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Micro balun design and fabrication at 910 MHz and mobile RFID reader miniaturization thereby

This article represents a microchip balun for UHF mobile RFID system at 910 MHz. This balun is designed by transforming coupled transmission lines into lumped-element equivalent circuit, and thus monolithic elements that art fabricated by IPD technology replace it for entirely miniaturized size. The corresponding balun performs under 3 dB return loss, with nearly 176 (176.42) degree of phase difference characteristic; therefore, this meets the UHF mobile RFID reader system consideration. (C) 2008 Wiley Periodicals, Inc.

Targeting breast and pancreatic cancer metastasis using a dual-cadherin antibody

The successful application of antibody-based therapeutics in either primary or metastatic cancer depends upon the selection of rare cell surface epitopes that distinguish cancer cells from surrounding normal epithelial cells. By contrast, as circulating tumor cells (CTCs) transit through the bloodstream, they are surrounded by hematopoietic cells with dramatically distinct cell surface proteins, greatly expanding the number of targetable epitopes. Here, we show that an antibody (23C6) against cadherin proteins effectively suppresses blood-borne metastasis in mouse isogenic and xenograft models of triple negative breast and pancreatic cancers. The 23C6 antibody is remarkable in that it recognizes both the epithelial E-cadherin (CDH1) and mesenchymal OB-cadherin (CDH11), thus overcoming considerable heterogeneity across tumor cells. Despite its efficacy against single cells in circulation, the antibody does not suppress primary tumor formation, nor does it elicit detectable toxicity in normal epithelial organs, where cadherins may be engaged within intercellular junctions and hence inaccessible for antibody binding. Antibody-mediated suppression of metastasis is comparable in matched immunocompetent and immunodeficient mouse models. Together, these studies raise the possibility of antibody targeting CTCs within the vasculature, thereby suppressing blood-borne metastasis.

Role of regulatory pathways and multi-omics approaches for carbon capture and mitigation in cyanobacteria

Cyanobacteria are known for their metabolic potential and carbon capture and sequestration capabilities. These cyanobacteria are not only an effective source for carbon minimization and resource mobilization into value-added products for biotechnological gains. The present review focuses on the detailed description of carbon capture mechanisms exerted by the various cyanobacterial strains, the role of important regulatory pathways, and their subsequent genes responsible for such mechanisms. Moreover, this review will also describe effectual mechanisms of central carbon metabolism like isoprene synthesis, ethylene production, MEP pathway, and the role of Glyoxylate shunt in the carbon sequestration mechanisms. This review also describes some interesting facets of using carbon assimilation mechanisms for valuable bio-products. The role of regulatory pathways and multi-omics approaches in cyanobacteria will not only be crucial towards improving carbon utilization but also will give new insights into utilizing cyanobacterial bioresource for carbon neutrality.

Fast and efficient method for computing ART

Angular radial transform (ART), which is the region-based shape descriptor of MPEG-7, has desirable properties for representing shape information in a small number of features with no redundancy. However, in order for ART to be useful, especially in limited computing environments, the computational cost of ART must be greatly reduced. In this paper, we derive symmetric/antisymmetric properties from the basis functions of ART and present a fast and efficient method to compute the ART coefficients using these properties. The proposed method significantly reduces the number of sinusoidal operations and multiplications in computing the coefficients of ART. Moreover, the memory requirements needed to store the ART basis functions in lookup tables are only 25% of the conventional method. The experimental results are presented to show the effectiveness of the proposed method.

Hijacking the Peptidoglycan Recycling Pathway of Escherichia coli to Produce Muropeptides

Soluble fragments of peptidoglycan called muropeptides are released from the cell wall of bacteria as part of their metabolism or as a result of biological stresses. These compounds trigger immune responses in mammals and plants. In bacteria, they play a major role in the induction of antibiotic resistance. The development of efficient methods to produce muropeptides is, therefore, desirable both to address their mechanism of action and to design new antibacterial and immunostimulant agents. Herein, we engineered the peptidoglycan recycling pathway of Escherichia coli to produce N-acetyl-β-D-glucosaminyl-(1→4)-1,6-anhydro-N-acetyl-β-D-muramic acid (GlcNAc-anhMurNAc), a common precursor of Gram-negative and Gram-positive muropeptides. Inactivation of the hexosaminidase nagZ gene allowed the efficient production of this key disaccharide, providing access to Gram-positive muropeptides through subsequent chemical peptide conjugation. E. coli strains deficient in both NagZ hexosaminidase and amidase activities further enabled the in vivo production of Gram-negative muropeptides containing meso-diaminopimelic acid, a rarely available amino acid.

Tackling inadequate vitamin D intakes within the population: fortification of dairy products with vitamin D may not be enough

Dietary recommendations for vitamin D are designed by authoritative agencies to prevent vitamin D deficiency in the population, and while individual target intakes around the globe vary, they are generally between 10 and 20 μg/day [400-800 IU/day], depending on age, assuming little or no sunshine exposure. National dietary surveys report usual intakes of vitamin D that are much lower than these targets, at about 3-7 μg/day [120-280 IU/day], depending on usual diet, age, sex, and mandatory or voluntary fortification practices, and there is widespread dietary inadequacy around the globe. While acknowledging the valuable contribution fortified milk makes to vitamin D intakes among consumers, particularly in children, and the continued need for fortification of milk and other dairy products, additional strategic approaches to fortification, including biofortification, of a wider range of foods, have the potential to increase vitamin D intakes in the population and minimize the prevalence of low serum 25(OH)D without increasing the risk of excessive dosing. Careful consideration must be given to the range of products used for fortification and the amount of vitamin D used in each; there is a need for well-designed and sustainable fortification, and biofortification strategies for vitamin D, which use a range of foods to accommodate dietary diversity. Clinical patients may require additional consideration in terms of addressing low vitamin D status.

Binding of piperine to mycobacterial RNA polymerase improves the efficacy of rifampicin activity against Mycobacterium leprae and nontuberculous mycobacteria

Piperine (PPN) is a known inhibitor of efflux pumps in Mycobacterium tuberculosis and in vitro synergism with rifampicin (RIF) has been proven. The current study evaluates the activity of PPN and synergism with RIF in rapidly and slowly growing nontuberculous mycobacteria (NTM). Also, to propose a possible mechanism of interaction of PPN with M. leprae (Mlp) RNA polymerase (RNAp). Minimal inhibitory concentration and drug combination assay was determined by resazurin microtiter assay and resazurin drug combination assay, respectively. In silico evaluation of PPN binding was performed by molecular docking and molecular dynamics (MD). PPN showed higher antimicrobial activity against rapidly growing NTM (32-128 mg/L) rather than for slowly growing NTM (≥ 256 mg/L). Further, 77.8% of NTM tested exhibited FICI ≤ 0.5 when exposed to PPN and RIF combination, regardless of growth speed. Docking and MD simulations showed a possible PPN binding site at the interface between β and β' subunits of RNAp, in close proximity to the trigger-helix and bridge-helix elements. MD results indicated that PPN binding hindered the mobility of these elements, which are essential for RNA transcription. We hypothesize that PPN binding might affect mycobacterial RNAp activity, and, possibly, RIF activity and that this mechanism is partially responsible for synergic behaviors with RIF reported in vitro. Communicated by Ramaswamy H. Sarma.

Optimal fault ride through compliance of offshore wind power plants with VSC-HVDC connection by meta-heuristic based tuning

This paper presents a novel iterative procedure augmented by electromagnetic transient type simulations and the state of the art mean variance mapping optimization algorithm. The aforementioned procedure enables the optimal tuning of coordinated fault ride through compliance strategies for offshore wind power plants with VSC-HVDC transmission. In particular, the formulated optimization task minimizes the electrical stresses experienced by the VSC-HVDC system and the offshore wind power plants during onshore faults. Moreover, it ensures that the onshore and offshore grid code profiles are not vicilated due to unwanted dynamics associated with the combined response of the VSC-HVDC system and the wind power plants. Two state of the art coordinated fault ride through strategies are optimized, namely the voltage drop and the frequency modulation technique. Simulation results demonstrate that the optimal tuning of coordinated fault ride through compliance strategies by the proposed iterative procedure enables improved dynamic response and reduced electrical stresses for the offshore wind power plant and the VSC-HVDC transmission. (C) 2016 Elsevier B.V. All rights reserved.

SFOD-Trans: semi-supervised fine-grained object detection framework with transformer module

As the labeling cost of object detection for medical images is very high, semi-supervised learning methods for medical images are investigated. In this paper, semi-supervised fine-grained object detection framework with transformer module (SFOD-Trans) is proposed for hepatic portal vein detection. It adopts Sparse R-CNN as the backbone. In detection model, the transformer module is introduced and contrastive loss is added to improve the performance of fine-grained object detection. In order to complete the information transfer both of labeled and unlabeled pictures, a new fusion module named normalized ROI fusion (NRF) is designed based on the characteristics of hepatic portal vein. We run a large number of experiments on a dataset of 1000 real CT scans. The results show that Average Precision (AP) and Average Recall (AR) of the proposed method reach 0.773 and 0.831 respectively with the 300 labeled and 1500 unlabeled samples. An overview of semi-supervised fine-grained object detection framework with transformer module (SFOD-Trans). There are two parallel branches to train supervised loss and semi-supervised loss respectively.

Experimental analysis of optimization techniques on the road passenger transportation problem

Analyzing the state of the art in a given field in order to tackle a new problem is always a mandatory task. Literature provides surveys based on summaries of previous studies, which are often based on theoretical descriptions of the methods. An engineer, however, requires some evidence from experimental evaluations in order to make the appropriate decision when selecting a technique for a problem. This is what we have done in this paper: experimentally analyzed a set of representative state-of-the-art techniques in the problem we are dealing with, namely, the road passenger transportation problem. This is an optimization problem in which drivers should be assigned to transport services, fulfilling some constraints and minimizing some function cost. The experimental results have provided us with good knowledge of the proper-ties of several methods, such as modeling expressiveness, anytime behavior, computational time, memory requirements, parameters, and free downloadable tools. Based on our experience, we are able to choose a technique to solve our problem. We hope that this analysis is also helpful for other engineers facing a similar problem. (C) 2008 Elsevier Ltd. All rights reserved.

Travelling Wave Expansion: A Model Fitting Approach to the Inverse Problem of Elasticity Reconstruction

In this paper, a novel approach to the problem of elasticity reconstruction is introduced. In this approach, the solution of the wave equation is expanded as a sum of waves travelling in different directions sharing a common wave number. In particular, the solutions for the scalar and vector potentials which are related to the dilatational and shear components of the displacement respectively are expanded as sums of travelling waves. This solution is then used as a model and fitted to the measured displacements. The value of the shear wave number which yields the best fit is then used to find the elasticity at each spatial point. The main advantage of this method over direct inversion methods is that, instead of taking the derivatives of noisy measurement data, the derivatives are taken on the analytical model. This improves the results of the inversion. The dilatational and shear components of the displacement can also be computed as a byproduct of the method, without taking any derivatives. Experimental results show the effectiveness of this technique in magnetic resonance elastography. Comparisons are made with other state-of-the-art techniques.

Incorporating artistic thinking into sustainability

Arts have great potential to facilitate sustainability. However, as a growing research field, it is still not clear how and what factors of arts can be applied to promote sustainable behavior. This paper summarizes five key factors of artistic thinking, including novelty, criticism, perfectionism, unique, and passion; each of them reveals clues to addressing sustainability issues. Meaningful examples are presented to demonstrate the proposed ideas, and the implications for researchers and practitioners are discussed. The results suggest that turning any form of art-making processes into service offerings can help companies work with their customers and co-create sustainable value with them. Artistic thinking also encourages hands-on programs and problem-based learning that benefit education for sustain ability. To link artistic thinking to behavior change, more art-making and art learning programs for citizens and communities should be developed. (C) 2018 Elsevier Ltd. All rights reserved.

Omicron variant of SARS-CoV-2: a review of existing literature

On November 24th, 2021 a case of a new viral variant of SARS-CoV-2 was reported by South Africa and Botswana to WHO, which later was designated as the variant of concern on 26th November 2021. It has around 60 mutations (50 non synonymous, 8 synonymous, and 2 non coding) as compared to the original parent strain of Wuhan. Different hypotheses have been put forward as an explanation for the origin like reverse zoonosis i.e. animal to human transmission, origin from an immune compromised patient or use of highly mutagenic drug like molnupiravir as treatment. A huge spike in cases around the globe is suggestive of a high rate of infectivity and transmissivity as compared to the previous known variants. With whatever cases have been documented so far, it is said that omicron causes mostly mild clinical illnesses and there is a less chance of hospitalization according to the clinicians. Among the reported cases, there were already vaccinated patients also. So there is a possibility that omicron might be able to evade the vaccine induced immunity due to a huge number of mutations (especially in the spike protein sequences). Until new vaccines specific to the pathogen are being developed, the coverage of the currently acceptable vaccines should be increased so that none is deprived of the mandatory doses and a third booster dose might help to reduce the chances of serious complications of this new strain beforehand. So an equal focus on the host and environment is required along with the pathogen.

Dual Referenced Composite Free Layer Design for Improved Switching Efficiency of Spin-Transfer Torque Random Access Memory

A composite free layer spin-transfer torque random access memory (STT-RAM) cell is proposed for ultra-high density memory. The structure consists of three layers-a high anisotropy interior layer and two low anisotropy outer layers that assist the switching of the interior layer via exchange coupling. Efficiencies (k(BT)/mu A) of 4.5 and 4.1 are achieved for the proposed structure with perpendicular and longitudinal anisotropies, respectively. An efficiency of 4.3 is obtained for the state-of-the-art single-layer dual-referenced structure with perpendicular anisotropy. Simulation of the conventional singlelayer structure with perpendicular anisotropy yields an efficiency of 1.6. Therefore, the proposed structure with perpendicular anisotropy achieves an improvement of 5% and 181% relative to the state-of-the-art dual-referenced and conventional STT-RAM cells, respectively. Furthermore, use of low anisotropy assistive layers enables reduction of Gilbert damping and an increase of partial spin polarization within those low anisotropy layers-not feasible with single layer structures that require high anisotropy for thermal stability. This significantly increases perpendicular and longitudinal efficiencies to 8.5 and 6.8, respectively. Therefore, this augmented proposed structure with perpendicular anisotropy achieves an improvement of 99% and 446% relative to the state-of-the-art dual-referenced and conventional STT-RAM cells, respectively.

Decommodify the 2030 Agenda: Why and How to Finance What Is Not Profitable?

The 2030 Agenda serves as a guide for current economic policy. Despite this, the dominant political and economic discourse still relies on the market for success. Incentives are being developed to create business opportunities that align with the sustainable development goals. However, funding for these projects ultimately depends on their potential profitability. As a result, economic growth is seen as a necessary condition for achieving the 2030 Agenda. This approach leaves culture and the arts behind, as they are difficult to commodify. The artist job market highlights the tension between the democratic value of the arts and the values of the capitalist system. This challenge is seen in both the field of cultural economics and in discussions of culture's role in meeting the 2030 Agenda's sustainable development goals. To address this, the study proposes incorporating culture into a funding strategy not based on the private market by using the employer of last resort or job guarantee policy for future applications. This will redirect focus from the economic value of the arts to their value for human development, ultimately realizing the goals set by the 2030 Agenda.

Stability of DRAM-devices with respect to 75 keV helium ion beam irradiation as required for ion projection lithography of critical layers

Ion projection lithography (IPL) is one of the next generation lithography techniques, targeting the 50-nm node and below. During the last year tool, mask and process development have made major advances. A resolution of 50 nm has been achieved locally, 75 nm over a field size of 12.5 x 12.5 mm(2). Compatibility of ion projection lithography with a standard semiconductor process has not been proven so far. Possible device damage is still seen as a critical issue. Therefore experimental investigation of the influence of ion beam irradiation on the functionality of state-of-the-art chips is necessary. We carried out the experiment with a state-of-the-art DRAM product processed at a high volume production site. The experiment demonstrates that 75 keV He+ ion beam exposure with ion doses as used for ion projection lithography would cause no detrimental damage. Thus IPL is suitable for semiconductor device fabrication. (C) 2002 Elsevier Science B.V. All rights reserved.

A multiclass TrAdaBoost transfer learning algorithm for the classification of mobile lidar data

A major challenge in the application of state-of-the-art deep learning methods to the classification of mobile lidar data is the lack of sufficient training samples for different object categories. The transfer learning technique based on pre-trained networks, which is widely used in deep learning for image classification, is not directly applicable to point clouds, because pre-trained networks trained by a large number of samples from multiple sources are not available. To solve this problem, we design a framework incorporating a state-of-the-art deep learning network, i.e. VoxNet, and propose an extended Multiclass TrAdaBoost algorithm, which can be trained with complementary training samples from other source datasets to improve the classification accuracy in the target domain. In this framework, we first train the VoxNet model with the combined dataset and extract the feature vectors from the fully connected layer, and then use these to train the Multiclass TrAdaBoost. Experimental results show that the proposed method achieves both improvement in the overall accuracy and a more balanced performance in each category.

Modular Construction of an MIL-101(Fe)@MIL-100(Fe) Dual-Compartment Nanoreactor and Its Boosted Photocatalytic Activity toward Tetracycline

Iron-based metal-organic frameworks (MOFs) have aroused extensive concern as prospective photocatalysts for antibiotic (e.g., tetracycline, TC) degradation. However, efficiencies of single and simple Fe-based MOFs still undergo restricted light absorption and weak charge separation. Assembly of different iron-based MOF building blocks into a hybrid MOF@MOF heterostructure reactor could be an encouraging strategy for the effective capture of antibiotics from the aqueous phase. This paper reports a new-style MIL-101(Fe)@MIL-100(Fe) photocatalyst, which was groundbreakingly constructed to realize a double win for boosting the performances of adsorption and photocatalysis. The optical response range, surface open sites, and charge separation efficiency of MIL-101(Fe)@MIL-100(Fe) can be regulated through accurate design and alteration. Attributed to the synergistic effects of double iron-based MOFs, MIL-101(Fe)@MIL-100(Fe) exhibits an excellent photocatalytic activity toward TC degradability compared to MIL-101(Fe) and MIL-100(Fe), which is even superior to those reported previously in the literature. Furthermore, the main active species of •O2- and h+ were proved through trapping tests of the photocatalytic process. Additionally, MIL-101(Fe)@MIL-100(Fe) possesses remarkable stability, maintaining more than 90% initial photocatalytic activity after the fifth cycle. In brief, MIL-101(Fe)@MIL-100(Fe) was highly efficient for TC degradation. Our work offers a new strategy for visible-light photodegradation of TC by exploring the double Fe-based MOF composite.

Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks With Jaccard Distance

Automatic skin lesion segmentation in dermoscopic images is a challenging task due to the low contrast between lesion and the surrounding skin, the irregular and fuzzy lesion borders, the existence of various artifacts, and various imaging acquisition conditions. In this paper, we present a fully automatic method for skin lesion segmentation by leveraging 19-layer deep convolutional neural networks that is trained end-to-end and does not rely on prior knowledge of the data. We propose a set of strategies to ensure effective and efficient learning with limited training data. Furthermore, we design a novel loss function based on Jaccard distance to eliminate the need of sample re-weighting, a typical procedure when using cross entropy as the loss function for image segmentation due to the strong imbalance between the number of foreground and background pixels. We evaluated the effectiveness, efficiency, as well as the generalization capability of the proposed framework on two publicly available databases. One is from ISBI 2016 skin lesion analysis towards melanoma detection challenge, and the other is the PH2 database. Experimental results showed that the proposed method outperformed other state-of-the-art algorithms on these two databases. Our method is general enough and only needs minimum pre- and post-processing, which allows its adoption in a variety of medical image segmentation tasks.

Channel decoupling network for cross-modality person re-identification

Cross-modality person re-identification (CM-ReID) is a very challenging problem due to the discrepancy in data distributions between visible and near-infrared modalities. To obtain a robust sharing feature representation, existing methods mainly focus on image generation or feature constrain to decrease the modality discrepancy, which ignores the large gap between mixed-spectral visible images and single-spectral near-infrared images. In this paper, we address the problem by decoupling the mixed-spectral visible images into three single-spectral subspaces R, G, and B. By aligning the spectrum, we noted that even using a single spectral image instead of the VIS images could result in a better performance. Based on the above observation, we further introduce a clear and effective three-path channel decoupling network (CDNet) for combining the three spectral images. Extensive experiments implemented on the benchmark CM-ReID datasets, SYSU-MM01 and RegDB indicated that our method achieved state-of-the-art performance and outperformed existing approaches by a large margin. On the RegDB dataset, the absolute gain of our method in terms of rank-1 and mAP is well over 15.4% and 8.5%, respectively, compared with the state-of-the-art methods.

Immune-mediated platelet transfusion refractoriness in a severely thrombocytopenic patient with myelodysplastic syndrome successfully treated with romiplostim

Immune-mediated platelet transfusion refractoriness due to anti-human leukocyte antigen (HLA) antibodies can occur in approximately 9% of patients with myelodysplastic syndromes (MDS) and can lead to an increased risk of clinically relevant bleeds and treatment delays. These patients are typically managed with frequent platelet transfusions; however, HLA-matched platelet transfusions are usually available only in large blood centers. For this reason, alloimunized thrombocytopenic MDS patients are notoriously difficult to manage. Here, we present a case of a MDS patient with an immune-mediated platelet transfusion refractoriness, severe thrombocytopenia and spontaneous subarachnoid hemorhage who we successfully treated with romiplostim, a thrombopoietin receptor agonist.

Global Trends in High-Power On-Board Chargers for Electric Vehicles

This paper provides a comprehensive review and analyses on the state-of-the-art and future trends for high-power conductive on-board chargers (OBCs) for electric vehicles. To provide a global context, a summary of global charging standards and electric vehicle (EV) related trends are presented, which demonstrates momentum toward the OBCs with higher power rating. High-power OBCs are either unidirectional or bidirectional, and they have either an integrated or non-integrated system architecture. Non-integrated high-power OBCs are studied both from industry and academia, and the former are used to illustrate the current state of the art. The latter are classified on the basis of the converter design approach, studied for their principle of operation, and compared over power density, weight, efficiency, and other metrics. In addition to non-integrated OBCs, recent advancements in propulsion-machine integrated OBC solutions are also presented. Other integrated OBC techniques, such as system integration with the EV's auxiliary power module and wireless charging systems, are also discussed. Finally, future charging strategies and functionalities in charging infrastructures are addressed, and global OBC trends are summarized.

Photoinduced Promiscuity of Cyclohexanone Monooxygenase for the Enantioselective Synthesis of α-Fluoroketones

The development of mild, efficient, and enantioselective methods for preparing chiral fluorinated compounds has been a long-standing challenge. Herein, we report a promiscuous cyclohexanone monooxygenase (CHMO) for the photoinduced synthesis of chiral α-fluoroketones via enantioselective reductive dehalogenation of α,α-halofluoroketones. Wild-type CHMO from Acinetobacter sp. possesses this promiscuous ability innately; however, the yield and stereoselectivity are low. A structure-guided rational design of CHMO improved the yield and stereoselectivity remarkably. Mechanistic studies and molecular simulations demonstrated that this photoinduced CHMO catalyzes the reductive dehalogenation via a novel electron transfer (ET)/proton transfer (PT) mechanism, distinct from that of previously reported reductases with similar promiscuity. This methodology was expanded to various substrates, and desirable chiral α-fluoroketones were obtained in high yields (up to 99 %) and e.r. values (up to 99:1).

On the Design of Logarithmic Multiplier Using Radix-4 Booth Encoding

This paper proposes an energy-efficient approximate multiplier which combines radix-4 Booth encoding and logarithmic product approximation. Additionally, a datapath pruning technique is proposed and studied to reduce the hardware complexity of the multiplier. Various experiments were conducted to evaluate the multiplier's error performance and efficiency in terms of energy and area utilization. The reported results are based on simulations using TSMC-180nm. Also, the applicability of the proposed multiplier is examined in image sharpening and convolutional neural networks. The applicability assessment shows that the proposed multiplier can replace an exact multiplier and deliver up to a 75% reduction in energy consumption and up to a 50% reduction in area utilization. Comparative analysis with the state-of-the-art multipliers indicates the potential of the proposed approach as a novel design strategy for approximate multipliers. When compared to the state-of-the-art approximate non-logarithmic multipliers, the proposed multiplier offers smaller energy consumption with the same level of applicability in image processing and classification applications. On the other hand, some state-of-the-art approximate logarithmic multipliers exhibit lower energy consumption than the proposed multiplier but deliver significant performance degradation for the selected application cases.

Histone deacetylase CsHDA6 mediates the regulated formation of the anti-insect metabolite α-farnesene in tea (Camellia sinensis)

α-Farnesene accumulated in tea plants following infestations by most insects, and mechanical wounding is the common factor. However, the specific mechanism underlying the wounding-regulated accumulation of α-farnesene in tea plants remains unclear. In this study, we observed that histone deacetylase inhibitor treatment induced the accumulation of α-farnesene. The histone deacetylase CsHDA6 interacted directly with CsMYC2, which was an important transcription factor in the jasmonic acid (JA) pathway, and co-regulated the expression of the key α-farnesene synthesis gene CsAFS. Wounding caused by insect infestation affected CsHDA6 production at the transcript and protein levels, while also inhibited the binding of CsHDA6 to the CsAFS promoter. The resulting increased acetylation of histones H3/H4 in CsAFS enhanced the expression of CsAFS and the accumulation of α-farnesene. In conclusion, our study demonstrated the effect of histone acetylation on the production of tea plant HIPVs and revealed the importance of the CsHDA6-CsMYC2 transcriptional regulatory module.

Impact of Parental Bos taurus and Bos indicus Origins on Copy Number Variation in Traditional Chinese Cattle Breeds

Copy number variation (CNV) is an important component of genomic structural variation and plays a role not only in evolutionary diversification but also in domestication. Chinese cattle were derived from Bos taurus and Bos indicus, and several breeds presumably are of hybrid origin, but the evolution of CNV regions (CNVRs) has not yet been examined in this context. Here, we of CNVRs, mtDNA D-loop sequence variation, and Y-chromosomal single nucleotide polymorphisms to assess the impact of maternal and paternal B. taurus and B. indicus origins on the distribution of CNVRs in 24 Chinese domesticated bulls. We discovered 470 genome-wide CNVRs, only 72 of which were shared by all three Y-lineages (B. taurus: Y1, Y2; B. indicus: Y3), whereas 265 were shared by inferred taurine or indicine paternal lineages, and 228 when considering their maternal taurine or indicine origins. Phylogenetic analysis uncovered eight taurine/indicine hybrids, and principal component analysis on CNVs corroborated genomic exchange during hybridization. The distribution patterns of CNVRs tended to be lineage-specific, and correlation analysis revealed significant positive or negative co-occurrences of CNVRs across lineages. Our study suggests that CNVs in Chinese cattle partly result from selective breeding during domestication, but also from hybridization and introgression.

GRANMA: Gradient Angle Model Algorithm on Wideband EMI Data for Land-Mine Detection

This letter presents a simple and fast algorithm to analyze wideband electromagnetic induction data for subsurface targets. A well-known four-parameter model is differentiated, resulting in a two-parameter model. A fast lookup table is used to find parameters as opposed to nonlinear optimization. The proposed approach provides a computationally faster way to reproduce the results of state-of-the-art methods. A detailed mathematical analysis of the model is given that describes the advantages and limitations of the proposed method.

Enhanced Recovery After Surgery Is Still Powerful for Colorectal Cancer Patients in COVID-19 Era

Purpose: To figure out whether enhanced recovery after surgery (ERAS) could effectively improve the prognosis of colorectal cancer (CRC) patients and reduce hospitalization expenses under the shadow of COVID-19, furthermore to alleviate the current situation of medical resource for the whole society. Methods: Patients who underwent CRC surgery in the department of gastrointestinal surgery of the First Affiliated Hospital from January 2020 to March 2022 were retrospectively enrolled. According to protocol adherence, all patients were divided into the ERAS group and the non-ERAS group. Short-term outcomes were compared between the two groups. Results: A total of 918 patients were enrolled in the study. Based on protocol adherence ≥70%, 265 patients were classified into the ERAS group and the other 653 patients were classified into the non-ERAS group. Patients in the ERAS group had shorter operation time (P < .01), less intraoperative blood loss (P < .01), shorter overall hospital stay (P < .01) and postoperative hospital stay (P < .01), less hospital costs (P < .01), earlier first flatus (P < .01), earlier first stool (P < .01), earlier food tolerance (P < .01), and lower postoperative complications (P < .01). Univariate and multivariate logistic regression analysis manifested that ERAS and cerebrovascular disease were predictive factors of postoperative overall complications. In univariate analyses, cerebrovascular disease (P = .033, OR = 2.225, 95% CI = 1.066-4.748), time of the surgery (P = .026, OR = 1.417, 95% CI = 1.043-1.925), and ERAS (P < .01, OR = 0.450, 95% CI = 0.307-0.661) were predictive factors. Furthermore, in the multivariate analysis, ERAS (P < .01, OR = 0.440, 95% CI = 0.295-0.656) and cerebrovascular disease (P = .016, OR = 2.575, 95% CI = 1.190-5.575) were independent predictive factors of postoperative overall complications. Conclusion: In summary, under the impact of the COVID-19 pandemic, ERAS could still reduce the financial burden of patients and reduce the incidence of short-term postoperative complications. However, whether the effects of ERAS were enhanced after the pandemic and the long-term outcomes of CRC obey ERAS remained to be further explored.

Oxidation state-specific fluorescent copper sensors reveal oncogene-driven redox changes that regulate labile copper(II) pools

Copper is an essential metal nutrient for life that often relies on redox cycling between Cu(I) and Cu(II) oxidation states to fulfill its physiological roles, but alterations in cellular redox status can lead to imbalances in copper homeostasis that contribute to cancer and other metalloplasias with metal-dependent disease vulnerabilities. Copper-responsive fluorescent probes offer powerful tools to study labile copper pools, but most of these reagents target Cu(I), with limited methods for monitoring Cu(II) owing to its potent fluorescence quenching properties. Here, we report an activity-based sensing strategy for turn-on, oxidation state-specific detection of Cu(II) through metal-directed acyl imidazole chemistry. Cu(II) binding to a metal and oxidation state-specific receptor that accommodates the harder Lewis acidity of Cu(II) relative to Cu(I) activates the pendant dye for reaction with proximal biological nucleophiles and concomitant metal ion release, thus avoiding fluorescence quenching. Copper-directed acyl imidazole 649 for Cu(II) (CD649.2) provides foundational information on the existence and regulation of labile Cu(II) pools, including identifying divalent metal transporter 1 (DMT1) as a Cu(II) importer, labile Cu(II) increases in response to oxidative stress induced by depleting total glutathione levels, and reciprocal increases in labile Cu(II) accompanied by decreases in labile Cu(I) induced by oncogenic mutations that promote oxidative stress.

A Three-Stage Self-Training Framework for Semi-Supervised Semantic Segmentation

Semantic segmentation has been widely investigated in the community, in which state-of-the-art techniques are based on supervised models. Those models have reported unprecedented performance at the cost of requiring a large set of high quality segmentation masks for training. Obtaining such annotations is highly expensive and time consuming, in particular, in semantic segmentation where pixel-level annotations are required. In this work, we address this problem by proposing a holistic solution framed as a self-training framework for semi-supervised semantic segmentation. The key idea of our technique is the extraction of the pseudo-mask information on unlabelled data whilst enforcing segmentation consistency in a multi-task fashion. We achieve this through a three-stage solution. Firstly, a segmentation network is trained using the labelled data only and rough pseudo-masks are generated for all images. Secondly, we decrease the uncertainty of the pseudo-mask by using a multi-task model that enforces consistency and that exploits the rich statistical information of the data. Finally, the segmentation model is trained by taking into account the information of the higher quality pseudo-masks. We compare our approach against existing semi-supervised semantic segmentation methods and demonstrate state-of-the-art performance with extensive experiments.

N-White Balancing: White Balancing for Multiple Illuminats Including Non-Uniform Illumination

In this paper, we propose a novel white balance adjustment for multi-illuminant scenes, called "N-white balancing," in which N source white points are mapped into a ground truth one. Most white balance adjustments focus on adjusting single-illuminant scenes. Several state-of-the-art methods for adjusting multi-illuminant scenes have been proposed, but they need to know the number of segments or the number of light sources in advance. Multi-color balance adjustments have been investigated to improve the performance of white balancing, but they also suffer from color distortion due to the rank deficiency problem. In contrast, the proposed method, N-white balancing, can correct multi-illuminant scenes even when we do not know the exact number of segments or light sources in a scene. In an experiment, the proposed method was demonstrated to outperform state-of-the-art methods under various illumination conditions such as single and multiple illuminants including non-uniform light sources.

Defining invasion in breast cancer: the role of basement membrane

Basement membrane (BM) is an amorphous, sheet-like structure separating the epithelium from the stroma. BM is characterised by a complex structure comprising collagenous and non-collagenous proteoglycans and glycoproteins. In the breast, the thickness, density and composition of the BM around the ductal lobular system vary during differing development stages. In pathological conditions, the BM provides a physical barrier that separates proliferating intraductal epithelial cells from the surrounding stroma, and its absence or breach in malignant lesions is a hallmark of invasion and metastases. Currently, diagnostic services often use special stains and immunohistochemistry (IHC) to identify the BM in order to distinguish in situ from invasive lesions. However, distinguishing BM on stained sections, and differentiating the native BM from the reactive capsule or BM-like material surrounding some invasive malignant breast tumours is challenging. Although diagnostic use of the BM is being replaced by myoepithelial cell IHC markers, BM is considered by many to be a useful marker to distinguish in situ from invasive lesions in ambiguous cases. In this review, the structure, function and biological and clinical significance of the BM are discussed in relation to the various breast lesions with emphasis on how to distinguish the native BM from alternative pathological tissue mimicking its histology.

Human Action Recognition Via Multi-modality Information

In this paper, we propose pyramid appearance and global structure action descriptors on both RGB and depth motion history images and a model-free method for human action recognition. In proposed algorithm, we firstly construct motion history image for both RGB and depth channels, at the same time, depth information is employed to filter RGB information, after that, different action descriptors are extracted from depth and RGB Mills to represent these actions, and then multimodality information collaborative representation and recognition model, in which multi-modality information are put into object function naturally, and information fusion and action recognition also be done together, is proposed to classify human actions. To demonstrate the superiority of the proposed method, we evaluate it on MSR Action3D and DHA datasets, the well-known dataset for human action recognition. Large scale experiment shows our descriptors are robust, stable and efficient, when comparing with the-state-of-the-art algorithms, the performances of our descriptors are better than that of them, further, the performance of combined descriptors is much better than just using sole descriptor. What is more, our proposed model outperforms the state-of-the-art methods on both MSR Action3D and DHA datasets.

Noninvasive Solution for Electrochemical Impedance Spectroscopy on Metallic Works of Art

Metallic works of art of cultural relevance are continuously subjected to corrosion as the environment becomes increasingly polluted. A fast and simple method to in situ assess the conservation conditions is therefore required. This paper describes the development and performance of dry and gel-based electrodes which can be used to assess the surface conservation state without the need to move the artifacts and which do not cause any damage to them. The electrodes can be used with a portable electrochemical impedance spectroscopy system, without employing electrochemical cells. The proposed solution does not provide all the information that one can obtain using an electrochemical cell, but it can discriminate between protective coatings. It can be used to assess the protective capability of corrosion product layers and natural patinas, and it can therefore enable a noninvasive routine surface assessment to be conducted that could be extremely useful for people working in the field of conservation of cultural heritage.

Addition of Camrelizumab to Transarterial Chemoembolization in Hepatocellular Carcinoma With Untreatable Progression

Purpose: The present retrospective study aimed to evaluate the efficacy and safety of camrelizumab addition to transarterial chemoembolization (TACE) in the treatment of hepatocellular carcinoma (HCC) with TACE-related untreatable progression (UP). Methods: Patients with HCC who received addition of camrelizumab due to UP after initial TACE treatment were enrolled at our institution between May 2019 and January 2021. Patients were assessed for tumor response, progression-free survival (PFS), and adverse events (AEs). Risk factors for PFS were evaluated with logistic regression analysis. Results: A total of 41 patients were included. The objective response rates (ORR) and disease control rates (DCR) were 24.4% and 61.0% at 2 to 3 months, and 12.2% and 58.5% at 6 months, respectively. The median PFS of the patients were 6 months (95% confidence interval [CI]: 3.8 months, 8.2 months). Of the 41 patients, 23 received camrelizumab combined with TACE (hereafter, camrelizumab-TACE) on whom 52 combined TACE procedures were performed, with a median of 2 procedures (range: 1-6) per patient. The remaining 18 patients received camrelizumab alone due to TACE contraindications. Multivariable analysis indicated that camrelizumab-TACE was an independent prognostic factor for PFS. Subgroup analysis showed a median PFS of 8 months in the camrelizumab-TACE group and 3 months in the camrelizumab monotherapy group (P < .001). No treatment-related mortalities occurred. Seventeen patients (41.5%) developed at least 1 type of AE after treatment with camrelizumab, with reactive cutaneous capillary endothelial proliferation (RCCEP) (n = 14, 34.1%) being the most common AE. Conclusion: Addition of camrelizumab to TACE offered an effective and safe treatment for HCC with UP.

In-air handwritten Chinese text recognition with temporal convolutional recurrent network

As a new human-computer interaction way, in-air handwriting allows users to perform gesture-based writing in the midair. However, most existing in-air handwriting systems mainly focus on recognizing either isolated characters/words or only a small number of texts, making those systems far from practical applications. Instead, here we present a 3D in-air handwritten Chinese text recognition (IAHCTR) system for the first time, and construct the first public large-scale IAHCT dataset. Moreover, a novel architecture, named the temporal convolutional recurrent network (TCRN), is proposed for online HCTR. Specifically, the TCRN first applies the 1-dimensional convolution to extract local contextual features from low-level trajectories, and then it utilizes the recurrent network to capture long-term dependencies of high-level outputs. Compared with the state-of-the-art architecture, the TCRN not only avoids the domain-specific knowledge for feature image extraction, but also attains higher training efficiency with a more compact model. Empirically, this TCRN also outperforms the single recurrent network with faster prediction and higher accuracy. Experiments on CASIA-OLHWDB2 & ICDAR-2013 demonstrate that the TCRN yields the best result in comparison to the state-of-the-art methods for online HCTR. (C) 2019 Elsevier Ltd. All rights reserved.

The effect of gestational weight gain on serum total oxidative stress, total antioxidant capacity and gut microbiota

This study aimed to investigate the effect of gestational weight gain on total oxidative stress (TOS), total antioxidant capacity (TAC), oxidative stress index (OSI), dietary antioxidant intake, and the gut microbiome. The study was carried out on 40 pregnant women divided as follows: a) normal prepregnancy weight and gestational weight gain of 11.5-16.0 kg (n=10) b) normal prepregnancy weight and gestational weight gain of >16.0 kg (n=10) c) obese before pregnancy and gestational weight gain of 5-9 kg (n=10) and d) obese before pregnancy and gestational weight gain of >9.0 kg (n=10). Serum TOS and TAC levels, dietary antioxidant intake, and microbiome diversity of the gut microbiome were evaluated during the third trimester of pregnancy. A positive correlation was found between body mass index (BMI) in the third trimester and serum TOS levels and OSI. In women with normal prepregnancy weight, an increase in the Firmicutes and Bacteroidetes phyla was observed when gestational weight gain was above the recommended values (p<0.05). In women who were obese before pregnancy, an increase only in the Bacteroidetes phylum was observed when gestational weight gain was above the recommended values (p<0.05). A positive correlation was found between Firmicutes/Bacteroidetes and OSI, and a negative correlation was found between Firmicutes/Bacteroidetes and dietary antioxidant intake (p<0.05). Prepregnancy body weight, high serum TOS level, and dietary antioxidant intake are determinant factors for microbial diversity, with increased serum TOS levels caused by increased gestational weight gain.

ERBB2 exon 20 insertions are rare in Brazilian non-small cell lung cancer

ERBB2 exon 20 insertions may impact the clinical management of lung cancer patients. However, the frequency of ERBB2 exon 20 insertions in lung cancer patients in Brazil is scarce. Here, we analyzed 722 Brazilian non-small cell lung cancer (NSCLC) patients from Barretos Cancer Hospital that were indicated to require routine lung cancer molecular testing. ERBB2 exon 20 insertions were evaluated by a targeted panel using next-generation sequencing (NGS). Clinicopathological and molecular data were collected from patient medical records. Among the 722 NSCLC patients, 85.2% had lung adenocarcinomas, 53.9% were male, 66.8% were quitter or current smokers, and 63.2% were diagnosed at an advanced stage of the disease. We identified 0.8% (6/722) of patients who harbored the insertion p.(Tyr772_Ala775dup) at exon 20 of the ERBB2 gene. All ERBB2 mutated patients were diagnosed with lung adenocarcinoma, were never smokers, and wild-type for EGFR, KRAS, and ALK hotspot alterations. Less than 1% of Brazilian NSCLC patients harbor ERBB2 exon 20 insertions, yet they could benefit in future from the new drugs in development.

Learning to Learn: Hierarchical Meta-Critic Networks

In recent years, deep reinforcement learning methods have achieved impressive performance in many different fields, including playing games, robotics, and dialogue systems. However, there are still a lot of restrictions here, one of which is the demand for massive amounts of sampled data. In this paper, a hierarchical meta-learning method based on the actor-critic algorithm is proposed for sample efficient learning. This method provides the transferable knowledge that can efficiently train an actor on a new task with a few trials. Specifically, a global basic critic, meta critic, and task specified network are shared within a distribution of tasks and are capable of criticizing any actor trying to solve any specified task. The hierarchical framework is applied to a critic network in the actor-critic algorithm for distilling meta-knowledge above the task level and addressing distinct tasks. The proposed method is evaluated on multiple classic control tasks with reinforcement learning algorithms, including the start-of-the-art meta-learning methods. The experimental results statistically demonstrate that the proposed method achieves state-of-the-art performance and attains better results with more depth of meta critic network.

Service Composition and Optimal Selection in Cloud Manufacturing: State-of-the-Art and Research Challenges

Increasing interest in the field of Cloud Manufacturing (CMfg) has been witnessed over the last few years. This study aims to identify current and state-of-the-art techniques and to synthesize quality attributes, objectives, and evaluation methodologies for service composition and optimal selection (SCOS) in the field of CMfg. We used a systematic literature review (SLR) methodology for a thorough analysis of 46 shortlisted primary studies, from a total of 5872 accumulated studies from ten electronic databases. NVivo analysis software was used for data coding and qualitative analysis. A review scope was primarily devised based on research goals, and to uncover potential search strings; a pilot study was formulated. Secondarily, research identification, key data extraction, and deductive coding-based data analysis were performed. Multi-variant distribution approaches were adopted for data categorization. We found that the research in this domain has increased due to the rapid manufacturing urge. Although a few studies were based on industrial evaluations; however, scientific and empirically validated methodologies are still needed in this domain. This study lays an overview of SCOS in the field of CMfg and enlightens the identified future research areas.

Investigation of micromixing in the ART plate reactor PR37 using the acetal cleavage method and different mixing models

The mixing time can strongly affect the yield of a desired product if side reactions occur. Mixing is therefore of great practical importance. Millireactors enable comparatively high flow rates at moderate pressure drops and are thus well suited for production purposes. In this work, the mixing performance of the millistructured reactor ART PR37 is investigated using the Bourne method. Four plate types with similar meandering, converging/ diverging process channels, but different hydraulic diameters have been studied over a wide range of Reynolds numbers. Mixing times have been calculated using two different mixing models, the IEM model and a recently introduced modified incorporation model. Mixing times were found to be less than 0.1 second for Reynolds numbers exceeding 150. For higher Reynolds numbers, mixing times in the range of 0.02 to 0.03 s are achievable without prohibitively high pressure drops. The ART PR37 is therefore a promising option for mixing-sensitive reactions.

Extraction of the association rules from artificial neural networks based on the multiobjective optimization

Artificial Neural Network (ANN) is one of the powerful techniques of machine learning. It has shown its effectiveness in both prediction and classification problems. However, in some fields there is still some reticence towards their use mainly the fact that they do not justify their answers. The lack of transparency on how ANN makes decisions motivated us to develop our rule extraction algorithm that extracts comprehensible rules with high accuracy and high fidelity. The aim is to generate a set of rules that mimic the decision of ANN and cover a larger set of patterns. The obtained rule sets should satisfy a well-balanced trade-off between the fidelity, the accuracy and the comprehensibility. The proposed algorithm consists of a three steps: ANN learning phase, rule extraction phase and rule simplification phase. The rule extraction phase is based on the extraction of the association rules while the rules simplification procedure is based on the laws of Boolean algebra. To evaluate the performance of our algorithm, the system has been studied using four datasets, and then compared with other rule extraction methods. The results show that our proposal offers a small set of rules having the highest accuracy and fidelity values.

Insight Into Factors Governing Formation, Synthesis and Stereochemical Configuration of DNA Adducts Formed by Mitomycins

Mitomycin C, (MC), an antitumor drug used in the clinics, is a DNA alkylating agent. Inert in its native form, MC is reduced to reactive mitosenes in cellulo which undergo nucleophilic attack by DNA bases to form monoadducts as well as interstrand crosslinks (ICLs). These properties constitute the molecular basis for the cytotoxic effects of the drug. The mechanism of DNA alkylation by mitomycins has been studied for the past 30 years and, until recently, the consensus was that drugs of the mitomycins family mainly target CpG sequences in DNA. However, that paradigm was recently challenged. Here, we relate the latest research on both MC and dicarbamoylmitomycin C (DMC), a synthetic derivative of MC which has been used to investigate the regioselectivity of mitomycins DNA alkylation as well as the relationship between mitomycins reductive activation pathways and DNA adducts stereochemical configuration. We also review the different synthetic routes to access mitomycins nucleoside adducts and oligonucleotides containing MC/DMC DNA adducts located at a single position. Finally, we briefly describe the DNA structural modifications induced by MC and DMC adducts and how site specifically modified oligonucleotides have been used to elucidate the role each adduct plays in the drugs cytotoxicity.

A 4.49nW/Pixel Light-to-Stimulus Duration Converter-Based Retinal Prosthesis Chip

This paper presents a 288-pixel retinal prosthesis (RP) chip implemented in a 0.18 mu m CMOS process. The proposed light-to-stimulus duration converter (LSDC) and biphasic stimulator generate a wide range of retinal stimuli proportional to the incident light intensity at a low supply voltage of 1V. The implemented chip shows 25.5 dB dynamic stimulation range and the state-of-the art low power consumption of 4.49 nW/pixel. Ex-vivo experiments were performed with a mouse retina and patch-clamp recording. The electrical artifact recorded by the patch electrode demonstrates that the proposed chip can generate electrical stimuli that have different pulse durations depending on the light intensity. Correspondingly, the spike counts in a retinal ganglion cell (RGC) were successfully modulated by the brightness of the light stimuli.

Environmental influence in the forested area toward human health: incorporating the ecological environment into art psychotherapy

This study on the development of a psychotherapy program based on the relationship between forests and human health focused on actively considering the natural ecological environment. This study categorized and compared an art psychotherapy program that simply moved to an outdoor space and a forest-art therapy program that actively utilized the forest environment as a medium. The characteristics of the natural environment, such as openness, change, and diversity, shortened the amount of time participants took to develop a rapport and open up and played a vital role in recovering mental health. After a bold attempt at integrating forest environment and art psychotherapy by going beyond outdoor art therapy, there were significant results pertaining to improvements in mental disorders in today's society, including stress vulnerabilities, depression, anxiety, and aggression. The research results verified that the developed forest-art therapy method had greater efficacy in relation to both the Stress Vulnerability - Interpersonal Sensitivity Scale and the Stress Vulnerability - Self-Regulation Scale.

Promoting the Sustainability of City Communities through 'Voluntary Arts Activities' at Regenerated Cultural Arts Spaces: A Focus on the Combination of the 'Democratization of Culture' and 'Cultural Democracy' Perspectives

Abandoned industrial facilities have become a nuisance in cities because the needs of society members are continuously changing. Idle industrial facilities might be considered to be merely abandoned and empty spaces, but they are in reality historic sites that illustrate the period of industrialization in the region. They are valuable because they serve to accumulate memories from the past. Recently, with the need for urban regeneration, there have been various discussions on converting the abandoned industrial facilities into cultural art spaces. They are intended to promote the sustainability of communities and cities by vitalizing the area. Considering the social dimensions of urban regeneration, it is necessary to render such a creative space as a 'Third Place' to promote the city's sustainability. Converted industrial facilities, through the medium of 'Voluntary Arts Activities,' have many elements that are suitable for the needs of a creative space, and even for a 'Third Place'. As opposed to the private sector, it is seen that when the public sector regenerates these facilities, they approach this issue in order to lower the cultural arts barrier. The public sector, which is a government-centered first sector, conducts regeneration projects based on the 'Democratization of Culture' perspective. However, in order to promote participation in the third sector, which is a community-based, non-profit sector that actually uses the space, it is important to approach the issue from the 'Cultural Democracy' perspective. Focusing on this aspect, this study aims to examine cases of public sector-led converted cultural arts spaces by 'Voluntary Arts Activities' in France and South Korea, namely 'Le Centquatre-Paris,' the 'Oil Tank Culture Park,' and the 'West Seoul Arts Center for Learning'. This will allow us to contemplate the possibility of sustainable spaces, individuals, communities and cities.

Erica the Rhino: A Case Study in Using Raspberry Pi Single Board Computers for Interactive Art

Erica the Rhino is an interactive art exhibit created by the University of Southampton, UK. Erica was created as part of a city wide art trail in 2013 called "Go! Rhinos", curated by Marwell Wildlife, to raise awareness of Rhino conservation. Erica arrived as a white fibreglass shell which was then painted and equipped with five Raspberry Pi Single Board Computers (SBC). These computers allowed the audience to interact with Erica through a range of sensors and actuators. In particular, the audience could feed and stroke her to prompt reactions, as well as send her Tweets to change her behaviour. Pi SBCs were chosen because of their ready availability and their educational pedigree. During the deployment, 'coding clubs' were run in the shopping centre where Erica was located, and these allowed children to experiment with and program the same components used in Erica. The experience gained through numerous deployments around the country has enabled Erica to be upgraded to increase reliability and ease of maintenance, whilst the release of the Pi 2 has allowed her responsiveness to be improved.

Is Selective Head Cooling Combined with Whole-Body Cooling the Most Effective Hypothermia Method for Neonatal Hypoxic-Ischemic Encephalopathy?

This study aimed to compare combined hypothermia (CH) to the 2 classical therapeutic hypothermia (TH) methods selective head cooling (SHC) and whole-body cooling (WBC). This retrospective cohort study included neonates who underwent CH, SHC, and WBC between 2012 and 2020. Mean rectal temperature was maintained at 33.5 ± 0.5°C by cooling the head and the body in the CH group, at 34.5 ± 0.5°C by cooling the head in the SHC group, and at 33.5 ± 0.5°C by cooling the body in the WBC group. The groups were compared in terms of side effects, magnetic resonance imaging (MRI) scores, and status at discharge. The study included 60 neonates in the CH group, 112 in the WBC group, and 27 in the SHC group. There was no significant difference in side effects between the groups (p > 0.05). There was no significant difference in brain MRI scores between the groups (p > 0.05); however, gray matter, white matter, and total MRI scores in the CH group were lower than in the WBC group. Duration of hospitalization was shorter in the CH group than in the other two groups (p = 0.022). CH was not associated with more side effects than the two classical TH methods. In addition, some of these findings suggest that CH might result in better clinical outcome than the two classical TH methods.

Deep Learning for Natural Language Parsing

Natural language processing problems (such as speech recognition, text-based data mining, and text or speech generation) are becoming increasingly important. Before effectively approaching many of these problems, it is necessary to process the syntactic structures of the sentences. Syntactic parsing is the task of constructing a syntactic parse tree over a sentence which describes the structure of the sentence. Parse trees are used as part of many language processing applications. In this paper, we present a multi-lingual dependency parser. Using advanced deep learning techniques, our parser architecture tackles common issues with parsing such as long-distance head attachment, while using 'architecture engineering' to adapt to each target language in order to reduce the feature engineering often required for parsing tasks. We implement a parser based on this architecture to utilize transfer learning techniques to address important issues related with limited-resourced language. We exceed the accuracy of state-of-the-art parsers on languages with limited training resources by a considerable margin. We present promising results for solving core problems in natural language parsing, while also performing at state-of-the-art accuracy on general parsing tasks.

Bag of indexes: a multi-index scheme for efficient approximate nearest neighbor search

During the last years, the problem of Content-Based Image Retrieval (CBIR) was addressed in many different ways, achieving excellent results in small-scale datasets. With growth of the data to evaluate, new issues need to be considered and new techniques are necessary in order to create an efficient yet accurate system. In particular, computational time and memory occupancy need to be kept as low as possible, whilst the retrieval accuracy has to be preserved as much as possible. For this reason, a brute-force approach is no longer feasible, and an Approximate Nearest Neighbor (ANN) search method is preferable. This paper describes the state-of-the-art ANN methods, with a particular focus on indexing systems, and proposes a new ANN technique called Bag of Indexes (BoI). This new technique is compared with the state of the art on several public benchmarks, obtaining 86.09% of accuracy on Holidays+Flickr1M, 99.20% on SIFT1M and 92.4% on GIST1M. Noteworthy, these state-of-the-art accuracy results are obtained by the proposed approach with a very low retrieval time, making it excellent in the trade off between accuracy and efficiency.

Molecular simulations of electrolyte structure and dynamics in lithium-sulfur battery solvents

The performance of modern lithium-sulfur (Li/S) battery systems critically depends on the electrolyte and solvent compositions. For fundamental molecular insights and rational guidance of experimental developments, efficient and sufficiently accurate molecular simulations are thus in urgent need. Here, we construct a molecular dynamics (MD) computer simulation model of representative state-of-the art electrolyte-solvent systems for Li/S batteries constituted by lithium-bis(trifluoromethane)sulfonimide (LiTFSI) and LiNO3 electrolytes in mixtures of the organic solvents 1,2-dimethoxyethane (DME) and 1,3-dioxolane (DOL). We benchmark and verify our simulations by comparing structural and dynamic features with various available experimental reference systems and demonstrate their applicability for a wide range of electrolyte-solvent compositions. For the state-of-the-art battery solvent, we finally calculate and discuss the detailed composition of the first lithium solvation shell, the temperature dependence of lithium diffusion, as well as the electrolyte conductivities and lithium transference numbers. Our model will serve as a basis for efficient future predictions of electrolyte structure and transport in complex electrode confinements for the optimization of modern Li/S batteries (and related devices).

Is now the time for a Rubiscuit or Ruburger? Increased interest in Rubisco as a food protein

Much of the research on Rubisco aims at increasing crop yields, with the ultimate aim of increasing plant production to feed an increasing global population. However, since the identification of Rubisco as the most abundant protein in leaf material, it has also been touted as a direct source of dietary protein. The nutritional and functional properties of Rubisco are on a par with those of many animal proteins, and are superior to those of many other plant proteins. Purified Rubisco isolates are easily digestible, nutritionally complete, and have excellent foaming, gelling, and emulsifying properties. Despite this potential, challenges in efficiently extracting and separating Rubisco have limited its use as a global foodstuff. Leaves are lower in protein than seeds, requiring large amounts of biomass to be processed. This material normally needs to be processed quickly to avoid degradation of the final product. Extraction of Rubisco from the plant material requires breaking down the cell walls and rupturing the chloroplast. In order to obtain high-quality protein, Rubisco needs to be separated from chlorophyll, and then concentrated for final use. However, with increased consumer demand for plant protein, there is increased interest in the potential of leaf protein, and many commercial plants are now being established aimed at producing Rubisco as a food protein, with over US$60 million of funding invested in the past 5 years. Is now the time for increased use of Rubisco in food production as a nitrogen source, rather than just providing a carbon source?

Beam Training and Alignment for RIS-Assisted Millimeter-Wave Systems: State of the Art and Beyond

Reconfigurable intelligent surface (RIS) has recently emerged as a promising paradigm for future cellular networks. Specifically, due to its capability in reshaping the propagation environment, RIS was introduced to address the blockage issue in millimeter-wave (mmWave) or even terahertz communications. The deployment of RIS, however, complicates the system architecture and poses a significant challenge for beam training (BT)/beam alignment (BA), a process that is required to establish a reliable link between the transmitter and the receiver. In this article, we first review several state-of-the-art beam training solutions for RIS-assisted mmWave systems and discuss their respective advantages and limitations. We also present a new multidirectional BT method, which can achieve decent BA performance with only a small amount of training overhead. Finally, we outline several important open issues in BT for RIS-assisted mmWave systems.

Evaluation of Expressive Arts Therapy on the Resilience of University Students in COVID-19: A Network Analysis Approach

As an alternative to traditional verbal counselling, expressive arts therapy has been shown to be an effective method of mental health care, particularly when dealing with stressful public interactions, such as those associated with COVID-19. However, few studies have been conducted to determine the efficacy of expressive arts therapy on the resilience of psychologically exposed university students during COVID-19. Furthermore, since network analysis appears to be a popular approach in psychological research, it has not been used in recent intervention studies for resilience. As a result, the current study utilized a network analysis approach to determine the efficacy of expressive arts therapy on the resilience of university students during the COVID-19 pandemic. A total of 263 students in a comprehensive university in China were selected for the therapy group between March and November 2021. In a pre-post design, students' resilience was assessed using the Resiliency Scale for University Students (RSUS). The extended Bayesian information criteria (EBIC) and graphical LASSO were used to estimate and define paired resilience networks, and the strength, betweenness, and closeness indices were utilized to determine the centrality of the six facets of resilience. Additionally, we verified the stability and accuracy. It was discovered that significant differences appeared between the paired networks before and after expressive arts therapy. Facets of self-efficacy, self-acceptance and problem-solving in resilience were notably improved after the therapy, with the variable of emotional stability sustained at the mean level. Meanwhile, the network analysis has highlighted the central variable of self-efficacy in the pre-intervention and support from friends in the post-intervention. The connectivity among the components of problem solving, support from friends, and support from family was enhanced, with support from friends playing the role of hub nod in the following network. By utilizing a network analytic approach, expressive arts therapy can be more targeted in intervening in resilience mechanisms. As a proxy for efficacious problem-solving, intervention should be calibrated to the cultivation of social support networks, especially in the support from friends.

A comparative characterization of SARS-CoV-2-specific T cells induced by mRNA or inactive virus COVID-19 vaccines

Unlike mRNA vaccines based only on the spike protein, inactivated severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) vaccines should induce a diversified T cell response recognizing distinct structural proteins. Here, we perform a comparative analysis of SARS-CoV-2-specific T cells in healthy individuals following vaccination with inactivated SARS-CoV-2 or mRNA vaccines. Relative to spike mRNA vaccination, inactivated vaccines elicit a lower magnitude of spike-specific T cells, but the combination of membrane, nucleoprotein, and spike-specific T cell response is quantitatively comparable with the sole spike T cell response induced by mRNA vaccine, and they efficiently tolerate the mutations characterizing the Omicron lineage. However, this multi-protein-specific T cell response is not mediated by a coordinated CD4 and CD8 T cell expansion but by selective priming of CD4 T cells. These findings can help in understanding the role of CD4 and CD8 T cells in the efficacy of the different vaccines to control severe COVID-19 after Omicron infection.

Adaptive Estimation of Active Contour Parameters Using Convolutional Neural Networks and Texture Analysis

In this paper, we propose a generalization of the level set segmentation approach by supplying a novel method for adaptive estimation of active contour parameters. The presented segmentation method is fully automatic once the lesion has been detected. First, the location of the level set contour relative to the lesion is estimated using a convolutional neural network (CNN). The CNN has two convolutional layers for feature extraction, which lead into dense layers for classification. Second, the output CNN probabilities are then used to adaptively calculate the parameters of the active contour functional during the segmentation process. Finally, the adaptive window size surrounding each contour point is re-estimated by an iterative process that considers lesion size and spatial texture. We demonstrate the capabilities of our method on a dataset of 164 MRI and 112CT images of liver lesions that includes low contrast and heterogeneous lesions as well as noisy images. To illustrate the strength of our method, we evaluated it against state of the art CNN-based and active contour techniques. For all cases, our method, as assessed by Dice similarity coefficients, performed significantly better than currently available methods. An average Dice improvement of 0.27 was found across the entire dataset over all comparisons. We also analyzed two challenging subsets of lesions and obtained a significant Dice improvement of 0.24 with our method (p <0.001, Wilcoxon).

Hand Pose Understanding With Large-Scale Photo-Realistic Rendering Dataset

Hand pose understanding is essential to applications such as human computer interaction and augmented reality. Recently, deep learning based methods achieve great progress in this problem. However, the lack of high-quality and large-scale dataset prevents the further improvement of hand pose related tasks such as 2D/3D hand pose from color and depth from color. In this paper, we develop a large-scale and high-quality synthetic dataset, PBRHand. The dataset contains millions of photo-realistic rendered hand images and various ground truths including pose, semantic segmentation, and depth. Based on the dataset, we firstly investigate the effect of rendering methods and used databases on the performance of three hand pose related tasks: 2D/3D hand pose from color, depth from color and 3D hand pose from depth. This study provides insights that photo-realistic rendering dataset is worthy of synthesizing and shows that our new dataset can improve the performance of the state-of-the-art on these tasks. This synthetic data also enables us to explore multi-task learning, while it is expensive to have all the ground truth available on real data. Evaluations show that our approach can achieve state-of-the-art or competitive performance on several public datasets.

Identifying transformational space for transdisciplinarity: using art to access the hidden third

A challenge for transdisciplinary sustainability science is learning how to bridge diverse worldviews among collaborators in respectful ways. A temptation in transdisciplinary work is to focus on improving scientific practices rather than engage research partners in spaces that mutually respect how we learn from each other and set the stage for change. We used the concept of Nicolescu's Hidden Third to identify and operationalize this transformative space, because it focused on bridging objective and subjective worldviews through art. Between 2014 and 2017, we explored the engagement of indigenous peoples from three inland delta regions in Canada and asa team of interdisciplinary scholars and students who worked together to better understand long-term social-ecological change in those regions. In working together, we identified five characteristics associated with respectful, transformative transdisciplinary space. These included (1) establishing an unfiltered safe place where (2) subjective and objective experiences and (3) different world views could come together through (4) interactive and (5) multiple sensory experiences. On the whole, we were more effective in achieving characteristics 2-5bringing together the subjective and objective experiences, where different worldviews could come togetherthan in achieving characteristic 1creating a truly unfiltered and safe space for expression. The novelty of this work is in how we sought to change our own engagement practices to advance sustainability rather than improving scientific techniques. Recommendations for sustainability scientists working in similar contexts are provided.

Deep Learning for Segmentation Using an Open Large-Scale Dataset in 2D Echocardiography

Delineation of the cardiac structures from 2D echocardiographic images is a common clinical task to establish a diagnosis. Over the past decades, the automation of this task has been the subject of intense research. In this paper, we evaluate how far the state-of-the-art encoder-decoder deep convolutional neural network methods can go at assessing 2D echocardiographic images, i.e. segmenting cardiac structures and estimating clinical indices, on a dataset, especially, designed to answer this objective. We, therefore, introduce the cardiac acquisitions for multi-structure ultrasound segmentation dataset, the largest publicly-available and fully-annotated dataset for the purpose of echocardiographic assessment. The dataset contains two and four-chamber acquisitions from 500 patients with reference measurements from one cardiologist on the full dataset and from three cardiologists on a fold of 50 patients. Results show that encoder-decoder-based architectures outperform state-of-the-art non-deep learning methods and faithfully reproduce the expert analysis for the end-diastolic and end-systolic left ventricular volumes, with a mean correlation of 0.95 and an absolute mean error of 9.5 ml. Concerning the ejection fraction of the left ventricle, results are more contrasted with a mean correlation coefficient of 0.80 and an absolute mean error of 5.6%. Although these results are below the inter-observer scores, they remain slightly worse than the intra-observer's ones. Based on this observation, areas for improvement are defined, which open the door for accurate and fully-automatic analysis of 2D echocardiographic images.

W-Band Graphene-Based Six-Port Receiver

We demonstrate a full-fledged millimeter-wave graphene-based six-port receiver frontend at 90 GHz employing graphene power detectors. Exploiting the high responsivity and wide dynamic range reported for the state-of-the-art graphene field-effect transistors (GFETs), graphene power detectors are demonstrated beyond the maximum oscillation frequency, f(max), of the graphene transistor. The proposed circuit is fabricated on thinned SiC substrate and its functionality is verified by demodulation of 10-Mbps ON-OFF keying (OOK) digitally modulated signal.

Tracking of targets of interest using labeled multi-Bernoulli filter with multi-sensor control

This paper proposes a novel multi-sensor control scheme for tracking of targets of interest in the presence of clutter and detection uncertainty, using labeled multi-Bernoulli filters. The focus is on multi-target tracking applications where multiple centrally connected and controllable sensors are used for tracking of particular targets of interest. We derive an efficient analytical approximation for a task-driven objective function for multi-sensor selective control, that can be computed immediately after the prediction step. Compared to the state-of-the-art, drastic reduction in computation is achieved. Numerical experiments, involving challenging multi-sensor control scenarios, demonstrate how the proposed method can lead to significant improvements in the tracking accuracy of the targets of interest, in comparison to the generic non-selective sensor control methods. It is also shown that while our proposed method has comparable performance to the state-of-art selective sensor control method (selective-PEECS) in terms of the meansquare-error (MSE) of tracking for targets of interest, it runs significantly faster than the state-of-art (both selective and non-selective) multi-sensor control algorithms. Indeed, the resulting reduction in computation time is shown to be in the order of tens to hundreds time, depending on the number of sensors to be controlled. (C) 2020 Elsevier B.V. All rights reserved.

Expert system based on artificial neural networks for content-based image retrieval

Clustering technique is essential for fast retrieval in large database. In this paper, new image clustering technique based on artificial neural networks is proposed for content-based image retrieval. Fuzzy-ART mechanism maps high-dimensional input features into the output neuron. Joint HSV histogram and average entropy computed from gray-level co-occurrence matrices in the localized image region is employed as input feature elements. Original Fuzzy-ART suffers unnecessary increase of the number of output neurons when the noise input is presented. Modified Fuzzy-ART mechanism resolves the problem by differently updating the committed node and uncommitted node, and checking the vigilance test again. To show the validity of the proposed algorithm, experiment results on image clustering performance and comparison with original Fuzzy-ART are presented in terms of recall rates. (c) 2005 Elsevier Ltd. All rights reserved.

Variable metabolic scaling breaks the law: from 'Newtonian' to 'Darwinian' approaches

Life's size and tempo are intimately linked. The rate of metabolism varies with body mass in remarkably regular ways that can often be described by a simple power function, where the scaling exponent (b, slope in a log-linear plot) is typically less than 1. Traditional theory based on physical constraints has assumed that b is 2/3 or 3/4, following natural law, but hundreds of studies have documented extensive, systematic variation in b. This overwhelming, law-breaking, empirical evidence is causing a paradigm shift in metabolic scaling theory and methodology from 'Newtonian' to 'Darwinian' approaches. A new wave of studies focuses on the adaptable regulation and evolution of metabolic scaling, as influenced by diverse intrinsic and extrinsic factors, according to multiple context-dependent mechanisms, and within boundary limits set by physical constraints.

Experimental studies of particle removal and probability of COVID-19 infection in passenger railcars

A series of experiments in stationary and moving passenger railcars was conducted to measure the removal rates of particles in the size ranges of SARS-CoV-2 viral aerosols, and the air changes per hour provided by the existing and modified air handling systems. The effect of ventilation and air filtration systems on removal rates and their effects on estimated probability (i.e., risk) of infection was evaluated in a range of representative conditions: (1) for two different ratios of recirculated air (RA) to outdoor air (OA) (90:10 RA:OA and 67:33 RA:OA); (2) using minimum efficiency reporting value (MERV) filters with standard (MERV-8) and increased (MERV-13) filtration ratings; and (3) in the presence and absence of a portable high-efficiency particulate-air (HEPA) room air purifier system operated at clean air delivery rate (CADR) of 150 and 550 cfm. The higher-efficiency MERV-13 filters significantly increased particle removal rates on average by 3.8 to 8.4 hr-1 across particle sizes ranging from 0.3 to 10 µm (p < 0.01) compared to MERV-8 filters. The different RA:OA ratios and the use of a portable HEPA air purifier system had little effect on particle removal rates. MERV-13 filters reduced the estimated probability of infection by 42% compared to the MERV-8 filter. The use of a HEPA-air purifier with a MERV-13 filter causes a 50% reduction in the estimated probability of infection. Upgrading the efficiency of HVAC filters from MERV-8 to MERV-13 in public transit vehicles is the most effective exposure control method resulting in a clear reduction in the removal rates of aerosol particles and the estimated probability of infection.

Cancer-related cognitive impairment in non-CNS cancer patients: Targeted review and future action plans in Europe

Cancer-related cognitive impairment (CRCI) has increasingly been identified over the last two decades in non-CNS system cancer patients. Across Europe, researchers have contributed to this effort by developing preclinical models, exploring underlying mechanisms and assessing cognitive and quality of life changes. The ultimate goal is to develop interventions to treat patients experiencing CRCI. To do so, new challenges need to be addressed requiring the implementation of multidisciplinary research groups. In this consensus paper, we summarize the state of the art in the field of CRCI combined with the future challenges and action plans in Europe. These challenges include data sharing/pooling, standardization of assessments as well as assessing additional biomarkers and neuroimaging investigations, notably through translational studies. We conclude this position paper with specific actions for Europe based on shared scientific expert opinion and stakeholders involved in the Innovative Partnership for Action Against Cancer, with a particular focus on cognitive intervention programs.

The co-occurrence network patterns and keystone species of microbial communities in cattle manure-corn straw composting

Microbes often form complex ecological networks in various habitats. Co-occurrence network analysis allows exploring the complex community interactions beyond the community diversities. This study explores the interspecific relationships within and between bacterial and fungal communities during composting of cow manure using co-occurrence network analysis. Furthermore, the keystone taxa that potentially exert a considerable impact on the microbiome were revealed by network analysis. The networks in the present study harbored more positive links. Specifically, the interactions/coupling within bacterial communities was tighter and the response to changes in external environmental conditions was more quickly during the composting process, while the fungal network had a better buffer capacity for changes in external environmental conditions. Interestingly, this result was authenticated in the bacterial-fungal (BF) network and the Mantel test of major modules and environmental variables. More than that, the Zi-Pi plot revealed that the keystone taxa including "module hubs" and "connectors" were all detected in these networks, which could prevent the dissociation of modules and networks.

Towards Detecting Building Facades with Graffiti Artwork Based on Street View Images

As a recognized type of art, graffiti is a cultural asset and an important aspect of a city's aesthetics. As such, graffiti is associated with social and commercial vibrancy and is known to attract tourists. However, positional uncertainty and incompleteness are current issues of open geo-datasets containing graffiti data. In this paper, we present an approach towards detecting building facades with graffiti artwork based on the automatic interpretation of images from Google Street View (GSV). It starts with the identification of geo-tagged photos of graffiti artwork posted on the photo sharing media Flickr. GSV images are then extracted from the surroundings of these photos and interpreted by a customized, i.e., transfer learned, convolutional neural network. The compass heading of the GSV images classified as containing graffiti artwork and the possible positions of their acquisition are considered for scoring building facades according to their potential of containing the artwork observable in the GSV images. More than 36,000 GSV images and 5000 facades from buildings represented in OpenStreetMap were processed and evaluated. Precision and recall rates were computed for different facade score thresholds. False-positive errors are caused mostly by advertisements and scribblings on the building facades as well as by movable objects containing graffiti artwork and obstructing the facades. However, considering higher scores as threshold for detecting facades containing graffiti leads to the perfect precision rate. Our approach can be applied for identifying previously unmapped graffiti artwork and for assisting map contributors interested in the topic. Furthermore, researchers interested on the spatial correlations between graffiti artwork and socio-economic factors can profit from our open-access code and results.

Present status and future perspectives of ALPPS (associating liver partition and portal vein ligation for staged hepatectomy)

First International Consensus Meeting, Hamburg, Germany, 27-28 February 2015 More than 160 participants took part in the conference for 2 days. A total of 58 world renown experts on ALPPS (associating liver partition and portal vein ligation for staged hepatectomy) were invited from all over the world. The faculty was divided into many different subgroups that were in contact during the 2-3 months before the conference analyzing all the most important aspects of this technique and summarizing it in a common structured work to be presented during the congress, giving final recommendations in the form of bulleted point statements. The aim was to gain a solid basis of preliminary agreement on many controversial aspects of ALPPS. A poster area was also organized with 35 posters reporting mostly mono-institutional experiences on single aspects of the technique from all five continents.

Enhanced self-adaptive differential evolution multi-Objective algorithm for coordination of directional overcurrent relays contemplating maximum and minimum fault points

In this study, a parameter tune free enhanced self-adaptive differential evolution multi-objective (ESA-DEMO) approach has been proposed for coordination of directional overcurrent relays. The advantages of the proposed method are: avoid the use of conventional single-objective function, which requires tuning of weighting parameters; avoid tuning of algorithm parameters; minimisation of primary, backup and coordination time interval; zero violation of coordination constraints in large interconnected network; and low computational resource consumption leading to fast algorithm execution time. The proposed method has been implemented on the highly interconnected 6-bus, IEEE 14- and 30-bus systems, where results have shown robustness and consistency of the algorithm. Moreover, two-fault point coordination criterion considering close- and far-end (maximum and minimum) faults has been performed. ESA-DEMO has been compared with popular genetic algorithms and state-of-the-art multi-objective algorithm for protection coordination study.

Fetal Congenital Heart Disease Echocardiogram Screening Based on DGACNN: Adversarial One-Class Classification Combined with Video Transfer Learning

Fetal congenital heart disease (FHD) is a common and serious congenital malformation in children. In Asia, FHD birth defect rates have reached as high as 9.3 0025. For the early detection of birth defects and mortality, echocardiography remains the most effective method for screening fetal heart malformations. However, standard echocardiograms of the fetal heart, especially four-chamber view images, are difficult to obtain. In addition, the pathophysiological changes in fetal hearts during different pregnancy periods lead to ever-changing two-dimensional fetal heart structures and hemodynamics, and it requires extensive professional knowledge to recognize and judge disease development. Thus, research on the automatic screening for FHD is necessary. In this paper, we proposed a new model named DGACNN that shows the best performance in recognizing FHD, achieving a rate of 85. The motivation for this network is to deal with the problem that there are insufficient training datasets to train a robust model. There are many unlabeled video slices, but they are tough and time-consuming to annotate. Thus, how to use these un-annotated video slices to improve the DGACNN capability for recognizing FHD, in terms of both recognition accuracy and robustness, is very meaningful for FHD screening. The architecture of DGACNN comprises two parts, that is, DANomaly and GACNN (Wgan-GP and CNN). DANomaly, similar to the ALOCC network, but incorporates cycle adversarial learning to train an end-to-end one-class classification (OCC) network that is more robust and has a higher accuracy than ALOCC in screening video slices. For the GACNN architecture, we use FCH (four chamber heart) video slices at around the end-systole, as screened by DANomaly, to train a WGAN-GP for the purpose of obtaining ideal low-level features that can robustly improve the FHD recognition accuracy. A few annotated video slices, as screened by DANomaly, can also be used for data augmentation so as to improve the FHD recognition further. The experiments show that the DGACNN outperforms other state-of-the-art networks by 10025; in recognizing FHD. A comparison experiment shows that the proposed network already outperforms the performance of expert cardiologists in recognizing FHD, reaching 84 025; in a test. Thus, the proposed architecture has high potential for helping cardiologists complete early FHD screenings.

Second-Harmonic Power Generation Limits in Harmonic Oscillators

Based on piecewise linear modeling of field-effect transistors, harmonic translations are deployed to analyze the fundamental limits for a maximum second-harmonic power generation for any given field-effect transistor. Optimum waveforms at the gate-source and drain-source terminals, which yield high second-harmonic power generation by the given transistor, are derived. Two oscillators are implemented in a TSMC 65-nm CMOS process. Transistors in these oscillators have optimum voltage waveforms at their terminals. Thus, they deliver a state-of-the-art second-harmonic output power while operating at relatively higher frequencies than related arts. One of the proposed oscillators has the maximum output power of 4.9 dBm and a peak dc-to-RF efficiency of 3% at 300 GHz. Each of the implemented oscillators occupies 0.16 mm(2) of the chip area.

Effects of Antiretroviral Therapy and HIV Exposure in Utero on Adverse Pregnancy and Infant Outcomes: A Prospective Cohort Study in Guangzhou, China

Objective This study aimed to evaluate the effects of in-utero exposure to HIV and ART on pregnancy outcome and early growth of children. Methods This cohort study enrolled 802 HIV-infected pregnant women between October 2009 and May 2018 in Guangzhou, China. The women were assigned to receive combination ART (cART) or mono/dual ART or no treatment. The primary outcomes were the combined endpoints of any adverse pregnancy outcome [including ectopic pregnancy, spontaneous abortion, stillbirth, preterm birth, small for gestational age (SGA)] and adverse early growth outcome (including infant death, HIV infection of mother-to-child transmission, and underweight, wasting and stunting of infants at 4 weeks of age). Results Adverse pregnancy outcomes occurred in 202 (35.1%) of all enrolled HIV-infected women, and 121 (31.3%) of all infants exhibited adverse effects on early growth at 4 weeks of age. The rates of adverse pregnancy outcomes, spontaneous abortion, ectopic pregnancy, stillbirth, infant death and perinatal HIV infection were higher among women not receiving ART, compared to those treated with cART or mono/dual ART (P < 0.05). However, women treated with cART had a higher rate of SGA, compared to untreated women (P < 0.05). No differences in early infant growth were observed among the different treatment regimens. Conclusion Our findings underscore the essentiality of prioritizing HIV-positive pregnant women for ART, as even mono/dual ART available in resource-limited countries could improve pregnancy outcomes and infant survival.

The Relationship Between Vertical Facial Type and Maxillary Anterior Alveolar Angle in Adults Using Cone-Beam Computed Tomography

Background Cone-beam computed tomography (CBCT) imaging provides detailed and thorough information about the dentofacial complex. However, not all aspects have been yet explored among different types of malocclusion. The maxillary anterior alveolus is one of the components of the maxillary bone which affects the upper lip position and the esthetics of the smile. The inclination of this alveolus may vary between the different vertical growth patterns of patients who may seek orthodontic treatment. The objective of this study was to investigate possible differences in maxillary anterior alveolar angle (MAAA) among orthodontically untreated adults with different vertical facial types in a Syrian sample. Methods CBCT images of 84 orthodontically untreated adult patients were included. Three groups of vertical facial type (n=28 for each group; 14 males, 14 females) were created using disproportionate multi-stratified random sampling. CBCT-derived lateral cephalograms were used to categorize the patients into three groups. Measurements were made at three regions (region 1 (R1), region 2 (R2), and region 3 (R3)), located in the maxillary anterior alveolar bone using OnDemand3D™ software (Cypermed Inc., Seoul, South Korea). Results No significant differences in the mean MAAA were detected between females and males for the three measured regions in all groups. Analysis of variance showed significant inter-group differences in the MAAA (p<0.05) for all measured regions. The hyperdivergent facial type group had the greatest MAAA mean value of 68.72° (± 6.01), 67.30° (± 4.15), and 68.01° (± 5.12) at R1 in the female, male, and the entire sample of both sexes respectively. Whereas the hypodivergent facial type group had the least mean MAAA values of 58.47° (± 5.34) at R3, 59.83° (± 6.23) at R2, and 59.23° (± 5.75) at R3 in the female, male, and the entire sample of both sexes respectively. Conclusions The maxillary anterior alveolar bone was more buccally inclined in the hypodivergent facial type. The MAA bone inclination did not differ between females and males in the same vertical facial type group.

Polyethylene Glycol 3350 Crystal Nephropathy in Association With Glomerular Mesangial Immunoglobin A Deposition

Polyethylene glycol (PEG) 3350, an active ingredient of over-the-counter MiraLAX, is a commonly used laxative in children and is produced by polymerization of ethylene glycol (EG). Masked EG toxicity secondary to contamination of PEG 3350 could occur. We present a 7-year-old child with developmental delay who presented with altered mental status and acute kidney injury (AKI) following intake of generic PEG 3350 for few days prior to presentation. There was high anion gap metabolic acidosis, hypernatremia, elevated osmolar gap, lactic acidosis, and AKI. Urinalysis showed tubular proteinuria, microscopic hematuria, and calcium oxalate crystals. Prior urinalyses were normal without hematuria or proteinuria. Renal biopsy revealed evidence of mesangial dominant immunoglobulin A (IgA) and complement 3 (C3) deposits along with dense tubular deposition of calcium oxalate crystals. He subsequently developed worsening oliguric AKI and required hemodialysis (HD) for several sessions. The AKI resolved within 2 weeks and further HD was not required. Mental status improved in few days. Follow-up urinalyses showed resolution of microscopic hematuria and crystalluria. We hypothesized that the generic PEG 3350 most likely was contaminated with EG leading to the presentation. A high index of suspicion of contamination of PEG 3350 with EG is required in patients presenting with unexplained high anion gap metabolic acidosis, elevated osmolar gap, lactic acidosis, AKI, calcium oxalate crystalluria, and oxalate crystals on renal biopsy. Further studies are needed to determine whether there is an association between transient glomerular mesangial IgA deposition and crystal nephropathy.

Combined i-Vector and Extreme Learning Machine Approach for Robust Speaker Identification and Evaluation with SITW 2016, NIST 2008, TIMIT Databases

In this article, a novel combined i-vector and an Extreme Learning Machine (ELM) is proposed for speaker identification. The ELM is chosen because it is fast to train and has a universal approximator property. Four combinations of features based on Mel Frequency Cepstral Coefficient and Power Normalized Cepstral Coefficient are used. Besides, seven fusion methods are exploited. The system is evaluated with three different databases, namely: the SITW 2006, NIST 2008, and the TIMIT database. This work employs the 2016 SITW database for the first time for speaker identification using the integration between the ELM and i-vector approach. From each database, 120 speakers with 1200 speech utterances are used (overall 360 speakers with 3600 speech utterances). Furthermore, comprehensive evaluations are exploited with a wide range of realistic background noise types (Stationary noise AWGN and Non-Stationary Noise types) with the handset effect. The proposed system is compared with the Gaussian Mixture Model-Universal Background Model (GMM-UBM) and other states of the art approaches. The results show that the i-vector method outperforms the GMM-UBM approach and other state- of-the-art methods under specific conditions, and that fusion techniques can be used to improve robustness to noise and handset effects.

State of the art in thermal insulation materials and aims for future developments

Insulation materials are the key tool in designing and constructing a energy thrifty buildings. This is demonstrated by the increasing thicknesses used in buildings, which also reflects in the growing sales of the branch. The European market of insulation materials is characterised by the domination of two groups of products inorganic fibrous materials and organic foamy materials. They all feature similar performance in terms of insulating capabilities, but otherwise present significant differences. These are discussed in detail in the following paper. Despite the fact that the thermal properties of the materials has not improved significantly of the last decade, a series of other features, like reaction to fire and moisture or mechanical properties have improved, sometimes even at the cost of insulation abilities. Furthermore, environmental and public health aspects play an increasing role, both in the search for 'optimum' materials for given applications, and in the aims set by the industry for future developments. These aims, examined within the legislative and market framework, are discussed in this paper, both as criteria for evaluating state of the art materials and as goals for future research developments. (C) 2004 Elsevier B.V. All rights reserved.

hTetro-Infi: A Reconfigurable Floor Cleaning Robot With Infinite Morphologies

The development of floor cleaning robots is an emerging area in robotics. Maximizing the area coverage is a foremost mission for a floor cleaning robot. Reconfigurable floor cleaning robots outperform floor cleaning robots with fixed morphology in the aspect of area coverage. A reconfigurable robot should be more flexible in changing its morphologies by considering the shapes of objects occupied in an environment to gain more coverage. Nevertheless, the state of the art methods of tiling robots considers only a limited number of morphologies for the reconfiguration, which is not sufficient to match the shape of an object. Therefore, this paper proposes a novel method to synthesize an appropriate morphology for a reconfigurable robot in accordance with the shape of an object. The proposed concept is named hTetro-Infi since it is not limited to a finite number of morphologies. The major novelty of the proposed concept overt the state of the art is the consideration of an infinite number of morphologies for the reconfiguration without sticking into a limited number of morphologies. Feedforward Neural Network (FNN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were used for determining the hinge angle required for synthesizing a given morphology. Different configurations of FNNs and ANFISs were trained and evaluated to find the most suitable configurations. The area coverage performance of the proposed hTetro-Infi was compared against that of the state of the art methods of an existing class of tiling robots, which considers only a limited number of morphologies, through simulations. According to the statistical conclusions, the proposed hTetro-Infi is capable of significantly improving area coverage compared to an existing tiling-theory based floor cleaning robot. Furthermore, the area coverage improvement of hTetro-Infi is noteworthy. Therefore, the proposed concept is beneficial in improving the abilities of a reconfigurable cleaning robot. Real-world experiments with the hardware platform of the robot for evaluating the performance is expected to be conducted in the next phase of the work. Furthermore, consideration of hTetro-Infi for navigation through confined areas is proposed for future work.

Epistemic community in transboundary river regime: a case study in the Mekong River Commission regarding mainstream hydropower development

Despite the importance of transboundary water management, cooperation mechanisms are limited, especially in the case of Mekong River basin where environmental and social aspects are threatened by recent anthropogenic pressures like hydropower development. Existing transboundary mechanism such as the Mekong River Commission (MRC) is challenged to facilitate the cooperation between riparian states. An epistemic community (EC) is considered to effectively influence international governance and is studied as part of transboundary river regimes. The existence of an MRC EC is part of that regime but understanding about its characteristics is yet limited. This research aims to fill in the gap by unraveling the main features of the EC in relation to hydropower development. We analyze shared causal beliefs and policy goals that developed in the EC framework of Haas applying literature review and semi-structured interviews of experts. Results show that the community experts share causal beliefs and policy goals only to a limited extent while disagreeing on many aspects. It resembles a "disciplined" or "professional" group rather than an EC. This suggests that the knowledge factor has not gained proper influence and attention in the region, resulting in incoherent policy advice leading to policymakers developing policies based on incomplete and fragmented knowledge. The role of the MRC in the decision-making process could become more relevant if it would facilitate the development of an EC. Bringing key stakeholders including policymakers and experts into a platform where policy goals and causal beliefs are facilitated to reach possible consensus is recommended. Narrowing the science-policy gap while acknowledging differences in interests and policy objectives is crucial to reach a sustainable transboundary management of the Mekong River given its rapid development, especially on hydropower.

Coupling predictive scheduling and reactive control in manufacturing hybrid control architectures: state of the art and future challenges

Nowadays, industrials are seeking for models and methods that are not only able to provide efficient overall production performance, but also for reactive systems facing a growing set of unpredicted events. One important research activity in that field focuses on holonic/multi-agent control systems that couple predictive/proactive and reactive mechanisms into agents/holons. Meanwhile, not enough attention is paid to the optimization of this coupling. The aim of this paper is to depict the main research challenges that are to be addressed before expecting a large industrial dissemination. Relying on an extensive review of the state of the art, three main challenges are highlighted: the estimation of the future performances of the system in reactive mode, the design of efficient switching strategies between predictive and reactive modes and the design of efficient synchronization mechanisms to switch back to predictive mode.

CycN-Net: A Convolutional Neural Network Specialized for 4D CBCT Images Refinement

Four-dimensional cone-beam computed tomography (4D CBCT) has been developed to provide a sequence of phase-resolved reconstructions in image-guided radiation therapy. However, 4D CBCT images are degraded by severe streaking artifacts and noise because the phase-resolved image is an extremely sparse-view CT procedure wherein a few under-sampled projections are used for the reconstruction of each phase. Aiming at improving the overall quality of 4D CBCT images, we proposed two CNN models, named N-Net and CycN-Net, respectively, by fully excavating the inherent property of 4D CBCT. To be specific, the proposed N-Net incorporates the prior image reconstructed from entire projection data based on U-Net to boost the image quality for each phase-resolved image. Based on N-Net, a temporal correlation among the phase-resolved images is also considered by the proposed CycN-Net. Extensive experiments on both XCAT simulation data and real patient 4D CBCT datasets were carried out to verify the feasibility of the proposed CNNs. Both networks can effectively suppress streaking artifacts and noise while restoring the distinct features simultaneously, compared with the existing CNN models and two state-of-the-art iterative algorithms. Moreover, the proposed method is robust in handling complicated tasks of various patient datasets and imaging devices, which implies its excellent generalization ability.

Emotion Based Automated Priority Prediction for Bug Reports

Issue tracking systems allow users to report bugs. Bug reports often contain product name, product component, description, and severity. Based on such information, triagers often manually prioritize the bug reports for investigation. However, manual prioritization is time consuming and cumbersome. DRONE is an automated state-of-the-art approach that recommends the priority level information of the bug reports. However, its performance for all levels of priorities is not uniform and may be improved. To this end, in this paper, we propose an emotion-based automatic approach to predict the priority for a report. First, we exploit natural language processing techniques to preprocess the bug report. Second, we identify the emotion-words that are involved in the description of the bug report and assign it an emotion value. Third, we create a feature vector for the bug report and predict its priority with a machine learning classifier that is trained with history data collected from the Internet. We evaluate the proposed approach on Eclipse open-source projects and the results of the cross-project evaluation suggest that the proposed approach outperforms the state-of-the-art. On average, it improves the F1 score by more than 6%.

Accurate and Robust Time Reconstruction for Deployed Sensor Networks

The notion of global time is of great importance for many sensor network applications. Time reconstruction methods aim to reconstruct the global time with respect to a reference clock. To achieve microsecond accuracy, MAC-layer timestamping is required for recording packet transmission and reception times. The timestamps, however, can be invalid due to multiple reasons, such as imperfect system designs, wireless corruptions, or timing attacks, etc. In this paper, we propose ART, an accurate and robust time reconstruction approach to detecting invalid timestamps and recovering the needed information. ART is much more accurate and robust than threshold-based approach, especially in dynamic networks with inherently varying propagation delays. We evaluate our approach in both testbed and a real-world deployment. Results show that: 1) ART achieves a high detection accuracy with low false-positive rate and low false-negative rate; 2) ART achieves a high recovery accuracy of less than 2 ms on average, much more accurate than previously reported results.

Comparison of non-linear mixture models: Sub-pixel classification

Sub-pixel level classification is essential for the successful description of many land cover patterns with spatial resolution of less than similar to1 km and has been widely used in global or continental scale land cover mapping with remote sensing data. This paper presents a general comparison of four non-linear models for sub-pixel classification: ARTMAP. ART-MMAP. Regression Tree (RT) and Multilayer Perceptron (MLP) with Back-Propagation (BP) algorithm. The comparison is based oil four factors: accuracy. model complexity, interpolation ability and error distribution. Two data sets, one simulated and one real world MODIS satellite image. were used to demonstrate the characteristics of each model. Experimental results show the superior performance of MLP with the simulated data set and better performance of ART-MMAP with the MODIS data set. (C) 2004 Elsevier Inc. All rights reserved.

Evolution, Current Challenges, and Future Possibilities in ECG Biometrics

Face and fingerprint are, currently, the most thoroughly explored biometric traits, promising reliable recognition in diverse applications. Commercial products using these traits for biometric identification or authentication are increasingly widespread, from smartphones to border control. However, increasingly smart techniques to counterfeit such traits raise the need for traits that are less vulnerable to stealthy trait measurement or spoofing attacks. This has sparked interest on the electrocardiogram (ECG), most commonly associated with medical diagnosis, whose hidden nature and inherent liveness information make it highly resistant to attacks. In the last years, the topic of ECG-based biometrics has quickly evolved toward the commercial applications, mainly by addressing the reduced acceptability and comfort by proposing new off-the-person, wearable, and seamless acquisition settings. Furthermore, researchers have recently started to address the issues of spoofing prevention and data security in ECG biometrics, as well as the potential of deep learning methodologies to enhance the recognition accuracy and robustness. In this paper, we conduct a deep review and discussion of 93 state-of-the-art publications on their proposed methods, signal datasets, and publicly available ECG collections. The extracted knowledge is used to present the fundamentals and the evolution of ECG biometrics, describe the current state of the art, and draw conclusions on prior art approaches and current challenges. With this paper, we aim to delve into the current opportunities as well as inspire and guide future research in ECG biometrics.

Long-distance WPT unconventional arrays synthesis

Two innovative array concepts are introduced for the design of long-distance Wireless Power Transfer (WPT) radiating systems. The achievable tradeoffs between complexity/cost mitigation and power focusing capabilities of unconventional WPT architectures with respect to state-of-the-art optimal WPT solutions are investigated. To this end, clustered or sparseWPT arrangements are introduced by formulating their syntheses either as excitation or as pattern matching problems then solved by ad-hoc versions of the Contiguous Partition Method and Compressive Sensing algorithms. Selected numerical examples are presented to assess the features and the potentialities of unconventional WPT designs also in comparison with traditional state-of-the-art optimal methods.

Associating Multi-Modal Brain Imaging Phenotypes and Genetic Risk Factors via a Dirty Multi-Task Learning Method

Brain imaging genetics becomes more and more important in brain science, which integrates genetic variations and brain structures or functions to study the genetic basis of brain disorders. The multi-modal imaging data collected by different technologies, measuring the same brain distinctly, might carry complementary information. Unfortunately, we do not know the extent to which the phenotypic variance is shared among multiple imaging modalities, which further might trace back to the complex genetic mechanism. In this paper, we propose a novel dirty multi-task sparse canonical correlation analysis (SCCA) to study imaging genetic problems with multi-modal brain imaging quantitative traits (QTs) involved. The proposed method takes advantages of the multi-task learning and parameter decomposition. It can not only identify the shared imaging QTs and genetic loci across multiple modalities, but also identify the modality-specific imaging QTs and genetic loci, exhibiting a flexible capability of identifying complex multi-SNP-multi-QT associations. Using the state-of-the-art multi-view SCCA and multi-task SCCA, the proposed method shows better or comparable canonical correlation coefficients and canonical weights on both synthetic and real neuroimaging genetic data. In addition, the identified modality-consistent biomarkers, as well as the modality-specific biomarkers, provide meaningful and interesting information, demonstrating the dirty multi-task SCCA could be a powerful alternativemethod inmulti-modal brain imaging genetics.

Functional neuroanatomical correlates of contingency judgement

Contingency judgement is an ability to detect relationships between events and is crucial in the allocation of attentional resources for reasoning, categorization, and decision making to control behaviour in our environment. Research has suggested that the allocation of attention is sensitive to the frequency of contingency information whether it constitutes a negative, zero or positive relationship. The aim of the present study was to explore the functional neuroanatomical correlates of contingency judgement with different frequencies and whether these are distinct from each other or whether they rely on a common mechanism. Using three contingency tasks within a streaming paradigm (one each for negative, zero, and positive contingency frequencies), we assessed brain activity by means of functional magnetic resonance imaging (fMRI) in 20 participants. Contingency frequency was manipulated between blocks which allowed us to determine the neural correlates of each of the three contingency tasks as well as the common areas of activation. The conjunction of task activation showed activity in left parietal cortices (BA 23, 40) and superior temporal gyrus (BA42). Further, the interaction analysis revealed distinct areas that mainly involve lateral (BA 45) and medial (BA 9) prefrontal cortices in the judgment of negative contingencies compared with positive and zero contingencies. We interpret the finding as evidence that the shared regions may be involved in coding, integration, and updating of associative relations and distinct regions may be involved in the investment of attentional resources to varied degrees in the computation of contingencies to make a judgment.

Novel Treatment of Ventilator Dyssynchrony From Central Alveolar Hypoventilation Syndrome Utilizing Scheduled 5-Hydroxytryptamine-3 Receptor Antagonist

Traumatic brain injury (TBI) occurs in a large percentage of surgical trauma patients and is one of the leading causes of death amongst young teens and adults. Furthermore, individuals with TBIs often require mechanical ventilation and admission to the intensive care unit. As a result of their TBIs, these patients can develop central alveolar hypoventilation (CAH) secondary to disruptions in neuromodulatory respiratory brainstem control and neural signal initiation and integration. Prior studies have primarily focused their attention on treatment of congenital disorders of CAH, and limited research is available on intubated trauma patients who have signs of ventilator dyssynchrony. Current case reports and animal studies have suggested that noradrenergic and specific serotonergic medications are able to target specific neurologic pathways in the respiratory circuit and induce ventilator synchrony. This case series describes the clinical course of TBI patients treated for ventilator dyssynchrony secondary to CAH with a daily scheduled 5-hydroxytryptamine-3 (5-HT3) receptor antagonist. All patients were ultimately extubated and discharged from the hospital.

Calibration-free, high-precision, and robust terahertz ultrafast metasurfaces for monitoring gastric cancers

Optical sensors, with great potential to convert invisible bioanalytical response into readable information, have been envisioned as a powerful platform for biological analysis and early diagnosis of diseases. However, the current extraction of sensing data is basically processed via a series of complicated and time-consuming calibrations between samples and reference, which inevitably introduce extra measurement errors and potentially annihilate small intrinsic responses. Here, we have proposed and experimentally demonstrated a calibration-free sensor for achieving high-precision biosensing detection, based on an optically controlled terahertz (THz) ultrafast metasurface. Photoexcitation of the silicon bridge enables the resonant frequency shifting from 1.385 to 0.825 THz and reaches the maximal phase variation up to 50° at 1.11 THz. The typical environmental measurement errors are completely eliminated in theory by normalizing the Fourier-transformed transmission spectra between ultrashort time delays of 37 ps, resulting in an extremely robust sensing device for monitoring the cancerous process of gastric cells. We believe that our calibration-free sensors with high precision and robust advantages can extend their implementation to study ultrafast biological dynamics and may inspire considerable innovations in the field of medical devices with nondestructive detection.

Techno-economic evaluation of biomass-to-fuels with solid-oxide electrolyzer

Thermochemical biomass-to-fuel conversion requires an increased hydrogen concentration in the syngas derived from gasification, which is currently achieved by water-gas-shift reaction and CO2 removal. State-of-the-art biomass-to-fuels convert less than half of the biomass carbon with the remaining emitted as CO2. Full conversion of biomass carbon can be achieved by integrating solid-oxide electrolyzer with different concepts: (1) steam electrolysis with the hydrogen produced injected into syngas, and (2) co-electrolysis of CO2 and H2O to convert the CO2 captured from the syngas. This paper investigates techno-economically steam- or co-electrolysis-based biomass-to-fuel processes for producing synthetic natural gas, methanol, dimethyl ether and jet fuel, considering system-level heat integration and optimal placement of steam cycles for heat recovery. The results show that state-of-the-art biomass-to-fuels achieve similar energy efficiencies of 48-51% (based on a lower heating value) for the four different fuels. The integrated concept with steam electrolysis achieves the highest energy efficiency: 68% for synthetic natural gas, 64% for methanol, 63% for dimethyl ether, and 56% for jet fuel. The integrated concept with co-electrolysis can enhance the state-of-the-art energy efficiency to 66% for synthetic natural gas, 61% for methanol, and 54% for jet fuel. The biomass-to-dimethyl ether with co-electrolysis only reaches an efficiency of 49%, due to additional heat demand. The levelized cost of the product of the integrated concepts highly depends on the price and availability of renewable electricity. The concept with co-electrolysis allows for additional operation flexibility without renewable electricity, resulting in high annual production. Thus, with limited annual available hours of renewable electricity, biomass-to-fuel with co-electrolysis is more economically convenient than that with steam electrolysis. For a plant scale of 60 MWth biomass input with the renewable electricity available for 1800 h annually, the levelized cost of product of biomass-to-synthesis-natural-gas with co-electrolysis is 35 $/GJ, 20% lower than that with steam-electrolysis.

Recent Advances in Artificial Intelligence and Tactical Autonomy: Current Status, Challenges, and Perspectives

This paper presents the findings of detailed and comprehensive technical literature aimed at identifying the current and future research challenges of tactical autonomy. It discusses in great detail the current state-of-the-art powerful artificial intelligence (AI), machine learning (ML), and robot technologies, and their potential for developing safe and robust autonomous systems in the context of future military and defense applications. Additionally, we discuss some of the technical and operational critical challenges that arise when attempting to practically build fully autonomous systems for advanced military and defense applications. Our paper provides the state-of-the-art advanced AI methods available for tactical autonomy. To the best of our knowledge, this is the first work that addresses the important current trends, strategies, critical challenges, tactical complexities, and future research directions of tactical autonomy. We believe this work will greatly interest researchers and scientists from academia and the industry working in the field of robotics and the autonomous systems community. We hope this work encourages researchers across multiple disciplines of AI to explore the broader tactical autonomy domain. We also hope that our work serves as an essential step toward designing advanced AI and ML models with practical implications for real-world military and defense settings.

Fast and Efficient Circuit Topologies for Finding the Maximum of n k-Bit Numbers

Finding the value and/or index of the maximum (or minimum) element of a set of n numbers (each with k-bits) is a fundamental arithmetic operation and is needed in many applications. This paper proposes several maximum-finder (or minimum-finder) circuit topologies, which are parallel. We wrote circuit generators at hardware description language level for our topologies and previous works. Then we synthesized these circuits for 20 different (n, k) cases (with values up to 64) and compared their efficiency in timing (latency), area, and energy. The timing complexity of our fastest topology is O(log n + log k), whereas the fastest in the literature is O(log n log k). The synthesis results showed that our fastest topology is 1.2-2.2 times (1.6 times on the average) faster than the state-of-the-art. In this paper, we argue that a more fair metric of area efficiency is area-timing product. In terms of ATP, our proposed topologies are better than the state-of-the-art in 19 out of the 20 cases. In terms of energy (i.e., power-timing product, abbreviated as PTP), we are better in 11 cases out of 20.

Bone marrow mesenchymal stem cells and exercise restore motor function following spinal cord injury by activating PI3K/AKT/mTOR pathway

Although many therapeutic interventions have shown promise in treating spinal cord injury, focusing on a single aspect of repair cannot achieve successful and functional regeneration in patients following spinal cord injury . In this study, we applied a combinatorial approach for treating spinal cord injury involving neuroprotection and rehabilitation, exploiting cell transplantation and functional sensorimotor training to promote nerve regeneration and functional recovery. Here, we used a mouse model of thoracic contusive spinal cord injury to investigate whether the combination of bone marrow mesenchymal stem cell transplantation and exercise training has a synergistic effect on functional restoration. Locomotor function was evaluated by the Basso Mouse Scale, horizontal ladder test, and footprint analysis. Magnetic resonance imaging, histological examination, transmission electron microscopy observation, immunofluorescence staining, and western blotting were performed 8 weeks after spinal cord injury to further explore the potential mechanism behind the synergistic repair effect. In vivo, the combination of bone marrow mesenchymal stem cell transplantation and exercise showed a better therapeutic effect on motor function than the single treatments. Further investigations revealed that the combination of bone marrow mesenchymal stem cell transplantation and exercise markedly reduced fibrotic scar tissue, protected neurons, and promoted axon and myelin protection. Additionally, the synergistic effects of bone marrow mesenchymal stem cell transplantation and exercise on spinal cord injury recovery occurred via the PI3K/AKT/mTOR pathway. In vitro, experimental evidence from the PC12 cell line and primary cortical neuron culture also demonstrated that blocking of the PI3K/AKT/mTOR pathway would aggravate neuronal damage. Thus, bone marrow mesenchymal stem cell transplantation combined with exercise training can effectively restore motor function after spinal cord injury by activating the PI3K/AKT/mTOR pathway.

RS-SSKD: Self-Supervision Equipped with Knowledge Distillation for Few-Shot Remote Sensing Scene Classification

While growing instruments generate more and more airborne or satellite images, the bottleneck in remote sensing (RS) scene classification has shifted from data limits toward a lack of ground truth samples. There are still many challenges when we are facing unknown environments, especially those with insufficient training data. Few-shot classification offers a different picture under the umbrella of meta-learning: digging rich knowledge from a few data are possible. In this work, we propose a method named RS-SSKD for few-shot RS scene classification from a perspective of generating powerful representation for the downstream meta-learner. Firstly, we propose a novel two-branch network that takes three pairs of original-transformed images as inputs and incorporates Class Activation Maps (CAMs) to drive the network mining, the most relevant category-specific region. This strategy ensures that the network generates discriminative embeddings. Secondly, we set a round of self-knowledge distillation to prevent overfitting and boost the performance. Our experiments show that the proposed method surpasses current state-of-the-art approaches on two challenging RS scene datasets: NWPU-RESISC45 and RSD46-WHU. Finally, we conduct various ablation experiments to investigate the effect of each component of the proposed method and analyze the training time of state-of-the-art methods and ours.

Transformer-Based Decoder Designs for Semantic Segmentation on Remotely Sensed Images

Transformers have demonstrated remarkable accomplishments in several natural language processing (NLP) tasks as well as image processing tasks. Herein, we present a deep-learning (DL) model that is capable of improving the semantic segmentation network in two ways. First, utilizing the pre-training Swin Transformer (SwinTF) under Vision Transformer (ViT) as a backbone, the model weights downstream tasks by joining task layers upon the pretrained encoder. Secondly, decoder designs are applied to our DL network with three decoder designs, U-Net, pyramid scene parsing (PSP) network, and feature pyramid network (FPN), to perform pixel-level segmentation. The results are compared with other image labeling state of the art (SOTA) methods, such as global convolutional network (GCN) and ViT. Extensive experiments show that our Swin Transformer (SwinTF) with decoder designs reached a new state of the art on the Thailand Isan Landsat-8 corpus (89.8% F1 score), Thailand North Landsat-8 corpus (63.12% F1 score), and competitive results on ISPRS Vaihingen. Moreover, both our best-proposed methods (SwinTF-PSP and SwinTF-FPN) even outperformed SwinTF with supervised pre-training ViT on the ImageNet-1K in the Thailand, Landsat-8, and ISPRS Vaihingen corpora.

Human and Scene Motion Deblurring Using Pseudo-Blur Synthesizer

Present-day deep learning-based motion deblurring methods utilize the pair of synthetic blur and sharp data to regress any particular framework. This task is designed for directly translating a blurry image input into its restored version as output. The aforementioned approach relies heavily on the quality of the synthetic blurry data, which are only available before the training stage. Handling this issue by providing a large amount of data is expensive for common usage. We answer this challenge by providing an on- the-fly blurry data augmenter that can be run during training and test stages. To fully utilize it, we incorporate an unorthodox scheme of deblurring framework that employs the sequence of blur-deblur-reblur-deblur steps. The reblur step is assisted by a reblurring module (synthesizer) that provides the reblurred version (pseudo-blur) of its sharp or deblurred counterpart. The proposed module is also equipped with hand-crafted prior extracted using the state-of-the-art human body statistical model. This prior is employed to map human and non-human regions during adversarial learning to fully perceive the characteristics of human-articulated and scene motion blurs. By engaging this approach, our deblurring module becomes adaptive and achieves superior outcomes compared to recent state-of-the-art deblurring algorithms.

Susceptibility versus resistance in alveolar echinococcosis (larval infection with Echinococcus multilocularis)

Epidemiological studies have demonstrated that the majority of human individuals exposed to infection with Echinococcus spp. eggs exhibit resistance to disease as shown by either seroconversion to parasite--specific antigens, and/or the presence of 'dying out' or 'aborted' metacestodes, not including hereby those individuals who putatively got infected but did not seroconvert and who subsequently allowed no development of the pathogen. For those individuals where infection leads to disease, the developing parasite is partially controlled by host immunity. In infected humans, the type of immune response developed by the host accounts for the subsequent trichotomy concerning the parasite development: (i) seroconversion proving infection, but lack of any hepatic lesion indicating the failure of the parasite to establish and further develop within the liver; or resistance as shown by the presence of fully calcified lesions; (ii) controlled susceptibility as found in the "conventional" alveolar echinococcosis (AE) patients who experience clinical signs and symptoms approximately 5-15 years after infection, and (iii) uncontrolled hyperproliferation of the metacestode due to an impaired immune response (AIDS or other immunodeficiencies). Immunomodulation of host immunity toward anergy seems to be triggered by parasite metabolites. Beside immunomodulating IL-10, TGFβ-driven regulatory T cells have been shown to play a crucial role in the parasite-modulated progressive course of AE. A novel CD4+CD25+ Treg effector molecule FGL2 recently yielded new insight into the tolerance process in Echinococcus multilocularis infection.

Atrophy patterns in early clinical stages across distinct phenotypes of Alzheimer's disease

Alzheimer's disease (AD) can present with distinct clinical variants. Identifying the earliest neurodegenerative changes associated with each variant has implications for early diagnosis, and for understanding the mechanisms that underlie regional vulnerability and disease progression in AD. We performed voxel-based morphometry to detect atrophy patterns in early clinical stages of four AD phenotypes: Posterior cortical atrophy (PCA, "visual variant," n=93), logopenic variant primary progressive aphasia (lvPPA, "language variant," n=74), and memory-predominant AD categorized as early age-of-onset (EOAD, <65 years, n=114) and late age-of-onset (LOAD, >65 years, n=114). Patients with each syndrome were stratified based on: (1) degree of functional impairment, as measured by the clinical dementia rating (CDR) scale, and (2) overall extent of brain atrophy, as measured by a neuroimaging approach that sums the number of brain voxels showing significantly lower gray matter volume than cognitively normal controls (n=80). Even at the earliest clinical stage (CDR=0.5 or bottom quartile of overall atrophy), patients with each syndrome showed both common and variant-specific atrophy. Common atrophy across variants was found in temporoparietal regions that comprise the posterior default mode network (DMN). Early syndrome-specific atrophy mirrored functional brain networks underlying functions that are uniquely affected in each variant: Language network in lvPPA, posterior cingulate cortex-hippocampal circuit in amnestic EOAD and LOAD, and visual networks in PCA. At more advanced stages, atrophy patterns largely converged across AD variants. These findings support a model in which neurodegeneration selectively targets both the DMN and syndrome-specific vulnerable networks at the earliest clinical stages of AD.

COVID-19 Vaccination Responses with Different Vaccine Platforms in Patients with Inborn Errors of Immunity

Patients with inborn errors of immunity (IEI) in Argentina were encouraged to receive licensed Sputnik, AstraZeneca, Sinopharm, Moderna, and Pfizer vaccines, even though most of the data of humoral and cellular responses combination on available vaccines comes from trials conducted in healthy individuals. We aimed to evaluate the safety and immunogenicity of the different vaccines in IEI patients in Argentina. The study cohort included adults and pediatric IEI patients (n = 118) and age-matched healthy controls (HC) (n = 37). B cell response was evaluated by measuring IgG anti-spike/receptor binding domain (S/RBD) and anti-nucleocapsid(N) antibodies by ELISA. Neutralization antibodies were also assessed with an alpha-S protein-expressing pseudo-virus assay. The T cell response was analyzed by IFN-γ secretion on S- or N-stimulated PBMC by ELISPOT and the frequency of S-specific circulating T follicular-helper cells (TFH) was evaluated by flow cytometry.No moderate/severe vaccine-associated adverse events were observed. Anti-S/RBD titers showed significant differences in both pediatric and adult IEI patients versus the age-matched HC cohort (p < 0.05). Neutralizing antibodies were also significantly lower in the patient cohort than in age-matched HC (p < 0.01). Positive S-specific IFN-γ response was observed in 84.5% of IEI patients and 82.1% presented S-specific TFH cells. Moderna vaccines, which were mainly administered in the pediatric population, elicited a stronger humoral response in IEI patients, both in antibody titer and neutralization capacity, but the cellular immune response was similar between vaccine platforms. No difference in humoral response was observed between vaccinated patients with and without previous SARS-CoV-2 infection.In conclusion, COVID-19 vaccines showed safety in IEI patients and, although immunogenicity was lower than HC, they showed specific anti-S/RBD IgG, neutralizing antibody titers, and T cell-dependent cellular immunity with IFN-γ secreting cells. These findings may guide the recommendation for a vaccination with all the available vaccines in IEI patients to prevent COVID-19 disease.

Stable, Low Power and Bit-Interleaving Aware SRAM Memory for Multi-Core Processing Elements

The machine learning and convolutional neural network (CNN)-based intelligent artificial accelerator needs significant parallel data processing from the cache memory. The separate read port is mostly used to design built-in computational memory (CRAM) to reduce the data processing bottleneck. This memory uses multi-port reading and writing operations, which reduces stability and reliability. In this paper, we proposed a self-adaptive 12T SRAM cell to increase the read stability for multi-port operation. The self-adaptive technique increases stability and reliability. We increased the read stability by refreshing the storing node in the read mode of operation. The proposed technique also prevents the bit-interleaving problem. Further, we offered a butterfly-inspired SRAM bank to increase the performance and reduce the power dissipation. The proposed SRAM saves 12% more total power than the state-of-the-art 12T SRAM cell-based SRAM. We improve the write performance by 28.15% compared with the state-of-the-art 12T SRAM design. The total area overhead of the proposed architecture compared to the conventional 6T SRAM cell-based SRAM is only 1.9 times larger than the 6T SRAM cell.