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Masked Dual-Rail Precharge Logic Encounters State-of-the-Art Power Analysis Methods

Latest evaluation of the state-of-the-art iMDPL logic style has shown small information leakage compared to its predecessor version MDPL. Concurrently, new advanced power analysis attacks specifically targeting iMDPL have been proposed. Up to now, these attacks are purely theoretic and have not been applied to an implementation. We present a comprehensive analysis of iMDPL, backed by real measurements collected from a 180 nm iMDPL prototype chip. We thoroughly study the extent of remaining information leakage of iMDPL by applying all relevant attacks. Our investigation shows the vulnerability of the target device, a standalone AES core, to several of the advanced attack methods. In comparison to conventional power analysis attacks, the advanced attacks need less power measurements to obtain meaningful results. With the help of logic level simulations routing imbalances between complementary mask trees are identified as a major source of leakage.

Solar irradiation accelerates the oxidation of Cr(III) by δ-manganese dioxide

Cr(VI) has been observed to be released from Cr(III)-bearing natural sources or residues when they are found alongside manganese and manganese oxides. However, relevant mechanism studies normally ignore the effect of simulated solar irradiation on this oxidation reaction. Therefore, we investigated the photochemical reaction between Cr(OH)3 and δ-MnO2, the common species of chromium and manganese oxide found in the environment. At pH 11, the oxidation of Cr(OH)3 by δ-MnO2 was accelerated under simulated solar irradiation, which had an oxidation rate 2.7-fold greater than that in the dark condition. Further investigation revealed that δ-MnO2, an n-type semiconductor with a 2.7 eV band gap, can be excited by light with wavelengths < 459 nm and produce photogenic electrons and holes. These photo-induced carriers reacted with surrounding molecules to form free radicals and participate the redox reactions. Free-radical quenching experiments indicated that hydroxyl radicals (•OH) are the main oxidants of Cr(III) under simulated solar irradiation. This work provides new mechanistic insight into the oxidation of Cr(III) to Cr(VI), which may help clarifying the environmental fate of Cr and the potential solar-triggered release of Cr(VI).

Three-Dimensional Covalent Organic Framework with scu-c Topology for Drug Delivery

Three-dimensional (3D) covalent organic frameworks (COFs) exemplify a new generation of crystalline extended solids with intriguing structures and unprecedented porosity. Notwithstanding substantial scope, the reticular synthesis of 3D COFs from pre-designed building units leading to new network topologies yet remains a demanding task owing to the shortage of 3D building units and inadequate reversibility of the linkages between the building units. In this work, by linking a tetragonal prism (8-connected) node with a square planar (4-connected) node, we report the first 3D COF with scu-c topology. The new COF, namely, TUS-84, features a two-fold interpenetrated structure with well-defined porosity and a Brunauer-Emmett-Teller surface area of 679 m2 g-1. In drug delivery applications, TUS-84 shows efficient drug loading and sustained release profile.

SWAP: A Server-Scale Communication-Aware Chiplet-Based Manycore PIM Accelerator

Processing-in-memory (PIM) is a promising technique to accelerate deep learning (DL) workloads. Emerging DL workloads (e.g., ResNet with 152 layers) consist of millions of parameters, which increase the area and fabrication cost of monolithic PIM accelerators. The fabrication cost challenge can be addressed by 2.5-D systems integrating multiple PIM chiplets connected through a network-on-package (NoP). However, server-scale scenarios simultaneously execute multiple compute-heavy DL workloads, leading to significant interchiplet data volume. State-of-the-art NoP architectures proposed in the literature do not consider the nature of DL workloads. In this article, we propose a novel server scale 2.5-D manycore architecture called SWAP that accounts for the traffic characteristics of DL applications. Comprehensive experimental evaluations with different system sizes as well as diverse emerging DL workloads demonstrate that SWAP achieves significant performance and energy consumption improvements with much lower fabrication cost than state-of-the-art NoP topologies.

Efficacy of a recombinant M-like protein, SimA as a subunit vaccine candidate against Streptococcus parauberis infection in olive flounder, Paralichthys olivaceus

Streptococcus parauberis, a gram-positive cocci, causes bacterial disease in farmed fish. The recent increase in S. parauberis infection in aquatic farms in South Korea has justified the importance of vaccine development for the prevention of this disease. In this study, we evaluated the effect of subunit vaccines prepared from recombinant M-like protein (SimA) and fibrinogen-binding protein (FBP) candidates with an aluminum hydroxide adjuvant against S. parauberis infection in olive flounder Paralichthys olivaceus. For the in vivo experiment, fish (average length, 7.18 cm; average weight, 3.5 g) were injected intraperitoneally with: phosphate buffer saline (PBS, group 1), PBS/aluminum hydroxide (group 2), FBP/aluminum hydroxide (group 3), SimA/aluminum hydroxide (group 4), and SimA/FBP/aluminum hydroxide (group 5). After 3 weeks, the fish in each group were boosted using PBS (group 1 and 2), FBP (group 3), SimA (group 4), and SimA/FBP (group 5) without adjuvant. We found that the relative percent survival of fish after S. parauberis exposure in group 2, 3, 4, and 5 was 6.25%, 18.75%, 50%, and 12.5%, respectively, whereas the mortality in groups 1 was 80%, respectively. We performed Western blot, ELISA, and quantitative real time RT-PCR (qRT-PCR) after vaccination to investigate the further efficacy of the vaccine. Western blot and ELISA of vaccinated fish serum confirmed the production of specific antibodies against SimA and FBP. Furthermore, results of qRT-PCR showed that recombinant protein SimA induced a remarkably specific-antibody response compared with that in FBP or control and increased the expression of various immune response-related genes including interleukin-8 (IL-8), toll-like receptor 2 (TLR2), tumor necrosis factor-α (TNF-α), CD4-1, and MHC II. Thus, these results indicate that SimA is a potent vaccine candidate for protection against S. parauberis infection.

Closed-Loop Neural Prostheses With On-Chip Intelligence: A Review and a Low-Latency Machine Learning Model for Brain State Detection

The application of closed-loop approaches in systems neuroscience and therapeutic stimulation holds great promise for revolutionizing our understanding of the brain and for developing novel neuromodulation therapies to restore lost functions. Neural prostheses capable of multi-channel neural recording, on-site signal processing, rapid symptom detection, and closed-loop stimulation are critical to enabling such novel treatments. However, the existing closed-loop neuromodulation devices are too simplistic and lack sufficient on-chip processing and intelligence. In this paper, we first discuss both commercial and investigational closed-loop neuromodulation devices for brain disorders. Next, we review state-of-the-art neural prostheses with on-chip machine learning, focusing on application-specific integrated circuits (ASIC). System requirements, performance and hardware comparisons, design trade-offs, and hardware optimization techniques are discussed. To facilitate a fair comparison and guide design choices among various on-chip classifiers, we propose a new energy-area (E-A) efficiency figure of merit that evaluates hardware efficiency and multi-channel scalability. Finally, we present several techniques to improve the key design metrics of tree-based on-chip classifiers, both in the context of ensemble methods and oblique structures. A novel Depth-Variant Tree Ensemble (DVTE) is proposed to reduce processing latency (e.g., by 2.5x on seizure detection task). We further develop a cost-aware learning approach to jointly optimize the power and latency metrics. We show that algorithm-hardware co-design enables the energy- and memory-optimized design of tree-based models, while preserving a high accuracy and low latency. Furthermore, we show that our proposed tree-based models feature a highly interpretable decision process that is essential for safety-critical applications such as closed-loop stimulation.

Semantic Graph Attention With Explicit Anatomical Association Modeling for Tooth Segmentation From CBCT Images

Accurate tooth identification and delineation in dental CBCT images are essential in clinical oral diagnosis and treatment. Teeth are positioned in the alveolar bone in a particular order, featuring similar appearances across adjacent and bilaterally symmetric teeth. However, existing tooth segmentation methods ignored such specific anatomical topology, which hampers the segmentation accuracy. Here we propose a semantic graph-based method to explicitly model the spatial associations between different anatomical targets (i.e., teeth) for their precise delineation in a coarse-to-fine fashion. First, to efficiently control the bilaterally symmetric confusion in segmentation, we employ a lightweight network to roughly separate teeth as four quadrants. Then, designing a semantic graph attention mechanism to explicitly model the anatomical topology of the teeth in each quadrant, based on which voxel-wise discriminative feature embeddings are learned for the accurate delineation of teeth boundaries. Extensive experiments on a clinical dental CBCT dataset demonstrate the superior performance of the proposed method compared with other state-of-the-art approaches.

Reverse logistics: the state of the art and future perspectives

This paper characterizes the state of the art in the area of reverse logistics by analyzing publications in the ScienceDirect and Web of Science (WOS) databases through bibliometric and systematic analysis. From a sample of 462 papers, leading journals, authors, research institutions and countries, as well as research partnerships were identified. It was noted that publications on the subject have steadily increased over the years, with the United States having the largest number of publications and partnerships. Brazilian researchers should seek partnerships with foreign institutions, since they have demonstrated the potential to enrich discussions and foster quality publications. This paper identified the main studies in this area, as well as the gaps that could guide future researches.

A State of the Art of Power Transmission Line Maintenance Robots

This paper dealt with a state-of-the-art maintenance robots for power transmission lines (PTL). The paper summarized the PTL maintenance robots that have been developed by eight major research institutions. The main features of the robot navigation were derived and classified through analysis of researches conducted by those institutions. The derived six main features were analyzed quantitatively and qualitatively through readiness level assessment. For this, system readiness level (SRL) was used to evaluate the readiness level of overall system. The SRL was obtained using. technology readiness level (TRL) and integration readiness level (IRL). In addition, the readiness level for each feature was reviewed through statistical analysis using box and whisker plot. In conclusion, we confirmed the status and direction of research and development on PTL maintenance robots by analyzing conventional robots with respect to six main features.

Antiskin Aging Effects of Indole Alkaloid N-Glycoside from Ginkgo Fruit ( Ginkgo biloba fruit) on TNF-α-Exposed Human Dermal Fibroblasts

Human skin aging has internal and external factors, both of which are characterized by TNF-α overproduction. Therefore, we aimed to identify a natural product that suppresses the damage that occurs in cutaneous dermal fibroblasts exposed to TNF-α. The protective effects of the indole alkaloid N-glycoside, ginkgoside B dimethyl ester (GBDE), isolated from ginkgo fruit (Ginkgo biloba fruit) were evaluated in TNF-α stimulated human dermal fibroblasts (HDFs). GBDE inhibited TNF-α-induced MMP-1 expression to 2.2 ± 0.1-fold (p < 0.01) and reversed the decrease in collagen levels to 0.4 ± 0.00-fold (p < 0.01) at 50 μM. The effect of GBDE was due to the suppression of the phospolylaton of MAPKs (ERK, 0.47 ± 0.05; JNK, 1.21 ± 0.07; p38, 0.77 ± 0.07-folds, p < 0.001) and Akt (0.14 ± 0.03-fold, p < 0.001) compared to the TNF-α group. GBDE also reduced the expression of COX-2 to 2.06 ± 0.12-fold (p < 0.001) and increased the expression of HO-1 to 10.64 ± 0.2-fold (p < 0.001). In addition, GBDE inhibited the expression of the pro-inflammatory cytokines (IL-8, 2.2 ± 0.0; IL-1β, 1.6 ± 0.0; IL-6, 2.0 ± 0.10-folds, p < 0.05). These results provide experimental evidence that GBDE can protect against skin damage, including aging.

BiTransformer: augmenting semantic context in video captioning via bidirectional decoder

Video captioning is an important problem involved in many applications. It aims to generate some descriptions of the content of a video. Most of existing methods for video captioning are based on the deep encoder-decoder models, particularly, the attention-based models (say Transformer). However, the existing transformer-based models may not fully exploit the semantic context, that is, only using the left-to-right style of context but ignoring the right-to-left counterpart. In this paper, we introduce a bidirectional (forward-backward) decoder to exploit both the left-to-right and right-to-left styles of context for the Transformer-based video captioning model. Thus, our model is called bidirectional Transformer (dubbed BiTransformer). Specifically, in the bridge of the encoder and forward decoder (aiming to capture the left-to-right context) used in the existing Transformer-based models, we plug in a backward decoder to capture the right-to-left context. Equipped with such bidirectional decoder, the semantic context of videos will be more fully exploited, resulting in better video captions. The effectiveness of our model is demonstrated over two benchmark datasets, i.e., MSVD and MSR-VTT,via comparing to the state-of-the-art methods. Particularly, in terms of the important evaluation metric CIDEr, the proposed model outperforms the state-of-the-art models with improvements of 1.2% in both datasets.

Multi-Modal Deep Guided Filtering for Comprehensible Medical Image Processing

Deep learning-based image processing is capable of creating highly appealing results. However, it is still widely considered as a "blackbox" transformation. In medical imaging, this lack of comprehensibility of the results is a sensitive issue. The integration of known operators into the deep learning environment has proven to be advantageous for the comprehensibility and reliability of the computations. Consequently, we propose the use of the locally linear guided filter in combination with a learned guidance map for general purpose medical image processing. The output images are only processed by the guided filter while the guidance map can be trained to be task-optimal in an end-to-end fashion. We investigate the performance based on two popular tasks: image super resolution and denoising. The evaluation is conducted based on pairs of multi-modal magnetic resonance imaging and cross-modal computed tomography and magnetic resonance imaging datasets. For both tasks, the proposed approach is on par with state-of-the-art approaches. Additionally, we can show that the input image's content is almost unchanged after the processing which is not the case for conventional deep learning approaches. On top, the proposed pipeline offers increased robustness against degraded input as well as adversarial attacks.

Intelligent Framework for Learning Physics with Aikido (Martial Art) and Registered Sensors

Physics is considered a tough academic subject by learners. To leverage engagement in the learning of this STEM area, teachers try to come up with creative ideas about the design of their classroom lessons. Sports-related activities can foster intuitive knowledge about physics (gravity, speed, acceleration, etc.). In this context, martial arts also provide a novel way of visualizing these ideas when performing the predefined motions needed to master the associated techniques. The recent availability of cheap monitoring hardware (accelerometers, cameras, etc.) allows an easy tracking of the aforementioned movements, which in the case of aikido, usually involve genuine circular motions. In this paper, we begin by reporting a user study among high-school students showing that the physics concept of moment of inertia can be understood by watching live exhibitions of specific aikido techniques. Based on these findings, we later present Phy + Aik, a tool for educators that enables the production of innovative visual educational material consisting of high-quality videos (and live demonstrations) synchronized/tagged with the inertial data collected by sensors and visual tracking devices. We think that a similar approach, where sensors are automatically registered within an intelligent framework, can be explored to teach other difficult-to-learn STEM concepts.

A Survey on Mobile Learning for Adult Learners: State-of-the-Art, Taxonomy, and Challenges

Adult learner performance has become a significant challenge in education. Adult learners are identified as being of productive age in engaging with multiple commitments, including studying. The multiple engagements make managing their time and focus difficult, contributing to a high institutional dropout rate. This situation has reduced the number of highly skilled and professional workers in the country because of the failure to meet academic requirements. In the current digital world, every aspect of education includes some technology elements. However, most adult learners are reluctant to utilize mobile technology for learning. Therefore, this review paper aims to provide state of the art on mobile learning (m-Learning) for adult learners. Considering a set of inclusion and exclusion criteria, 135 related articles from IEEE, Google Scholar, Springer Link, Science Direct, Taylor and Francis, and Wiley digital databases were extracted and reviewed. State of the art were discussed in terms of functions of m-Learning, effectiveness, and m-Learning as a tool. Keywords from articles were extracted and the taxonomy of m-Learning was discussed under five classifications (behavioural intention, technological support, educational content, learner coordination, and instructional design), representing the same meaning and features discussed in the articles. The findings can raise awareness among mobile learning practitioners to provide more effective services for adult learners. Meanwhile, higher education institutions can be better redesigned in terms of pedagogy to fit the needs of adult learners with mobile learning.

An Optimized Approach for Pansharpening Very High Resolution Multispectral Images

State-of-the-art pansharpening methods generally inject the spatial details extracted from the panchromatic (Pan) image into the multispectral (MS) images by considering different injection models. The fusion performances severely rely on the accuracy of the modeling and the estimation of model parameters. In this letter, we propose an optimized approach to avoid explicitly modeling the detail injection process. The solution employs the gradient field of the Pan image for spatial enhancement. The low-pass (LP) version of the fused bands are constrained to be the most similar to the original MS bands to preserve the spectral characteristics. We use the local correlation coefficients between the MS band and the LP version of the Pan image to adjust the two sources of information based on a simple observation, and it is further optimized by considering the overall quality index Q4. Experimental results demonstrate that the proposed method outperforms the state-of-the-art multiresolution analysis-based methods.

Patient-Derived Organoids for In Vivo Validation of In Vitro Data

Patient-derived organoids are promising tumor models for functional validation of next-generation sequencing-based therapy recommendations. In times of rapidly advancing precision oncology approaches in everyday clinical processes, reliable and valid tumor models are required. Tumor organoids consist of tumor "stem" cells, differentiated (epithelial) tumor, and stroma cells. The cellular architecture and interactions closely mimic the original patient tumor. These organoids can be implanted into immunodeficient mice, generating patient-derived organoid-derived xenografts, thus enabling in vitro to in vivo transfer. Most importantly, the high clinical relevance of PDO models is maintained in this conversion. This protocol describes in detail the methods and techniques as well as the materials necessary to generate in vitro PDO and in vivo PDO-derived xenograft models. The elaborate process description starts with the processing of freshly obtained tumor tissue. The proceedings include tissue processing, organoid culturing, PDO implantation into immunodeficient mice, tumor explantation, and finally tumor preservation. All these proceedings are described in this timely chronological order. This protocol will enable researchers to generate PDO models from freshly received tumor tissue and generate PDO-derived xenografts. Models generated according to these methods are suitable for a very broad research spectrum.

A survey of recent grinding wheel topography models

This paper provides a survey of grinding wheel topography models. Recent 1D, 2D, and 3D models are reviewed, and the important model components for a state-of-the-art 3D topography model are identified. Future directions for topography modeling are recommended and, based on this survey, a general modelling approach using grain size, shape, arrangement, and wheel dressing is proposed. (c) 2005 Elsevier Ltd. All rights reserved.

User-Driven Fine-Tuning for Beat Tracking

The extraction of the beat from musical audio signals represents a foundational task in the field of music information retrieval. While great advances in performance have been achieved due the use of deep neural networks, significant shortcomings still remain. In particular, performance is generally much lower on musical content that differs from that which is contained in existing annotated datasets used for neural network training, as well as in the presence of challenging musical conditions such as rubato. In this paper, we positioned our approach to beat tracking from a real-world perspective where an end-user targets very high accuracy on specific music pieces and for which the current state of the art is not effective. To this end, we explored the use of targeted fine-tuning of a state-of-the-art deep neural network based on a very limited temporal region of annotated beat locations. We demonstrated the success of our approach via improved performance across existing annotated datasets and a new annotation-correction approach for evaluation. Furthermore, we highlighted the ability of content-specific fine-tuning to learn both what is and what is not the beat in challenging musical conditions.

Locative Meaning-making: An Arts-based Approach to Learning for Sustainable Development

The term sustainable development is often criticized for having lost credibility due to a lack of clear-cut delineation. The same holds true for education designed to foster sustainable development often referred to as education for sustainable development (ESD). This contribution agrees that the term suffers from a want of meaning, but argues that the persistent hunt for a definition-i.e., a fixed generic description-produces rather than resolves this deficit. What sustainable development means is context and time dependent and is therefore necessarily ambiguous, open-ended and dynamic. Hence, the success of ESD depends on the paradoxical imperative of reducing vagueness while at the same time maintaining ambiguity. This paper explores how this can be established and proposes a process informed by the arts. Drawing from dialogic practices, site-specific theatre and a project conducted in a British village, this writing discusses elements that constitute a process of. context-based meaning finding.. It concludes that ESD essentially starts with and revolves around re-embedding SD in life and the act of living, engaging people in place through processes in which communities yield their own, context and time specific interpretations of sustainable development.

Detecting Intimate Partner Violence During Pregnancy Using Municipal Pregnancy Registration Records: An Administrative Data Analysis

Intimate partner violence (IPV) during pregnancy needs to be prevented because it leads to negative health outcomes for both the mother and offspring. However, it is not easy to detect women who suffer from IPV by health practitioners or public health staff due to stigma attached to it or hesitation of the women to disclose it. The aim of this study is to develop a scale using pregnancy registration records to detect IPV during pregnancy. We used administrative data of pregnancy registration records of Adachi City, Tokyo, in the 2016 fiscal year (N = 5,990). IPV was assessed at the first interview or another opportunity for further assessment by a public health nurse. The data include registration information, demographics, health and perinatal status, and social environment. Multiple logistic regression model was used to predict IPV. IPV cases were found for 24 (0.4%) cases. Subsequent child (odds ratio [OR]: 3.45, 95% confidence interval [CI] [1.02-11.6]), single marital status (OR: 7.96, 95% CI [2.88-22.2]), thinness (OR: 3.17, 95% CI [1.13-8.90]), past pregnancy of four or more times (OR: 5.25, 95% CI [1.35-20.4]), having trouble with family member (OR: 5.45, 95% CI [1.95, 15.2]), and poverty (OR: 6.27, 95% CI [2.25-17.5]) showed significant association with IPV. These variables detected IPV with good predictive power (area under receiver operating characteristic curve = 0.89, 95% CI [0.81-0.98]). We showed strong detectability of IPV during pregnancy using a scale based on pregnancy registration records in which IPV was not asked directly. The current study is useful to detect IPV during pregnancy and prevent further adverse health outcomes due to IPV during pregnancy.

Prolonged Therapy Is Not Associated with Delayed Identification of Recurrent Intra-Abdominal Infection

Background: The Study to Optimize Peritoneal Infection Therapy (STOP-IT) Trial identified an association between prolonged antibiotic therapy and delayed identification of recurrent intra-abdominal infection (IAI). However, this association has not been observed in other studies. The purpose of this study was to evaluate the association between recurrent IAIs and the duration of antibiotic agents. Patients and Methods: Adult patients from 2016 to 2020 who underwent a source control procedure for a colon-related complicated IAI were identified. Patients not meeting the inclusion criteria were excluded. Demographics, comorbidities, post-operative antibiotic duration, and presence of secondary intra-abdominal infection were recorded. The primary outcome was the time to identification of secondary IAI. Delayed identification of recurrent infection was identified as 10 or more days following source control procedure. Statistical analysis using χ2, Fisher exact, and Wilcoxon rank sum were used where appropriate. Results: Seventy-six of the patients identified met inclusion criteria, and 17 (22.4%) of those patients had a recurrent IAI. Patients with recurrent infections were slightly younger (64 vs. 60 years; p = 0.01) and had lower rates of pre-operative anticoagulation (50.8% vs. 17.6%). There were no differences in the initial length of antibiotic therapy after source control between the recurrent infection and non-recurrent groups (p = 0.6). There was a difference in total days of antibiotic use between the two groups, with the recurrent infection group averaging 10 more days of antibiotic use than the non-recurrence group (p < 0.0001). In those patients with a recurrence, there were no differences in median days to identification (9 vs. 11.5 days; p = 0.29) or the rate of those with delayed identification of recurrent infection (44.4% vs. 75%; p = 0.33). Conclusions: Similar to the STOP-IT Trial we failed to identify an association between the duration of post-operative antibiotic agents and recurrent infection. However, we further failed to identify an association between the prolonged post-operative courses and the timing of identification of the recurrent infection. Further evaluation is needed to determine if prolonged therapy delays the identification of recurrent infection.

Learning to Predict Sequences of Human Visual Fixations

Most state-of-the-art visual attention models estimate the probability distribution of fixating the eyes in a location of the image, the so-called saliency maps. Yet, these models do not predict the temporal sequence of eye fixations, which may be valuable for better predicting the human eye fixations, as well as for understanding the role of the different cues during visual exploration. In this paper, we present a method for predicting the sequence of human eye fixations, which is learned from the recorded human eye-tracking data. We use least-squares policy iteration (LSPI) to learn a visual exploration policy that mimics the recorded eye-fixation examples. The model uses a different set of parameters for the different stages of visual exploration that capture the importance of the cues during the scanpath. In a series of experiments, we demonstrate the effectiveness of using LSPI for combining multiple cues at different stages of the scanpath. The learned parameters suggest that the low-level and high-level cues (semantics) are similarly important at the first eye fixation of the scanpath, and the contribution of high-level cues keeps increasing during the visual exploration. Results show that our approach obtains the state-of-the-art performances on two challenging data sets: 1) OSIE data set and 2) MIT data set.

Web mining in soft computing framework: Relevance, state of the art and future directions

This paper summarizes the different characteristics of web data, the basic components of web mining and its different types, and their current states of the art. The reason for considering web mining, a separate field from data mining, is explained. The limitations of some of the existing web mining methods and tools are enunciated, and the significance of soft computing (comprising fuzzy logic (FL), artificial neural networks (ANNs), genetic algorithms (GAs), and rough sets (RSs) highlighted. A survey of the existing literature on "soft web mining" is provided along with the commercially available systems. The prospective areas of web mining where the application of soft computing needs immediate attention are outlined with justification. Scope for future research in developing "soft web mining" systems is explained. An extensive bibliography is also provided.

Innovative application research on the combination of art design and engineering practice education under the background of new media

Under the background of the continuous impact of new media, with the deepening of art education reform, more creative teaching thinking and teaching methods are integrated into the art design education classroom in universities. In the continuous progress of art design major, how to change the traditional art education classroom and make the teaching more innovative has become one of the research contents that the teaching workers focus on. Therefore, in order to export interdisciplinary talents to the society, it is an inevitable trend for universities to act interdisciplinary teaching and learning mode in combination with the background of new engineering and the widely used STEAM concept. This paper analyzes the teaching mode of art design major in higher education, and then analyzes the current situation of engineering practice courses in universities. According to the characteristics of art students and engineering students, it explores the interdisciplinary teaching mode of introducing art design elements into engineering practice courses, which can provide the orderly development of art education with reference in universities.

Sensitivity analysis of coupled interconnects for RFIC applications

This paper investigates the sensitivity of on-wafer coupled interconnects to the Si CMOS process parameters. Experiments are conducted to emulate state-of-the-art and future technologies. Some important parameters characterizing the coupled interconnects have been examined. The influence of the process parameters on transmission, reflection, near-end, and far-end crosstalk capacities of the coupled interconnects are discussed.

Influence of Polymer Characteristics on the Self-Assembly of Polymer-Grafted Metal-Organic Framework Particles

Polymer-grafted metal-organic frameworks (MOFs) can combine the properties of MOFs and polymers into a single, matrix-free composite material. Herein, we examine polymer-grafted MOF particles (using UiO-66 as a model system) to examine how the molecular weight, grafting density, and chemical functionality of the polymer graft affects the preparation of free-standing self-assembled MOF monolayers (SAMMs). The physical properties of the monolayers are influenced by the choice of polymer, and robust, flexible monolayers were achieved more readily with poly(methyl acrylate) when compared to poly(methyl methacrylate) or poly(benzyl methacrylate). Molecular dynamics simulations were carried out to provide insights into the orientation and ordering of MOFs in the monolayers with respect to MOF size, graft length, and hydrophobicity. The relationship between molecular weight and graft density of the polymer brush was investigated and related to polymer brush conformation, offering design rules for further optimizations to balance mechanical strength, MOF weight fraction, and processability for this class of hybrid materials.

Estimation of functional diversity and species traits from ecological monitoring data

The twin crises of climate change and biodiversity loss define a strong need for functional diversity monitoring. While the availability of high-quality ecological monitoring data is increasing, the quantification of functional diversity so far requires the identification of species traits, for which data are harder to obtain. However, the traits that are relevant for the ecological function of a species also shape its performance in the environment and hence, should be reflected indirectly in its spatiotemporal distribution. Thus, it may be possible to reconstruct these traits from a sufficiently extensive monitoring dataset. Here, we use diffusion maps, a deterministic and de facto parameter-free analysis method, to reconstruct a proxy representation of the species' traits directly from monitoring data and use it to estimate functional diversity. We demonstrate this approach with both simulated data and real-world phytoplankton monitoring data from the Baltic Sea. We anticipate that wider application of this approach to existing data could greatly advance the analysis of changes in functional biodiversity.

An Automatic Learning-Based Framework for Robust Nucleus Segmentation

Computer-aided image analysis of histopathology specimens could potentially provide support for early detection and improved characterization of diseases such as brain tumor, pancreatic neuroendocrine tumor (NET), and breast cancer. Automated nucleus segmentation is a prerequisite for various quantitative analyses including automatic morphological feature computation. However, it remains to be a challenging problem due to the complex nature of histopathology images. In this paper, we propose a learning-based framework for robust and automatic nucleus segmentation with shape preservation. Given a nucleus image, it begins with a deep convolutional neural network (CNN) model to generate a probability map, on which an iterative region merging approach is performed for shape initializations. Next, a novel segmentation algorithm is exploited to separate individual nuclei combining a robust selection-based sparse shape model and a local repulsive deformable model. One of the significant benefits of the proposed framework is that it is applicable to different staining histopathology images. Due to the feature learning characteristic of the deep CNN and the high level shape prior modeling, the proposed method is general enough to perform well across multiple scenarios. We have tested the proposed algorithm on three large-scale pathology image datasets using a range of different tissue and stain preparations, and the comparative experiments with recent state of the arts demonstrate the superior performance of the proposed approach.

Flat-Start Single-Stage Discriminatively Trained HMM-Based Models for ASR

In recent years, end-to-end approaches to automatic speech recognition have received considerable attention as they are much faster in terms of preparing resources. However, conventional multistage approaches, which rely on a pipeline of training hidden Markov models (HMM)-GMM models and tree-building steps still give the state-of-the-art results on most databases. In this study, we investigate flat-start one-stage training of neural networks using lattice-free maximum mutual information (LF-MMI) objective function with HMM for large vocabulary continuous speech recognition. We thoroughly look into different issues that arise in such a setup and propose a standalone system, which achieves word error rates (WER) comparable with that of the state-of-the-art multi-stage systems while being much faster to prepare. We propose to use full biphones to enable flat-start context-dependent (CD) modeling and show through experiments that our CD modeling approach can be almost as effective as regular tree-based CD modeling. We show that our flat-start LF-MMI setup together with this tree-free CD modeling technique achieves 10 to 25 % relative WER reduction compared to other end-to-end methods on well-known databases. The improvements are larger for smaller databases.

BioThreads: A Novel VLIW-Based Chip Multiprocessor for Accelerating Biomedical Image Processing Applications

We discuss BioThreads, a novel, configurable, extensible system-on-chip multiprocessor and its use in accelerating biomedical signal processing applications such as imaging photoplethysmography (IPPG). BioThreads is derived from the LE1 open-source VLIW chip multiprocessor and efficiently handles instruction, data and thread-level parallelism. In addition, it supports a novel mechanism for the dynamic creation, and allocation of software threads to uncommitted processor cores by implementing key POSIX Threads primitives directly in hardware, as custom instructions. In this study, the BioThreads core is used to accelerate the calculation of the oxygen saturation map of living tissue in an experimental setup consisting of a high speed image acquisition system, connected to an FPGA board and to a host system. Results demonstrate near-linear acceleration of the core kernels of the target blood perfusion assessment with increasing number of hardware threads. The BioThreads processor was implemented on both standard-cell and FPGA technologies; in the first case and for an issue width of two, full real-time performance is achieved with 4 cores whereas on a mid-range Xilinx Virtex6 device this is achieved with 10 dual-issue cores. An 8-core LE1 VLIW FPGA prototype of the system achieved 240 times faster execution time than the scalar Microblaze processor demonstrating the scalability of the proposed solution to a state-of-the-art FPGA vendor provided soft CPU core.

3-D object retrieval using topic model

In the last few years, extensive effort has been spent to develop better performed 3-D object retrieval methods. View-based methods have attracted a significant amount of attention, not only because their state-of-art performance, but also they merely require some of a 3-D object's 2-D view images. However, most recent approaches only deal with the images' primordial-extracted features and ignore their hidden relationships. Considering these latent characters, a visual-topic-model 3-D object retrieval approach is introduced in this paper. In this framework, dense scale invariant feature transform(dense-SIFT) descriptors are extracted from a set of views of each 3-D object, and all the dense-SIFT descriptors are grouped into bag-of-word features using k-means clustering. Then, the topic distribution of a 3-D object is generated via latent dirichlet allocation (LDA) given its bag-of-word features. Gibbs sampling is applied in the learning and inference processing of LDA. We conduct experiments on the Princeton Shape Benchmark (PSB) and National Taiwan University 3D model database (NTU), and the experimental results demonstrate that the proposed method can achieve better retrieval effectiveness than the state-of-the-art methods under several standard evaluation measures.

A plant-based mutant huntingtin model-driven discovery of impaired expression of GTPCH and DHFR

Pathophysiology associated with Huntington's disease (HD) has been studied extensively in various cell and animal models since the 1993 discovery of the mutant huntingtin (mHtt) with abnormally expanded polyglutamine (polyQ) tracts as the causative factor. However, the sequence of early pathophysiological events leading to HD still remains elusive. To gain new insights into the early polyQ-induced pathogenic events, we expressed Htt exon1 (Httex1) with a normal (21), or an extended (42 or 63) number of polyQ in tobacco plants. Here, we show that transgenic plants accumulated Httex1 proteins with corresponding polyQ tracts, and mHttex1 induced protein aggregation and affected plant growth, especially root and root hair development, in a polyQ length-dependent manner. Quantitative proteomic analysis of young roots from severely affected Httex1Q63 and unaffected Httex1Q21 plants showed that the most reduced protein by polyQ63 is a GTP cyclohydrolase I (GTPCH) along with many of its related one-carbon (C1) metabolic pathway enzymes. GTPCH is a key enzyme involved in folate biosynthesis in plants and tetrahydrobiopterin (BH4) biosynthesis in mammals. Validating studies in 4-week-old R6/2 HD mice expressing a mHttex1 showed reduced levels of GTPCH and dihydrofolate reductase (DHFR, a key folate utilization/alternate BH4 biosynthesis enzyme), and impaired C1 and BH4 metabolism. Our findings from mHttex1 plants and mice reveal impaired expressions of GTPCH and DHFR and may contribute to a better understanding of mHtt-altered C1 and BH4 metabolism, and their roles in the pathogenesis of HD.

DYANOM-Dykstra's projection based atomic norm solver

In this paper, we propose a fast monotonic algorithm named DYANOM to find the optimal minimizer of the atomic norm minimization problem (ANM), which has extensive applications ranging from spectral estimation, direction of arrival estimation to system identification. DYANOM is faster as well as more accurate than the state-of-the-art methods and enjoys monotonicity property. We present numerical simulations in the context of frequency estimation and compare its numerical performance with the stateof-the-art methods. (c) 2020 Elsevier B.V. All rights reserved.

Matching Software-Generated Sketches to Face Photographs With a Very Deep CNN, Morphed Faces, and Transfer Learning

Sketches obtained from eyewitness descriptions of criminals have proven to be useful in apprehending criminals, particularly when there is a lack of evidence. Automated methods to identify subjects depicted in sketches have been proposed in the literature, but their performance is still unsatisfactory when using software-generated sketches and when tested using extensive galleries with a large amount of subjects. Despite the success of deep learning in several applications including face recognition, little work has been done in applying it for face photograph-sketch recognition. This is mainly a consequence of the need to ensure robust training of deep networks by using a large number of images, yet limited quantities are publicly available. Moreover, most algorithms have not been designed to operate on software-generated face composite sketches which are used by numerous law enforcement agencies worldwide. This paper aims to tackle these issues with the following contributions: 1) a very deep convolutional neural network is utilised to determine the identity of a subject in a composite sketch by comparing it to face photographs and is trained by applying transfer learning to a state-of-the-art model pretrained for face photograph recognition; 2) a 3-D morphable model is used to synthesise both photographs and sketches to augment the available training data, an approach that is shown to significantly aid performance; and 3) the UoM-SGFS database is extended to contain twice the number of subjects, now having 1200 sketches of 600 subjects. An extensive evaluation of popular and state-of-the-art algorithms is also performed due to the lack of such information in the literature, where it is demonstrated that the proposed approach comprehensively outperforms state-of-the-art methods on all publicly available composite sketch datasets.

Comparison of three techniques for analysis of data from an Aerosol Time-of-Flight Mass Spectrometer

The Aerosol Time-of-Flight Mass Spectrometer (ATOFMS) is one of few instruments able to measure the size and mass spectra of individual airborne particles with high temporal resolution. Data analysis is challenging and in the present study, we apply three different techniques (PMF. ART-2a and K-means) to a regional ATOFMS dataset collected at Harwell, UK. For the first time, Positive Matrix Factorization (PMF) was directly applied to single particle mass spectra as opposed to clusters already generated by the other methods. The analysis was performed on a total of 56,898 single particle mass spectra allowing the extraction of 10 factors, their temporal trends and size distributions, named CNO-COOH (cyanide, oxidized organic nitrogen and carboxylic acids), SUL (sulphate), NH4-OOA (ammonium and oxidized organic aerosol), NaCl, EC+ (elemental carbon positive fragments), OC-Arom (aromatic organic carbon), EC- (elemental carbon negative fragments), K (potassium), NIT (nitrate) and OC-CHNO (organic nitrogen). The 10 factor solution from single particle PMF analysis explained 45% of variance of the total dataset, but the factors are well defined from a chemical point of view. Different EC and OC components were separated: fresh EC (factor EC) from aged EC (factor EC+) and different organic families (factors NH4-OOA, OC-Arom, OC CHNO and CNO-COOH). A comparison was conducted between PMF, K-means cluster analysis and the ART-2a artificial neural network. K-means and ART-2a give broadly overlapping results (with 9 clusters, each describing the full composition of a particle type), while PMF, by effecting spectral deconvolution, was able to extract and separate the different chemical species contributing to particles, but loses some information on internal mixing. Relationships were also examined between the estimated volumes of ATOFMS PMF factors and species concentrations measured independently by GRAEGOR and AMS instruments, showing generally moderate to strong correlations. (C) 2012 Elsevier Ltd. All rights reserved.

Analysis Operator Learning and its Application to Image Reconstruction

Exploiting a priori known structural information lies at the core of many image reconstruction methods that can be stated as inverse problems. The synthesis model, which assumes that images can be decomposed into a linear combination of very few atoms of some dictionary, is now a well established tool for the design of image reconstruction algorithms. An interesting alternative is the analysis model, where the signal is multiplied by an analysis operator and the outcome is assumed to be sparse. This approach has only recently gained increasing interest. The quality of reconstruction methods based on an analysis model severely depends on the right choice of the suitable operator. In this paper, we present an algorithm for learning an analysis operator from training images. Our method is based on l(p)-norm minimization on the set of full rank matrices with normalized columns. We carefully introduce the employed conjugate gradient method on manifolds, and explain the underlying geometry of the constraints. Moreover, we compare our approach to state-of-the-art methods for image denoising, inpainting, and single image super-resolution. Our numerical results show competitive performance of our general approach in all presented applications compared to the specialized state-of-the-art techniques.

Pharmacological evaluation of newly synthesized benzimidazole derivative for anti-Alzheimer potential

Backgound: Alzheimer disease (AD) is a disastrous disease characterized by accretion of amyloid-beta plaques, neurofibrillary tangles inducing oxidative stress, loss of neuronal functions and continuous progression of cognitive impairment leading to severe dementia.Material and Methods: The newly synthesized benzimidazole derivative 4-chloro-3-(2-phenyl-1H-benzimidazole-1-sulfonyl) benzoic acid (CB) was evaluated for its anti-Alzheimer activity using in silico, in vivo, in vitro and molecular techniques (ELISA, WB & IHC).Results: In-silico studies revealed that CB has atomic contact energy values of -3.9 to -8.9 kcal/mol against selected targets. In vitro assay showed that CB caused acetylcholinesterase (AChE) and diphenyl-1-picrylhydrazyl inhibition. In-vivo findings revealed improvement in dementia as observed in the morris water maze test and Ymaze test. Amyloid-beta disaggregation, increased level of anti-oxidants, decreased expressions of inflammatory markers and enhanced cellular architecture were found in the cortex and hippocampus of treated rats in the histopathological examination, immunohistochemistry analysis, enzyme-linked immunosorbent assay and western blot analysis.Conclusions: This study revealed that CB possess different binding affinities with the Alzheimer-related targets and it possess anti-Alzheimer activity, mediated via AChE and amyloid-beta inhibition, anti-oxidant and anti-inflammatory pathways.

Improving the Performance and Energy Efficiency of GPGPU Computing through Integrated Adaptive Cache Management

Hardware caches are widely employed in GPGPUs to achieve higher performance and energy efficiency. Incorporating hardware caches in GPGPUs, however, does not immediately guarantee enhanced performance and energy efficiency due to high cache contention and thrashing. To address the inefficiency of GPGPU caches, various adaptive techniques (e.g., warp limiting) have been proposed. However, relatively little work has been done in the context of creating an architectural framework that tightly integrates adaptive cache management techniques and investigating their effectiveness and interaction. To bridge this gap, we propose IACM, integrated adaptive cache management for high-performance and energy-efficient GPGPU computing. IACM integrates the state-of-the-art adaptive cache management techniques (i.e., cache indexing, bypassing, and warp limiting) in a unified architectural framework. Our quantitative evaluation demonstrates that IACM significantly improves the performance and energy efficiency of various GPGPU workloads over the baseline architecture (i.e., 98.1 and 61.9 percent on average, respectively), achieves considerably higher performance than the state-of-the-art technique (i.e., 361.4 percent at maximum and 7.7 percent on average), and delivers significant performance and energy-efficiency gains over the baseline GPGPU architecture enhanced with advanced architectural technologies.

Ultra-fast, High-Bandwidth Coherent cw THz Spectrometer for Non-destructive Testing

Continuous wave THz (cw THz) systems define the state-of-the-art in terms of spectral resolution in THz spectroscopy. Hitherto, acquisition of broadband spectra in a cw THz system was always connected with slow operation. Therefore, high update rate applications like inline process monitoring and non-destructive testing are served by time domain spectroscopy (TDS) systems. However, no fundamental restriction prevents cw THz technology from achieving faster update rates and be competitive in this field. In this paper, we present a fully fiber-coupled cw THz spectrometer. Its sweep speed is two orders of magnitude higher compared to commercial state-of-the-art systems and reaches a record performance of 24 spectra per second with a bandwidth of more than 2THz. In the single-shot mode, the same system reaches a peak dynamic range of 67dB and exceeds a value of 100dB with averaging of 7min, which is among the highest values ever reported. The frequency steps can be as low as 40MHz. Due to the fully homodyne detection, each spectrum contains full amplitude and phase information. This demonstration of THz-spectroscopy at video-rate is an essential step towards applying cw THz systems in non-destructive, in line testing.

RNA interference (RNAi)-based therapeutics for treatment of rare neurologic diseases

Advances in genome sequencing have greatly facilitated the identification of genomic variants underlying rare neurodevelopmental and neurodegenerative disorders. Understanding the fundamental causes of rare monogenic disorders has made gene therapy a possible treatment approach for these conditions. RNA interference (RNAi) technologies such as small interfering RNA (siRNA), microRNA (miRNA), and short hairpin RNA (shRNA), and other oligonucleotide-based modalities such as antisense oligonucleotides (ASOs) are being developed as potential therapeutic approaches for manipulating expression of the genes that cause a variety of neurological diseases. Here, we offer a brief review of the mechanism of action of these RNAi approaches; provide deeper discussion of the advantages, challenges, and specific considerations related to the development of RNAi therapeutics for neurological disease; and highlight examples of rare neurological diseases for which RNAi therapeutics hold great promise.

Investigation of murine host sex as a biological variable in epithelial barrier function and muscle contractility in human intestinal organoids

Intestinal failure (IF) occurs when intestinal surface area or function is not sufficient to support digestion and nutrient absorption. Human intestinal organoid (HIO)-derived tissue-engineered intestine is a potential cure for IF. Research to date has demonstrated successful HIO transplantation (tHIO) into mice with significant in vivo maturation. An area lacking in the literature is exploration of murine host sex as a biological variable (SABV) in tHIO function. In this study, we investigate murine host SABV in tHIO epithelial barrier function and muscle contractility. HIOs were generated in vitro and transplanted into nonobese diabetic, severe combined immunodeficiency gamma chain deficient male and female mice. tHIOs were harvested after 8-12 weeks in vivo. Reverse transcriptase polymerase chain reaction and immunohistochemistry were conducted to compare tight junctions and contractility-related markers in tHIOs. An Ussing chamber and contractility apparatus were used to evaluate tHIO epithelial barrier and muscle contractile function, respectively. The expression and morphology of tight junction and contractility-related markers from tHIOs in male and female murine hosts is not significantly different. Epithelial barrier function as measured by transepithelial resistance, short circuit current, and fluorescein isothiocyanate-dextran permeability is no different in tHIOs from male and female hosts, although these results may be limited by HIO epithelial immaturity and a short flux time. Muscle contractility as measured by total contractile activity, amplitude, frequency, and tension is not significantly different in tHIOs from male and female hosts. The data suggest that murine host sex may not be a significant biological variable influencing tHIO function, specifically epithelial barrier maintenance and muscle contractility, though limitations exist in our model.

Features combination for art authentication studies: brushstroke and materials analysis of Amadeo de Souza-Cardoso

This work presents a tool to support authentication studies of paintings attributed to the modernist Portuguese artist Amadeo de Souza-Cardoso (1887-1918). The strategy adopted was to quantify and combine the information extracted from the analysis of the brushstroke with information on the pigments present in the paintings. The brushstroke analysis was performed combining Gabor filter and Scale Invariant Feature Transform. Hyperspectral imaging and elemental analysis were used to compare the materials in the painting with those present in a database of oil paint tubes used by the artist. The outputs of the tool are a quantitative indicator for authenticity, and a mapping image that indicates the areas where materials not coherent with Amadeo's palette were detected, if any. This output is a simple and effective way of assessing the results of the system. The method was tested in twelve paintings obtaining promising results.

Recent Advances in Neuropsychological Outcomes and Intervention in Pediatric Stroke

Over the past 15 years, there have been significant advances in the treatment of acute and chronic medical consequences of stroke in childhood. Given high rates of survival in pediatric stroke, practitioners are tasked with treating the ongoing motor and neuropsychological sequelae in patients over the course of their development. This article provides a review of the current literature on neuropsychological outcomes in pediatric stroke, including intelligence, academics, language, visual-spatial skills, attention, executive functions, memory, and psychosocial function. Recent developments in functional neuroimaging are discussed, with a particular focus on language outcomes. We further review the current research on cognitive and behavioral rehabilitation and introduce intervention models in pediatric stroke. In the final section, we discuss future directions for clinical practice and research in pediatric stroke.

Update on management of genitourinary syndrome of menopause: A practical guide

The term genitourinary syndrome of menopause (GSM) emerged following a consensus conference held in May 2013. GSM is a more descriptive term than vulvovaginal atrophy (VVA) and does not imply pathology. However there are concerns that GSM is all encompassing and includes not only symptoms resulting from estrogen deficiency, but also those arising from the effects of ageing and other processes on the bladder and pelvic floor. Focusing on symptoms related to estrogen deficiency, the update provides a practical guide for health and allied health professionals on the impact of GSM on women and their partners, assessment, management and areas for future research. As GSM is a chronic condition, long term therapy is required. Hormonal, nonhormonal, laser and alternative and complementary therapies are described.

Aspect-Based Sentiment Analysis in Hindi Language by Ensembling Pre-Trained mBERT Models

Sentiment Analysis is becoming an essential task for academics, as well as for commercial companies. However, most current approaches only identify the overall polarity of a sentence, instead of the polarity of each aspect mentioned in the sentence. Aspect-Based Sentiment Analysis (ABSA) identifies the aspects within the given sentence, and the sentiment that was expressed for each aspect. Recently, the use of pre-trained models such as BERT has achieved state-of-the-art results in the field of natural language processing. In this paper, we propose two ensemble models based on multilingual-BERT, namely, mBERT-E-MV and mBERT-E-AS. Using different methods, we construct an auxiliary sentence from this aspect and convert the ABSA problem to a sentence-pair classification task. We then fine-tune different pre-trained BERT models and ensemble them for a final prediction based on the proposed model; we achieve new, state-of-the-art results for datasets belonging to different domains in the Hindi language.

Multi-Channel Generative Framework and Supervised Learning for Anomaly Detection in Surveillance Videos

Recently, most state-of-the-art anomaly detection methods are based on apparent motion and appearance reconstruction networks and use error estimation between generated and real information as detection features. These approaches achieve promising results by only using normal samples for training steps. In this paper, our contributions are two-fold. On the one hand, we propose a flexible multi-channel framework to generate multi-type frame-level features. On the other hand, we study how it is possible to improve the detection performance by supervised learning. The multi-channel framework is based on four Conditional GANs (CGANs) taking various type of appearance and motion information as input and producing prediction information as output. These CGANs provide a better feature space to represent the distinction between normal and abnormal events. Then, the difference between those generative and ground-truth information is encoded by Peak Signal-to-Noise Ratio (PSNR). We propose to classify those features in a classical supervised scenario by building a small training set with some abnormal samples of the original test set of the dataset. The binary Support Vector Machine (SVM) is applied for frame-level anomaly detection. Finally, we use Mask R-CNN as detector to perform object-centric anomaly localization. Our solution is largely evaluated on Avenue, Ped1, Ped2, and ShanghaiTech datasets. Our experiment results demonstrate that PSNR features combined with supervised SVM are better than error maps computed by previous methods. We achieve state-of-the-art performance for frame-level AUC on Ped1 and ShanghaiTech. Especially, for the most challenging Shanghaitech dataset, a supervised training model outperforms up to 9% the state-of-the-art an unsupervised strategy.

MyStoryPlayer: experiencing multiple audiovisual content for education and training

There are several education and training cases where multi-camera view is a traditional way to work: performing arts and news, medical surgical actions, sport actions, instruments playing, speech training, etc. In most cases, users need to interact with multi camera and multi audiovisual to create among audiovisual segments their own relations and annotations with the purpose of: comparing actions, gesture and posture; explaining actions; providing alternatives, etc. Most of the present solutions are based on custom players and/or specific applications which force to create custom streams from server side, thus leading to restrictions on the user activity as to establishing dynamically additional relations. Web based solutions would be more appreciated and are complex to be realized for the problems related to the video desynchronization. In this paper, MyStoryPlayer/ECLAP solution is presented. The major contributions to the state of the art are related to: (i) the semantic model to formalize the relationships and play among audiovisual determining synchronizations, (ii) the model and modality to save and share user experiences in navigating among lessons including several related and connected audiovisual, (iii) the design and development of algorithm to shorten the production of relationships among media, (iv) the design and development of the whole system including its user interaction model, and (v) the solution and algorithm to keep the desynchronizations limited among media in the event of low network bandwidth. The proposed solution has been developed for and it is in use within ECLAP (European Collected Library of Performing Arts) for accessing and commenting performing arts training content. The paper also reports validation results about performance assessment and tuning, and about the usage of tools on ECLAP services. In ECLAP, users may navigate in the audiovisual relationships, thus creating and sharing experience paths. The resulting solution includes a uniform semantic model, a corresponding semantic database for the knowledge, a distribution server for semantic knowledge and media, and the MyStoryPlayer Client for web applications.

Low-Complexity Methodology for Complex Square-Root Computation

In this brief, we propose a low-complexity methodology to compute a complex square root using only a circular coordinate rotation digital computer (CORDIC) as opposed to the state-of-the-art techniques that need both circular as well as hyperbolic CORDICs. Subsequently, an architecture has been designed based on the proposed methodology and implemented on the ASIC platform using the UMC 180-nm Technology node with 1.0 V at 5 MHz. Field programmable gate array (FPGA) prototyping using Xilinx' Virtex-6 (XC6v1x240t) has also been carried out. After thorough theoretical analysis and experimental validations, it can be inferred that the proposed methodology reduces 21.15% slice look up tables (on FPGA platform) and saves 20.25% silicon area overhead and decreases 19% power consumption (on ASIC platform) when compared with the state-of-the-art method without compromising the computational speed, throughput, and accuracy.

Assessing genetic and agronomic gains in rice yield in sub-Saharan Africa: A meta-analysis

Research for development efforts for increasing rice yield in sub-Saharan Africa (SSA) have largely concentrated on genetic improvement and agronomy for more than 50 years. Here we perform the first meta-analysis to quantify genetic gain - yield increase through use of new variety and calculated by yield difference between new variety and variety popularly grown in the target site, and agronomic gain - difference in yield between improved agronomic practices and the control in SSA using 208 paired observations from 40 studies across 12 countries. Among the studies, 41 %, 34 %, and 25 % were from irrigated lowland, rainfed lowland, and rainfed upland rice, respectively. Seventy percent of the studies reported in this paper were conducted on research stations. In agronomic practices, inorganic fertilizer management practices accounted for 78 % of the studies, of which 48 % were nitrogen (N) management. In each study, we identified four types of varieties: check variety (VC), variety with highest yield in the control (VHC), variety with highest yield under improved agronomic practices (VHT), and variety with largest yield difference between improved agronomic practices and control (VHR). VHT was the same as VHC in 35 % of observations, whereas VHR and VHT were the same in 51 %. These indicate that it is possible to develop varieties adapted to different agronomic practices and high-yielding varieties tend to be responsive to improved agronomic practices. On average, total gain in yield with improved agronomic practices and VHT was 1.6 t/ha. Agronomic practice accounted for 75 % of the total variation in total yield gain with variety and agronomic practice by variety interaction responsible for 19 % and 6 %, respectively. Genetic gains in yield with VHC, VHT, and VHR were 0.7, 0.3, and -0.3 t/ha in control, and 0.4, 0.9, and 0.5 t/ha in improved agronomic practices. Agronomic gain in yield averaged 0.5, 0.8, 1.4, and 1.6 t/ha in VHC, VC, VHT, and VHR, respectively. Agronomic gain in yield of VHT was higher than genetic gain under improved agronomic practices in 54 % of observations. Agronomic gain was highest in irrigated lowland rice, followed by rainfed lowland rice. Higher agronomic gain in yield was also associated with larger difference in N application rate between improved agronomic practices and control. Whereas agronomic practices had larger contribution to total gain in yield than genetic improvement in this study, future assessment of agronomic and genetic gains in yield is warranted. Such assessment should focus more on rainfed rice systems, where agronomic gain was small, take into account genetic improvement rate over time and integrated agronomic practices rather than single intervention like nutrient management practice only, and be conducted in farmers' fields.

ICT Usage for Cross-Curricular Connections in Music and Visual Arts during Emergency Remote Teaching in Slovenia

Due to the COVID-19 pandemic, the entire process of teaching and learning moved online. This forced teachers and pupils to heavily rely on information and communications technology (ICT) and make adjustments to the new mode of teaching and learning in educational institutions. We conducted a qualitative case study by interviewing 24 teachers from Slovene primary schools focusing on the implementation of cross-curricular connections in music and visual arts content with the support of ICT during the period of emergency remote teaching. We found that when planning and implementing the cross-curricular learning process, teachers insufficiently took advantage of possibilities offered by modern ICT. The manner of implementing cross-curricular connections showed uncertainties in terms of understanding their specifics, resulting in the inefficient transfer of concepts taught, the results of which were seen in pupils' work. This might additionally show the negative influence of parental supervision on the creative thinking and expression of pupils. The present study emphasizes the lack of ICT competences on the part of all participants in the educational process. Our findings show the need to educate teachers by eliminating the uncertainties related to the implementation of distant cross-curricular connections while meaningfully applying ICT adapted to pupils' competences.

Molecular and functional characterization of a ladderlectin-like molecule from ayu (Plecoglossus altivelis)

Ladderlectin is a member of C-type lectins (CTLs) in teleost fish and involved in innate immune defense. In this study, ayu (Plecoglossus altivelis) ladderlecin-like (PaLL-like) sequence was cloned, which encodes a polypeptide of 172 amino acids that includes a signal peptide and characteristic C-type lectin-like domains (CTLDs). Phylogenetically, PaLL-like was most closely related to its teleost counterpart from shishamo smelt (Spirinchus lanceolatus). Expression analysis revealed a ubiquitous expression profile, with highest expression detected in liver and its expression was up-regulated following Vibiro anguillarum infection. Similar to canonical CTLs, PaLL-like exhibited carbohydrate-binidng capacities to a wide range of well-defined mono-/di-saccharides and likely confer PaLL-like the ability to agglutinate all tested bacterial, including three Gram-positive species (i.e., Listeria monocytogenes, Staphylococcus aureus and Streptococcus iniae) and eight Gram-negative species (i.e., Edwardsiella tarda, Aeromonas (A.) hydrophila, Escherichia coli, Vibrio (V.) harveyi, V. anguillarum, V. parahemolyticus, A. versoni and V. vulnificus), in a calcium-dependent manner. Further functional studies revealed that PaLL-like displayed immunomodulatory activities leading to enhanced bactericidal activity of serum, pathogen opsonization and macrophage activation with increased expression of pro-inflammatory cytokines (i.e., PaIL-1β and PaTNF-α). Collectively, these immunomodulatory activities of PaLL-like suppressed proliferations of V. anguillarum in targeted tissued in vivo and likely contributed to the increased survival rate of infected-fish. Overall, our results demonstrated PaLL-like is a critical component of innate immunity and provides protective effects against bacterial infection.

Prostate Cancer Grading: Use of Graph Cut and Spatial Arrangement of Nuclei

Tissue image grading is one of the most important steps in prostate cancer diagnosis, where the pathologist relies on the gland structure to assign a Gleason grade to the tissue image. In this grading scheme, the discrimination between grade 3 and grade 4 is the most difficult, and receives the most attention from researchers. In this study, we propose a novel method (called nuclei-based method) that 1) utilizes graph theory techniques to segment glands and 2) computes a gland-score (based on the spatial arrangement of nuclei) to estimate how similar a segmented region is to a gland. Next, we create a fusion method by combining this nuclei-based method with the lumen-based method presented in our previous work to improve the performance of grade 3 versus grade 4 classification problem (the accuracy is now improved to 87.3% compared to 81.1% of the lumen-based method alone). To segment glands, we build a graph of nuclei and lumina in the image, and use the normalized cut method to partition the graph into different components, each corresponding to a gland. Unlike most state-of-the-art lumen-based gland segmentation method, the nuclei-based method is able to segment glands without lumen or glands with multiple lumina. Moreover, another important contribution in this research is the development of a set of measures to exploit the difference in nuclei spatial arrangement between grade 3 images (where nuclei form closed chain structure on the gland boundary) and grade 4 image (where nuclei distribute more randomly in the gland). These measures are combined to generate a single gland-score value, which estimates how similar a segmented region (which is a set of nuclei and lumina) is to a gland.

Learning an Occlusion-Aware Network for Video Deblurring

Video deblurring is a challenging task since the blur is caused by camera shake, object motions, etc. The success of the state-of-the-art methods stems mainly from exploiting the temporal information of neighboring frames through alignment. When there exists occlusion among the sequence, these approaches become less effective for inaccurate alignment. In this paper, we propose an effective occlusion-aware network to handle the occlusion for video deblurring. The proposed module first generates a coarse pixel-wise alignment filter to explore the temporal information and then learns an adaptive affine transformation to deal with the occluded areas. In addition, a self-attention mechanism is developed to better model the occluded pixels. To further improve the performance, we progress a multi-scale strategy and train the network in an end-to-end manner. Both quantitative and qualitative experimental results show that the proposed method achieves favorable performance against state-of-the-art methods on the benchmark datasets. The code and trained models are available at: https://github.com/XQLuck/code.git

Map art style transfer with multi-stage framework

We propose a multi-stage framework to create the stylized map art images. Existing techniques are successful in transferring style in photos. Yet, the noise in results and the harmonization in the generated art images still need to be investigated. We address these issues with a proposed algorithm that defines a good portrait for map art application in the initial round. A refinement strategy is then applied to produce the final map arts that meet the aforementioned expectations. Beside our plausible results, the objective evaluation presented in this paper shows that our proposed method can interactively achieve better and appealing map art results in the comparison with those of other works. In addition, our method can also create ocean or landscape stylized paintings using our map art collage.

Abstraction-perception preserving cartoon face synthesis

Portrait cartoonization aims at translating a portrait image to its cartoon version, which guarantees two conditions, namely, reducing textural details and synthesizing cartoon facial features (e.g., big eyes or line-drawing nose). To address this problem, we propose a two-stage training scheme based on GAN, which is powerful for stylization problems. The abstraction stage with a novel abstractive loss is used to reduce textural details. Meanwhile, the perception stage is adopted to synthesize cartoon facial features. To comprehensively evaluate the proposed method and other state-of-the-art methods for portrait cartoonization, we contribute a new challenging large-scale dataset named CartoonFace10K. In addition, we find that the popular metric FID focuses on the target style yet ignores the preservation of the input image content. We thus introduce a novel metric FISI, which compromises FID and SSIM to focus on both target features and retaining input content. Quantitative and qualitative results demonstrate that our proposed method outperforms other state-of-the-art methods.

Experimental investigation of multistage electrodialysis for seawater desalination

Electrodialysis (ED) is currently used for selective removal of ions and brackish water desalination, while for seawater desalination ED is considered to be too energy intensive. This research focuses on the viability of ED using multiple stages for seawater desalination. With staging, the driving force is adapted to the governing conditions at that specific stage, operating at its individual optimum at lower energy consumption. An ED multistage configuration is examined that contains up to four stages. We compare single stage with multistage ED and investigate the effect of operation parameters. Different current densities are applied and optimized and residence time is compared to describe both transmembrane salt and water fluxes. We showed that desalination from 500 mM to 200 mM is possible, but that for these desalination conditions a multistage and single-stage system perform equal. Operation of each stage of the multistage close to limiting current density shows that desalination of synthetic seawater close to drinking water quality is possible. To reach this, the energy consumption is 3.6 kWh/m3 and at least 4 stages are needed. Although outlet concentrations between ED and RO are different, this non-optimized ED system showed double the energy consumption of the state-of-the-art desalination technology RO.

Model-Based Generation of Large Databases of Cardiac Images: Synthesis of Pathological Cine MR Sequences From Real Healthy Cases

Collecting large databases of annotated medical images is crucial for the validation and testing of feature extraction, statistical analysis, and machine learning algorithms. Recent advances in cardiac electromechanical modeling and image synthesis provided a framework to generate synthetic images based on realistic mesh simulations. Nonetheless, their potential to augment an existing database with large amounts of synthetic cases requires further investigation. We build upon these works and propose a revised scheme for synthesizing pathological cardiac sequences from real healthy sequences. Our new pipeline notably involves a much easier registration problem to reduce potential artifacts, and takes advantage of mesh correspondences to generate new data from a given case without additional registration. The output sequences are thoroughly examined in terms of quality and usability on a given application: the assessment of myocardial viability, via the generation of 465 synthetic cine MR sequences (15 healthy and 450 with pathological tissue viability [random location, extent, and grade, up to myocardial infarct]). We demonstrate that: 1) our methodology improves the state-of-the-art algorithms in terms of realism and accuracy of the simulated images and 2) our methodology is well-suited for the generation of large databases at small computational cost.

STUDIES AND EXPERIMENTS FOR DETERMINATION OF DEGRADATION OF PAINTINGS IN MUSEUM ART GALLERIES CAUSED BY ARTIFICIAL LIGHT SOURCES

Light displays a vital role in the exhibition environment especially in Art gallery. The exhibits' colour details cannot be realized without light. Both natural light and artificial light affect the exhibits, it reduces the strength of the paint and material, causes the fading of paper and paint colour, this damage cannot be recovered. So, the relationship between the exhibits protection and the art visual became one of the core issues of Art gallery design research. Museums and art galleries collect, preserve, and display historical /cultural artefacts and various achievements of historical days. Effective exhibit lighting must balance with exhibition and observation and museums visual lighting effects. This should be done by providing lighting levels as per standard as well as keeping the IR (infrared) and UV (ultraviolet) components of light sources minimum. There are different types of light sources (i.e. fluorescent, halogen and incandescent lamps) with IR and / or UV components, and those sources are being used for many years. Now in present days, there is tremendous development in lighting industries (i.e. lighting sources, lighting design etc.). Recently, solid state light sources (i.e. LEDs and OLEDs) are being developed and they have less power consumption and contains no IR range and minimum UV interval of radiation. So in modern art galleries, low wattage highly efficient LEDs are being used to minimize the fading effect on painting. It should be remembered that till now the inefficient halogen sources have best colour rendering property. The experimental work described in this paper reveals that colour rendering of a painting is different under different light sources, it is true but the colour fading of the painting fades very much slow when LEDs are being used.

Electrical measurement of the junction temperature and thermal resistance of HBTs

A simple method is proposed to derive the junction temperature and the bias- and temperature-dependent thermal resistance of heterojunction bipolar transistors (HBTs) using a radio frequency (RF) signal. The method exploits the thermal dependence of the current gain of a transistor whose dissipated power is modified by applying an RF signal. The new method is used to derive the junction temperature and thermal resistance of a power HBT. The results are compared with state-of-the-art techniques.

Application of spray-dried erythromycin fermentation residue as a soil amendment: antibiotic resistance genes, nitrogen cycling, and microbial community structure

Erythromycin fermentation residue (EFR) after spray drying could be reused as a soil amendment. However, the effects of spray-dried EFR on antibiotic resistance genes (ARGs), nitrogen cycling, and microbial community structure in soil are rarely reported. In this study, a pot experiment was conducted by adding spray-dried EFR to soil. For the application of 1.0% spray-dried EFR, the residual erythromycin (ERY) could be rapidly removed with the half-life of 22.2 d; the total relative abundance of ARGs increased at first, but decreased to the initial level of the control group in the end; genes related to ammonium assimilation (glnA, gltB, gltD), ammonification (gdhA, gudB, cynT, cynS, ncd2), denitrification (narI, narG, narH), assimilatory nitrate reduction (nirA, nasA), and dissimilatory nitrate reduction (nirD) were enriched; soil microbial community structure presented temporary variation. Network analysis showed significant negative correlations between ARGs and nitrogen cycling genes. The addition of 6.0% spray-dried EFR resulted in the amplification of ARGs and inhibition of nitrogen cycling. This work provides new insights into the effects of spray-dried EFR on ARGs, nitrogen cycling, and microbial community structure within the fertilized soil.

Management of patient with acrometageria for routine dental treatment: A case report

The population of special needs patients in dental offices is growing. Therefore, the demand for well-trained, educated practitioners must increase to fit the need. Conditions such as intellectual developmental disorder, Down syndrome, and autism spectrum disorder are more readily encountered in dental settings. However, it is equally appropriate to identify management techniques for patients with less common conditions. A case is reported in which a 38-year-old Caucasian male with a history significant for acrometageria and associated signs of Mallampati Class IV, micrognathia, decreased mouth opening, decreased thyromental distance, and decreased cervical range of motion presented for routine dental treatment under intravenous sedation. Providers should recognize appropriate management techniques to safely and effectively care for a wide patient demographic.

CPFNet: Context Pyramid Fusion Network for Medical Image Segmentation

Accurate and automatic segmentation of medical images is a crucial step for clinical diagnosis and analysis. The convolutional neural network (CNN) approaches based on the U-shape structure have achieved remarkable performances in many different medical image segmentation tasks. However, the context information extraction capability of single stage is insufficient in this structure, due to the problems such as imbalanced class and blurred boundary. In this paper, we propose a novel Context Pyramid Fusion Network (named CPFNet) by combining two pyramidal modules to fuse global/multi-scale context information. Based on the U-shape structure, we first design multiple global pyramid guidance (GPG) modules between the encoder and the decoder, aiming at providing different levels of global context information for the decoder by reconstructing skip-connection. We further design a scale-aware pyramid fusion (SAPF) module to dynamically fuse multi-scale context information in high-level features. These two pyramidal modules can exploit and fuse rich context information progressively. Experimental results show that our proposed method is very competitive with other state-of-the-art methods on four different challenging tasks, including skin lesion segmentation, retinal linear lesion segmentation, multi-class segmentation of thoracic organs at risk and multi-class segmentation of retinal edema lesions.

State-of-the-art cryogenic machining and processing

This article is a state-of-the-art review of the use of cryogenic cooling using liquefied gases in machining. The review is classified into two major categories, namely cryogenic processing and cryogenic machining. In cryogenic processing also known as cryo-processing, the cutting tool material is subjected to cryogenic temperatures as a part of its heat treatment process. The majority of the reported studies identify that cryo-processing can considerably increase cutting tool life especially for high speed steel tools. It also identified that, in cryogenic machining, a cryogen is used as a cooling substance during cutting operations. The cryogen can be used to freeze the workpiece material and/or cutting tool. This article concludes that cryogenic cooling has demonstrated significant improvements in machinability by changing the material properties of the cutting tool and/or workpiece material at the cutting zone, altering the coefficient of friction and reducing the cutting temperature.

A new Airport Collaborative Decision Making algorithm based on Deferred Acceptance in a two-sided market

The main objective of Airport Collaborative Decision Making (A-CDM) is to allow the stakeholders working together in more efficiently and transparently way to share data and to enhance Air Traffic Management (ATM) processes. The state-of-the-art approaches for A-CDM, currently implemented in many airports in both Europe as well as the United States, are considered mature and well accepted. In many cases it usually focuses on the information sharing and only takes into account the preferences of Air Traffic Control (ATC) units and those of the airlines. This inherently leads to only satisfying the preferences of a limited number of stakeholders within the airport area. In this paper we extend current state-of-the-art approaches to include the preferences of the Airport Management in the A-CDM. The model that we propose is based on the Deferred Acceptance (DA) allocation mechanism from Game Theory and addresses the problem of slot allocation in the Compression step of the classic CDM algorithm currently used. Dealing with this market by using the DA-CDM model enables assigning flights to slots through a one-to-one relationship that respects the preferences of each allocation and is always guaranteed to provide a stable result. (C) 2014 Elsevier Ltd. All rights reserved.

Planning Sustainable Community-Based Tourism in the Context of Thailand: Community, Development, and the Foresight Tools

Community-Based Tourism (CBT) has been heavily promoted in Thailand, particularly in rural communities. Tourism transforms the natural and culturally significant attractions of rural communities into consumer products. Tourism development also makes a direct connection between tourism and the community. Therefore, before starting a tourism development project, the interests of local residents need to be investigated and allowances made for their inclusion. This paper introduces foresight tools, community arts, and a community goal-setting technique for putting the authority of future tourism development in the hands of the local community, which is appropriate for tourism development in rural Thailand. This study aims to give the local community useful tools to design their own future development by helping its members develop an understanding of what tourism is and how it impacts their community. A series of inclusive workshops was used to emphasize the need to understand the opportunities and repercussions of tourism as a community, what is at stake, and how important it is to participate in development projects. This methodology was chosen to advance the community members' ability to generate ideas about what kind of tourism products the community has the potential to develop. Furthermore, it aims to get locals to understand that tourism development is more than just getting that development started. Reaching their future goals requires continuing their tourism activities. Therefore, the two workshops we held advocated community arts and community goal-setting techniques as foresight tools to empower communities to design their future and gave support to those communities to improve the quality of their participation in tourism.

Probing Tissue Microarchitecture of the Baby Brain via Spherical Mean Spectrum Imaging

During the first years of life, the human brain undergoes dynamic spatially-heterogeneous changes, involving differentiation of neuronal types, dendritic arborization, axonal ingrowth, outgrowth and retraction, synaptogenesis, and myelination. To better quantify these changes, this article presents a method for probing tissue microarchitecture by characterizing water diffusion in a spectrum of length scales, factoring out the effects of intra-voxel orientation heterogeneity. Our method is based on the spherical means of the diffusion signal, computed over gradient directions for a set of diffusion weightings (i.e., b-values). We decompose the spherical mean profile at each voxel into a spherical mean spectrum (SMS), which essentially encodes the fractions of spin packets undergoing fine-to coarse-scale diffusion processes, characterizing restricted and hindered diffusion stemming respectively from intra-and extra-cellular water compartments. From the SMS, multiple orientation distribution invariant indices can be computed, allowing for example the quantification of neurite density, microscopic fractional anisotropy (mu FA), per-axon axial/radial diffusivity, and free/restricted isotropic diffusivity. We show that these indices can be computed for the developing brain for greater sensitivity and specificity to development related changes in tissue microstructure. Also, we demonstrate that our method, called spherical mean spectrum imaging (SMSI), is fast, accurate, and can overcome the biases associated with other state-of-the-art microstructure models.

Neurocognitive impairment in the deficit subtype of schizophrenia

Schizophrenia is a heterogeneous disorder characterized by numerous diverse signs and symptoms. Individuals with prominent, persistent, and idiopathic negative symptoms are thought to encompass a distinct subtype of schizophrenia. Previous work, including studies involving neuropsychological evaluations, has supported this position. The present study sought to further examine whether deficit patients are cognitively distinct from non-deficit patients with schizophrenia. A comprehensive neurocognitive battery including tests of verbal memory, vigilance, processing speed, reasoning, and working memory was administered to 657 patients with schizophrenia. Of these, 144 (22 %) patients were classified as deficit patients using a proxy identification method based on severity, persistence over time, and possible secondary sources (e.g., depression) of negative symptoms. Deficit patients with schizophrenia performed worse on all tests of cognition relative to non-deficit patients. These patients were characterized by a generalized cognitive impairment on the order of about 0.4 standard deviations below that of non-deficit patients. However, when comparing deficit patients to non-deficit patients who also present with negative symptoms, albeit not enduring or primary, no group differences in cognitive performance were found. Furthermore, a discriminant function analysis classifying patients into deficit/non-deficit groups based on cognitive scores demonstrated only 62.3 % accuracy, meaning over one-third of individuals were misclassified. The deficit subtype of schizophrenia is not markedly distinct from non-deficit schizophrenia in terms of neurocognitive performance. While deficit patients tend to have poorer performance on cognitive tests, the magnitude of this effect is relatively modest, translating to over 70 % overlap in scores between groups.

Subpixel Line Localization With Normalized Sums of Gradients and Location Linking With straightness and Omni-Directionality

This paper presents a method to localize line locations with subpixel accuracy and a method to link the locations based on a linking distance. This paper first proposes a subpixel line localization method based on normalized sums of gradients (NSG) calculated by dividing pixel sum of gradients by the sum of gradient lengths within the pixel neighborhood. The proposed NSG method is compared with current state-of-the art based on a Taylor series approximation of intensity surface and the normal vector derived from the Hessian matrix. Comparative experiments for subpixel line localization methods were performed with simulated and natural images and confirmed the proposed subpixel localization method provided superior accuracy and faster localization under most combinations of varying line width and noise strengths than the state-of-the art localization method. The proposed linking method was also designed to have more straightness and omni-directionality than a current state-of-the art method. Experimental comparison of linking methods confirmed the proposed linking method provided superior linking quality than current state-of-the art.

Robust global and local color matching in stereoscopic omnidirectional content

Shooting a live-action immersive 360-degree experience, i.e. omnidirectional content (ODC) is a technological challenge as there are many technical limitations which need to be overcome, especially for capturing and post-processing in stereoscopic 3D (S3D). In this paper, we introduce a novel approach and entire system for stitching and color mismatch correction and detection in S3D omnidirectional content, which consists of three main modules: pre-processing, spherical color correction and color mismatch evaluation. The system and its individual modules are evaluated on two datasets, including a new dataset which will be publicly available with this paper. We show that our system outperforms the state of the art in color correction of S3D ODC and demonstrate that our spherical color correction module even further improves the results of the state of the art approaches.

Residents' Perception of a Collaborative Approach with Artists in Culture-Led Urban Regeneration: A Case Study of the Changdong Art Village in Changwon City in Korea

This study asserts that the higher the degree of artists' and residents' participation in a culture-led renewal project, the higher the level of residents' satisfaction. Engaging artist groups and introducing cultural programs can facilitate building a collaborative network between artists and residents. This paper adopts an experimental study method and defines the experimental and control groups as follows: the experimental group (Changwon city) has relatively high artist participation, and the control group (Sacheon, Gimhae, Miryang cities) have relatively low artist involvement. Multiple regression analysis was conducted utilizing 192 valid survey data in R studio software. The significant variables were compared between the experimental group (Model 1) and the control group (Model 2). As a result, the relative effects of "1. experience (or amount) of residents' participation in urban renewal programs", "2. reflection of residents' opinions", and "3. neighborly trust" on "residents' satisfaction with urban regeneration projects and expected outcomes" was shown to be greater in the experimental group. The result implies that the involvement of cultural entities and the operation of arts programs increase residents' will to participate in renewal projects and to build neighborly trust. Further, collection and reflection of residents' opinions about the renewal works were more smoothly completed when mediated by artist participation and using cultural content.

Experimental Models of Mouse and Human Hematopoietic Stem Cell Transplantation

Experimental hematopoietic stem cell transplantation (HSCT) is an invaluable tool in determining the function and characteristics of hematopoietic stem cells (HSC) from experimental mouse and human donor groups. These groups could include, but are not limited to, genetically altered populations (gene knockout/knockin models), ex vivo manipulated cell populations, or in vivo modulated cell populations. The basic fundamentals of this process involve taking cells from a mouse/human donor source and putting them into another mouse (recipient) after preconditioning of the recipient with either total body irradiation (TBI) for mouse donor cells or into sublethally irradiated immune-deficient mice for human donor cells. Then, at pre-determined time points post-transplant, sampling a small amount of peripheral blood (PB) and at the termination of the evalaution, bone marrow (BM) to determine donor contribution and function by phenotypic analysis. Exploiting the congenic mouse strains of C57BL/6 (CD45.1- CD45.2+), BoyJ (CD45.1+ CD45.2-), and their F1-crossed hybrid C57BL/6 × BoyJ (CD45.1+ CD45.2+), we are able to quantify donor, competitor, and recipient mouse cell contributions to the engraftment state. Human donor cell engraftment (e.g., from the cord blood [CB], mobilized PB, or BM) is assessed by human cell phenotyping in sublethally irradiated immune-deficient mouse recipients (e.g., NOD scid gamma mice that are deficient in B cells, T cells, and natural killer cells and have defective dendritic cells and macrophages). Engraftment of cells from primary mouse recipients into secondary mice allows for an estimation of the self-renewal capacity of the original donor HSC. This chapter outlines concepts, methods, and techniques for mouse and human cell models of HSCT and for assessment of donor cells collected and processed in hypoxia versus ambient air.

An Annotation-Free Restoration Network for Cataractous Fundus Images

Cataracts are the leading cause of vision loss worldwide. Restoration algorithms are developed to improve the readability of cataract fundus images in order to increase the certainty in diagnosis and treatment for cataract patients. Unfortunately, the requirement of annotation limits the application of these algorithms in clinics. This paper proposes a network to annotation-freely restore cataractous fundus images (ArcNet) so as to boost the clinical practicability of restoration. Annotations are unnecessary in ArcNet, where the high-frequency component is extracted from fundus images to replace segmentation in the preservation of retinal structures. The restoration model is learned from the synthesized images and adapted to real cataract images. Extensive experiments are implemented to verify the performance and effectiveness of ArcNet. Favorable performance is achieved using ArcNet against state-of-the-art algorithms, and the diagnosis of ocular fundus diseases in cataract patients is promoted by ArcNet. The capability of properly restoring cataractous images in the absence of annotated data promises the proposed algorithm outstanding clinical practicability.

Low-area and accurate inner product and digital filters based on stochastic computing

The inner product is a key operation in various applications, such as signal processing and pattern recognition. Research has shown that this function, when implemented in stochastic computing (SC) domain, can result in significant reduction in area cost and power consumption compared to its equivalent counterpart in the conventional binary-encoded (BE) deterministic computing. However, existing designs of SC inner product are disadvantaged due to high BE-SC conversion circuits, hence high overall area cost. They also suffer from correlation-induced errors that affect their accuracy performance. In this work, we propose a novel inner product design method for the SC domain that has high accuracy, low area cost, and most importantly, the circuit is correlation-insensitive. Experimental results show that the proposed design on average reduces 85.7% of hardware footprint when compared to its equivalent BE counterpart. We show that it outperforms current state-of-the-art SC designs in terms of area savings, both in computation and conversion costs. Furthermore, it achieves better (or comparable) accuracy performance compared to existing works, especially in designs having large number of inputs with low stochastic number lengths. Moreover, SC FIR filter based on the proposed design method outperforms state-of-the-art SC filters in terms of area and accuracy. (C) 2021 Elsevier B.V. All rights reserved.

Corneal reflections and skin contrast yield better memory of human and virtual faces

Virtual faces have been found to be rated less human-like and remembered worse than photographic images of humans. What it is in virtual faces that yields reduced memory has so far remained unclear. The current study investigated face memory in the context of virtual agent faces and human faces, real and manipulated, considering two factors of predicted influence, i.e., corneal reflections and skin contrast. Corneal reflections referred to the bright points in each eye that occur when the ambient light reflects from the surface of the cornea. Skin contrast referred to the degree to which skin surface is rough versus smooth. We conducted two memory experiments, one with high-quality virtual agent faces (Experiment 1) and the other with the photographs of human faces that were manipulated (Experiment 2). Experiment 1 showed better memory for virtual faces with increased corneal reflections and skin contrast (rougher rather than smoother skin). Experiment 2 replicated these findings, showing that removing the corneal reflections and smoothening the skin reduced memory recognition of manipulated faces, with a stronger effect exerted by the eyes than the skin. This study highlights specific features of the eyes and skin that can help explain memory discrepancies between real and virtual faces and in turn elucidates the factors that play a role in the cognitive processing of faces.

Geometric Occlusion Analysis in Depth Estimation Using Integral Guided Filter for Light-Field Image

Unlike traditional multi-view images, sampling in angular domain of light field images is distributed in different directions. Therefore, an angular sampling image (ASI), comprising of possible matching points extracted from each view, is available for each point. In this paper, we analyze the geometric relationship between ASIs and reference sub-aperture images, and then prove the occlusion boundary similarity. Based on the geometric relationship in extreme cases, we show that some points in ASI have higher reliability than other points for depth calculation. An integral guided filter is then built based on the sub-aperture image to predict occlusion probabilities in ASIs. The filter is independent of ASIs and has no requirement for high angular resolution so that it is easy to apply to the cost volume calculation. We integrate the filter into our depth estimation framework and other state-of-the-art depth estimation frameworks. Experimental results demonstrate that the proposed filter is more effective to occluded point detection in ASIs than other methods. Results from different data sets show that our method outperforms the existing state-of-the-art depth estimation methods, especially along occlusion boundaries.

Randomized Trials With Repeatedly Measured Outcomes: Handling Irregular and Potentially Informative Assessment Times

Randomized trials are often designed to collect outcomes at fixed points in time after randomization. In practice, the number and timing of outcome assessments can vary among participants (i.e., irregular assessment). In fact, the timing of assessments may be associated with the outcome of interest (i.e., informative assessment). For example, in a trial evaluating the effectiveness of treatments for major depressive disorder, not only did the timings of outcome assessments vary among participants but symptom scores were associated with assessment frequency. This type of informative observation requires appropriate statistical analysis. Although analytic methods have been developed, they are rarely used. In this article, we review the literature on irregular assessments with a view toward developing recommendations for analyzing trials with irregular and potentially informative assessment times. We show how the choice of analytic approach hinges on assumptions about the relationship between the assessment and outcome processes. We argue that irregular assessment should be treated with the same care as missing data, and we propose that trialists adopt strategies to minimize the extent of irregularity; describe the extent of irregularity in assessment times; make their assumptions about the relationships between assessment times and outcomes explicit; adopt analytic techniques that are appropriate to their assumptions; and assess the sensitivity of trial results to their assumptions.

What happened at 1500-1000 cal. BP in Central Australia? Timing, impact and archaeological signatures

This paper reviews the late Holocene archaeology of Central Australia. The last 1500 years saw significant changes in the archaeological record in this part of the Australian and zone, with shifts in settlement pattern, site histories, resource use, tool inventories and rock art. Much of the evidence points to regional population growth, beginning 1500-1000 cal. BP and coinciding with expansion of summer-rainfall grassland and more frequent palaeoflood events. Hunter-gatherer groups appear to have increased their use of marginal or outlying areas as these became seasonally accessible. Responses to the demographic changes, especially in the better-watered ranges, include more extended occupation of existing sites, more processing of acacia and grass seeds, and an increase in territoriality reflected in the greater differentiation of rock art complexes after 1500 cal. BP. The archaeological changes are not scaled commensurately with the modest environmental shifts at this time, indicating that human-environment interactions were not linear. A human-environment threshold may have been breached 1500-1000 years ago, with existing socio-economic or historical factors acting to amplify the effects of small environmental changes. However, it remains difficult to fully characterize the nature of these human-environment interactions, despite the fine-grained archaeological record now available. An unresolved problem for this emerging picture of climatic amelioration and population growth is that Aboriginal settlement in Central Australia was expanding at a time when ENSO-driven variability appears to have been at its highest.

Low-cost multiple object tracking for embedded vision applications

This paper presents a low-cost multiple object tracking (MOT) technique by employing a novel appearance update model for object appearance modeling using K-means. The state-of-the-art work has attained a very high accuracy without considering the real-time aspects necessitated by currently trending embedded vision platforms. The major research on multiple object tracking is used to update the appearance model in every frame while discounting its persistent nature. The proposed appearance update model reduces the computational cost of the state-of-the-art MOT 6-fold by exploiting this facet of persistent appearance over the sequence of frames. To ensure accuracy, the proposed model is tested on different publicly available standard datasets with challenging situations for both indoor and outdoor scenarios. The experimental results illustrate that our model successfully achieves multiple object tracking while coping with long-term and complete occlusion. The proposed method achieves the same accuracy in comparison with the state-of-the-art baseline methods. Moreover, and most importantly, the proposed method is cost-effective in terms of computing and/or memory requirements in comparison to the state-of-the-art techniques. All these traits make our design very suitable for real-time and embedded video surveillance applications with low computing/memory resources.

Food and nutrition security: challenges of post-harvest handling in Kenya

Presently, close to 1 billion people suffer from hunger and food insecurity. Statistics in Kenya indicates that over 10 million people suffer from chronic food insecurity and poor nutrition, 2-4 million people require emergency food assistance at any given time with nearly 30 % of Kenya's children being undernourished, 35 % stunted while micro-nutrient deficiency is wide spread. Key among the challenges contributing to inadequate foods include lack of certified seeds, seasonal production (rain-fed), high post-harvest losses and wastages, poor transportation, low value additions which reduce their market competitiveness. The present paper examines some of the underlying causes for high food wastage experience in Kenya and the associated challenges in addressing these problems. The paper also provides an overview of some of the basic solutions that have been recommended by various stakeholders. However, in spite of the recent efforts made to mitigate food wastage, there is still an urgent need to address these gaps through participatory, innovative community based interventions that will create resilience to climate change and enhance livelihoods of smallholder farmers in diverse ecosystems.

Aryl Hydrocarbon Receptor Agonism Antagonizes the Hypoxia-driven Inflammation in Cystic Fibrosis

Hypoxia contributes to the exaggerated yet ineffective airway inflammation that fails to oppose infections in cystic fibrosis (CF). However, the potential for impairment of essential immune functions by HIF-1α (hypoxia-inducible factor 1α) inhibition demands a better comprehension of downstream hypoxia-dependent pathways that are amenable for manipulation. We assessed here whether hypoxia may interfere with the activity of AhR (aryl hydrocarbon receptor), a versatile environmental sensor highly expressed in the lungs, where it plays a homeostatic role. We used murine models of Aspergillus fumigatus infection in vivo and human cells in vitro to define the functional role of AhR in CF, evaluate the impact of hypoxia on AhR expression and activity, and assess whether AhR agonism may antagonize hypoxia-driven inflammation. We demonstrated that there is an important interferential cross-talk between the AhR and HIF-1α signaling pathways in murine and human CF, in that HIF-1α induction squelched the normal AhR response through an impaired formation of the AhR:ARNT (aryl hydrocarbon receptor nuclear translocator)/HIF-1β heterodimer. However, functional studies and analysis of the AhR genetic variability in patients with CF proved that AhR agonism could prevent hypoxia-driven inflammation, restore immune homeostasis, and improve lung function. This study emphasizes the contribution of environmental factors, such as infections, in CF disease progression and suggests the exploitation of hypoxia:xenobiotic receptor cross-talk for antiinflammatory therapy in CF.

Sporadic occurrence of multiple paragangliomas causing carotid and coronary arterial encasement

We describe a case of a 32-year-old man with a paraganglioma causing encasement of ostioproximal segments of the left internal carotid artery and left external carotid artery with concurrent presence of bilobulated mediastinal paraganglioma, with similar imaging characteristics, causing encasement of the coronary arteries.

Enhanced plasmid DNA utilization in transiently transfected CHO-DG44 cells in the presence of polar solvents

Although the protein yields from transient gene expression (TGE) with Chinese hamster ovary (CHO) cells have recently improved, the amount of plasmid DNA (pDNA) needed for transfection remains relatively high. We describe a strategy to reduce the pDNA amount by transfecting CHO-DG44 cells with 0.06 μg pDNA/10(6) cells (10% of the optimal amount) in the presence of nonspecific (filler) DNA and various polar solvents including dimethylsufoxide, dimethyl formamide, acetonitrile, dimethyl acetamide (DMA), and hexamethyl phosphoramide (HMP). All of the polar solvents with the exception of HMP increased the production of a recombinant antibody in comparison to the untreated control transfection. In the presence of 0.25% DMA, the antibody yield in a 7-day batch culture was 500 mg/L. This was fourfold higher than the yield from the untreated control transfection. Mechanistic studies revealed that the polar solvents did not affect polyethylenimine-mediated pDNA delivery into cells or nuclei. The steady-state transgene mRNA level was elevated in the presence of each of the polar solvents tested, while the transgene mRNA half-life remained the same. These results indicated that the polar solvents enhanced transgene transcription. When screening a panel of recombinant antibodies and Fc-fusion proteins for production in the presence of the polar solvents, the highest increase in yield was observed following DMA addition for 11 of the 12 proteins. These results are expected to enhance the applicability of high-yielding TGE processes with CHO-DG44 cells by decreasing the amount of pDNA required for transfection.

Simulation of Postoperative Facial Appearances via Geometric Deep Learning for Efficient Orthognathic Surgical Planning

Orthognathic surgery corrects jaw deformities to improve aesthetics and functions. Due to the complexity of the craniomaxillofacial (CMF) anatomy, orthognathic surgery requires precise surgical planning, which involves predicting postoperative changes in facial appearance. To this end, most conventional methods involve simulation with biomechanical modeling methods, which are labor intensive and computationally expensive. Here we introduce a learning-based framework to speed up the simulation of postoperative facial appearances. Specifically, we introduce a facial shape change prediction network (FSC-Net) to learn the nonlinear mapping from bony shape changes to facial shape changes. FSC-Net is a point transform network weakly-supervised by paired preoperative and postoperative data without point-wise correspondence. In FSC-Net, a distance-guided shape loss places more emphasis on the jaw region. A local point constraint loss restricts point displacements to preserve the topology and smoothness of the surface mesh after point transformation. Evaluation results indicate that FSC-Net achieves $15\times $ speedup with accuracy comparable to a state-of-the-art (SOTA) finite-element modeling (FEM) method.

Evaluation of uranium and plutonium isotopes in marine samples from Veracruz coastline (Mexico)

Activity ratios (A.R.) of 234U/238U and activity concentration of 238+234U and 239+240Pu were measured in collected seawaters and sand beach samples from various locations along of littoral of Mexican state of Veracruz. Uranium and plutonium were separated and concentrated in a liquid-liquid partition chromatography, afterwards, detected and analyzed by means of alpha spectrometric technique. The 234U/238U activity ratio (AR) ranges from 0.72 to 1.11 in sand beach and from 0.77 to 1.22 in seawater. The activity concentration was found in sea water from 0.31 to 1.94 Bq/L for 234+238U and from 15 to 137 μBq/L for 239+240Pu, in sand beach samples was found to be from 0.64 to 3.86 Bq/kg for 234+238U and from 33 to 249 μBq/kg for 239+240Pu.

Lipopolysaccharide distinctively alters human microglia transcriptomes to resemble microglia from Alzheimer's disease mouse models

Alzheimer's disease (AD) is the most common form of dementia, and risk-influencing genetics implicates microglia and neuroimmunity in the pathogenesis of AD. Induced pluripotent stem cell (iPSC)-derived microglia (iPSC-microglia) are increasingly used as a model of AD, but the relevance of historical immune stimuli to model AD is unclear. We performed a detailed cross-comparison over time on the effects of combinatory stimulation of iPSC-microglia, and in particular their relevance to AD. We used single-cell RNA sequencing to measure the transcriptional response of iPSC-microglia after 24 h and 48 h of stimulation with prostaglandin E2 (PGE2) or lipopolysaccharide (LPS)+interferon gamma (IFN-γ), either alone or in combination with ATPγS. We observed a shared core transcriptional response of iPSC-microglia to ATPγS and to LPS+IFN-γ, suggestive of a convergent mechanism of action. Across all conditions, we observed a significant overlap, although directional inconsistency to genes that change their expression levels in human microglia from AD patients. Using a data-led approach, we identify a common axis of transcriptomic change across AD genetic mouse models of microglia and show that only LPS provokes a transcriptional response along this axis in mouse microglia and LPS+IFN-γ in human iPSC-microglia. This article has an associated First Person interview with the first author of the paper.

Universal Framework for Joint Image Restoration and 3D Body Reconstruction

Recent works have demonstrated excellent state-of-the-art achievements in image restoration and 3D body reconstruction from an input image. The 3D body reconstruction task, however, relies heavily on the input image's quality. A straightforward way to solve this issue is by generating vast degraded datasets and using them in a re-finetuned or newly-crafted body reconstruction network. However, in future usage, these datasets may become obsolete, leaving the newly-crafted network outdated. Unlike this approach, we design a universal framework that is able to utilize prior state-of-the-art restoration works and then self-boosts their performances during test-time while jointly carrying out the 3D body reconstruction. The self-boosting mechanism is adopted via test-time parameter adaptation capable of handling various types of degradation. To accommodate, we also propose a strategy that generates pseudo-data on the fly during test-time, allowing both restoration and reconstruction modules to be learned in a self-supervised manner. With this advantage, the universal framework intelligently enhances the performance without any new dataset or new neural network model involvement. Our experimental results show that using the proposed framework and pseudo-data strategies significantly improves the performances of both scenarios.

Hybrid localization with temporal post-processing

Precise localization of mobile devices is a promising and challenging task. Due to multipath propagation state of the art localization techniques provide only coarse position estimates. An approach to improve position estimates consists in hybrid localization, i.e., in combining several coarse position estimates to obtain one better position estimate. The position estimates to be combined can stem from different spatial measurements taken at the same time instant or from the same spatial measurement performed at different time instants.

Optimization of Edge Quality in the Slot-Die Coating Process of High-Capacity Lithium-Ion Battery Electrodes

Understanding and reducing edge elevations at the lateral edges are crucial aspects to reduce reject rates during electrode production for lithium-ion batteries (LIB). Herein, different process conditions to reduce edge elevations at the lateral edges of water-based, shear-thinning coatings in the production of LIB electrodes are presented. The reduction of edge elevations is transferred from state-of-the-art electrodes to high-capacity electrodes. The developed process configuration greatly reduces reject caused by cutting off the edge areas in the industrial roll-to-roll process for electrode production. Compared with state-of-the art electrodes, the reject rate for high-capacity electrode production is significantly higher because the edge geometry in crossweb direction of the electrodes is wider. An optimization can be achieved by a combined adjustment of the coating gap and the slot-die angle to the substrate (angle of attack) to affect the pressure field in the coating bead. Therefore, a systematic investigation and optimization of these process parameters are presented. In addition, the investigation of the process stability of the coating is required. Based on this optimization, a reduction of edge elevations for high-capacity electrode coatings (5 mAh cm(-2)) of 69% and ultrathick high-capacity electrode coatings (7 mAh cm(-2)) of 48% is possible.

Environmental challenges of COVID-19 pandemic: resilience and sustainability - A review

The emergence of novel respiratory disease (COVID-19) caused by SARS-CoV-2 has become a public health emergency worldwide and perturbed the global economy and ecosystem services. Many studies have reported the presence of SARS-CoV-2 in different environmental compartments, its transmission via environmental routes, and potential environmental challenges posed by the COVID-19 pandemic. None of these studies have comprehensively reviewed the bidirectional relationship between the COVID-19 pandemic and the environment. For the first time, we explored the relationship between the environment and the SARS-CoV-2 virus/COVID-19 and how they affect each other. Supporting evidence presented here clearly demonstrates the presence of SARS-CoV-2 in soil and water, denoting the role of the environment in the COVID-19 transmission process. However, most studies fail to determine if the viral genomes they have discovered are infectious, which could be affected by the environmental factors in which they are found.The potential environmental impact of the pandemic, including water pollution, chemical contamination, increased generation of non-biodegradable waste, and single-use plastics have received the most attention. For the most part, efficient measures have been used to address the current environmental challenges from COVID-19, including using environmentally friendly disinfection technologies and employing measures to reduce the production of plastic wastes, such as the reuse and recycling of plastics. Developing sustainable solutions to counter the environmental challenges posed by the COVID-19 pandemic should be included in national preparedness strategies. In conclusion, combating the pandemic and accomplishing public health goals should be balanced with environmentally sustainable measures, as the two are closely intertwined.

Shotgun metaproteomic profiling of biomimetic anaerobic digestion processes treating sewage sludge

Two parallel anaerobic digestion lines were designed to match a "bovid-like" digestive structure. Each of the lines consisted of two continuous stirred tank reactors placed in series and separated by an acidic treatment step. The first line was inoculated with industrial inocula whereas the second was seeded with cow digestive tract contents. After 3 months of continuous sewage sludge feeding, samples were recovered for shotgun metaproteomic and DNA-based analysis. Strikingly, protein-inferred and 16S ribosomal DNA tags based taxonomic community profiles were not consistent. PCA however revealed a similar clustering pattern of the samples, suggesting that reproducible methodological and/or biological factors underlie this observation. The performances of the two digestion lines did not differ significantly and the cow-derived inocula did not establish in the reactors. A low throughput metagenomic dataset (3.4 × 10(6) reads, 1.1 Gb) was also generated for one of the samples. It allowed a substantial increase of the analysis depth (11 vs. 4% of spectral identification rate for the combined samples). Surprisingly, a high proportion of proteins from members of the "Candidatus Competibacter" group, a key microbial player usually found in activated sludge plants, was retrieved in our anaerobic digester samples. Data are available via ProteomeXchange with identifier PXD002420 (http://proteomecentral.proteomexchange.org/dataset/PXD002420).

Demystifying racemic natural products in the homochiral world

Natural products possess structural complexity, diversity and chirality with attractive functions and biological activities that have significantly impacted drug discovery initiatives. Chiral natural products are abundant in nature but rarely occur as racemates. The occurrence of natural products as racemates is very intriguing from a biosynthetic point of view; as enzymes are chiral molecules, enzymatic reactions generating natural products should be stereospecific and lead to single-enantiomer products. Despite several reports in the literature describing racemic mixtures of stereoisomers isolated from natural sources, there has not been a comprehensive review of these intriguing racemic natural products. The discovery of many more natural racemates and their potential enzymatic sources in recent years allows us to describe the distribution and chemical diversity of this 'class of natural products' to enrich discussions on biosynthesis. In this Review, we describe the chemical classes, occurrence and distribution of pairs of enantiomers in nature and provide insights about recent advances in analytical methods used for their characterization. Special emphasis is on the biosynthesis, including plausible enzymatic and non-enzymatic formation of natural racemates, and their pharmacological significance.

Overview of Wind Park Control Strategies

This paper describes the concept of wind turbine control system (WTCS), wind park control system, state-of-the-art information communication technologies, and wind park control strategies from various sources. Concept of WTCSs and wind park control systems has been briefly explained. Their typical structures and functions have been explained. Relevant theoretical background of power systems has been touched upon as well.

A multimodal approach using deep learning for fall detection

A computational system able to automatically and efficiently detect and classify falls would be beneficial for monitoring the elderly population and speed up the assistance proceedings, reducing the risk of prolonged injuries and death. One of the most common problems in such systems is the high number of false-positives in their recognition scheme, which may cause an overload on surveillance system calls. We address this problem by proposing different topologies of a multimodal convolution neural network, which is trained to detect falls based on RGB images and information from accelerometers. We train and evaluate our networks with the UR Fall Detection dataset and UP-Fall dataset, and provide an extensive comparison with state-of-the-art models. Our model reached good results on UR Fall Detection dataset and achieved the state-of-art on UP-Fall detection dataset, relying on easily available sensors to do so, demonstrating it can be a scalable solution for robust fall detection in the real world.

Medical Image Imputation From Image Collections

We present an algorithm for creating high-resolution anatomically plausible images consistent with acquired clinical brain MRI scans with large inter-slice spacing. Although large data sets of clinical images contain a wealth of information, time constraints during acquisition result in sparse scans that fail to capture much of the anatomy. These characteristics often render computational analysis impractical as many image analysis algorithms tend to fail when applied to such images. Highly specialized algorithms that explicitly handle sparse slice spacing do not generalize well across problem domains. In contrast, we aim to enable the application of existing algorithms that were originally developed for high-resolution research scans to significantly undersampled scans. We introduce a generative model that captures a fine-scale anatomical structure across subjects in clinical image collections and derives an algorithm for filling in the missing data in scans with large inter-slice spacing. Our experimental results demonstrate that the resulting method outperforms the state-of-the-art upsampling super-resolution techniques, and promises to facilitate subsequent analysis not previously possible with scans of this quality. Our implementation is freely available at https://github.com/adalca/papago.

Social Workers' Involvement in Developing and Implementing Social Programs for Older Adults During the COVID-19 Pandemic in Nigeria: A Concept Paper and Suggestions for Action Plans

Social workers, especially in the Global North/developed countries such as the United States of America, Australia, Canada, and the United Kingdom, have been actively involved in implementing social programs to improve the psychosocial, health, and wellbeing of older adults during the COVID-19 pandemic. However, this is not the case in the Global South/developing countries like Nigeria, Ghana, etc. This concept paper aims to describe the current state of Nigerian social workers' role in developing and implementing social programs for older adults during the COVID-19 pandemic and to identify action plans for further strengthening their involvement. We systematically reviewed the literature to identify Nigerian social workers' role in developing and implementing social programs for older adults during COVID-19. Our review reflected that social workers are rarely involved in developing and implementing social programs; when involved, their involvement is on a consultation basis, which limits their active involvement in multidisciplinary team of COVID-19 prevention and vaccination ad hoc committees in Nigeria.

Full-BAPose: Bottom Up Framework for Full Body Pose Estimation

We present Full-BAPose, a novel bottom-up approach for full body pose estimation that achieves state-of-the-art results without relying on external people detectors. The Full-BAPose method addresses the broader task of full body pose estimation including hands, feet, and facial landmarks. Our deep learning architecture is end-to-end trainable based on an encoder-decoder configuration with HRNet backbone and multi-scale representations using a disentangled waterfall atrous spatial pooling module. The disentangled waterfall module leverages the efficiency of progressive filtering, while maintaining multi-scale fields-of-view comparable to spatial pyramid configurations. Additionally, it combines multi-scale features obtained from the waterfall flow with the person-detection capability of the disentangled adaptive regression and incorporates adaptive convolutions to infer keypoints more precisely in crowded scenes. Full-BAPose achieves state-of-the art performance on the challenging CrowdPose and COCO-WholeBody datasets, with AP of 72.2% and 68.4%, respectively, based on 133 keypoints. Our results demonstrate that Full-BAPose is efficient and robust when operating under a variety conditions, including multiple people, changes in scale, and occlusions.

Hevin/Sparcl1 drives pathological pain through spinal cord astrocyte and NMDA receptor signaling

High endothelial venule protein/SPARC-like 1 (hevin/Sparcl1) is an astrocyte-secreted protein that regulates synapse formation in the brain. Here we show that astrocytic hevin signaling plays a critical role in maintaining chronic pain. Compared with WT mice, hevin-null mice exhibited normal mechanical and heat sensitivity but reduced inflammatory pain. Interestingly, hevin-null mice have faster recovery than WT mice from neuropathic pain after nerve injury. Intrathecal injection of WT hevin was sufficient to induce persistent mechanical allodynia in naive mice. In hevin-null mice with nerve injury, adeno-associated-virus-mediated (AAV-mediated) re-expression of hevin in glial fibrillary acidic protein-expressing (GFAP-expressing) spinal cord astrocytes could reinstate neuropathic pain. Mechanistically, hevin is crucial for spinal cord NMDA receptor (NMDAR) signaling. Hevin-potentiated N-Methyl-D-aspartic acid (NMDA) currents are mediated by GluN2B-containing NMDARs. Furthermore, intrathecal injection of a neutralizing Ab against hevin alleviated acute and persistent inflammatory pain, postoperative pain, and neuropathic pain. Secreted hevin that was detected in mouse cerebrospinal fluid (CSF) and nerve injury significantly increased CSF hevin abundance. Finally, neurosurgery caused rapid and substantial increases in SPARCL1/HEVIN levels in human CSF. Collectively, our findings support a critical role of hevin and astrocytes in the maintenance of chronic pain. Neutralizing of secreted hevin with monoclonal Ab may provide a new therapeutic strategy for treating acute and chronic pain and NMDAR-medicated neurodegeneration.

Evolutionary joint selection to improve human action recognition with RGB-D devices

Interest in RGB-D devices is increasing due to their low price and the wide range of possible applications that come along. These devices provide a marker-less body pose estimation by means of skeletal data consisting of 3D positions of body joints. These can be further used for pose, gesture or action recognition. In this work, an evolutionary algorithm is used to determine the optimal subset of skeleton joints, taking into account the topological structure of the skeleton, in order to improve the final success rate. The proposed method has been validated using a state-of-the-art RGB action recognition approach, and applying it to the MSR-Action3D dataset. Results show that the proposed algorithm is able to significantly improve the initial recognition rate and to yield similar or better success rates than the state-of-the-art methods. (C) 2013 Elsevier Ltd. All rights reserved.

Status and development of PEM fuel cell technology

Fuel cells are an emerging technology with applications in transportation, stationary and portable power generation, With Outputs ranging from mW to MW. The most promising and most widely researched, developed and demonstrated type of fuel cells is proton exchange membrane (PEM) fuel cell. State of the art in PEM fuel cell technology and challenges in their development and widespread applications are discussed. Copyright (C) 2007 John Wiley & Sons, Ltd.

Disentangled-Multimodal Adversarial Autoencoder: Application to Infant Age Prediction With Incomplete Multimodal Neuroimages

Effective fusion of structural magnetic resonance imaging (sMRI) and functional magnetic resonance imaging (fMRI) data has the potential to boost the accuracy of infant age prediction thanks to the complementary information provided by different imaging modalities. However, functional connectivity measured by fMRI during infancy is largely immature and noisy compared to the morphological features from sMRI, thus making the sMRI and fMRI fusion for infant brain analysis extremely challenging. With the conventional multimodal fusion strategies, adding fMRI data for age prediction has a high risk of introducing more noises than useful features, which would lead to reduced accuracy than that merely using sMRI data. To address this issue, we develop a novel model termed as disentangled-multimodal adversarial autoencoder (DMM-AAE) for infant age prediction based on multimodal brain MRI. Specifically, we disentangle the latent variables of autoencoder into common and specific codes to represent the shared and complementary information among modalities, respectively. Then, cross-reconstruction requirement and common-specific distance ratio loss are designed as regularizations to ensure the effectiveness and thoroughness of the disentanglement. By arranging relatively independent autoencoders to separate the modalities and employing disentanglement under cross-reconstruction requirement to integrate them, our DMM-AAE method effectively restrains the possible interference cross modalities, while realizing effective information fusion. Taking advantage of the latent variable disentanglement, a new strategy is further proposed and embedded into DMM-AAE to address the issue of incompleteness of the multimodal neuroimages, which can also be used as an independent algorithm for missing modality imputation. By taking six types of cortical morphometric features from sMRI and brain functional connectivity from fMRI as predictors, the superiority of the proposed DMM-AAE is validated on infant age (35 to 848 days after birth) prediction using incomplete multimodal neuroimages. The mean absolute error of the prediction based on DMM-AAE reaches 37.6 days, outperforming state-of-the-art methods. Generally, our proposed DMM-AAE can serve as a promising model for prediction with multimodal data.