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Deep supervised hashing network with integrated regularisation

Hashing has been widely deployed to approximate nearest neighbour search for large-scale multimedia retrieval tasks due to storage and retrieval efficiency. State-of-the-art supervised hashing methods for image retrieval construct deep structures to simultaneously learn image representation and generate good hash codes, and the key step among them is simultaneously learned feature representation and binary hash code. Existing methods use similarity and regularity loss to train deep hashing systems, but these two functions usually work together but not cooperative, which may lead to inadequate performance of the whole system. In this study, a new method for training deep hashing system to learn compact binary codes is presented. The deep supervised hashing network with integrated regularisation (DSHIR) system develop the zero division restriction as a new part of the loss function, which settles the problem of cooperatively guiding the system generate similarity preserving binary codes. DSHIR system also modifies the similarity handling loss to better extract features from image data, which promotes the performance compared to existing end-to-end deep hashing systems. Experiments show that DSHIR yields about 10 per cent higher mean average precision on CIFAR-10 dataset, and also promote on other evaluation indexes compared with state-of-the-art systems.

Antimicrobial Weapons of Pseudomonas aeruginosa

Pseudomonas aeruginosa is a robust and versatile organism capable of surviving and prospering in a diverse array of environments and is an opportunistic pathogen of humans. One reason for the success of this pathogen is the large arsenal of antimicrobial weapons that it possesses. Here we focus our attention on these antimicrobial weapons and how they give P. aeruginosa a survival edge in polymicrobial environments. We define antimicrobial weapons as components produced by P. aeruginosa that are used to kill, inhibit growth and/or subvert key cellular functions in other microbes. P. aeruginosa has a large and complex genome and encodes an armament of antimicrobial weapons that fall into two subclasses; those that are delivered directly to competing microbes using a contact-dependent method, and those that are secreted in a contact-independent manner into the environment to then be available to target neighbouring cells. This chapter provides an overview of the major antimicrobial weapons possessed by P. aeruginosa, captures recent advances in the field and discusses how these could be targeted as a therapeutic intervention, or potentially harnessed to combat infection.

IFRAD: A Fast Feature Descriptor for Remote Sensing Images

Feature description is a necessary process for implementing feature-based remote sensing applications. Due to the limited resources in satellite platforms and the considerable amount of image data, feature description-which is a process before feature matching-has to be fast and reliable. Currently, the state-of-the-art feature description methods are time-consuming as they need to quantitatively describe the detected features according to the surrounding gradients or pixels. Here, we propose a novel feature descriptor called Inter-Feature Relative Azimuth and Distance (IFRAD), which will describe a feature according to its relation to other features in an image. The IFRAD will be utilized after detecting some FAST-alike features: it first selects some stable features according to criteria, then calculates their relationships, such as their relative distances and azimuths, followed by describing the relationships according to some regulations, making them distinguishable while keeping affine-invariance to some extent. Finally, a special feature-similarity evaluator is designed to match features in two images. Compared with other state-of-the-art algorithms, the proposed method has significant improvements in computational efficiency at the expense of reasonable reductions in scale invariance.

[A collaborative project to improve emergency room admissions through codesign]

Design is a project approach that is gradually opening up in the hospital sector with the ambition of contributing to the improvement of hospitality for the benefit of patients and caregivers. This is the challenge taken up by the University Hospital of Montpellier (34) by creating an experimental project that integrates design practices in the emergency room.

Adherence with early allied health assessments in specialist burn services

Appropriate multidisciplinary allied health assessment during the early stages of admission following burn injury positively influences recovery and quality of life. Variation in allied health care may affect patient outcomes. We aimed to explore adherence in providing early allied health assessments in accordance with local parameters. Associations between the number of assessments and hospital length of stay (LOS) were also explored. The Burns Registry of Australia and New Zealand was queried for adult (≥ 16 years) burn injured patients admitted to a specialist burn service for > 48 hours between July 2016 and June 2020. Quality indicator data relating to allied health assessment processes were examined; patients were grouped according to the number of assessments they received within 48 hours of admission. Of the 5789 patients included in the study, 5598 (97%) received at least one allied health assessment within 48 hours of admission and 3976 (69%) received all three assessments. A greater proportion of patients who received no assessments were admitted on a Saturday. Patients receiving three assessments had more severe injuries compared to their counterparts who received fewer assessments. Hospital LOS was not associated with the number of allied health assessments during an acute admission following burn injury after accounting for confounding factors, particularly TBSA. Multidisciplinary allied health teams provide routine burn care to Australian and New Zealand burns patients at a consistent level. Further, this study provides evidence that allied health input is prioritised towards patients with increasing severity of burn injury, playing an integral role in early rehabilitation.

Medial femoral condyle free flap for carpo-metacarpal instability following hamate comminute fracture

Complete reconstruction of the hamate bone has been reported in the literature mostly following cancer excision or avascular necrosis. For the exiguity of the tissue deficit, bone grafting has usually been used as treatment option for its rapidity and easiness to perform, even if a variable amount of bone resorption may occur. In traumatic cases, microbial contamination may jeopardize the success of a well performed bone graft and vascularised bone grafts may represent a better reconstructive option. Here we describe the first case reported in the literature of a patient underwent complete hamate reconstruction following trauma with an osseous medial femoral condyle free flap as vascularized arthrodesis between the capitate and the 4th MTC base, in order to stabilize the 4th and 5th finger and the ulnar carpo-metacarpal joint.

List Scheduling Algorithm for Heterogeneous Systems by an Optimistic Cost Table

Efficient application scheduling algorithms are important for obtaining high performance in heterogeneous computing systems. In this paper, we present a novel list-based scheduling algorithm called Predict Earliest Finish Time (PEFT) for heterogeneous computing systems. The algorithm has the same time complexity as the state-of-the-art algorithm for the same purpose, that is, O(v(2).p) for v tasks and p processors, but offers significant makespan improvements by introducing a look-ahead feature without increasing the time complexity associated with computation of an optimistic cost table (OCT). The calculated value is an optimistic cost because processor availability is not considered in the computation. Our algorithm is only based on an OCT that is used to rank tasks and for processor selection. The analysis and experiments based on randomly generated graphs with various characteristics and graphs of real-world applications show that the PEFT algorithm outperforms the state-of-the-art list-based algorithms for heterogeneous systems in terms of schedule length ratio, efficiency, and frequency of best results.

Potential effects of particulate matter from combustion during services on human health and on works of art in medieval churches in Cyprus

Indoor and outdoor particulate matter (PM(0.3-10)) number concentrations were established in two medieval churches in Cyprus. In both churches incense was burnt occasionally during Mass. The highest indoor PM(0.5-1) concentrations compared with outdoors (10.7 times higher) were observed in the church that burning of candles indoors was allowed. Peak indoor black carbon concentration was 6.8 mu g m(-3) in the instances that incense was burning and 13.4 mu g m(-3) in the instances that the candles were burning (outdoor levels ranged between 0.6 and 1.3 mu g m(-3)). From the water soluble inorganic components determined in PM(10), calcium prevailed in all samples indoors or outdoors, whilst high potassium concentration indoors were a clear marker of combustion. Indoor sources of PM were clearly identified and their emission strengths were estimated via modeling of the results. Indoor estimated PM(0.3-10) mass concentrations exceeded air quality standards for human health protection and for the preservation of works of art. (C) 2010 Elsevier Ltd. All rights reserved.

Inter-examiner reliability of the Doha agreement meeting classification system of groin pain in male athletes

The Doha agreement classification is used to classify groin pain in athletes. We evaluated the inter-examiner reliability of this classification system. We prospectively recruited 48 male athletes (66 symptomatic sides) with groin pain between 10-2017 and 03-2020 at a sports medicine hospital in Qatar. Two examiners (23 and 10 years of clinical experience) performed history taking, and a standardized clinical examination blinded to each other's findings. Examiners classified groin pain using the Doha agreement terminology (adductor-, inguinal-, iliopsoas-, pubic-, hip-related groin pain, or other causes of groin pain). Multiple entities were ranked in order of perceived clinical importance. Each side was classified separately for bilateral groin pain. Inter-examiner reliability was calculated using Cohen's Kappa statistic (κ). Inter-examiner reliability was slight to moderate for adductor- (κ = 0.40), inguinal- (κ = 0.44), iliopsoas- (κ = 0.57), and pubic-related groin pain (κ = 0.12), substantial for hip-related groin pain (κ = 0.62), and slight for "other causes of groin pain" (κ = 0.13). Ranking entities in order of perceived clinical importance improved inter-examiner reliability for adductor-, inguinal-, and iliopsoas-related groin pain (κ = 0.52-0.65), but not for pubic (κ = 0.12), hip (κ = 0.51), and "other causes of groin pain" (κ = 0.03). For participants with unilateral groin pain classified with a single entity (n = 7), there was 100% agreement between the two examiners. Inter-examiner reliability of the Doha agreement meeting classification system varied from slight to substantial, depending on the clinical entity. Agreement between examiners was perfect when athletes were classified with a single clinical entity of groin pain, but lower when athletes were classified with multiple clinical entities.

A Joint Learning Framework With BERT for Spoken Language Understanding

Intent classification and slot filling are two essential tasks for spoken language understanding. Recently, joint learning has been shown to be effective for the two tasks. However, most joint learning methods only consider joint learning using shared parameters on the surface level rather than the semantic level, and these methods suffer from small-scale human-labeled training data, resulting in poor generalization capabilities, especially for rare words. In this paper, we propose a novel encoder-decoder framework based multi-task learning model, which conducts joint training for intent classification and slot filling tasks. For the encoder of our model, we encode the input sequence as context representations using bidirectional encoder representation from transformers (BERT). For the decoder, we implement two-stage decoder process in our model. In the first stage, we use an intent classification decoder to detect the user's intent. In the second stage, we leverage the intent contextual information into the slot filling decoder to predict the semantic concept tags for each word. We conduct experiments on three popular benchmark datasets: ATIS, Snips and Facebook multilingual task-oriented datasets. The experimental results show that our proposed model outperforms the state-of-the-art approaches and achieves new state-of-the-art results on both three datasets.

Taxonomy, phylogeny and molecular epidemiology of Echinococcus multilocularis: From fundamental knowledge to health ecology

Alveolar echinococcosis, caused by the tapeworm Echinococcus multilocularis, is one of the most severe parasitic diseases in humans and represents one of the 17 neglected diseases prioritised by the World Health Organisation (WHO) in 2012. Considering the major medical and veterinary importance of this parasite, the phylogeny of the genus Echinococcus is of considerable importance; yet, despite numerous efforts with both mitochondrial and nuclear data, it has remained unresolved. The genus is clearly complex, and this is one of the reasons for the incomplete understanding of its taxonomy. Although taxonomic studies have recognised E. multilocularis as a separate entity from the Echinococcus granulosus complex and other members of the genus, it would be premature to draw firm conclusions about the taxonomy of the genus before the phylogeny of the whole genus is fully resolved. The recent sequencing of E. multilocularis and E. granulosus genomes opens new possibilities for performing in-depth phylogenetic analyses. In addition, whole genome data provide the possibility of inferring phylogenies based on a large number of functional genes, i.e. genes that trace the evolutionary history of adaptation in E. multilocularis and other members of the genus. Moreover, genomic data open new avenues for studying the molecular epidemiology of E. multilocularis: genotyping studies with larger panels of genetic markers allow the genetic diversity and spatial dynamics of parasites to be evaluated with greater precision. There is an urgent need for international coordination of genotyping of E. multilocularis isolates from animals and human patients. This could be fundamental for a better understanding of the transmission of alveolar echinococcosis and for designing efficient healthcare strategies.

Therapeutic effects of JLX001 on neuronal necroptosis after cerebral ischemia-reperfusion in rats

In recent years, more attention has been given to novel patterns of cell death observed during ischemia/reperfusion (I/R). Necroptosis is a regulable secondary cell death pathway; necroptosis is different from traditional forms of cell death, and it is regulated by the RIPK1-RIPK3-MLKL signaling pathway. JLX001 is the double hydrochloride of the natural compound cyclovirobuxine D (CVB-D). Previous studies have confirmed that CVB-D exerts a significant effect on cardiovascular and cerebrovascular diseases and that JLX001 can reduce ischemic brain injury by inhibiting cell apoptosis. For the first time, this project explored the in vivo and in vitro inhibitory effects of the therapeutic administration of JLX001 on the neuronal necroptosis caused by cerebral ischemia-reperfusion injury (CIRI). The middle cerebral artery occlusion reperfusion (MCAO/R) model was used to simulate I/R injury in rats in vivo, and oxygen-glucose deprivation and reperfusion (OGD/R) was used to simulate I/R injury in vitro. After the administration of JLX001, the relative expression of necroptosis-related molecules was measured by ELISA, RT-PCR, HE staining, immunofluorescence and Western blotting. The results showed that JLX001 significantly reduced pathological damage and the cerebral infarction rate in rat brain tissues, and the expression of neuronal necroptosis-related molecules was reduced, suggesting that JLX001 may regulate CIRI through the classic RIPK1-RIPK3-MLKL necroptosis pathway.

Immunohistochemical localisation and effect of matrix metalloproteinases and their inhibitors on canine spontaneous periodontitis

Periodontitis is commonly observed in dogs. In human medicine, it is well documented that matrix metalloproteinases (MMPs) are involved in the destruction of the periodontium. Therefore, the aim of this prospective study was to investigate the impact of MMPs and their inhibitors, the TIMPs (tissue inhibitors of metalloproteinases), on canine periodontitis. The oral cavities of 57 dogs were examined clinically and radiologically. Gingival biopsies were obtained from the examined dogs and histologically analysed via haematoxylin and eosin stained sections. Immunohistological detection of MMP-2, MMP-3, MMP-8 and MMP-9 as well as TIMP-1 and TIMP-2 was performed by the avidin-biotin peroxidase complex technique. All sections were evaluated by light microscopy. Statistically significant positive correlations were detected between the histologically verified degree of inflammation and the expression of MMP-2, MMP-3, MMP-8 and MMP-9 as well as between changes in collagen fibre content and the occurrence of MMP-2, MMP-8 and MMP-9. Concerning TIMP-1 and TIMP-2, non-significant, generally negative correlations were observed. In summary, in canine periodontitis, an increased expression of the above mentioned MMPs and a tendentially decreased expression of TIMPs are present. In conclusion, in canine periodontitis, a MMP-TIMP imbalance is suggestive of contributing to the destruction of the periodontium.

Understanding oxide degradation mechanisms in ultra-thin SiO2 through high-speed, high-resolution in-situ measurements

A model is proposed and validated for the degradation mechanisms occurring in ultra-thin SiO2 at real operation conditions, based on high-resolution, high-speed in-situ measurements. This state-of-the-art set-up proves that oxide degradation still occurs at low stress conditions and allows distinguishing quantitatively the SILC-contribution from the contribution due to trapping.

Efficacy and safety of integrated traditional Chinese and Western medicine against COVID-19: A systematic review and meta-analysis

Although plenty of clinical trials have confirmed the efficacy and safety of integrated traditional Chinese and Western medicine (ITCWM) against COVID-19, the role of ITCWM remains controversial. So we conducted a systematic review and meta-analysis of published studies in eight major databases that report the outcomes of interest in COVID-19 patients receiving ITCWM. RevMan5.4 software was used for meta-analysis, while the quality of RCTs was assessed by the Cochrane risk of bias tool and the retrospective studies were assessed by Newcastle-Ottawa Scale. Eventually, a total of 53 studies with 5425 COVID-19 patients was identified. The meta-analysis results showed that ITCWM was significantly better than western medicine treatment (WMT) alone in the percentage of cases changing to severe/critical [RR = 0.40, 95%CI (0.33, 0.49), p < .00001, I2 = 10%], overall clinical effectiveness [RR = 1.26, 95% CI (1.18, 1.35), p < .00001, I2 = 50%], time to defervescencer [MD = -1.45, 95% CI (-1.82, -1.07), p < .00001, I2 = 83%], disappearing time of cough [MD = -2.11, 95% CI (-2.98, -1.25), p < .00001, I2 = 93%], time of RT-PCR negativity [MD = -3.35, 95% CI (-4.74, -1.95), p < .00001, I2 = 92%], length of hospital stay [MD = -4.05, 95% CI (-5.24, -2.85), p < .00001, I2 = 91%], improvement in CT scan [RR = 1.22, 95% CI (1.17, 1.28), p < .00001, I2 = 46%], TCM syndrome score [MD = -3.95, 95% CI (-5.07, -2.82), p < .00001, I2 = 92%], disappearance rate of fever [RR = 1.23, 95% CI (1.10, 1.38), p < .00001, I2 = 85%], disappearance rate of cough [RR = 1.43, 95% CI (1.25, 1.63), p < .00001, I2 = 60%], level of CRP [MD = -9.23, 95% CI (-10.94, -7.52), p < .00001, I2 = 97%], and WBC [MD = -9.23, 95% CI (-10.94, -7.52), p < .00001, I2 = 97%]. There is no significant difference between ITCWM and WMT in the adverse reaction rate [RR = 0.85, 95% CI(0.71, 1.03), p = .10, I2 = 25%]. Our results showed evidence of clinical efficacy and safety benefit in COVID-19 patients treated with ITCWM. In spite of some limitations, the rapidly developing global pandemic warrants further high-quality and multicenter clinical studies to confirm the contribution of ITCWM.

Paint with stitches: a style definition and image-based rendering method for random-needle embroidery

Random-needle Embroidery is a graceful Chinese art designated as Intangible Cultural Heritage, which "draws" beautiful images with thousands of free-form threads. In this paper, we explore techniques for automatically translating an input image into an art image with the random-needle style. The key idea is to generate rendering primitives of this art first, from which the corresponding dictionary is learned to further sparsely code the contents in the input image. To this end, we first define the artistic style of Random-needle Embroidery by introducing the notion of "stitch", i.e., collection of threads arranged in a certain pattern, as the basic rendering primitive. Then, we adopt sparse coding to generate a stitch dictionary which gives a compact representation of the generated stitches. During runtime, new and more image content-adaptive stitches can be synthesized by optimizing a linear combination of stitch dictionary atoms via sparse representation. Then, the synthesized stitches are placed on the canvas sequentially and connected to adjacent stitches by stitch quilting. After placing all the stitches, a blank filling strategy is proposed and adopted to fill the uncovered areas on the canvas. The experimental results show our method can generate engaging images with the random-needle style. Moreover, our rendering image is better than those obtained by using two other state-of-the art methods.

Generating Adversarial Examples in One Shot With Image-to-Image Translation GAN

Deep Neural Networks (DNNs) provide state-of-the-art results for most machine learning and computer vision tasks. However, they have been found susceptible to adversarial examples. In the recent literature, many ways of generating adversarial examples have been discovered. In this work, we propose a novel method to generate adversarial examples with generative adversarial networks (GANs). Compared to traditional optimisation-based methods, our method provides a fast yet powerful alternative for adversary generation. Unlike other GAN-based approaches in the literature which learn to generate an intermediate perturbation vector, our method generates adversarial examples from benign input images in a straightforward manner. By directly generating adversarial examples from given input images, our method produces perturbations that better align with the underlying edge and shape contained in the inputs, hence more natural-looking and imperceptible to human eyes. We evaluate our method on the MNIST and the CIFAR-10 dataset and demonstrate that it outperforms the state-of-the-art GAN-based attack AdvGAN with similar attack capability in terms of distortion. We show that our method produces competitive results to notable optimisation-based attacks in the literature including the strongest Carlini Wagner (CW) attack.

Hydrodynamic performance optimization and experimental verification of underwater glider based on parametric method

In this paper, the wing body fusion method is used to complete the design of underwater glider. On this basis, the traditional optimization algorithm of underwater gliding wing shape is improved. Based on the improved Hicks Henne algorithm and genetic algorithm, the shape optimization of underwater glider is completed. Through the further optimization of the overall performance, the overall shape of the glider is improved and the maximum lift drag ratio is increased. Finally, the physical experiment of the optimized shape is carried out according to the experimental water area of the circulating water tank. Through the comparative analysis of the data, the accuracy of the numerical calculation is verified.

Preference dynamics with multimodal user-item interactions in social media recommendation

Recommender systems elicit the interests and preferences of individuals and make recommendations accordingly, a main challenge for expert and intelligent systems. An essential problem in recommender systems is to learn users' preference dynamics, that is, the constant evolution of the explicit or the implicit information, which is diversified throughout time according to the user actions. Also, in real settings data sparsity degrades the recommendation accuracy. Hence, state-of-the-art methods exploit multimodal information of users-item interactions to reduce sparsity, but they ignore preference dynamics and do not capture users' most recent preferences. In this article, we present a Temporal Collective Matrix Factorization (TCMF) model, making the following contributions: (i) we capture preference dynamics through a joint decomposition model that extracts the user temporal patterns, and (ii) co-factorize the temporal patterns with multimodal user-item interactions by minimizing a joint objective function to generate the recommendations. We evaluate the performance of TCMF in terms of accuracy and root mean square error, and show that the proposed model significantly outperforms state-of-the-art strategies. (C) 2017 Elsevier Ltd. All rights reserved.

Adolescent Mental Health Resilience and Combinations of Caregiver Monitoring and Warmth: A Person-centred Perspective

Caregiver monitoring and warmth have protective mental health effects for adolescents, including vulnerable adolescents. However, combinations of the aforesaid parenting behaviours and their relationship with adolescent mental health are underexplored, especially among younger and older South African (SA) adolescents challenged by structural disadvantage. Hence, the purpose of this study was to investigate unique profiles of caregiver monitoring and warmth and their associations with depression and conduct problems as reported by younger and older adolescents from disadvantaged SA communities. Latent profile and linear regression analyses were used to examine cross-sectional survey data generated by 891 adolescents from two disadvantaged SA communities (62.2% aged 13-17 [average age: 16.13]; 37.5% aged 18-24 [average age: 20.62]). Two profiles emerged. The first, i.e. substantial caregiver warmth and some monitoring, was associated with younger and older adolescent reports of statistically significantly fewer symptoms of depression and conduct problems. The second, i.e. caregiver monitoring without much warmth, was associated with significantly more symptoms of depression or conduct problems among younger and older adolescents. Traditional gender effects (i.e. higher depression symptoms among girls; higher conduct problem symptoms among boys) were amplified when caregiver monitoring was combined with low warmth. In short, protecting the mental health of younger and older adolescents from disadvantaged communities requires higher levels of caregiver warmth combined with moderate levels of caregiver supervision. Because stressors associated with disadvantaged communities jeopardise warm parenting, supporting caregiver resilience to those stressors is integral to supporting adolescent mental health.

Hyperspectral Video Tracker Based on Spectral Deviation Reduction and a Double Siamese Network

The advent of hyperspectral cameras has popularized the study of hyperspectral video trackers. Although hyperspectral images can better distinguish the targets compared to their RGB counterparts, the occlusion and rotation of the target affect the effectiveness of the target. For instance, occlusion obscures the target, reducing the tracking accuracy and even causing tracking failure. In this regard, this paper proposes a novel hyperspectral video tracker where the double Siamese network (D-Siam) forms the basis of the framework. Moreover, AlexNet serves as the backbone of D-Siam. The current study also adopts a novel spectral-deviation-based dimensionality reduction approach on the learned features to match the input requirements of the AlexNet. It should be noted that the proposed dimensionality reduction method increases the distinction between the target and background. The two response maps, namely the initial response map and the adjacent response map, obtained using the D-Siam network, were fused using an adaptive weight estimation strategy. Finally, a confidence judgment module is proposed to regulate the update for the whole framework. A comparative analysis of the proposed approach with state-of-the-art trackers and an extensive ablation study were conducted on a publicly available benchmark hyperspectral dataset. The results show that the proposed tracker outperforms the existing state-of-the-art approaches against most of the challenges.

Long-Term Person Tracking for Unmanned Aerial Vehicle Based on Human-Machine Collaboration

Unmanned Aerial Vehicle (UAV) has been widely used in military reconnaissance, smart transportation, public security and other fields. UAV-based person tracking is attracting incremental attention for its wide application requirements. Currently, some state-of-the-art visual tracking methods have achieved promising performance in common scenarios. However, in the scene of UAV-based person tracking, there will be long-term target disappearance and unpredictable dramatic target appearance changes, which still pose a huge challenge to UAV-based person tracking. In this work, a human-machine hybrid augmented tracking system based on eye tracking is proposed to cope with the challenge. During tracking, through the interaction between humans and machines, humans can provide real-time guidance and corrections to the tracker, and the tracker can also learn interesting targets from humans to enhance itself. The experimental results show that human-in-the-loop can remarkable improve the success rate and robustness of the tracking and our tracking system outperforms the state-of-the-art tracker in complex environments.

MMSE-Optimal Spectral Amplitude Estimation Given the STFT-Phase

In this letter, we derive a minimum mean squared error (MMSE) optimal estimator for clean speech spectral amplitudes, which we apply in single channel speech enhancement. As opposed to state-of-the-art estimators, the optimal estimator is derived for a given clean speech spectral phase. We show that the phase contains additional information that can be exploited to distinguish outliers in the noise from the target signal. With the proposed technique, incorporating the phase can potentially improve the PESQ-MOS by 0.5 in babble noise as compared to state-of-the-art amplitude estimators. In a blind setup we achieve a PESQ improvement of around 0.25 in voiced speech.

An Adaptive Mean-Shift Framework for MRI Brain Segmentation

An automated scheme for magnetic resonance imaging (MRI) brain segmentation is proposed. An adaptive mean-shift methodology is utilized in order to classify brain voxels into one of three main tissue types: gray matter, white matter, and Cerebro-spinal fluid. The MRI image space is represented by a high-dimensional feature space that includes multimodal intensity features as well as spatial features. An adaptive mean-shift algorithm clusters the joint spatial-intensity feature space, thus extracting a representative set of high-density points within the feature space, otherwise known as modes. Tissue segmentation is obtained by a follow-up phase of intensity-based mode clustering into the three tissue categories. By its nonparametric nature, adaptive mean-shift can deal successfully with nonconvex clusters and produce convergence modes that are better candidates for intensity based classification than the initial voxels. The proposed method is validated on 3-D single and multimodal datasets, for both simulated and real MRI data. It is shown to perform well in comparison to other state-of-the-art methods without the use of a preregistered statistical brain atlas.

NEDD8 Ultimate Buster 1 Long (NUB1L) Protein Suppresses Atypical Neddylation and Promotes the Proteasomal Degradation of Misfolded Proteins

Neddylation is a posttranslational modification that controls diverse biological processes by covalently conjugating the ubiquitin-like protein NEDD8 to specific targets. Neddylation is commonly mediated by NEDD8-specific enzymes (typical neddylation) and, sometimes, by ubiquitin enzymes (atypical neddylation). Although typical neddylation is known to regulate protein function in many ways, the regulatory mechanisms and biological consequence of atypical neddylation remain largely unexplored. Here we report that NEDD8 conjugates were accumulated in the diseased hearts from mouse models and human patients. Proteotoxic stresses induced typical and atypical neddylation in cardiomyocytes. Loss of NUB1L exaggerated atypical neddylation, whereas NUB1L overexpression repressed atypical neddylation through promoting the degradation of NEDD8. Activation of atypical neddylation accumulated a surrogate misfolded protein, GFPu. In contrast, suppression of atypical neddylation by NUB1L overexpression enhanced GFPu degradation. Moreover, NUB1L depletion accumulated a cardiomyopathy-linked misfolded protein, CryAB(R120G), whereas NUB1L overexpression promoted its degradation through suppressing neddylation of ubiquitinated proteins in cardiomyocytes. Consequently, NUB1L protected cells from proteotoxic stress-induced cell injury. In summary, these data indicate that NUB1L suppresses atypical neddylation and promotes the degradation of misfolded proteins by the proteasome. Our findings also suggest that induction of NUB1L could potentially become a novel therapeutic strategy for diseases with increased proteotoxic stress.

Conquering Data Variations in Resolution: A Slice-Aware Multi-Branch Decoder Network

Fully convolutional neural networks have made promising progress in joint liver and liver tumor segmentation. Instead of following the debates over 2D versus 3D networks (for example, pursuing the balance between large-scale 2D pretraining and 3D context), in this paper, we novelly identify the wide variation in the ratio between intra- and inter-slice resolutions as a crucial obstacle to the performance. To tackle the mismatch between the intra- and inter-slice information, we propose a slice-aware 2.5D network that emphasizes extracting discriminative features utilizing not only in-plane semantics but also out-of-plane coherence for each separate slice. Specifically, we present a slice-wise multi-input multi-output architecture to instantiate such a design paradigm, which contains a Multi-Branch Decoder (MD) with a Slice-centric Attention Block (SAB) for learning slice-specific features and a Densely Connected Dice (DCD) loss to regularize the inter-slice predictions to be coherent and continuous. Based on the aforementioned innovations, we achieve state-of-the-art results on the MICCAI 2017 Liver Tumor Segmentation (LiTS) dataset. Besides, we also test our model on the ISBI 2019 Segmentation of THoracic Organs at Risk (SegTHOR) dataset, and the result proves the robustness and generalizability of the proposed method in other segmentation tasks.

An Implicit Contour Morphing Framework Applied to Computer-Aided Severe Weather Forecasting

We propose a contour morphing framework that allows large deformations and topological changes while guaranteeing non self-intersecting contours. Instead of explicitly matching contour points, the proposed algorithm combines an implicit contour representation with state-of-the-art deformable image registration. Synthetic and real-life examples from a meteorological application are presented to demonstrate the efficacy of the framework.

Cost-Effectiveness Analysis in Performance Assessments: A Case Study of the Objective Structured Clinical Examination

Medical education assessments are becoming more complex, resulting in the inappropriateness of traditional methods primarily consisting of direct observations, oral examinations, and multiple-choice tests. Advancements in research methods have led to the formation of new modalities, namely performance assessments, which are, on the other hand, always costly in development and implementation. Proposing using the Program Effectiveness and Cost Generalization flow within an assessment context (PRECOG-A), this brief report explores the real financial cost drivers associated with an assessment case in the context of medical education, presents the steps in bridging the effectiveness with its psychometric properties via cost-effectiveness analysis, and evaluates the two-side outcomes for further evaluation decision-making. Referentially providing a framework to investigators and researchers, the illustration of PRECOG-A in this study outlines instructional guidelines for conducting cost-effectiveness analysis in a performance assessment.

Analyzing Lymphoma Development and Progression Using HDACi in Mouse Models

Besides the physiological role of histone deacetalylases in maintaining normal cellular integrity, the acetylation landscape is changed in cancer cells, which has been implicated as a potential target in cancer therapy. The overexpression of certain HDACs correlates with specific cancer types. Therefore, the development of specific HDAC inhibitors may extend the therapeutic strategy for cancer therapy. Here, we describe how to investigate the therapeutic potential of specific HDACi by treatment in a mouse model for B-cell lymphoma, exemplified by the HDAC6 inhibitor Marbostat-100.

EgNRT2.3 and EgNAR2 expression are controlled by nitrogen deprivation and encode proteins that function as a two-component nitrate uptake system in oil palm

Oil palm (Elaeis guineensis Jacq.) is an important crop for oil and biodiesel production. Oil palm plantations require extensive fertilizer additions to achieve a high yield. Fertilizer application decisions and management for oil palm farming rely on leaf tissue and soil nutrient analyses with little information available to describe the key players for nutrient uptake. A molecular understanding of how nutrients, especially nitrogen (N), are taken up in oil palm is very important to improve fertilizer use and formulation practice in oil palm plantations. In this work, two nitrate uptake genes in oil palm, EgNRT2.3 and EgNAR2, were cloned and characterized. Spatial expression analysis showed high expression of these two genes was mainly found in un-lignified young roots. Interestingly, EgNRT2.3 and EgNAR2 were up-regulated by N deprivation, but their expression pattern depended on the form of N source. Promoter analysis of these two genes confirmed the presence of regulatory elements that support these expression patterns. The Xenopus oocyte assay showed that EgNRT2.3 and EgNAR2 had to act together to take up nitrate. The results suggest that EgNRT2.3 and EgNAR2 act as a two-component nitrate uptake system in oil palm.

A 0.6V 785-nW Multimodal Sensor Interface IC for Ozone Pollutant Sensing and Correlated Cardiovascular Disease Monitoring

In this article, we present the design and analysis of a 785-nW multimodal sensor interface IC for ozone pollutant sensing and correlated cardiovascular disease monitoring based on electrocardiography (ECG) and photoplethysmography (PPG). The proposed hybrid dc offset current cancellation (DCOC) along with a 4-M Omega gain-regulated cascode transimpedance amplifier (RGC-TIA) enable PPG readout power reduction by 37x, compared with the state-of-the-art PPG sensor interfaces. The ozone sensing channel proposes an adaptive architecture to enable low V-DD operation, achieving a 300x power reduction, compared with the state-of-the-art gas sensing readouts. The ozone sensing channel's performance was also verified using custom resistive metal-oxide sensors for concentrations from 50 to 900 ppb. The sensor interface IC is fabricated in a 65-nm CMOS, integrating a 165-nW voltage-mode ECG channel, a 532-nW current- mode PPG channel, 76-nW resistive-mode ozone channel, and 12.6-nW peripheral circuits, all at 0.6 V. The total system power consumption including the LED and a custom digital readout IC is 10.98-15.51 mu W, which is 41x-57x less than prior ozone/CVD joint monitoring sensor interface systems.

Fertility and contraception: The experience of Spanish women born in the first half of the twentieth century

New data based on retrospective interviews with older informants enable us to review the history of contraceptive use among Spanish women over much of the twentieth century. This source is unique because it includes cohorts of women whose reproductive lives took place before, during, and after the baby boom. Traditional contraceptive methods (withdrawal and periodic abstinence) were central to the experience of the first set of women, while the last set made full use of modern as well as some traditional methods. For the first cohorts, traditional methods spearheaded the historic decline in fertility, while among the last set of women modern methods led to a precipitous decline towards the below-replacement fertility that continues in Spain today. There is no evidence that the modest increases in fertility during the baby boom in Spain were the result of a decline in the use of contraception among married women.

Real-time tracking-with-detection for coping with viewpoint change

We consider real-time visual tracking with targets undergoing viewpoint changes. The problem is evaluated on a new and extensive dataset of vehicles undergoing large viewpoint changes. We propose an evaluation method in which tracking accuracy is measured under real-time computational complexity constraints and find that state-of-the-art agnostic trackers, as well as class detectors, are still struggling with this task. We study tracking schemes fusing real-time agnostic trackers with a non-real-time class detector used for template update, with two dominating update strategies emerging. We rigorously analyze the template update latency and demonstrate that such methods significantly outperform stand-alone trackers and class detectors. Results are demonstrated using two different trackers and a state-of-the-art classifier, and at several operating points of algorithm/hardware computational speed.

Total Synthesis of Natural Products using Gold Catalysis

Gold catalysis is an extremely enthusiastic field of investigation in the catalysis area. The development of alternative, highly inventive, precompetitive techniques based on gold catalysis has paved the way for executing a broad spectrum of chemical transformations from uncomplicated starting materials. The total synthesis of natural products is a complex and more complicated task. An amalgamation of natural product synthesis through gold-catalysis has been a thought-provoking job. The protocol has solved several problems related to the synthesis of numerous complicated natural products. Thus, this review has outlined some of the most notable benchmarks from the last seven years (2015-2021) on gold catalysis and their application in the total synthesis of numerous natural products. The strategy acquired by the authors to accomplish the total synthesis will be elaborately discussed by emphasizing the role of the gold-catalyzed reactions.

The connections between art and science in Antarctica: Activating Science*Art

Art may be made as a guide to understanding sense of place, and also as a pathway to understanding and valuing scientific ideas. Here we consider this connection in the context of a selected history of artists working in Antarctica, from early explorers to the modern era. This provides a parallel trajectory for the nature, realisation and purpose of the art. We then consider the interaction between art and science and the nature of interdisciplinary work by looking at work produced in a sea ice-based science field camp by an artist collecting data - both scientific and art focused. The artist participated in two field campaigns a year apart, allowing comparison of the evolution of both the artistic practice and the science data collection. Furthermore, the collection of data that served both needs provides a unique point of connection between two fields of endeavour, which are typically considered as separate.

INSPECTION OF THE STATE (GENERAL AND INSTRUMENTAL) OF HISTORICAL TRANSLUCENT STRUCTURES OF THE PUSHKIN STATE MUSEUM OF FINE ARTS

The article is focused on the general and instrumental survey of historical translucent structures in the Pushkin State Museum of Fine Arts in 2018. It is shown that they don't complying with the current requirements, neither in heat transfer resistance nor in air permeability. The improvement recommendations have been developed. It is noted that in case of preservation of metal window frames (according to the requirements of the law on protection of cultural heritage sites) the large-scale computer calculations should be performed to determine the best ways of window restoration.

Electrocoagulation of Corrugated Box Industrial Effluents and Optimization by Response Surface Methodology

The electrocoagulation method using stainless steel anodes was applied to a corrugated cardboard box manufacturing plant's wastewater with high COD content. The effects of current density, processing time and stirring speed on response functions were studied using the Response Surface Methodology (RSM). The removal efficiency of chemical oxygen demand (COD) and energy consumption were selected as response functions. The Central Composite Design (CCD) was chosen to explain the single and combined effects of independent variables on response functions. The COD concentration of the real industrial wastewater used in the experiments was 9130 mg L-1. The maximum COD removal efficiency of 91.6% is obtained with 19.78 Wh g-1 energy consumption. Current density and treatment time were effective parameters for both COD removal and energy consumption. Optimization for maximum COD removal with minimum energy consumption showed 80.9% of COD removal with 6.7 Wh g-1 of energy consumption at 15 mA cm-2, 700 rpm, and 28 min treatment time. The variables are optimized with a few experiments using the response surface method.

Chips for Everyone: A Multifaceted Approach in Electrical Engineering Outreach

This paper reports on a multifaceted approach in electrical engineering outreach focused on the area of semiconductor technology. The activities developed can be used in combination for a very wide range of audiences in both age and stage of education, as has been demonstrated with great success. Moreover, the project has developed cross-disciplinary activities designed to engage nonscientific audiences and has used entirely nonscientific venues, such as art galleries. The suite of activities, given the umbrella title Chips for Everyone, includes: Chips for Everyone: drop-in activities for fairs, shows, shopping centers; Chips with Relish: interactive workshops for groups of school pupils; Chips with Flair: an arts-science collaboration in music, art, video, and engineering to present a new perspective on semiconductor technology. To achieve this diverse mix of outreach activities, the Chips for Everyone team represents a very broad spectrum of skills, its members being engineering academics, musicians, artists, education academics, public engagement specialists, and student teachers in technology. The development process is quite generic and could be applied in other science, technology, engineering, and mathematics (STEM) areas. The subject focus of the project is semiconductor technology, a technology that influences the daily lives of everyone and yet is largely invisible. The activities seek to engage, engender interest, and promote informed discussion about this technology and engineering in general. From modest beginnings as a filler during the setting up of another outreach program, Chips for Everyone has developed into a major program reaching over 25 000 young people and families in school workshops, shopping centers, and art exhibitions in Scotland and across the U. K. The development method for the activities is innovative and creative, using the complimentary skills of research academics and students in both electronic engineering and technology initial teacher education (ITE).

Cussac Cave (Dordogne, France): The role of the rock support in the parietal art distribution, technical choices, and intentional and unintentional marks on the cave walls

In this article, we present the relationships observed between the properties of the rock support in Cussac Cave and the choices made by the Paleolithic artists: the lithology of the limestone and the speleogenesis of the cave resulted in the creation of the vast rock surfaces on which the artists realized monumental engravings. The cartography of the formation processes in the cave and the petrographic analysis of samples collected from the ground show that following a superficial dissolution of the limestone, the rock became softer, thus facilitating engraving, even with a soft tool. The analyses (X-ray Diffraction, X-ray Fluorescence, Raman Spectrometry) indicate a relative concentration of goethite, responsible for the orangish patina visible on the wall surfaces. When engraved, the lighter material under this patina is exposed in the bottom of the incised lines, creating contrasting colors that contribute to the visibility of the depictions. Finally, the alteration of the limestone created a surface that also facilitated the realization of more tenuous marks, such as finger-tracings, as well as involuntary marks made by resting hands. Published by Elsevier Ltd.

Study on the change of teachers' role in the teaching of new art curriculum

The new curriculum of fine arts is a newly emerging thing in the situation of education reform. There are many facets of traditional art education which should be improved, including the fact that the teacher's classroom teaching is dull, the teaching material is limited, the training mode is single, the "classroom" is understood by the teacher as the normative teaching content instead of a platform for communication, and the teacher can not find his role. In the new curriculum, teachers must change their ideas, so that in the classroom teaching students can take advantage of the teaching materials interestingly and efficiently. Both teachers and students are supposed to find their roles in the new curriculum.

Assessment of Mitochondrial Dysfunctions After Sirtuin Inhibition

Posttranslational modifications are important for protein functions and cellular signaling pathways. The acetylation of lysine residues is catalyzed by histone acetyltransferases (HATs) and removed by histone deacetylases (HDACs), with the latter being grouped into four phylogenetic classes. The class III of the HDAC family, the sirtuins (SIRTs), contributes to gene expression, genomic stability, cell metabolism, and tumorigenesis. Thus, several specific SIRT inhibitors (SIRTi) have been developed to target cancer cell proliferation. Here we provide an overview of methods to study SIRT-dependent cell metabolism and mitochondrial functionality. The chapter describes metabolic flux analysis using Seahorse analyzers, methods for normalization of Seahorse data, flow cytometry and fluorescence microscopy to determine the mitochondrial membrane potential, mitochondrial content per cell and mitochondrial network structures, and Western blot analysis to measure mitochondrial proteins.

Analysis of works of art down to the nanometric scale

The material analysis of works of art aims to better understand the techniques of the ancient cultures and to preserve the cultural heritage for future generations. The analysis brings to light new and unique information for authentification, for conservation and more generally in the domain of history of artistic techniques. Until now, the methods were intensively developed and adapted to the specific, precious character of the works of art. Works of art are examined from the macro to the micro down to the nano scale thanks to TEM, atomic force microscopy, ion beam techniques, or synchrotron radiation spectrometries. Various examples will be developed in order to demonstrate the efficiency of the materials science methods for another entrance door to the cultural heritage artefacts. (c) 2006 Elsevier B.V. All rights reserved.

Face recognition using color local binary pattern from mutually independent color channels

In this article, a high performance face recognition system based on local binary pattern (LBP) using the probability distribution functions (PDFs) of pixels in different mutually independent color channels which are robust to frontal homogenous illumination and planer rotation is proposed. The illumination of faces is enhanced by using the state-of-the-art technique which is using discrete wavelet transform and singular value decomposition. After equalization, face images are segmented by using local successive mean quantization transform followed by skin color-based face detection system. Kullback-Leibler distance between the concatenated PDFs of a given face obtained by LBP and the concatenated PDFs of each face in the database is used as a metric in the recognition process. Various decision fusion techniques have been used in order to improve the recognition rate. The proposed system has been tested on the FERET, HP, and Bosphorus face databases. The proposed system is compared with conventional and the state-of-the-art techniques. The recognition rates obtained using FVF approach for FERET database is 99.78% compared with 79.60 and 68.80% for conventional gray-scale LBP and principle component analysis-based face recognition techniques, respectively.

P/M aluminum matrix composites: an overview

This paper reviews the state of art concerning powder metallurgy (P/M) aluminum matrix composites. Among all the metal matrix composites (MMCs) aluminum could be the most widely used metal as matrix due to its low density coupled with high stiffness. There are different manufacturing methods which can be applied for this composite. From these, P/M could be remarked as a highly effective and economic method compared with other alternatives. (C) 2002 Elsevier Science B.V. All rights reserved.

Enhancement, ethics and society: towards an empirical research agenda for the medical humanities and social sciences

For some time now, bioethicists have paid close attention to issues associated with 'enhancement'; specifically, the appropriate use and regulation of substances and artefacts understood by some to improve the functioning of human bodies beyond that associated with 'normal' function. Medical humanities scholars (aside from philosophers and lawyers) and social scientists have not been frequent participants in debates around enhancement, but could shine a bright light on the range of dilemmas and opportunities techniques of enhancement are purported to introduce. In this paper, we argue that empirical research into the notion and practice of enhancement is necessary and timely. Such work could fruitfully engage with-and further develop-existing conceptual repertoires within the medical humanities and social sciences in ways that would afford benefit to scholars in those disciplines. We maintain that empirical engagements could also provide important resources to bioethicists seeking to regulate new enhancements in ways that are sensitive to societal context and cultural difference. To this end, we outline an empirical agenda for the medical humanities and social sciences around enhancement, emphasising especially how science and technology studies could bring benefits to-and be benefitted by-research in this area. We also use the example of (pharmaceutical) cognitive enhancement to show how empirical studies of actual and likely enhancement practices can nuance resonant bioethical debates.

The solar noise barrier project: 2. The effect of street art on performance of a large scale luminescent solar concentrator prototype

Noise barriers have been used worldwide to reduce the impact of sound generated from traffic on nearby areas. A common feature to appear on these noise barriers are all manner of graffiti and street art. In this work we describe the relative performance of a large area luminescent solar concentrator (LSC) noise barrier before and after application of street art to one surface. Comparisons are made of performance of East/West facing panels during a sunny day. It is shown that the edge mounted solar cells that are further away from the artwork perform at about 80% of their original performance level, while cells mounted nearby show greater performance decreases, suggesting that the effect of street art is primarily a localized effect. Furthermore, we demonstrate that illumination by sunlight from the rear side of the panel, opposite to the artwork shows less of a performance drop. In summary, the overall performance of a large-scale prototype LSC device is affected by the application of street art due to blocking solar access to the surface, but the effect is mostly confined to areas in the immediate vicinity of the surface modification, and the remaining panel area continues to function at a reasonable level. (C) 2017 The Authors. Published by Elsevier Ltd.

A study on skeletonization of complex petroglyph shapes

In this paper, we present a study on skeletonization of real-world shape data. The data stem from the cultural heritage domain and represent contact tracings of prehistoric petroglyphs. Automated analysis can support the work of archeologists on the investigation and categorization of petroglyphs. One strategy to describe petroglyph shapes is skeleton-based. The skeletonization of petroglyphs is challenging since their shapes are complex, contain numerous holes and are often incomplete or disconnected. Thus they pose an interesting testbed for skeletonization. We present a large real-world dataset consisting of more than 1100 petroglyph shapes. We investigate their properties and requirements for the purpose of skeletonization, and evaluate the applicability of state-of-the-art skeletonization and skeleton pruning algorithms on this type of data. Experiments show that pre-processing of the shapes is crucial to obtain robust skeletons. We propose an adaptive pre-processing method for petroglyph shapes and improve several state-of-the-art skeletonization algorithms to make them suitable for the complex material. Evaluations on our dataset show that 79.8 % of all shapes can be improved by the proposed pre-processing techniques and are thus better suited for subsequent skeletonization. Furthermore we observe that a thinning of the shapes produces robust skeletons for 83.5 % of our shapes and outperforms more sophisticated skeletonization techniques.

VPP-ART: An Efficient Implementation of Fixed-Size-Candidate-Set Adaptive Random Testing Using Vantage Point Partitioning

Adaptive random testing (ART) is an enhancement of random testing (RT), and aims to improve the RT failure-detection effectiveness by distributing test cases more evenly in the input domain. Many ART algorithms have been proposed, with fixed-size-candidate-set ART (FSCS-ART) being one of the most effective and popular. FSCS-ART ensures high failure-detection effectiveness by selecting as the next test case the candidate farthest from previously executed test cases. Although FSCS-ART has good failure-detection effectiveness, it also faces some challenges, including heavy computational overheads. In this article, we propose an enhanced version of FSCS-ART, vantage point partitioning ART (VPP-ART). VPP-ART addresses the FSCS-ART computational overhead problem using VPP, while maintaining the failure-detection effectiveness. VPP-ART partitions the input domain space using a modified vantage point tree (VP-tree) and finds the approximate nearest executed test cases of a candidate test case in the partitioned subdomains-thereby significantly reducing the time overheads compared with the searches required for FSCS-ART. To enable the FSCS-ART dynamic insertion process, we modify the traditional VP-tree to support dynamic data. The simulation results show that VPP-ART has a much lower time overhead compared to FSCS-ART, but also delivers similar (or better) failure-detection effectiveness, especially in the higher dimensional input domains. According to statistical analyses, VPP-ART can improve on the FSCS-ART failure-detection effectiveness by approximately 50-58%. VPP-ART also compares favorably with the KD-tree-enhanced fixed-size-candidate-set ART (KDFC-ART) algorithms (a series of enhanced ART algorithms based on the KD-tree). Our experiments also show that VPP-ART is more cost-effective than FSCS-ART and KDFC-ART.

Constrained particle filtering for movement identification in forearm prosthesis

We formulate the problem of movement identification for the forearm prosthesis using a nonlinear state-space system and the hypothesis of muscle synergies. The synergy activation coefficients contain task-specific information and can be used to identify limb movements. In the proposed framework, the measurements are EMG data and the system state consists of muscle synergy activation coefficients, which are physiologically constrained to be nonnegative on average. Particle filters are the state-of-the-art techniques for optimal state estimation in nonlinear and non-Gaussian systems. However, the very numerical nature of the particle filters, which constitutes their strength, becomes their major weakness in handling constraints on the state. In this paper, we solve the movement identification problem by introducing a constrained particle filter termed as mean density truncation (MiND). We show that MiND minimally perturbs the unconstrained distribution of the state while simultaneously satisfying the desired constraints on the unknown state. We recorded EMG data from forearm muscles of 12 participants for identification of hand and wrist movements. The proposed particle filtering with MiND provided an accurate stream of synergy activation coefficients (p < 0.001) which were used for movement identification with error rates significantly lower (p < 0.05) than currently used heuristics and Linear Discriminate Analysis. (C) 2019 Elsevier B.V. All rights reserved.

A Versatile Biomimic Nanotemplating Fluidic Assay for Multiplex Quantitative Monitoring of Viral Respiratory Infections and Immune Responses in Saliva and Blood

The last pandemic exposed critical gaps in monitoring and mitigating the spread of viral respiratory infections at the point-of-need. A cost-effective multiplexed fluidic device (NFluidEX), as a home-test kit analogous to a glucometer, that uses saliva and blood for parallel quantitative detection of viral infection and body's immune response in an automated manner within 11 min is proposed. The technology integrates a versatile biomimetic receptor based on molecularly imprinted polymers in a core-shell structure with nano gold electrodes, a multiplexed fluidic-impedimetric readout, built-in saliva collection/preparation, and smartphone-enabled data acquisition and interpretation. NFluidEX is validated with Influenza A H1N1 and SARS-CoV-2 (original strain and variants of concern), and achieves low detection limit in saliva and blood for the viral proteins and the anti-receptor binding domain (RBD) Immunoglobulin G (IgG) and Immunoglobulin M (IgM), respectively. It is demonstrated that nanoprotrusions of gold electrodes are essential for the fine templating of antibodies and spike proteins during molecular imprinting, and differentiation of IgG and IgM in whole blood. In the clinical setting, NFluidEX achieves 100% sensitivity and 100% specificity by testing 44 COVID-positive and 25 COVID-negative saliva and blood samples on par with the real-time quantitative polymerase chain reaction (p < 0.001, 95% confidence) and the enzyme-linked immunosorbent assay.

Survey of Interoperability in Electronic Health Records Management and Proposed Blockchain Based Framework: MyBlockEHR

Interoperability in Electronic Health Records (EHR) is significant for the seamless sharing of information amongst different healthcare stakeholders. Interoperability in EHR aims to devise agreements in its interpretation, access, and storage with security, privacy, and trust. A study and survey of the state-of-the-art literature, prototypes, and projects in standardization of the EHR structure, privacy-preservation, and EHR sharing are very essential. The presented work conducts a systematic literature review to address four research questions. 1) What are the different standards for common interpretation, representation, and modeling of EHR to achieve semantic interoperability? 2) What are the different privacy-preservation techniques and security standards for EHR data storage? 3) How mature is blockchain technology for building interoperable, privacy-preserving solutions for EHR storage and sharing? 4) What is the state-of-the-art for cross-chain interoperability for EHR sharing? An exhaustive study of these questions establishes the potential of a blockchain-based EHR management framework in privacy preservation, access control and efficient storage. The study also unveils challenges in the adoption of blockchain in EHR management with the state-of-the-art maturity of cross-chain interoperable solutions for sharing EHR amongst stakeholders on different blockchain platforms. The research gaps culminate in proposing a blockchain-based EHR framework called as MyBlockEHR with privacy preservation and access control design. The proposed framework employs partitioning of EHR to on-chain and off-chain storages for performance guarantees with the retrieval of valid off-chain data. The framework is deployed on the Ethereum test network with Solidity smart contracts. It is observed that different test cases on the partitioning of the EHR data, yielded better read-write throughput and effective gas price than fully on-chain storage.

Spectral Stochastic Simulation of a Ferromagnetic Cylinder Rotating at High Speed

This paper addresses what the influence of uncertainty on ferromagnetic material properties is on the torque yielded by ferromagnetic cylinders rotating at high speeds. To that end, two state-of-the-art high-order stochastic solution approaches, the stochastic collocation and the stochastic Galerkin method, are applied to a nonlinear convection-diffusion problem with random coefficients and their performance is compared.

Strategic Decisions to Enhance the Internationalization of the Performing Arts and Their Sustainability: The Case of Flamenco

Determining how to operate in foreign markets is challenging for the performing arts (PA) because the particular nature of their activities necessarily entails sustainable complexities. This study aims to extend understanding of the internationalization of PA to shed light on the strategic decisions adopted by cultural agents to achieve an economic and cultural objective-generating international income while ensuring the symbolic value of cultural products rooted in local values. A longitudinal empirical case of one leading enterprise in international flamenco production with a successful history in international markets is reported here, and in-depth insights into four strategic decisions that can boost the sustainable internationalization of PA are gained: why (motivations), what (product), where (market selection), and how (entry modes). The results have theoretical and practical implications for a cultural sector with few examples of internationalization that is seeking for international markets to become sustainable while being subject to public financing cuts, strong competition, and globalization. The important role of intermediaries in bridging the gaps between different actors of the PA value chain and in assuring sustainable cultural management of the internationalization process is also identified.

Scour of rock due to the impact of plunging high velocity jets Part I: A state-of-the-art review

This paper presents the state-of-the-art on methods to estimate rock scour due to the impingement of plunging high velocity water jets. The following topics are addressed: empirical formulae, semi-empirical and analytical approaches, determination of extreme pressure fluctuations at plunge pool bottoms and, finally, the transfer of these pressure fluctuations in joints underneath concrete slabs or rock blocks. Available methods on rock scour have been thoroughly investigated on their ability to represent the main physical-mechanical processes that govern scour. This reveals lack of knowledge on turbulence and aeration effects, as well as on transient pressure flow conditions in rock joints. These aspects may significantly influence the destruction of the rock mass and should be accounted for in scour evaluation methods. Their relevance has been experimentally investigated by dynamic pressure measurements at modeled plunge pool bottoms and inside underlying one-and two-dimensional rock joints. Test results are described and discussed in Part 11 of this paper.

Carried Object Detection in Videos Using Color Information

Automatic baggage detection has become a subject of significant practical interest in recent years. In this paper, we propose an approach to baggage detection in CCTV video footage that uses color information to address some of the vital shortcomings of state-of-the-art algorithms. The proposed approach consists of typical steps used in baggage detection, namely, the estimation of moving direction of humans carrying baggage, construction of human-like temporal templates, and their alignment with the best matched view-specific exemplars. In addition, we utilize the color information to define the region that most likely belongs to a human torso in order to reduce the false positive detections. A key novel contribution is the person's viewing direction estimation using machine learning and shoulder shape related features. Further enhancement of baggage detection and segmentation is achieved by exploiting the CIELAB color space properties. The proposed system has been extensively tested for its effectiveness, at each stage of improvement, on PETS 2006 dataset and additional CCTV video footage captured to cover specific test scenarios. The experimental results suggest that the proposed algorithm is capable of superseding the functional performance of state-of-the-art baggage detection algorithms.

The effect of modulation of gut microbiome profile on radiation-induced carcinogenesis and survival

Non-lethal doses of ionizing radiation (IR) delivered to humans because of terrorist events, nuclear accidents or radiotherapy can result in carcinogenesis. Means of protecting against carcinogenesis are lacking. We questioned the role of the gut microbiome in IR-induced carcinogenesis. The gut microbiome was modulated by administering broad spectrum antibiotics (Ab) in the drinking water. Mice were given Ab 3 weeks before and 3 weeks after 3 Gy total body irradiation (TBI) or for 6 weeks one month after TBI. Three weeks of Ab treatment resulted in a 98% reduction in total 16S rRNA counts for 4 out of 6 of the phylum groups detected. However, 3 more weeks of Ab treatment (6 weeks total) saw an expansion in the phylum groups Proteobacteria and Actinobacteria. The Ab treatment altered the bacteria diversity in the gut, and shortened the lifespan when Ab were administered before and after TBI. Mortality studies indicated that the adverse Ab lifespan effects were due to a decrease in the time in which solid tumors started to appear and not to any changes in hematopoietic or benign tumors. In contrast, when Ab were administered one month after TBI, lifespan was unchanged compared to the control TBI group. Use of broad-spectrum antibiotics to simulate the germ-free condition did not afford an advantage on carcinogenesis or lifespan.

Pandemic's silver lining

The intense international focus on the COVID-19 pandemic has provided a unique opportunity to use a wide array of novel tools to carry out scientific studies on the SARS-CoV-2 virus. The value of these comparative studies extends far beyond their consequences for SARS-CoV-2, providing broad implications for health-related science. Here we specifically discuss the impacts of these comparisons on advances in vaccines, the analysis of host humoral immunity, and antibody discovery. As an extension, we also discuss potential synergies between these areas.Abbreviations: CoVIC: The Coronavirus Immunotherapeutic Consortium; EUA: Emergency Use Authorization.

Arts, place, and sacrifice zones: restoration of damaged relational values in a Chilean sacrifice zone

This paper aims to unpack the relational dimension of place and placemaking by analysing how creative actions underpin relational values towards socio-spatial restoration in the sacrifice zone affecting the communities of Quintero and Puchuncavi (QPSZ) in Chile. Sacrifice zones are places permanently subject to environmental damage and lack of environmental regulation. For affected populations in environmentally degraded areas, creative actions such as murals, music, and street performances have become a way to re-establish connections both among humans, and between humans and the environment. To date, little has been theorized on this connection. With this in mind, we use network analysis to analyze which and how relational values are mobilized by artistic actions, and to examine ensuing socio-spatial transformations. Drawing insights from 35 interviews with activists, artists, and residents in QPSZ, we observed relational effects of arts, especially in creation processes, and in representations of local elements and life histories. The materiality of artistic practices raised as a force of placemaking, and so did artistic spaces as promoters of networking and social cohesion, essential for socio-spatial restoration. By bringing together insights from aesthetic politics, human geography, and relational values, this paper contributes to the emerging literature on art committed to tackling socio-environmental crises, and to political-ecological theories on the transformation of degraded areas.

A high speed modulo (2(n)-2(p)+1) multiplier design

In this express, an optimized architecture for modulo (2(n) - 2(p) + 1) multipliers is proposed. Compared with the state-of-art, synthesized results demonstrate that the proposed multipliers can achieve an average delay savings of about 11.8%. With the increase of n, the average delay savings also increases remarkably.

Cyber Attacks and Faults Discrimination in Intelligent Electronic Device-Based Energy Management Systems

Intelligent electronic devices (IEDs) along with advanced information and communication technology (ICT)-based networks are emerging in the legacy power grid to obtain real-time system states and provide the energy management system (EMS) with wide-area monitoring and advanced control capabilities. Cyber attackers can inject malicious data into the EMS to mislead the state estimation process and disrupt operations or initiate blackouts. A machine learning algorithm (MLA)-based approach is presented in this paper to detect false data injection attacks (FDIAs) in an IED-based EMS. In addition, stealthy construction of FDIAs and their impact on the detection rate of MLAs are analyzed. Furthermore, the impacts of natural disturbances such as faults on the system are considered, and the research work is extended to distinguish between cyber attacks and faults by using state-of-the-art MLAs. In this paper, state-of-the-art MLAs such as Random Forest, OneR, Naive Bayes, SVM, and AdaBoost are used as detection classifiers, and performance parameters such as detection rate, false positive rate, precision, recall, and f-measure are analyzed for different case scenarios on the IEEE benchmark 14-bus system. The experimental results are validated using real-time load flow data from the New York Independent System Operator (NYISO).

Optimal Distributed Nonlinear Battery Control

Energy storage plays a more important role than ever before, due to the transition to smart grid along with higher penetration of renewable resources. In this paper, we describe our optimal nonlinear battery control algorithm that can handle multiple batteries connected to the grid in a distributed and cost-optimal fashion, while maintaining low complexity of O(N-2). In contrast to the state-of-the-art models, we use a high accuracy nonlinear battery model with 2% error. We present three distributed solutions: 1) a circular negotiation ring, providing convergence rates independent of the number of batteries; 2) a mean circular negotiation ring, converging very quickly for a low number of batteries; 3) a bisection method has a convergence rate independent of battery capacities. We compare our algorithm to the state-of-the-art algorithms and show that we can decrease the utility cost of an actual building by up to 50% compared with the batteryless case by 30% over the load-following heuristic and by 60% over a state-of-the-art optimal control algorithm designed using a linear battery model. For a constant load profile, optimal linear control incurs costs higher by 150% for model predictive control and 250% for single-trajectory solutions than for our algorithm.

Relationship Induced Multi-Template Learning for Diagnosis of Alzheimer's Disease and Mild Cognitive Impairment

As shown in the literature, methods based on multiple templates usually achieve better performance, compared with those using only a single template for processing medical images. However, most existing multi-template based methods simply average or concatenate multiple sets of features extracted from different templates, which potentially ignores important structural information contained in the multi-template data. Accordingly, in this paper, we propose a novel relationship induced multi-template learning method for automatic diagnosis of Alzheimer's disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI), by explicitly modeling structural information in the multi-template data. Specifically, we first nonlinearly register each brain's magnetic resonance (MR) image separately onto multiple pre-selected templates, and then extract multiple sets of features for this MR image. Next, we develop a novel feature selection algorithm by introducing two regularization terms to model the relationships among templates and among individual subjects. Using these selected features corresponding to multiple templates, we then construct multiple support vector machine (SVM) classifiers. Finally, an ensemble classification is used to combine outputs of all SVM classifiers, for achieving the final result. We evaluate our proposed method on 459 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, including 97 AD patients, 128 normal controls (NC), 117 progressive MCI (pMCI) patients, and 117 stable MCI (sMCI) patients. The experimental results demonstrate promising classification performance, compared with several state-of-the-art methods for multi-template based AD/MCI classification.

Dickkopf-1 as a promising therapeutic target for autoimmune diseases

Dickkopf-1 (DKK-1) is mostly known as a mature inhibitor of classic Wnt signaling pathways, which plays a critically role in regulating bone formation and bone metastasis. In recent years, the roles of DKK-1 played in bone resorption, bone formation, immune homeostasis and inflammation have been investigated. The role of DKK-1 in the pathogenesis and treatment of autoimmune diseases (ADs), including rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), etc, has attracted widespread attention. Various studies have found that DKK-1 may be used as a biomarker for the occurrence and development of ADs, and as a potential target for the treatment of ADs. In this review, we have briefly summed up the intricate immunological functions and regulatory mechanisms of DKK-1 in ADs, aiming to further learning more about the role of DKK-1 involved in the pathogenesis of ADs and provide an outlook for the potential future researches.

Deep reinforcement learning for stock portfolio optimization by connecting with modern portfolio theory

With artificial intelligence and data quality development, portfolio optimization has improved rapidly. Traditionally, researchers in the financial market have utilized the modern portfolio theory for portfolio optimization; however, with the recent development of artificial intelligence, attempts to optimize portfolios with reinforcement learning are increasing. Many studies have developed reinforcement learning and deep learning algorithms and conducted portfolio optimization research. However, in reality, thus far, the securities industry thus has used the modern portfolio theory, which is sufficiently valuable. Nevertheless, to the best of our knowledge, there has yet to be an attempt to combine modern portfolio theory and reinforcement learning. To bridge this gap in the literature, we propose a novel deep reinforcement learning approach that combines the modern portfolio theory and a deep learning approach. As far as we know, we are the first to combine recent deep learning technology and traditional financial theory. Specifically, we solved the multimodal problem through the Tucker decomposition of a model with the input of technical analysis and stock return covariates. The results show that the proposed method outperforms state-of-the-art algorithms regarding the Sharpe ratio, annualized return, and maximum drawdown. In addition, the proposed method dynamically changes the weight according to the market trend, unlike other state-of-the-art algorithms.

Multi-Task Siamese Network for Retinal Artery/Vein Separation via Deep Convolution Along Vessel

Vascular tree disentanglement and vessel type classification are two crucial steps of the graph-based method for retinal artery-vein (A/V) separation. Existing approaches treat them as two independent tasks and mostly rely on ad hoc rules (e.g. change of vessel directions) and hand-crafted features (e.g. color, thickness) to handle them respectively. However, we argue that the two tasks are highly correlated and should be handled jointly since knowing the A/V type can unravel those highly entangled vascular trees, which in turn helps to infer the types of connected vessels that are hard to classify based on only appearance. Therefore, designing features and models isolatedly for the two tasks often leads to a suboptimal solution of A/V separation. In view of this, this paper proposes a multi-task siamese network which aims to learn the two tasks jointly and thus yields more robust deep features for accurate A/V separation. Specifically, we first introduce Convolution Along Vessel (CAV) to extract the visual features by convolving a fundus image along vessel segments, and the geometric features by tracking the directions of blood flow in vessels. The siamese network is then trained to learn multiple tasks: i) classifying A/V types of vessel segments using visual features only, and ii) estimating the similarity of every two connected segments by comparing their visual and geometric features in order to disentangle the vasculature into individual vessel trees. Finally, the results of two tasks mutually correct each other to accomplish final A/V separation. Experimental results demonstrate that our method can achieve accuracy values of 94.7%, 96.9%, and 94.5% on three major databases (DRIVE, INSPIRE, WIDE) respectively, which outperforms recent state-of-the-arts.

Force Control Strategy and Bench Press Experimental Research of a Cable Driven Astronaut Rehabilitative Training Robot

This paper presents a cable driven astronaut rehabilitative training (ART) robot, which can provide astronauts with multiple physical exercises including bench press, running, and deep squat to alleviate and resist the adverse effects induced by space adaptation syndrome. First, the modular reconfiguration of the ART driven by a cable is proposed to simulate the load characteristics of the gravity environment. Second, in order to improve the accuracy of the ART, the force control strategy is presented. The controller consists of two parts: high-level controller, which to calculate the desired cable tension and low-level controller, which to make each cable achieve the desired tension. Finally, to validate the effect of the ART, three (adult male) weight lifters were recruited to perform several bench press exercises with the barbell and ART. The analysis of the surface electromyography (sEMG) of triceps brachii and pectoralis major during real bench press and bench press simulated by the ART was conducted. The performance of the ART and the data of sEMG demonstrate the controller is effective and the ART can effectively assist astronauts to carry out bench press exercises in space.

A Novel, Efficient Implementation of a Local Binary Convolutional Neural Network

In order to reduce the computational complexity of convolutional neural networks (CNNs), the local binary convolutional neural network (LBCNN) has been proposed. In the LBCNN, a convolutional layer is divided into two sublayers. Sublayer 1 is a sparse ternary-weighted convolutional layer, and Sublayer 2 is a 1x1 convolutional layer. With the use of two sublayers, the LBCNN has lower computational complexity and uses less memory than the CNN. In this brief, we propose a platform that includes a weight preprocessor and layer accelerator for the LBCNN. The proposed weight preprocessor takes advantage of the sparsity in the LBCNN and encodes the weight offline. The layer accelerator effectively uses the encoded data to reduce computational complexity and memory accesses for an inference. When compared to the state-of-the-art design, the experimental results show that the number of clock cycles are reduced by 76.32%, and memory usage is reduced by 39.41%. The synthesized results show that the clock period is reduced by 4.76%; the cell area is reduced by 46.48%, and the power consumption is reduced by 40.87%. The inference accuracy is the same as that of the state-of-the-art design.

Looking Inside Category: Subcategory-Aware Object Recognition

In this paper, we present a subcategory-aware recognition framework to boost category level object classification performance. Different from the existing monolithic model approaches, we aim to automatically leverage the embedded subcategory structure to assist the further category level recognition. Motivated by the observation of considerable intra-class diversities and inter-class ambiguities in many current object classification data sets, we explicitly split data into subcategories by ambiguity-guided subcategory mining. The resulting subcategories are seamlessly integrated into the state-of-the-art detection-assisted classification framework. In particular, we build the instance affinity graph by combining both intra-class similarity and inter-class ambiguity. Visual subcategories, which correspond to the dense subgraphs, are detected by the graph shift algorithm. We then train an individual model for each subcategory rather than an attempt to represent an object category with a monolithic model. Related samples, which are informative for subcategory classification, are utilized to regularize each subcategory model. Finally, the responses from subcategory models are aggregated by subcategory-aware kernel regression. The extensive experiments over the PASCAL visual object challenge (VOC) 2007 and PASCAL VOC 2010 databases show the state-of-the-art performance from our framework.

Axially Extended-Volume C-Arm CT Using a Reverse Helical Trajectory in the Interventional Room

C-arm computed tomography (CT) is an innovative technique that enables a C-arm system to generate 3-D images from a set of 2-D X-ray projections. This technique can reduce treatment-related complications and may improve interventional efficacy and safety. However, state-of-the-art C-arm systems rely on a circular short scan for data acquisition, which limits coverage in the axial direction. This limitation was reported as a problem in hepatic vascular interventions. To solve this problem, as well as to further extend the value of C-arm CT, axially extended-volume C-arm CT is needed. For example, such an extension would enable imaging the full aorta, the peripheral arteries or the spine in the interventional room, which is currently not feasible. In this paper, we demonstrate that performing long object imaging using a reverse helix is feasible in the interventional room. This demonstration involved developing a novel calibration method, assessing geometric repeatability, implementing a reconstruction method that applies to real reverse helical data, and quantitatively evaluating image quality. Our results show that: 1) the reverse helical trajectory can be implemented and reliably repeated on a multiaxis C-arm system; and 2) a long volume can be reconstructed with satisfactory image quality using reverse helical data.

Reassigned time-frequency representations of discrete time signals and application to the Constant-Q Transform

In this paper we provide a formal justification of the use of time frequency reassignment techniques on time frequency transforms of discrete time signals. State of the art techniques indeed rely on formulae established in the continuous case which are applied, in a somehow inaccurate manner, to discrete time signals. Here, we formally derive a general framework for discrete time reassignment. To illustrate its applicability and generality this framework is applied to a specific transform: the Constant-Q Transform.

Limited view CT reconstruction and segmentation via constrained metric labeling

This paper proposes a new discrete optimization framework for tomographic reconstruction and segmentation of CT volumes when only a few projection views are available. The problem has important clinical applications in coronary angiographic imaging. We first show that the limited view reconstruction and segmentation problem can be formulated as a 'constrained' version of the metric labeling problem. This lays the groundwork for a linear programming framework that brings metric labeling classification and classical algebraic tomographic reconstruction (ART) together in a unified model. If the imaged volume is known to be comprised of a finite set of attenuation coefficients (a realistic assumption), given a regular limited view reconstruction, we view it as a task of voxels reassignment subject to maximally maintaining consistency with the input reconstruction and the objective of ART simultaneously. The approach can reliably reconstruct (or segment) Volumes With several multiple contrast objects. We present evaluations using experiments on cone beam computed tomography. (C) 2008 Elsevier Inc. All rights reserved.

Viscous pressure forming (VPF): state-of-the-art and future trends

Viscous pressure forming (VPF) is a recently developed flexible sheet forming process. Different from conventional sheet metal forming technology, VPF uses a kind of semi-solid, flowable and viscous material as the pressure-carrying medium. In VPF, the viscous medium is applied on one side or both sides of sheet blank to improve its formability. VPF offers a new approach to the forming of high strength, difficult-to-form materials and complex-shaped parts. In this paper, the state-of-the-art of VPF in formability analysis of sheet metals, pressure control, numerical simulation, applications and forming devices is presented. VPF has shown potential application to superalloy and aluminum alloy. Several complex shaped parts formed by VPF meet the requirements of shape and dimensional accuracy and the thickness distributions are more uniform. Finally, critical issues for the future development of VPF are presented. (C) 2004 Elsevier B.V. All rights reserved.

State-of-the-Art: AI-Assisted Surrogate Modeling and Optimization for Microwave Filters

Microwave filters are indispensable passive devices for modern wireless communication systems. Nowadays, electromagnetic (EM) simulation-based design process is a norm for filter designs. Many EM-based design methodologies for microwave filter design have emerged in recent years to achieve efficiency, automation, and customizability. The majority of EM-based design methods exploit low-cost models (i.e., surrogates) in various forms, and artificial intelligence techniques assist the surrogate modeling and optimization processes. Focusing on surrogate-assisted microwave filter designs, this article first analyzes the characteristic of filter design based on different design objective functions. Then, the state-of-the-art filter design methodologies are reviewed, including surrogate modeling (machine learning) methods and advanced optimization algorithms. Three essential techniques in filter designs are included: 1) smart data sampling techniques; 2) advanced surrogate modeling techniques; and 3) advanced optimization methods and frameworks. To achieve success and stability, they have to be tailored or combined together to achieve the specific characteristics of the microwave filters. Finally, new emerging design applications and future trends in the filter design are discussed.

The role of soil as a carbon sink in coastal salt-marsh and agropastoral systems at La Pletera, NE Spain

To evaluate the potential of natural and modified salt-marsh soils to store organic carbon and their soil properties, we investigated six soil environments located at La Pletera salt-marsh area, NE Spain. Namely, Ruderal (RU), rubbles over marsh for construction purpose, ELY covered by Elymus elymoides (Raf.) meadows, ART under Arthrocnemurn fruticosum L., SAL under Salicornia patula Duval Jouve, AGR under Zea mays L., and AME under Medicago saliva L. as artificial meadow. Soils were sampled at three depths (0-5, 5-20 and 20-40 cm). At 0-5 cm depth, soil organic carbon (SOC) was higher in ART soil (40.08 g kg(-1)) with respect to ELY, AME, AGR, SAL and RU (23.63, 11.45, 5.77, 4.40 and 3.18 g kg(-1) respectively). Glomalin (TGRSP) in ART had the same trend, with 8.88 g kg(-1) decreased by 51%, 77%, 89%, 92% and 94% in ELY, AME, AGR, SAL and RU soil respectively, indicating that in ART recalcitrant organic carbon may prevail. ART and ELY soils had higher SOC and GRSP than AGR and AME soils at 0-5 and 5-20 cm (on average + 70% and 57%) but SOC values were similar at 20-40 cm depth and glomalin was even higher in AGR and AME soils at this depth suggesting migration of stable organic compounds in cultivated soils. The water stable aggregates (WSA) analysed in the 0.25-2 mm and 2-5.6 mm fractions was also higher in ART and ELY soils (approximate to 90%) at 0-5 and 5-20 cm with respect to the other investigated soils. Higher WSA (fraction 0.25-5 mm) was found in AGR and AME soils at 20-40 cm corroborating that at higher GRSP corresponds higher aggregation. Potential carbon loss as C-CO2 (Mg ha(-1)) was evaluated at 0-5 cm depth and was much lower in ART soil. Accordingly, C-CO2/SOC ratio assigned to ART soil 1.85% of SOC loss against 8.26%, 11.64%, 18.90%, 20.37% and 22.72% of ELY, AME, RU, SAL and AGR soils respectively, indicating that only ART and ELY soils may exert clear carbon sequestration ability. The soil under annual Salicornia patula Duval Jouve (SAL) showed very low SOC (4.40 g kg(-1)) and the highest carbon loss potential (22.72%) due to shortage of organic decaying debris at surface. Also, C-TGRSP resulted higher in ELY and ART soils (2.51 and 1.31 Mg ha(-1)respectively) and C-TGRSP/SOC ratio demonstrated glomalin carbon enrichment in this order: ART > AME > ELY > AGR > SAL > RU, suggesting that carbon sequestration capacity may be assigned to ART and ELY soils, major carbon sinks in the Pletera salt-marsh area. Conversely, RU, AGR and AME soils, identified as ancient salt-marsh converted into agropastoral systems or altered for tourism purposes showed worse soil properties and higher sensitivity to carbon destabilization. Statistical treatment of data by factor analysis corroborated the obtained results outlining the importance of ART and ELY soils in maintaining best soil properties and the highest carbon storage capacity.

Learning structured visual dictionary for object tracking

In this paper, we propose a visual tracking algorithm by incorporating the appearance information gathered from two collaborative feature sets and exploiting its geometric structures. A structured visual dictionary (SVD) can be learned from both appearance and geometric structure, thereby enhancing its discriminative strength between the foreground object and the background. Experimental results show that the proposed tracking algorithm using SVD (SVDTrack) performs favorably against the state-of-the-art methods. (C) 2013 Elsevier B.V. All rights reserved.

The single cell transcriptional landscape of esophageal adenocarcinoma and its modulation by neoadjuvant chemotherapy

Immune checkpoint blockade has recently proven effective in subsets of patients with esophageal adenocarcinoma (EAC) but little is known regarding the EAC immune microenvironment. We determined the single cell transcriptional profile of EAC in 8 patients who were treatment-naive (n = 4) or had received neoadjuvant chemotherapy (n = 4). Analysis of 52,387 cells revealed 10 major cell subsets of tumor, immune and stromal cells. Prior to chemotherapy tumors were heavy infiltrated by T regulatory cells and exhausted effector T cells whilst plasmacytoid dendritic cells were markedly expanded. Two dominant cancer-associated fibroblast populations were also observed whilst endothelial populations were suppressed. Pathological remission following chemotherapy associated with broad reversal of immune abnormalities together with fibroblast transition and an increase in endothelial cells whilst a chemoresistant epithelial stem cell population correlated with poor response. These findings reveal features that underlie and limit the response to current immunotherapy and identify a range of novel opportunities for targeted therapy.

Imagery re-scripting for PTSD: session content and its relation to symptom improvement

Imagery rescripting (ImRs) is a therapy technique that, unlike traditional re-living techniques, focuses less on exposure and verbal challenging of cognitions and instead encourages patients to directly transform the intrusive imagery to change the depicted course of events in a more desired direction. However, a comprehensive account of how and in what circumstances ImRs brings about therapeutic change is required if treatment is to be optimised, and this is yet to be developed. The present study reports on the development of a coding scheme of ImRs psychotherapy elements identified in the literature as potential ImRs mechanisms. The codes were assessed in relation to short-term outcomes of 27 individuals undergoing ImRs for post-traumatic stress disorder. The timing of the change in the image, degree of activation of the new image and associated cognitive, emotional and physiological processes, self-guided rescripting, rescript believability, narrative coherence and cognitive and emotional shift were identified as being related to symptom change and so are potentially important factors for the re-scripting process.

Protective role for C3aR in experimental chronic pyelonephritis

Emerging evidence suggest that C3aR plays important roles in homeostasis, host defense and disease. Although it is known that C3aR is protective in several models of acute bacterial infections, the role for C3aR in chronic infection is largely unknown. Here we show that C3aR is protective in experimental chronic pyelonephritis. Global C3aR deficient (C3ar-/- ) mice had higher renal bacterial load, more pronounced renal histological lesions, increased renal apoptotic cell accumulation, tissue inflammation and extracellular matrix deposition following renal infection with uropathogenic E. coli (UPEC) strain IH11128, compared to WT control mice. Myeloid C3aR deficient (Lyz2-C3ar-/- ) mice exhibited a similar disease phenotype to global C3ar-/- mice. Pharmacological treatment with a C3aR agonist reduced disease severity in experimental chronic pyelonephritis. Furthermore, macrophages of C3ar-/- mice exhibited impaired ability to phagocytose UPEC. Our data clearly demonstrate a protective role for C3aR against experimental chronic pyelonephritis, macrophage C3aR plays a major role in the protection, and C3aR is necessary for phagocytosis of UPEC by macrophages. Our observation that C3aR agonist curtailed the pathology suggests a therapeutic potential for activation of C3aR in chronic infection.

The influence of weather and temperature on pedestrian walking characteristics on the zigzag bridge

With the increasing number of tourists in recent years, ensuring the safety of visitors in tourist attractions has become an enormous challenge for safety management. At present, many experiments have been conducted to study pedestrian dynamics, but empirical data on tourists' movement state under different weather conditions are still few. Therefore, a series of field experiments were conducted to analyze the effect of external weather and temperature on pedestrians' movement characteristics. The results show that pedestrians are more concentrated in the middle and inner tracks during the turning process to seek the shortest path on rainy days. Moreover, it is found that pedestrians speed up under the conditions with low (below 10 °C) and high (over 30 °C) temperatures. The average speed of pedestrians is 0.677 m/s as the temperature is below 0 °C, which is much higher than the average speed of pedestrians in other temperature ranges. In addition, the speed of pedestrians changed more dramatically under the low-temperature conditions. It is hoped that this research can provide a reference for crowd control and rational design of pedestrian facilities.

Drowning case complicated with a cardiopulmonary arrest and severe ARDS saved with a good neurological outcome by ECMO: A case report

Cardiopulmonary arrest (CPA) due to drowning has an extremely high mortality rate, and very few cases have good neurological outcomes. Severe respiratory failure can occur even after resuscitation. A 66 year old woman with a history of refractory epilepsy had a CPA due to drowning. Approximately 20 min after drowning, she was resuscitated and transported to the hospital, and extracorporeal membrane oxygenation (ECMO) was introduced on day two due to continued severe respiratory failure caused by acute respiratory distress syndrome (ARDS). After the introduction of ECMO, her respiratory status gradually improved and ECMO was discontinued on day 12. Approximately 6 months after drowning, she visited our hospital for a follow-up with a cerebral performance category of 1. Since cases of CPA due to drowning with a short drowning time or hypothermia are expected to have good neurological outcomes, the introduction of ECMO should be considered as a treatment for ARDS after resuscitation.

A 0.7-V 17.4-mu W 3-Lead Wireless ECG SoC

This paper presents a fully integrated sub-1 V 3-lead wireless ECG System-on-Chip (SoC) for wireless body sensor network applications. The SoC includes a two-channel ECG front-end with a driven-right-leg circuit, an 8-bit SAR ADC, a custom-designed 16-bit microcontroller, two banks of 16 kb SRAM, and a MICS band transceiver. The microcontroller and SRAM blocks are able to operate at sub-/near-threshold regime for the best energy consumption. The proposed SoC has been implemented in a standard 0.13-mu m CMOS process. Measurement results show the microcontroller consumes only 2.62 pJ per instruction at 0.35 V. Both microcontroller and memory blocks are functional down to 0.25 V. The entire SoC is capable of working at single 0.7-V supply. At the best case, it consumes 17.4 mu W in heart rate detection mode and 74.8 mu W in raw data acquisition mode under sampling rate of 500 Hz. This makes it one of the best ECG SoCs among state-of-the-art biomedical chips.

Making the Invisible Visible: Eco-Art and Design against the Anthropocene

This paper examines a series of art and design installations in the public realm that aim to raise awareness or activate change regarding pressing ecological issues. Such works tend to place environmental responsibility on the shoulders of the individual citizen, aiming to educate but also to implicate them in the age of the Anthropocene. How and what these works aim to accomplish, are key to a better understanding the means of knowledge transfer and potential agents of change in the Anthropocene. We study three cases in this paper. These are examined through: (1) their potential to raise awareness or activate behavior change; (2) how well they are capable of making the catastrophic situations, which are invisible to most people, visible; and (3) how well they enable systemic change in the catastrophic situations. In the three cases studied, we find that they are successful in helping to raise awareness and even change individual behavior, they are successful in rendering the invisible visible, but they are incapable of engendering any systemic change of the catastrophic situations depicted.

HEAR: Fog-Enabled Energy-Aware Online Human Eating Activity Recognition

Eating activity recognition (EAR) plays an important role in ensuring healthy eating habits. Recent advancements of the Internet of Things (IoT) have bolstered automated EAR through various wearable edge devices. State-of-the-art work uses some offline trained classifiers at the fog device to recognize eating activities. However, the eating habits of a person change quite frequently and vary from person to person. Therefore, the classifiers should be updated continuously with new data to adapt to these changes and be personalized over time through online learning. To the best of our knowledge, no state-of-the-art work has addressed this issue so far. In this article, we propose an online learning methodology called human eating activity recognition (HEAR) by introducing an online update phase. We also design an algorithm to be used in the online update phase that provides approximate true labels for the new data. Moreover, we also design a wearable neckband as the edge device to capture eating activity data (<italic>Chewing, Swallowing, Talking, and Idle</italic>) in a lab environment. Through a detailed experimental evaluation on 12 users, we show that an online learned neural network (OLNN) classifier using our HEAR methodology performs better than any state-of-the-art offline trained classifier. We also demonstrate that our OLNN classifier is energy efficient compared to the competitive offline trained classifiers.

Proteome Analysis Unravels Mechanism Underling the Embryogenesis of the Honeybee Drone and Its Divergence with the Worker (Apis mellifera lingustica)

The worker and drone bees each contain a separate diploid and haploid genetic makeup, respectively. Mechanisms regulating the embryogenesis of the drone and its mechanistic difference with the worker are still poorly understood. The proteomes of the two embryos at three time-points throughout development were analyzed by applying mass spectrometry-based proteomics. We identified 2788 and 2840 proteins in the worker and drone embryos, respectively. The age-dependent proteome driving the drone embryogenesis generally follows the worker's. The two embryos however evolve a distinct proteome setting to prime their respective embryogenesis. The strongly expressed proteins and pathways related to transcriptional-translational machinery and morphogenesis at 24 h drone embryo relative to the worker, illustrating the earlier occurrence of morphogenesis in the drone than worker. These morphogenesis differences remain through to the middle-late stage in the two embryos. The two embryos employ distinct antioxidant mechanisms coinciding with the temporal-difference organogenesis. The drone embryo's strongly expressed cytoskeletal proteins signify key roles to match its large body size. The RNAi induced knockdown of the ribosomal protein offers evidence for the functional investigation of gene regulating of honeybee embryogenesis. The data significantly expand novel regulatory mechanisms governing the embryogenesis, which is potentially important for honeybee and other insects.

Improved Modelling of Tool Tracking Errors by Modelling Dependent Marker Errors

Accurate understanding of equipment tracking error is essential for decision making in image guided surgery. For tools tracked using markers attached to a rigid body, existing error estimation methods use the assumption that the individual marker errors are independent random variables. This assumption is not valid for all tracking systems. This paper presents a method to estimate a more accurate tracking error function, consisting of a systematic and random component. The proposed method does not require detailed knowledge of the tracking system physics. Results from a pointer calibration are used to demonstrate that the proposed method provides a better match to observed results than the existing state of the art. A simulation of the pointer calibration process is then used to show that existing methods can underestimate the pointer calibration error by a factor of two. A further simulation of laparoscopic camera tracking is used to show that existing methods cannot model important variations in system performance due to the angular arrangement of the tracking markers. By arranging the markers such that the systematic errors are nearly identical for all markers, the rotational component of the tracking error can be reduced, resulting in a significant reduction in target tracking errors.

The Efficacy of Safinamide in the Management of Parkinson's Disease: A Systematic Review

Parkinson's disease (PD) is a chronic neurodegenerative disease that is challenging to treat due to its progressive nature and its weaning response to therapy. Safinamide, a monoamine oxidase type-B inhibitor (MAOB-I), has shown promise in managing dyskinesias caused by levodopa (L-dopa), carbidopa, and PD features such as pain and depression. This systematic review aimed to evaluate safinamide's efficacy as a monotherapy and an add-on in tackling these issues. We composed this systematic review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Our group searched the following databases: Manchester University Library, ScienceDirect, Google Scholar, PubMed, PubMed Central, and MedLine for articles produced in the last ten years using various search terms and criteria, which we outlined in the search strategy and eligibility criteria sections. We excluded 722 out of the initially screened 730 records for multiple reasons, such as titles and abstracts being irrelevant to the topic, articles without free full access, articles originally not in the English language, and articles that did not score 70% or above on their respective quality assessment tools. The studies explored supported safinamide's use in managing motor fluctuations, pain, depression, and improving patients' quality of life.

Integrin binding peptides facilitate growth and interconnected vascular-like network formation of rat primary cortical vascular endothelial cells in vitro

Neovascularization and angiogenesis in the brain are important physiological processes for normal brain development and repair/regeneration following insults. Integrins are cell surface adhesion receptors mediating important function of cells such as survival, growth and development during tissue organization, differentiation and organogenesis. In this study, we used an integrin-binding array platform to identify the important types of integrins and their binding peptides that facilitate adhesion, growth, development, and vascular-like network formation of rat primary brain microvascular endothelial cells. Brain microvascular endothelial cells were isolated from rat brain on post-natal day 7. Cells were cultured in a custom-designed integrin array system containing short synthetic peptides binding to 16 types of integrins commonly expressed on cells in vertebrates. After 7 days of culture, the brain microvascular endothelial cells were processed for immunostaining with markers for endothelial cells including von Willibrand factor and platelet endothelial cell adhesion molecule. 5-Bromo-2'-dexoyuridine was added to the culture at 48 hours prior to fixation to assess cell proliferation. Among 16 integrins tested, we found that α5β1, αvβ5 and αvβ8 greatly promoted proliferation of endothelial cells in culture. To investigate the effect of integrin-binding peptides in promoting neovascularization and angiogenesis, the binding peptides to the above three types of integrins were immobilized to our custom-designed hydrogel in three-dimensional (3D) culture of brain microvascular endothelial cells with the addition of vascular endothelial growth factor. Following a 7-day 3D culture, the culture was fixed and processed for double labeling of phalloidin with von Willibrand factor or platelet endothelial cell adhesion molecule and assessed under confocal microscopy. In the 3D culture in hydrogels conjugated with the integrin-binding peptide, brain microvascular endothelial cells formed interconnected vascular-like network with clearly discernable lumens, which is reminiscent of brain microvascular network in vivo. With the novel integrin-binding array system, we identified the specific types of integrins on brain microvascular endothelial cells that mediate cell adhesion and growth followed by functionalizing a 3D hydrogel culture system using the binding peptides that specifically bind to the identified integrins, leading to robust growth and lumenized microvascular-like network formation of brain microvascular endothelial cells in 3D culture. This technology can be used for in vitro and in vivo vascularization of transplants or brain lesions to promote brain tissue regeneration following neurological insults.

Temporal Memory Relation Network for Workflow Recognition From Surgical Video

Automatic surgical workflow recognition is a key component for developing context-aware computer-assisted systems in the operating theatre. Previous works either jointly modeled the spatial features with short fixed-range temporal information, or separately learned visual and long temporal cues. In this paper, we propose a novel end-to-end temporal memory relation network (TMRNet) for relating long-range and multi-scale temporal patterns to augment the present features. We establish a long-range memory bank to serve as a memory cell storing the rich supportive information. Through our designed temporal variation layer, the supportive cues are further enhanced by multi-scale temporal-only convolutions. To effectively incorporate the two types of cues without disturbing the joint learning of spatio-temporal features, we introduce a non-local bank operator to attentively relate the past to the present. In this regard, our TMRNet enables the current feature to view the long-range temporal dependency, as well as tolerate complex temporal extents. We have extensively validated our approach on two benchmark surgical video datasets, M2CAI challenge dataset and Cholec80 dataset. Experimental results demonstrate the outstanding performance of our method, consistently exceeding the state-of-the-art methods by a large margin (e.g., 67.0% v.s. 78.9% Jaccard on Cholec80 dataset).

Stopping power accuracy and achievable spatial resolution of helium ion imaging using a prototype particle CT detector system

A precise relative stopping power map of the patient is crucial for accurate particle therapy. Charged particle imaging determines the stopping power either tomographically - particle computed tomography (pCT) - or by combining prior knowledge from particle radiography (pRad) and x-ray CT. Generally, multiple Coulomb scattering limits the spatial resolution. Compared to protons, heavier particles scatter less due to their lower charge/mass ratio. A theoretical framework to predict the most likely trajectory of particles in matter was developed for light ions up to carbon and was found to be the most accurate for helium comparing for fixed initial velocity. To further investigate the potential of helium in particle imaging, helium computed tomography (HeCT) and radiography (HeRad) were studied at the Heidelberg Ion-Beam Therapy Centre (HIT) using a prototype pCT detector system registering individual particles, originally developed by the U.S. pCT collaboration. Several phantoms were investigated: modules of the Catphan QA phantom for analysis of spatial resolution and achievable stopping power accuracy, a paediatric head phantom (CIRS) and a custommade phantom comprised of animal meat enclosed in a 2 % agarose mixture representing human tissue. The pCT images were reconstructed applying the CARP iterative reconstruction algorithm. The MTF10% was investigated using a sharp edge gradient technique. HeRad provides a spatial resolution above that of protons (MTF1010%=6.07 lp/cm for HeRad versus MTF10%=3.35 lp/cm for proton radiography). For HeCT, the spatial resolution was limited by the number of projections acquired (90 projections for a full scan). The RSP accuracy for all inserts of the Catphan CTP404 module was found to be 2.5% or better and is subject to further optimisation. In conclusion, helium imaging appears to offer higher spatial resolution compared to proton imaging. In future studies, the advantage of helium imaging compared to other imaging modalities in clinical applications will be further explored.

Households' Assets Dynamics and Ecotourism Choices in the Western Highlands of Cameroon

Ecotourism is increasingly accepted as a suitable alternative for sustaining rural livelihoods. In spite of this trend, quantitative assessments of relationships between household assets and ecotourism choices, and the policy implications thereof, currently account for only a negligible number of studies in sub-Saharan Africa. This paper contributes to this evidence gap by analyzing the extent to which households' assets drive ecotourism choices on a representative sample of 200 households in Cameroon. The Principal Component Analysis (PCA) and the Human Development Index (HDI) were used to construct indices for ecotourism choices. The ordinary least square and logit models were also employed to estimate the effect of various household assets on ecotourism choices. A high preference was observed for the production and sale of arts and crafts items and the promotion of cultural heritage sites as key ecotourism choices. More women are found to participate in conservation education, as opposed to culture-related activities such as arts and crafts. Access to education and training were inversely related to cultural festival promotion. The results suggest the need to: (i) stem the overdependence on conservation sites for wood supply to the arts and crafts sector, (ii) enforce endogenous cultural institutional regulations, including those that increase female participation in guiding future ecotourism choices. This paper contributes to ecotourism development and conservation theory, with regards to unbundling household level predictors of ecotourism choices, and has implications on the design of policies to implement environmentally less-demanding ecotourism activities.

Safety of Magnetic Resonance Imaging After Implantation of Stainless Steel Embolization Coils

Stainless steel embolization coils (SSEC) have been used for over four decades for vascular occlusion. Recently, the safety of these coils in a magnetic resonance environment has been called into question, with important ramifications for thousands of patients with existing coils in place. We performed a retrospective chart review at five tertiary care pediatric centers evaluating all children and young adults with implanted SSEC who underwent magnetic resonance imaging (MRI). Data reviewed included demographics, coil implantation, MRI studies, and follow-up evaluations. Complications such as heating, discomfort, or device migration were specifically sought. Two hundred and ninety-seven patients with implanted SSEC underwent 539 MRI examinations. The median age at SSEC implantation was 2.3 years (1 week-23.2 years). The MRI studies were performed a median of 7.4 years (4 days-23.1 years) after implantation. No patients experienced any reported complications associated with their MRI examinations during the study or at median follow-up post-MRI of 4.8 years (1 day-23 years). In this large, retrospective review of patients with implanted SSEC undergoing MRI, there were no reported adverse events. These findings support the recent change by Cook Medical Inc. of their standard embolization coils from a designation of magnetic resonance unsafe to conditional.

Motion Correlation Discovery for Visual Tracking

Motion information plays an important role in identifying moving objects, which has not been well utilized in state of-the-art tracking algorithms. In this letter, we propose a unified framework integrating two tracking problems, i.e., pixel-level foreground probabilistic inference and motion parameter estimation. Our model employs motion fields to propagate probability forward, and discovers motion patterns in the spatial domain to distinguish targets from the background. It takes advantage of continuity and inertia of both target and camera motion, and provides reliable evidence to resolve confusion caused by appearance similarity between targets and the background. Target localization is effectively achieved from the pixel-level foreground probabilistic map. Experimental results demonstrate that the proposed method significantly improves our baseline method, and achieves performance comparable to state-of-the-art tracking methods with more complex features.

The AIM Photonics MPW: A Highly Accessible Cutting Edge Technology for Rapid Prototyping of Photonic Integrated Circuits

Silicon photonics has been heralded for a number of high technology fields, but access to a high quality technology has been limited to vertically integrated design/fabrication companies, or fabless companies with significant resources to engage high volume fabs. More recently, research and development hubs have developed and released process design kits and multi-project wafer programs to lower the barrier. We present the first silicon photonics multi-project wafer (MPW) service produced in a state-of-the-art 300 mm fabrication facility. The MPW service is enabled by a best-in-class process design kit (PDK) which allows designers to layout and obtain photonic integrated circuits (PICs) that work properly on the first run. The fabrication of these circuits is carried out at the SUNY Polytechnic Institute which operates a world class 300 mm cleanroom that, besides silicon photonics, develops sub-7 nm CMOS architectures. The industrial-level management of this facility and its equipment provides high quality photonic devices which are repeatable from run-to-run along with rapid turnaround time. The devices that are available to designers via the process design kit are produced by Analog Photonics and have been verified on actual runs. The performance of these devices is comparable to the state-of-the-art and enables a wide variety of silicon photonic applications.

A novel agonist for the HGF receptor MET promotes differentiation of human pluripotent stem cells into hepatocyte-like cells

Hepatocyte growth factor (HGF) is the natural ligand of the MET receptor tyrosine kinase. This ligand-receptor couple is essential for the maturation process of hepatocytes. Previously, the rational design of a synthetic protein based on the assembly of two K1 domains from HGF led to the production of a potent and stable MET receptor agonist. In this study, we compared the effects of K1K1 with HGF during the differentiation of hepatocyte progenitors derived from human induced pluripotent stem cells (hiPSCs). In vitro, K1K1, in the range of 20 to 200 nM, successfully substituted for HGF and efficiently activated ERK downstream signaling. Analysis of the levels of hepatocyte markers showed typical liver mRNA and protein expression (HNF4α, albumin, alpha-fetoprotein, CYP3A4) and phenotypes. Although full maturation was not achieved, the results suggest that K1K1 is an attractive candidate MET agonist suitable for replacing complex and expensive HGF treatments to induce hepatic differentiation of hiPSCs.

Transformer-Based Model for Electrical Load Forecasting

Amongst energy-related CO2 emissions, electricity is the largest single contributor, and with the proliferation of electric vehicles and other developments, energy use is expected to increase. Load forecasting is essential for combating these issues as it balances demand and production and contributes to energy management. Current state-of-the-art solutions such as recurrent neural networks (RNNs) and sequence-to-sequence algorithms (Seq2Seq) are highly accurate, but most studies examine them on a single data stream. On the other hand, in natural language processing (NLP), transformer architecture has become the dominant technique, outperforming RNN and Seq2Seq algorithms while also allowing parallelization. Consequently, this paper proposes a transformer-based architecture for load forecasting by modifying the NLP transformer workflow, adding N-space transformation, and designing a novel technique for handling contextual features. Moreover, in contrast to most load forecasting studies, we evaluate the proposed solution on different data streams under various forecasting horizons and input window lengths in order to ensure result reproducibility. Results show that the proposed approach successfully handles time series with contextual data and outperforms the state-of-the-art Seq2Seq models.

Comparison of bacterial diversity in full scale anammox bioreactors operated under different conditions

Bacterial community structure of full-scale anammox bioreactor is still mainly unknown. It has never been analyzed whether different anammox bioreactor configurations might result in the development of different bacterial community structures among these systems. In this work, the bacterial community structure of six full-scale autotrophic nitrogen removal bioreactors located in The Netherlands and China operating under three different technologies and with different influent wastewater characteristics was studied by the means of pyrotag sequencing evaluation of the bacterial assemblage yielded a great diversity in all systems. The most represented phyla were the Bacteroidetes and the Proteobacteria, followed by the Planctomycetes. 14 OTUs were shared by all bioreactors, but none of them belonged to the Brocadiales order. Statistical analysis at OTU level showed that differences in the microbial communities were high, and that the main driver of the bacterial assemblage composition was different for the distinct phyla identified in the six bioreactors, depending on bioreactor technology or influent wastewater characteristics.

Estimating the onset of spring from a complex phenology database: trade-offs across geographic scales

Phenology is an important indicator of ecological response to climate change. Yet, phenological responses are highly variable among species and biogeographic regions. Recent monitoring initiatives have generated large phenological datasets comprised of observations from both professionals and volunteers. Because the observation frequency is often variable, there is uncertainty associated with estimating the timing of phenological activity. "Status monitoring" is an approach that focuses on recording observations throughout the full development of life cycle stages rather than only first dates in order to quantify uncertainty in generating phenological metrics, such as onset dates or duration. However, methods for using status data and calculating phenological metrics are not standardized. To understand how data selection criteria affect onset estimates of springtime leaf-out, we used status-based monitoring data curated by the USA National Phenology Network for 11 deciduous tree species in the eastern USA between 2009 and 2013. We asked, (1) How are estimates of the date of leaf-out onset, at the site and regional levels, influenced by different data selection criteria and methods for calculating onset, and (2) at the regional level, how does the timing of leaf-out relate to springtime minimum temperatures across latitudes and species? Results indicate that, to answer research questions at site to landscape levels, data users may need to apply more restrictive data selection criteria to increase confidence in calculating phenological metrics. However, when answering questions at the regional level, such as when investigating spatiotemporal patterns across a latitudinal gradient, there is low risk of acquiring erroneous results by maximizing sample size when using status-derived phenological data.

Surface similarity parameter: A new machine learning loss metric for oscillatory spatio-temporal data

Supervised machine learning approaches require the formulation of a loss functional to be minimized in the training phase. Sequential data are ubiquitous across many fields of research, and are often treated with Euclidean distance-based loss functions that were designed for tabular data. For smooth oscillatory data, those conventional approaches lack the ability to penalize amplitude, frequency and phase prediction errors at the same time, and tend to be biased towards amplitude errors. We introduce the surface similarity parameter (SSP) as a novel loss function that is especially useful for training machine learning models on smooth oscillatory sequences. Our extensive experiments on chaotic spatio-temporal dynamical systems indicate that the SSP is beneficial for shaping gradients, thereby accelerating the training process, reducing the final prediction error, increasing weight initialization robustness, and implementing a stronger regularization effect compared to using classical loss functions. The results indicate the potential of the novel loss metric particularly for highly complex and chaotic data, such as data stemming from the nonlinear two-dimensional Kuramoto-Sivashinsky equation and the linear propagation of dispersive surface gravity waves in fluids.

Bacterial catabolism of acetovanillone, a lignin-derived compound

Bacterial catabolic pathways have considerable potential as industrial biocatalysts for the valorization of lignin, a major component of plant-derived biomass. Here, we describe a pathway responsible for the catabolism of acetovanillone, a major component of several industrial lignin streams. Rhodococcus rhodochrous GD02 was previously isolated for growth on acetovanillone. A high-quality genome sequence of GD02 was generated. Transcriptomic analyses revealed a cluster of eight genes up-regulated during growth on acetovanillone and 4-hydroxyacetophenone, as well as a two-gene cluster up-regulated during growth on acetophenone. Bioinformatic analyses predicted that the hydroxyphenylethanone (Hpe) pathway proceeds via phosphorylation and carboxylation, before β-elimination yields vanillate from acetovanillone or 4-hydroxybenzoate from 4-hydroxyacetophenone. Consistent with this prediction, the kinase, HpeHI, phosphorylated acetovanillone and 4-hydroxyacetophenone. Furthermore, HpeCBA, a biotin-dependent enzyme, catalyzed the ATP-dependent carboxylation of 4-phospho-acetovanillone but not acetovanillone. The carboxylase's specificity for 4-phospho-acetophenone (kcat/KM = 34 ± 2 mM-1 s-1) was approximately an order of magnitude higher than for 4-phospho-acetovanillone. HpeD catalyzed the efficient dephosphorylation of the carboxylated products. GD02 grew on a preparation of pine lignin produced by oxidative catalytic fractionation, depleting all of the acetovanillone, vanillin, and vanillate. Genomic and metagenomic searches indicated that the Hpe pathway occurs in a relatively small number of bacteria. This study facilitates the design of bacterial strains for biocatalytic applications by identifying a pathway for the degradation of acetovanillone.

Interpretable intrusion detection for next generation of Internet of Things

This paper presents a new framework for intrusion detection in the next-generation Internet of Things. MinMax normalization strategy is used to collect and preprocess data. The Marine Predator algorithm is then used to select relevant features to be used in the learning process. The selected features are then trained with an advanced and state-of-the-art recurrent neural network that includes an attention mechanism. Finally, Shapely values are calculated to determine how much each feature contributes to the final output. The dataset NSL-KDD was used for intensive simulations. The results show the advantages of the proposed system as well as its superiority over state-of-the-art methods. In fact, the proposed solution achieved a rate of more than 94% for both true negative and true position, while the rates of the existing solutions are below 90% for the challenging NSL-KDD datasets.