Miracle-dz/UniFAR-FTUs huggingface dataset:
Heterogeneous Graph Attention Network

Graph neural network, as a powerful graph representation technique based on deep learning, has shown superior performance and attracted considerable research interest. However, it has not been fully considered in graph neural network for heterogeneous graph which contains different types of nodes and links. The heterogeneity and rich semantic information bring great challenges for designing a graph neural network for heterogeneous graph. Recently, one of the most exciting advancements in deep learning is the attention mechanism, whose great potential has been well demonstrated in various areas. In this paper, we first propose a novel heterogeneous graph neural network based on the hierarchical attention, including node-level and semantic-level attentions. Specifically, the node-level attention aims to learn the importance between a node and its meta-path based neighbors, while the semantic-level attention is able to learn the importance of different meta-paths. With the learned importance from both node-level and semantic-level attention, the importance of node and meta-path can be fully considered. Then the proposed model can generate node embedding by aggregating features from meta-path based neighbors in a hierarchical manner. Extensive experimental results on three real-world heterogeneous graphs not only show the superior performance of our proposed model over the state-of-the-arts, but also demonstrate its potentially good interpretability for graph analysis.
POI Semantic Model with a Deep Convolutional Structure

When using the electronic map, POI retrieval is the initial and important step, whose quality directly affects the user experience. Similarity between user query and POI information is the most critical feature in POI retrieval. An accurate similarity calculation is challenging since the mismatch between a query and a retrieval text may exist in the case of a mistyped query or an alias inquiry. In this paper, we propose a POI latent semantic model based on deep networks, which can effectively extract query features and POI information features for the similarity calculation. Our model describes the semantic information of complex texts at multiple layers, and achieves multi-field matches by modeling POI's name and detailed address respectively. Our model is evaluated by the POI retrieval ranking datasets, including the labeled data of relevance and real-world user click data in POI retrieval. Results show that our model significantly outperforms our competitors in POI retrieval ranking tasks. The proposed algorithm has become a critical component of an online system serving millions of people everyday.
Streptococcus agalactiae Sequence Type 283 in Farmed Fish, Brazil

In 2016 and 2017, we characterized outbreaks caused by Streptococcus agalactiae serotype III sequence type (ST) 283 in Nile tilapia farms in Brazil. Whole-genome multilocus sequence typing clustered the fish isolates together with the zoonotic ST283 and other STs related to cases in humans, frogs, dogs, cattle, and dolphins.
Assessment of veterinary drugs present in pork kidney from a Midwest US retail market

ABSTRACT A total of 1040 pork kidneys were purchased from 4 retail stores located in a Midwestern US town and screened for antibiotics with the Charm-KIS™ screening test. Six samples (0.6%) tested positive with the Charm-KIS™. Sixty-five samples from each retail location and the 18 Charm-KIS™ positive or ‘caution’ samples were also subjected to ELISA to determine the presence of commonly used veterinary drugs including flunixin, ractopamine, sulfamethazine, and/or tetracycline of the 278 samples assessed by ELISA, flunixin, ractopamine, sulfamethazine, and tetracycline residues were found to be 0%, 22%, 4%, and 10% ELISA positive respectively, and had greater than limit of quantitation concentrations as measured by LC-MS/MS. All residue levels determined by LC-MS/MS were well below US tolerances, regardless of analyte. These findings suggest that veterinary drugs are being used in accordance with US regulations and that veterinary drug residues in pork do not pose a health concern to US consumers.
Characteristics and Source of Dissolved Organic Matter in Lake Hulun, A Large Shallow Eutrophic Steppe Lake in Northern China

Lake Hulun, the fifth largest lake in China, is a typical eutrophic steppe lake located in the Hulun Buir Prairie. The dissolved organic matter (DOM) in the water of Lake Hulun has a high concentration. However, little is known about the occurrence characteristics and source of the DOM in Lake Hulun. The spatial and temporal distribution characteristics of DOM concentration in Lake Hulun were thoroughly surveyed, and the optical characteristics, fluorescence components and sources of DOM were analyzed by excitation emission matrix (EMM) and parallel factor analysis (PARAFAC) technology. The DOM concentration was 6.46–42.87 mg C/L, and was highest in summer and lowest in winter. The difference in the spatial distribution of DOM in winter was significant due to the ice over, and showed a trend where the concentration near the shore was higher than that in the center of the lake. Three humic-like components and one component consisting of a mixture of humic-like and protein-like substances of DOM were identified, with the former being prevalent. The humification index of DOM was 2.22–9.92, indicating that the DOM has a high degree of humification. The DOM is mainly derived from terrestrial sources, with the highest proportion (91.0% ± 8.1%) found in winter and the lowest (66.2% ± 7.7%) in summer. Given that the DOM in Lake Hulun is mainly dominated by humic-like components with a high degree humification, the DOM may have low bioactivity. However, this is just a preliminary analysis and judgment, and it is necessary to conduct other experiments such as biodegradation experiments to further study the bioavailability of DOM in Lake Hulun.
CROSS RESISTANCE OF DIPEL 2x RESISTANT STRAIN OF PINK BOLLWORM PECTINOPHORA GOSSYPIELLA (SAUND.) TO SOME CONVENTIONAL AND BIOINSECTICIDES

A field strain of pink bollworm Pectinophora gossypiella (Saund.) collected from Sharquia Governorate, Egypt was exposed to the selection pressure of the for-mulation of Bacillus thuringiensis subsp. Kurstaki, Dipel 2x under laboratory condi-tions. Resistance ratio attained 16-fold based on the susceptible strain after 14 gen-erations of selection. Study the response of Dipel 2x resistant strain to some insecti-cides indicated that there is no cross resistance to the conventional insecticides, es-fenvalerate, chlorpyriphos and thiodicarb or the bioinsecticides Ecotech and Agerin. These data may be emphasizing the possibility of rotation the Dipel 2x with these insecticides in pest control program of pink bollworm to manage resistance to Bt products.
Technical notes: Syntax-aware Representation Learning With Pointer Networks

This is a work-in-progress report, which aims to share preliminary results of a novel sequence-to-sequence schema for dependency parsing that relies on a combination of a BiLSTM and two Pointer Networks (Vinyals et al., 2015), in which the final softmax function has been replaced with the logistic regression. The two pointer networks co-operate to develop a latent syntactic knowledge, by learning the lexical properties of "selection" and the lexical properties of "selectability", respectively. At the moment and without fine-tuning, the parser implementation gets a UAS of 93.14% on the English Penn-treebank (Marcus et al., 1993) annotated with Stanford Dependencies: 2-3% under the SOTA but yet attractive as a baseline of the approach.
Oxidative Stress in Animal Models of Acute and Chronic Renal Failure

Introduction Kidney disease is a worldwide health and economic burden, with rising prevalence. The search for biomarkers for earlier and more effective disease screening and monitoring is needed. Oxidative stress has been linked to both, acute kidney injury (AKI) and chronic kidney disease (CKD). The aim of our study was to investigate whether the concentrations of systemic markers of oxidative stress and antioxidant status are affected by AKI and CKD, and to identify potential biomarkers. Methods In adult male Wistar rats, AKI was induced by bilateral nephrectomy, and CKD was induced by 5/6 nephrectomy. Blood was collected 48 hours after surgery in AKI and 6 months after surgery in CKD. Advanced oxidation protein products (AOPP), thiobarbituric acid reactive substances (TBARS), advanced glycation end products (AGEs), fructosamine, total antioxidant capacity (TAC), and ferric reducing antioxidant power (FRAP) were measured. Results Impaired renal function was confirmed by high concentrations of plasma creatinine and urea in AKI and CKD animals. AOPP and fructosamine were higher by 100% and 54% in AKI, respectively, and by 100% and 199% in CKD, respectively, when compared to corresponding control groups. Similarly, there was approximately a twofold increase in AGEs (by 92%) and TAC (by 102%) during AKI. In CKD, concentrations of FRAP, as an antioxidative status marker, were doubled (by 107%) when compared to the control group, but concentration of TAC, another marker of antioxidative status, did not differ between the groups. Conclusions AKI and CKD led to increased systemic oxidative stress. AOPP and fructosamine could be considered potential biomarkers for both, acute and chronic kidney damage. On the other hand, AGEs, TAC, and FRAP seem to be disease specific, which could help to differentiate between acute and chronic kidney injuries. However, this needs further validation in clinical studies.
Co-occurring medical conditions among individuals with ASD-associated disruptive mutations

ABSTRACT Children with autism spectrum disorder (ASD) are at risk for co-occurring medical conditions, many of which have also been reported among individuals with mutations in ASD-associated genes. This study examined rates of co-occurring medical conditions across 301 individuals with disruptive mutations to 1 of 18 ASD-risk genes in comparison to rates of conditions in an idiopathic ASD sample. Rates of gastrointestinal problems, seizures, physical anomalies, and immune problems were generally elevated, with significant differences in rates observed between groups. Results may inform medical care of individuals with ASD-associated mutations and research into mechanisms of co-occurring medical conditions in ASD.
Vertical dispersal of nucleopolyhedrovirus occlusion bodies in soil by the earthworm Amynthas gracilus: a field-based estimation

ABSTRACT The soil is the most important environmental reservoir for nucleopolyhedrovirus occlusion bodies (OBs). We employed a diet + soil bioassay technique to demonstrate that the earthworm Amynthas gracilus was capable of vertical transport of Spodoptera frugiperda multiple nucleopolyhedrovirus (SfMNPV) occlusion bodies (OBs) under field conditions. Incubation of OBs with earthworms in sand did not adversely affect the prevalence of virus-induced mortality, but variation in insect mortality was markedly increased, possibly due to changes in the particle size distribution following earthworm processing of OB-contaminated sand. We conclude that earthworms are likely to interact frequently with soil OB populations.
Relationship of Preceptor Ad-hoc Entrustment Decisions to Students’ Clinical Skills Performance

This article was migrated. The article was marked as recommended. Using a workplace evaluation instrument that included clinical skills evaluation and supervisory EPA scale, this study provided empirical evidence of the relationship between preceptor evaluation of student clinical ability and their entrustment decisions at the medical student level, and advanced the understanding of general conditions that ad-hoc entrustment decision are based upon. A total of 4217 evaluations from 353 third-year medical students were included in the study. The analyses focused on entrustment decisions for seven EPAs (EPA1, 2, 3, 5, 6, 7, and 9) and the relationship with related clinical skill (e.g. history taking) performance. Pearson’s correlations showed statistically significant and positive correlations within clerkships (r= 0.43-0.75) and across clerkships (r= 0.70-0.77). Analyses of individual entrustment level and skill rating also revealed that the lowest level of entrustment was predominantly associated with the rating of developing on corresponding skills, whereas the highest level of entrustment with the rating of approaching advanced/ advanced; the link between the three middle entrustment levels and particular skill ratings were less salient. Overall, these patterns of association between individual entrustment level and skill rating varied by EPAs as well as level of entrustment. Limitations of the current study were also discussed.
Learning‐Based Animation of Clothing for Virtual Try‐On

This paper presents a learning‐based clothing animation method for highly efficient virtual try‐on simulation. Given a garment, we preprocess a rich database of physically‐based dressed character simulations, for multiple body shapes and animations. Then, using this database, we train a learning‐based model of cloth drape and wrinkles, as a function of body shape and dynamics. We propose a model that separates global garment fit, due to body shape, from local garment wrinkles, due to both pose dynamics and body shape. We use a recurrent neural network to regress garment wrinkles, and we achieve highly plausible nonlinear effects, in contrast to the blending artifacts suffered by previous methods. At runtime, dynamic virtual try‐on animations are produced in just a few milliseconds for garments with thousands of triangles. We show qualitative and quantitative analysis of results.
EV-IMO: Motion Segmentation Dataset and Learning Pipeline for Event Cameras

We present the first event-based learning approach for motion segmentation in indoor scenes and the first event-based dataset – EV-IMO– which includes accurate pixel-wise motion masks, egomotion and ground truth depth. Our approach is based on an efficient implementation of the SfM learning pipeline using a low parameter neural network architecture on event data. In addition to camera egomotion and a dense depth map, the network estimates independently moving object segmentation at the pixel-level and computes per-object 3D translational velocities of moving objects. We also train a shallow network with just 40k parameters, which is able to compute depth and egomotion.Our EV-IMO dataset features 32 minutes of indoor recording with up to 3 fast moving objects in the camera field of view. The objects and the camera are tracked using a VICON® motion capture system. By 3D scanning the room and the objects, ground truth of the depth map and pixel-wise object masks are obtained. We then train and evaluate our learning pipeline on EV-IMO and demonstrate that it is well suited for scene constrained robotics applications. SUPPLEMENTARY MATERIAL The supplementary video, code, trained models, appendix and a dataset will be made available at http://prg.cs. umd.edu/EV-IMO.html.
TF-ChIP Method for Tissue-Specific Gene Targets

Chromatin immunoprecipitation (ChIP) is an assay developed in order to define the dynamic nature of transcription processes. This method has been widely employed to identify methylated and acetylated DNA sequences in a variety of organs both in animals and humans. Nevertheless, this technique is significantly less employed to study transcriptional targets of specific nuclear signaling factors (TFs) and the data published so far have mainly used cell culture material and have been hardly reproduced in ex-vivo tissue. As nuclear signaling underlies important adaptive and maladaptive responses in chronic conditions such as cancer and neurodegeneration, there is a need for streamlining the upfront workflow of TF-ChIP for subsequent target sequencing. Based on the typical low concentration of the signaling transcriptional complex and the complexity/length of the ChIP Seq protocol, the field of cellular signaling has been confronted with a major roadblock in identifying clinically relevant targets of pathological and physiological signaling pathways. The present protocol offers a standardized procedure for detecting signaling targets in any whole tissue or specific dissected regions. The advantages of the protocol compared to the existing published methods are: (1) the small amount of starting material; appropriate for tissue subregions; (2) the optimization of DNA fragmentation from whole tissue; (3) suitability for sparsely populated tissues (i.e., brain); (4) the specificity of the TF-targeting readout; and (5) high DNA quality for sequencing or hybridization. The present protocol is highly detailed and can be reproduced using both fresh and fresh-frozen tissue. This is particularly relevant in the clinical setting, where specimen integrity is often the limiting step and where transcriptional target profiling is therapeutically relevant. The method is centered on Notch signaling but can be applied to a variety of nuclear signaling pathways as long as specific antibodies are available for pull down. Taken the superior yield/readout of this procedure, ChIP may finally provide relevant information about dynamic downstream gene changes in vivo for use in both basic research and clinical applications.
Nuclear Matrix Elements for β and ββ Decays and Quenching of the Weak Coupling gA in QRPA

Recent experimental studies at RCNP on single and double beta NMEs relevant to DBDs are briefly reviewed. The axial-vector single beta NMEs are shown to be uniformly reduced by the renormalization (quenching) factor 0.5-0.6 with respect to the QRPA model NMEs due to non-nucleonic and nuclear medium effects, which are not explicitly included in the model. Impact of the reduction of the axial-vector NMEs on the DBD NMEs and DBD experiments is discussed.
Evaluating the Economic Benefits of a Smart-Community Microgrid with Centralized Electrical Storage and Photovoltaic Systems

In this paper, an innovative method for managing a smart-community microgrid (SCM) with a centralized electrical storage system (CESS) is proposed. The method consists of day-ahead optimal power flow (DA–OPF) for day-ahead SCM managing and its subsequent evaluation, considering forecast uncertainties. The DA–OPF is based on a data forecast system that uses a deep learning (DL) long short-term memory (LSTM) network. The OPF problem is formulated as a mathematical mixed-integer nonlinear programming (MINLP) model. Following this, the developed DA–OPF strategy was evaluated under possible operations, using a Monte Carlo simulation (MCS). The MCS allowed us to obtain potential deviations of forecasted data during possible day-ahead operations and to evaluate the impact of the data forecast errors on the SCM, and that of unit limitation and the emergence of critical situations. Simulation results on a real existing rural conventional community endowed with a centralized community renewable generation (CCRG) and CESS, confirmed the effectiveness of the proposed operation method. The economic analysis showed significant benefits and an electricity price reduction for the considered community if compared to a conventional distribution system, as well as the easy applicability of the proposed method due to the CESS and the developed operating systems.
A fully soft switched high step‐up SEPIC‐boost DC‐DC converter with one auxiliary switch

This paper proposed a new single‐ended primary inductor converter (SEPIC)‐boost DC‐DC converter that uses only one auxiliary switch to create soft switching condition for all semiconductor devices. The auxiliary circuit comprises one power switch (Sa), one resonant inductor (Lr), one resonant capacitor (Cr), and one diode (Do2). The auxiliary switch (Sa) controls the resonance during switching instants. The converter has simple structure and its control circuit remains pulse width modulation (PWM). Besides, the proposed converter has high voltage gain without using any transformer or coupled inductors. In addition, the auxiliary switch is not located in the main power path. Moreover, using soft switching techniques is the best way for reducing the size, weight, and volume of the converter. Furthermore, reduction of input inrush current and voltage stress for the main switch is obtained by using SEPIC‐boost structure. A laboratory prototype converter is designed and implemented. The experimental results presented confirm the theoretical and features of the proposed converter.
Association between caesarean section delivery and obesity in childhood: a longitudinal cohort study in Ireland

Objectives To investigate the association between caesarean section (CS) birth and body fat percentage (BF%), body mass index (BMI) and being overweight or obese in early childhood. Design Prospective longitudinal cohort study. Setting Babies After Screening for Pregnancy Endpoints: Evaluating the Longitudinal Impact on Neurological and Nutritional Endpoints cohort. Participants Infants born to mothers recruited from the Screening for Pregnancy Endpoints study, Cork University Maternity Hospital between November 2007 and February 2011. Outcome measure Overweight or obese defined according to the International Obesity Task Force criteria. Results Of the 1305 infants, 362 (27.8%) were delivered by CS. On regression analysis, BF% at 2 months did not differ significantly by delivery mode. Infants born by CS had a higher mean BMI at 6 months compared with those born vaginally (adjusted mean difference=0.24; 95% CI 0.06 to 0.41, p value=0.009). At 2 years, no difference was seen across the exposure groups in the risk of being overweight or obese. At 5 years, the association between prelabour CS and the risk of overweight or obesity was not statistically significant (adjusted relative risk ratio, aRRR=1.37; 95% CI 0.69 to 2.69) and the association remained statistically nonsignificant when children who were macrosomic at birth were excluded from the model (aRRR=0.86; 95% CI 0.36 to 2.08). Conclusion At 6 months of age, children born by CS had a significantly higher BMI but this did not persist into future childhood. There was no evidence to support an association between mode of delivery and long-term risk of obesity in the child.
Analysis of large-scale UAV images using a multi-scale hierarchical representation

Abstract Unmanned aerial vehicle (UAV)-based imaging systems have many superiorities compared with other platforms, such as high flexibility and low cost in collecting images, providing wide application prospects. However, the acquisition of the UAV-based image commonly results in very high resolution and very large-scale images, which poses great challenges for subsequent applications. Therefore, an efficient representation of large-scale UAV images is necessary for the extraction of the required information in a reasonable time. In this work, we proposed a multi-scale hierarchical representation, i.e. binary partition tree, for analyzing large-scale UAV images. More precisely, we first obtained an initial partition of images by an oversegmentation algorithm, i.e. the simple linear iterative clustering. Next, we merged the similar superpixels to build an object-based hierarchical structure by fully considering the spectral and spatial information of the superpixels and their topological relationships. Moreover, objects of interest and optimal segmentation were obtained using object-based analysis methods with the hierarchical structure. Experimental results on processing the post-seismic UAV images of the 2013 Ya’an earthquake and the mosaic of images in the South-west of Munich demonstrate the effectiveness and efficiency of our proposed method.