Miracle-dz/UniFAR-FTUs huggingface dataset:
Genome-wide association and epistatic interactions of flowering time in soybean cultivar

Genome-wide association studies (GWAS) have enabled the discovery of candidate markers that play significant roles in various complex traits in plants. Recently, with increased interest in the search for candidate markers, studies on epistatic interactions between single nucleotide polymorphism (SNP) markers have also increased, thus enabling the identification of more candidate markers along with GWAS on single-variant-additive-effect. Here, we focused on the identification of candidate markers associated with flowering time in soybean (Glycine max). A large population of 2,662 cultivated soybean accessions was genotyped using the 180k Axiom® SoyaSNP array, and the genomic architecture of these accessions was investigated to confirm the population structure. Then, GWAS was conducted to evaluate the association between SNP markers and flowering time. A total of 93 significant SNP markers were detected within 59 significant genes, including E1 and E3, which are the main determinants of flowering time. Based on the GWAS results, multilocus epistatic interactions were examined between the significant and non-significant SNP markers. Two significant and 16 non-significant SNP markers were discovered as candidate markers affecting flowering time via interactions with each other. These 18 candidate SNP markers mapped to 18 candidate genes including E1 and E3, and the 18 candidate genes were involved in six major flowering pathways. Although further biological validation is needed, our results provide additional information on the existing flowering time markers and present another option to marker-assisted breeding programs for regulating flowering time of soybean.
Sensor-based Continuous Authentication of Smartphones' Users Using Behavioral Biometrics: A Survey

Mobile devices and technologies have become increasingly popular, offering comparable storage and computational capabilities to desktop computers allowing users to store and interact with sensitive and private information. The security and protection of such personal information are becoming more and more important since mobile devices are vulnerable to unauthorized access or theft. User authentication is a task of paramount importance that grants access to legitimate users at the point-of-entry and continuously through the usage session. This task is made possible with today's smartphones' embedded sensors that enable continuous and implicit user authentication by capturing behavioral biometrics and traits. In this paper, we survey more than 140 recent behavioral biometric-based approaches for continuous user authentication, including motion-based methods (27 studies), gait-based methods (23 studies), keystroke dynamics-based methods (20 studies), touch gesture-based methods (29 studies), voice-based methods (16 studies), and multimodal-based methods (33 studies). The survey provides an overview of the current state-of-the-art approaches for continuous user authentication using behavioral biometrics captured by smartphones' embedded sensors, including insights and open challenges for adoption, usability, and performance.
A Novel Analysis Dictionary Learning Model Based Hyperspectral Image Classification Method

Supervised hyperspectral image (HSI) classification has been acknowledged as one of the fundamental tasks of hyperspectral data analysis. Witnessing the success of analysis dictionary learning (ADL)-based method in recent years, we propose an ADL-based supervised HSI classification method in this paper. In the proposed method, the dictionary is modeled considering both the characteristics within the spectrum and among the spectra. Specifically, to reduce the influence of strong nonlinearity within each spectrum on classification, we divide the spectrum into some segments, and based on this we propose HSI classification strategy. To preserve the relationships among spectra, similarities among pixels are introduced as constraints. Experimental results on several benchmark hyperspectral datasets demonstrate the effectiveness of the proposed method for HSI classification.
A systematic review of digital badges in health care education

A challenge to competency‐based medical education is the verification of skill acquisition. Digital badges represent an innovative instruction strategy involving the credentialing of competencies to provide evidence for achievement. Despite increasing interest in digital badges, there has been no synthesis of the health care education literature regarding this credentialing strategy. The present authors proposed to address this gap by conducting the first systematic review of digital badges in health care education, to reveal pedagogical and research limitations, and to provide an evidence‐based foundation for the design and implementation of digital badges.
Artistic Text Stylization for Visual-Textual Presentation Synthesis

In this research, we study a specific task of visual-textual presentation synthesis, where artistic text is generated and embedded in a background photo. The art form of visual-textual presentation is widely used in graphic design such as posters, billboards and trademarks, and therefore is of high application value. We propose a new framework to complete this task. First, the shape of the target text is adjusted and the textures are rendered to match the reference style image to generate artistic text. By considering both aesthetics and seamlessness, the layout where the artistic text is placed is determined. Finally the artistic text is blended with the background photo to obtain the visual-textual presentations. The experimental results demonstrate the effectiveness of the proposed framework in creating professionally designed visual-textual presentations.
Keep Your Dimensions on a Leash: True Cognate Detection using Siamese Deep Neural Networks

Automatic Cognate Detection helps NLP tasks of Machine Translation, Information Retrieval, and Phylogenetics. Cognate words are defined as word pairs across languages which exhibit partial or full lexical similarity and mean the same (e.g., hund-hound in German-English). In this paper, we use a Siamese Feed-forward neural network with word-embeddings to detect such word pairs. Our experiments with various embedding dimensions show larger embedding dimensions can only be used for large corpora sizes for this task. On a dataset built using linked Indian Wordnets, our approach beats the baseline approach with a significant margin (up to 71%) with the best F-score of 0.85% on the Hindi-Gujarati language pair.
Role of Oxidative Stress and Metal Toxicity in the Progression of Alzheimer’s Disease

Alzheimer’s disease (AD) is one of the life-threatening neurodegenerative disorders in the elderly (>60 years) and incurable across the globe to date. AD is caused by the involvement of various genetic, environmental and lifestyle factors that affect neuronal cells to degenerate over the period of time. The oxidative stress is engaged in the pathogenesis of various disorders and its key role is also linked to the etiology of AD. AD is attributed by neuronal loss, abnormal accumulation of Amyloid-β (Aβ) and neurofibrillary tangles (NFTs) with severe memory impairments and other cognitive dysfunctions which lead to the loss of synapses and neuronal death and eventual demise of the individual. Increased production of reactive oxygen species (ROS), loss of mitochondrial function, altered metal homeostasis, aberrant accumulation of senile plaque and mitigated antioxidant defense mechanism all are indulged in the progression of AD. In spite of recent advances in biomedical research, the underlying mechanism of disruption of redox balance and the actual source of oxidative stress is still obscure. This review highlights the generation of ROS through different mechanisms, the role of some important metals in the progression of AD and free radical scavenging by endogenous molecule and supplementation of nutrients in AD.
Towards Discourse Parsing-inspired Semantic Storytelling

Previous work of ours on Semantic Storytelling uses text analytics procedures including Named Entity Recognition and Event Detection. In this paper, we outline our longer-term vision on Semantic Storytelling and describe the current conceptual and technical approach. In the project that drives our research we develop AI-based technologies that are verified by partners from industry. One long-term goal is the development of an approach for Semantic Storytelling that has broad coverage and that is, furthermore, robust. We provide first results on experiments that involve discourse parsing, applied to a concrete use case, "Explore the Neighbourhood! ", which is based on a semi-automatically collected data set with documents about noteworthy people in one of Berlin's districts. Though automatically obtaining annotations for coherence relations from plain text is a non-trivial challenge, our preliminary results are promising. We envision our approach to be combined with additional features (NER, coreference resolution, knowledge graphs
Ranking and Discovering Anomalous Neighborhoods in Attributed Multiplex Networks

The attributed multiplex network is a set of attributed networks in which each network represents a different type of interaction between the same set of nodes. Individual networks are termed as layers or dimensions and network nodes are characterized by attribute vectors. Neighborhood, in general, refers to any dense connected subgraph. We refer neighborhood1 as subgraph induced on graph node and its neighbors. It is usually observed that majority of the nodes in multilayer networks are active only on small number of layers except some outliers [18]. However, node activity is not strictly correlated to the edges incident in a node. A node might be active at few layers with relatively large number of incident edges and at the same time, multi-active node might not have many links even on single layer. Moreover, each layer has distinct importance in the multiplex networks2 and the structure and size of neighborhood formed by these multiplex nodes are different on each layer. Nodes with different attributes come together on different layers in the attributed multiplex networks. This node and layer heterogeneity should be considered while identifying anomalous neighborhoods in the attributed multiplex networks. Thus, a measure is required to quantify the quality of neighborhoods formed by active nodes on different layers. Existing approaches do not consider heterogeneity among network layers and do quantify the structure of networks either separately for each layer or its aggregated network and ignore the attributes of nodes. In this work, we define a novel quality measure Multi-Normality which utilizes the structure and attributes together of each layer and detect attribute coherence in neighborhoods between layers. We also propose an algorithm exhausting multi-normality to identify anomalous neighborhoods in multiplex networks and is named as Anomaly Detection of Entity Neighborhoods in Multiplex Networks (ADENMN). We evaluate the effectiveness of the proposed algorithm in anomaly detection by comparing its performance with three existing baseline approaches including ADOMS, AMM and AGG+AD on five real-world attributed multiplex networks including Amazon, YouTube, Noordin top terrorist network, DBLP_C, and Aarhus. The results of experiments demonstrate that multi-normality outperforms baseline algorithms.
Posterior Analysis for Normalized Random Measures with Independent Increments

Abstract. One of the main research areas in Bayesian Nonparametrics is the proposal and study of priors which generalize the Dirichlet process. In this paper, we provide a comprehensive Bayesian non‐parametric analysis of random probabilities which are obtained by normalizing random measures with independent increments (NRMI). Special cases of these priors have already shown to be useful for statistical applications such as mixture models and species sampling problems. However, in order to fully exploit these priors, the derivation of the posterior distribution of NRMIs is crucial: here we achieve this goal and, indeed, provide explicit and tractable expressions suitable for practical implementation. The posterior distribution of an NRMI turns out to be a mixture with respect to the distribution of a specific latent variable. The analysis is completed by the derivation of the corresponding predictive distributions and by a thorough investigation of the marginal structure. These results allow to derive a generalized Blackwell–MacQueen sampling scheme, which is then adapted to cover also mixture models driven by general NRMIs.
Semi-supervised Grasp Detection by Representation Learning in a Vector Quantized Latent Space

For a robot to perform complex manipulation tasks, it is necessary for it to have a good grasping ability. However, vision based robotic grasp detection is hindered by the unavailability of sufficient labelled data. Furthermore, the application of semi-supervised learning techniques to grasp detection is under-explored. In this paper, a semi-supervised learning based grasp detection approach has been presented, which models a discrete latent space using a Vector Quantized Variational AutoEncoder (VQ-VAE). To the best of our knowledge, this is the first time a Variational AutoEncoder (VAE) has been applied in the domain of robotic grasp detection. The VAE helps the model in generalizing beyond the Cornell Grasping Dataset (CGD) despite having a limited amount of labelled data by also utilizing the unlabelled data. This claim has been validated by testing the model on images, which are not available in the CGD. Along with this, we augment the Generative Grasping Convolutional Neural Network (GGCNN) architecture with the decoder structure used in the VQ-VAE model with the intuition that it should help to regress in the vector-quantized latent space. Subsequently, the model performs significantly better than the existing approaches which do not make use of unlabelled images to improve the grasp.
Epileptic Seizure Detection and Classification using Time-Frequency Features in EEG Signals

The use of EEG signal to diagnose several brain abnormalities is well-established in the literature. Particularly, epileptic seizure can be detected using EEG signals and several works were done in this field. The joint time-frequency domain features proved to be an improvement in classification results. This can be attributed to the non-stationarity inherent feature of the EEG signals. Therefore, shifting to the joint time-frequency domain benefits in utilizing the variations in time with respect to frequency and vice versa to obtain better outcomes. The Bayesian classifier is utilized to obtain the ranking of the best features. The information gain criterion was built to test the system and obtain the results.
Cognitive impairment profile differences in patients with psychogenic non-epileptic seizures and epilepsy patients with generalized seizures

ABSTRACT Objective: This study aimed to compare cognitive skills in epilepsy patients and psychogenic non-epileptic seizures (PNES) patients who have no history of frequent seizures to those of a healthy control group. Methods: This prospective cross-sectional study was conducted between January 2017 and June 2019 and included 103 epilepsy patients, 100 PNES patients, and 101 healthy controls, aged 18 to 60 years. Patients with fewer than 12 annual seizure episodes and no history of seizures during the last 30 days were added to the study. A sociodemographic data form, the Beck Anxiety and Depression Scales, Montreal Cognitive Assessment, Digit Span test, and Stroop Color-Word Interference Test were used. Results: Compared to the control group, sub-component scores were significantly lower in naming, attention, abstract thinking, and delayed recall for the epilepsy and PNES groups. Sub-component scores in orientation, executive functioning, and delayed recall were significantly lower in the epilepsy group compared to the PNES group. Digits Backward subtest scores and total Digit Span test scores were significantly lower in the epilepsy and PNES groups. Patients in the epilepsy and PNES groups took longer to complete the Stroop test and made more errors and corrections compared to the healthy controls. There was a negative correlation between the duration of the disease and the total Digit Span score in the epilepsy group. Conclusion: These findings suggest that the epilepsy group showed reduced cognitive scores even though they did not suffer from frequent seizures and were not drug-resistant.
Radiological predictors of hemorrhagic transformation after acute ischemic stroke: An evidence-based analysis

Hemorrhagic transformation (HT) is one of the most common adverse events related to acute ischemic stroke (AIS) that affects the treatment plan and clinical outcome. Identification of a sensitive radiological marker may influence the controversial thrombolytic decision in the setting of AIS and may at a minimum indicate more intensive monitoring or further prophylactic interventions. In this article we summarize possible radiological biomarkers and the role of different radiological modalities including computed tomography (CT), magnetic resonance imaging, angiography, and ultrasound in predicting HT. Different radiological indices of early ischemic changes, large ischemic lesion volume, severe blood flow restriction, blood-brain barrier disruption, poor collaterals and high blood flow velocities have been reported to be associated with higher risk of HT. The current levels of evidence of the available studies highlight the role of the different CT perfusion parameters in predicting HT. Further large standardized studies are recommended to compare the sensitivity and specificity of the different radiological markers combined and delineate the most reliable predictor.
Gene therapy delivering a paraoxonase 1 variant offers long-term prophylactic protection against nerve agents in mice

Paraoxonase 1 variant IF-11 offers long-term prophylactic protection against chemical warfare nerve agents in mice. Long-lasting protection Nerve agents are the most toxic and lethal warfare chemical compounds. The toxic effect is due to inhibition of the acetylcholinesterase enzymes. Current antidotes prevent death but are ineffective against complications including brain damage and behavioral impairments. To prevent these deleterious consequences, now, Betapudi et al. developed a prophylactic gene therapy delivering the gene coding for a variant of the nerve agent scavenger paraoxonase 1 (PON1). When injected systemically in mice, the treatment exerted long-lasting protection against different types of nerve agents without signs of toxicity. The results suggest that gene therapy might be a viable option as prophylactic treatment in subjects at risk of exposure to nerve agents such as military personnel in war zones. Chemical warfare nerve agents are organophosphorus chemical compounds that induce cholinergic crisis, leaving little or no time for medical intervention to prevent death. The current chemical treatment regimen may prevent death but does not prevent postexposure complications such as brain damage and permanent behavioral abnormalities. In the present study, we have demonstrated an adeno-associated virus 8 (AAV8)–mediated paraoxonase 1 variant IF-11 (PON1-IF11) gene therapy that offers asymptomatic prophylactic protection to mice against multiple lethal doses of G-type chemical warfare nerve agents, namely, tabun, sarin, cyclosarin, and soman, for up to 5 months in mice. A single injection of liver-specific adeno-associated viral particles loaded with PON1-IF11 gene resulted in expression and secretion of recombinant PON1-IF11 in milligram quantities, which has the catalytic power to break down G-type chemical warfare nerve agents into biologically inactive products in vitro and in vivo in rodents. Mice containing milligram concentrations of recombinant PON1-IF11 in their blood displayed no clinical signs of toxicity, as judged by their hematological parameters and serum chemistry profiles. Our study unfolds avenues to develop a one-time application of gene therapy to express a near-natural and circulating therapeutic PON1-IF11 protein that can potentially protect humans against G-type chemical warfare nerve agents for several weeks to months.
Pain in Brazilian people experiencing homelessness

Abstract Background: Previous studies conducted in developed countries have shown that homeless people experience a high rate of pain. In this population, pain is not only underdiagnosed but is also undertreated. In Brazil, the pain of people experiencing homelessness is unknown and this is the first study on this subject in the country. Objective: To assess and characterize the prevalence of pain in homeless people living on the streets in the center of São Paulo, Brazil, and to understand its implications to general activities according to the Brief Pain Inventory (BPI) questionnaire. Method: This is a descriptive-exploratory cross-sectional study with a quantitative approach conducted with 69 homeless people from the central region of São Paulo, Brazil. The data were captured as protected health information and analyzed with a sociodemographic form, the BPI questionnaire, the McGill Pain Questionnaire (MPQ), and the Wong–Baker Face Scale. Results: The sample consisted mainly of men who spend the night on the streets, of black and miscegenated Brazilian people, with a mean age of 51 years. This population lived an average of 12 years on the streets, presented low levels of education, and used psychoactive substances. We observed a prevalence of severe musculoskeletal pain in the spine, with intermittent activity and duration of several days. The most common self-reported worsening factor was body movement and the most common factor of improvement was use of medication, but only 15% reported pain relief. Pain greatly interfered with all BPI's Activities of Daily Living, in particular with sleep (87.2%). The MPQ was difficult for the subjects to understand. There was no evidence of an association between pain and sex. Our results suggest that the longer the time living on the street, the less pain symptoms are acknowledged by the subjects, due to the accommodation phenomenon.
Mitotic Spindle Attachment to the Holocentric Chromosomes of Cuscuta europaea Does Not Correlate With the Distribution of CENH3 Chromatin

The centromere is the region on a chromosome where the kinetochore assembles and spindle microtubules attach during mitosis and meiosis. In the vast majority of eukaryotes, the centromere position is determined epigenetically by the presence of the centromere-specific histone H3 variant CENH3. In species with monocentric chromosomes, CENH3 is confined to a single chromosomal region corresponding to the primary constriction on metaphase chromosomes. By contrast, in holocentrics, CENH3 (and thus centromere activity) is distributed along the entire chromosome length. Here, we report a unique pattern of CENH3 distribution in the holocentric plant Cuscuta europaea. This species expressed two major variants of CENH3, both of which were deposited into one to three discrete regions per chromosome, whereas the rest of the chromatin appeared to be devoid of CENH3. The two CENH3 variants fully co-localized, and their immunodetection signals overlapped with the positions of DAPI-positive heterochromatic bands containing the highly amplified satellite repeat CUS-TR24. This CENH3 distribution pattern contrasted with the distribution of the mitotic spindle microtubules, which attached at uniform density along the entire chromosome length. This distribution of spindle attachment sites proves the holocentric nature of C. europaea chromosomes and also suggests that, in this species, CENH3 either lost its function or acts in parallel to an additional CENH3-free mechanism of kinetochore positioning.