Miracle-dz/UniFAR-FTUs huggingface dataset:
SCRIBER: accurate and partner type-specific prediction of protein-binding residues from proteins sequences

Abstract Motivation Accurate predictions of protein-binding residues (PBRs) enhances understanding of molecular-level rules governing protein–protein interactions, helps protein–protein docking and facilitates annotation of protein functions. Recent studies show that current sequence-based predictors of PBRs severely cross-predict residues that interact with other types of protein partners (e.g. RNA and DNA) as PBRs. Moreover, these methods are relatively slow, prohibiting genome-scale use. Results We propose a novel, accurate and fast sequence-based predictor of PBRs that minimizes the cross-predictions. Our SCRIBER (SeleCtive pRoteIn-Binding rEsidue pRedictor) method takes advantage of three innovations: comprehensive dataset that covers multiple types of binding residues, novel types of inputs that are relevant to the prediction of PBRs, and an architecture that is tailored to reduce the cross-predictions. The dataset includes complete protein chains and offers improved coverage of binding annotations that are transferred from multiple protein–protein complexes. We utilize innovative two-layer architecture where the first layer generates a prediction of protein-binding, RNA-binding, DNA-binding and small ligand-binding residues. The second layer re-predicts PBRs by reducing overlap between PBRs and the other types of binding residues produced in the first layer. Empirical tests on an independent test dataset reveal that SCRIBER significantly outperforms current predictors and that all three innovations contribute to its high predictive performance. SCRIBER reduces cross-predictions by between 41% and 69% and our conservative estimates show that it is at least 3 times faster. We provide putative PBRs produced by SCRIBER for the entire human proteome and use these results to hypothesize that about 14% of currently known human protein domains bind proteins. Availability and implementation SCRIBER webserver is available at http://biomine.cs. vcu.edu/servers/SCRIBER/. Supplementary information Supplementary data are available at Bioinformatics online.
Cross-Sectional and Cumulative Longitudinal Central Nervous System Penetration Effectiveness Scores Are Not Associated With Neurocognitive Impairment in a Well Treated Aging Human Immunodeficiency Virus-Positive Population in Switzerland

Abstract Background Neurocognitive impairment (NCI) in people with human immunodeficiency virus (PWH) remains a concern despite potent antiretroviral therapy (ART). Higher central nervous system (CNS) penetration effectiveness (CPE) scores have been associated with better CNS human immunodeficiency virus (HIV) replication control, but the association between CPE and NCI remains controversial. Methods The Neurocognitive Assessment in the Metabolic and Aging Cohort (NAMACO) study is a subgroup of the Swiss HIV Cohort Study (SHCS) that invited patients aged ≥45 years enrolled in the SHCS and followed-up at NAMACO-affiliated centers in Switzerland to participate between May 2013 and November 2016. In total, 981 patients were enrolled, all of whom underwent standardized neurocognitive assessment. Neurocognitive impairment, if present, was characterized using Frascati criteria. The CPE scores of NAMACO study participants with undetectable plasma HIV-ribonucleic acid at enrollment (909 patients) were analyzed. Cross-sectional CPE scores (at neurocognitive assessment) were examined as potential predictors of NCI in multivariate logistic regression models. The analysis was then repeated taking CPE as a cumulative score (summarizing CPE scores from ART initiation to the time of neurocognitive assessment). Results Most patients were male (80%) and Caucasian (92%). Neurocognitive impairment was present in 40%: 27% with HIV-associated NCI (mostly asymptomatic neurocognitive impairment), and 13% with NCI related to other factors. None of the CPE scores, neither cross-sectional nor cumulative, was statistically significantly associated with NCI. Conclusions In this large cohort of aviremic PWH, we observed no association between NCI, whether HIV-associated or related to other factors, and CPE score, whether cross-sectional or cumulative.
Comparisons of the efficacy and recurrence of adenomyomectomy for severe uterine diffuse adenomyosis via laparotomy versus laparoscopy: a long-term result in a single institution

Purpose Studies have shown that adenomyomectomy can effectively treat women with adenomyosis in a short period of time. However, the long-term efficacy of adenomyomectomy has rarely been reported. The objective of this study was to determine whether laparotomy is superior to laparoscopic surgery in the long-term efficacy of double-flap method adenomyomectomy for severe diffuse adenomyosis. Methods Between March 2011 and September 2018, a total of 148 patients with severe uterine diffuse adenomyosis who underwent laparoscopic (group A, n=72) and laparotomic (group B, n=76) double-flap adenomyomectomy were recruited. Adenomyomectomy efficacy and adenomyosis recurrence after surgery between groups A and B were comparatively analyzed. Results The effective rate at 6-year follow up after surgery was higher in group B (75.0%) than that in group A (62.1%), while the 6-year cumulative recurrence rate was higher in group A (27.8%) than that in group B (17.1%), but the differences did not reach statistical significance between the two groups (P>0.05). The recurrence rate was lower in patients who were treated with gonadotropin-releasing hormone agonist (GnRHa) plus Mirena or oral contraceptives post-surgically than that in patients who were treated with only GnRHa post-surgically in groups A (51.6% vs 9.8%, P<0.01) and B (33.3% vs 6.5%, P<0.05). Moreover, the recurrence rate of adenomyosis patients with endometriosis was higher than that of adenomyosis patients without endometriosis in group A (55.0% vs 17.3%, P<0.05) and group B (36.0% vs 7.8%, P<0.05). Conclusion The long-term outcomes of laparoscopic and laparotomic double-flap adenomyomectomy can be achieved for severe diffuse uterine adenomyosis, but laparotomy seems to have advantages over laparoscopy. Postoperative drug use may be beneficial to reduce the recurrence of adenomyosis, especially for adenomyosis with endometriosis.
Rotation equivariant and invariant neural networks for microscopy image analysis

Abstract Motivation Neural networks have been widely used to analyze high-throughput microscopy images. However, the performance of neural networks can be significantly improved by encoding known invariance for particular tasks. Highly relevant to the goal of automated cell phenotyping from microscopy image data is rotation invariance. Here we consider the application of two schemes for encoding rotation equivariance and invariance in a convolutional neural network, namely, the group-equivariant CNN (G-CNN), and a new architecture with simple, efficient conic convolution, for classifying microscopy images. We additionally integrate the 2D-discrete-Fourier transform (2D-DFT) as an effective means for encoding global rotational invariance. We call our new method the Conic Convolution and DFT Network (CFNet). Results We evaluated the efficacy of CFNet and G-CNN as compared to a standard CNN for several different image classification tasks, including simulated and real microscopy images of subcellular protein localization, and demonstrated improved performance. We believe CFNet has the potential to improve many high-throughput microscopy image analysis applications. Availability and implementation Source code of CFNet is available at: https://github.com/bchidest/CFNet. Supplementary information Supplementary data are available at Bioinformatics online.
Tissue response to applied loading using different designs of penile compression clamps

Background: Penile compression devices (PCD) or clamps are applied to compress the urethra and prevent urinary incontinence (UI). PCDs are more secure and less likely to leak than pads, allowing men the opportunity to participate in short-term, vigorous activities. However, they are uncomfortable, can cause pressure ulcers (PU) and affect penile blood flow. No objective assessment of tissue health has been undertaken to assess and compare different PCD designs and to provide guidance on safe use. Objective: This study was designed to evaluate existing PCDs in terms of their physiological response and potential for pressure-induced injury. Design, setting and participants: Six men with post-prostatectomy UI tested four selected PCDs at effective pressures, in a random order, in a controlled laboratory setting. Outcome measurements and statistical analysis: Using objective methods for assessing skin injury, PCDs were measured in situ for their effects on circulatory impedance, interface pressures and inflammatory response. Results and limitations: There was evidence for PCD-induced circulatory impedance in most test conditions. Interface pressures varied considerably between both PCDs and participants, with a mean value of 137.4±69.7 mmHg. In some cases, penile skin was noted to be sensitive to loading with elevated concentration of the cytokine IL-1α after 10 mins wear, indicating an inflammatory response. IL-1α levels were restored to baseline 40 mins following PCD removal. Conclusion: Skin health measures indicated tissue and blood flow compromise during the 50 mins of testing using all PCDs. Although there was an elevation in pro-inflammatory cytokines, PCDs did not cause sustained irritation and skin health measures recovered 40 mins after PCD removal. This research indicates that application of a clamp for one hour with an equal clamp free time before reapplication is likely to be safe. Longer periods are often recommended by manufacturers but have yet to be tested.
Microvascular decompression and radiofrequency for the treatment of trigeminal neuralgia: a meta-analysis

Purpose Surgical treatments are used for trigeminal neuralgia (TN) when drug treatment fails. Surgical options can be divided into two categories: ablation (destructive) or non-ablation. Microvascular decompression (MVD) is primarily a non-ablation option, while radiofrequency thermocoagulation/rhizotomy (RF) is an ablation option. The aim of this study was to compare outcomes of MVD versus RF in the treatment of TN. Materials and methods This article evaluates the clinical results and economic effectiveness of trigeminal nerve RF and MVD for the treatment of TN. This review was conducted according to the methodological standards described in the Cochrane Handbook for Systematic Reviews of Interventions and the Preferred Reporting Items for Systematic Reviews and Meta-analysis statement. The PubMed, Embase and Cochrane libraries were searched in January 2018. We have registered our review at the Review Registry. Results Nine studies were included in this review. The sample size was 2163 participants. The results showed that compared with RF, MVD had a lower risk of requiring a secondary procedure. The MVD group also had a lower risk of facial numbness. There was no significant difference in postoperative medication use between the two groups. Compared to RF, MVD was more likely to increase the risk of hypacusis and hypesthesia and to decrease the risk of facial pain and dysesthesia. The total cost of MVD, including the operation, hospital stay and additional procedures, was much higher than that of RF. Conclusion MVD had a lower risk of requiring a secondary procedure and facial numbness after surgery. RF could be considered in patients who are unfit for MVD or refused invasive treatment.
DeepHunter: a coverage-guided fuzz testing framework for deep neural networks

The past decade has seen the great potential of applying deep neural network (DNN) based software to safety-critical scenarios, such as autonomous driving. Similar to traditional software, DNNs could exhibit incorrect behaviors, caused by hidden defects, leading to severe accidents and losses. In this paper, we propose DeepHunter, a coverage-guided fuzz testing framework for detecting potential defects of general-purpose DNNs. To this end, we first propose a metamorphic mutation strategy to generate new semantically preserved tests, and leverage multiple extensible coverage criteria as feedback to guide the test generation. We further propose a seed selection strategy that combines both diversity-based and recency-based seed selection. We implement and incorporate 5 existing testing criteria and 4 seed selection strategies in DeepHunter. Large-scale experiments demonstrate that (1) our metamorphic mutation strategy is useful to generate new valid tests with the same semantics as the original seed, by up to a 98% validity ratio; (2) the diversity-based seed selection generally weighs more than recency-based seed selection in boosting the coverage and in detecting defects; (3) DeepHunter outperforms the state of the arts by coverage as well as the quantity and diversity of defects identified; (4) guided by corner-region based criteria, DeepHunter is useful to capture defects during the DNN quantization for platform migration.
Academic Performance Estimation with Attention-based Graph Convolutional Networks

Student's academic performance prediction empowers educational technologies including academic trajectory and degree planning, course recommender systems, early warning and advising systems. Given a student's past data (such as grades in prior courses), the task of student's performance prediction is to predict a student's grades in future courses. Academic programs are structured in a way that prior courses lay the foundation for future courses. The knowledge required by courses is obtained by taking multiple prior courses, which exhibits complex relationships modeled by graph structures. Traditional methods for student's performance prediction usually neglect the underlying relationships between multiple courses; and how students acquire knowledge across them. In addition, traditional methods do not provide interpretation for predictions needed for decision making. In this work, we propose a novel attention-based graph convolutional networks model for student's performance prediction. We conduct extensive experiments on a real-world dataset obtained from a large public university. The experimental results show that our proposed model outperforms state-of-the-art approaches in terms of grade prediction. The proposed model also shows strong accuracy in identifying students who are at-risk of failing or dropping out so that timely intervention and feedback can be provided to the student.
Success prediction in MOOCs A case study

Success prediction in Massive Open Online Courses (MOOCs) is now tackled in numerous works, but still needs new case studies to compare the solutions proposed. We study here a speciﬁc dataset from a French MOOC provided by the OpenClassrooms company, featuring 12 courses. We exploit various features present in the literature and test several classiﬁcation models.
Predicting the Quality of Collaborative Problem Solving Through Linguistic Analysis of Discourse

Collaborative problem solving in computer-supported environments is of critical importance to the modern workforce. Coworkers or collaborators must be able to co-create and navigate a shared problem space using discourse and non-verbal cues. Analyzing this discourse can give insights into how consensus is reached and can estimate the depth of their understanding of the problem. This study uses Coh-Metrix, a natural language processing tool that measures cohesion, to analyze participant discourse from a recent multi-modal learning analytics study where novice programmers collaborated to use a block-based programming language to instruct a robot on how to solve a series of mazes. We significantly correlated thirty-five Coh-Metrix indices from the transcripts of dyads' discourse with collaboration, learning gains, and multimodal sensor values. We then fit a variety of machine learning classifiers to predict collaboration using the indices generated by Coh-Metrix as features. This study paves the way for real-time detection of (un)productive interactions from multimodal data and could lead to real-time interventions to support collaborative learning.
Light-weight Internet-scale Universal Storage

Many new applications, for example Wikis, social networks, and distributed recommender systems, require the ecient integration of decentralized and heterogenous data sources at a large scale. In this paper, we present our vision of a universal storage for RDF-like triple data based on a structured P2P system and the universal relation model as its key enabling technologies to achieve flexibility, robustness, and eciency for large-scale distributed data storage and query processing. We outline the steps we have already accomplished successfully and discuss a roadmap to achieve the final goal of a practical, light-weight implementation of our storage system.
Exploring the Link Between Motivations and Gaming

A student’s ability to regulate their thoughts, emotions and behaviors in the face of temptation is linked to their task specific motivational goals and dispositions. Behavioral tasks are designed to strain a targeted resource to differentiate individuals through measures of their performance. In this paper, we explore how student behavior on differentially gamed learning material relates to estimates of student motivational goals and dispositions. We leverage observations of students in two different courses using an intelligent tutoring system over an entire academic year. We use a previously validated heuristic model of gaming detection to label instances of gaming. Each student’s tendency to game is estimated separately using highly gamed and non-highly gamed sections of the course. Each estimate of student gaming is compared to precourse self-reported measures of student motivations. Results indicate that in naturalistic settings, gaming on more challenging materials is less influenced by student motivations and potentially a result of adaptive learning behaviors. Similarly, student gaming estimates using only non-highly gamed material are significantly related to all targeted motivation measures. Implications and future directions are discussed.
Implicit and Explicit Emotions in MOOCs

Understanding the affect expressed by learners is essential for enriching the learning experience in Massive Open Online Courses (MOOCs). However, online learning environments, especially MOOCs, pose several challenges in understanding the different types of affect experienced by a learner. In this paper, we define two categories of emotions, explicit emotions as those collected directly from the student through self-reported surveys, and implicit emotions as those inferred unobtrusively during the learning process. We also introduce positivity as a measure to study the valence reported by students chronologically, and use it to derive insights into their emotion patterns and their association with learning outcomes. We show that implicit and explicit emotions expressed by students within the context of a MOOC are independent of each other, however, they correlate better with students’ behavior compared to their valence.
A Meta-Learning Augmented Bidirectional Transformer Model for Automatic Short Answer Grading

We introduce ml-BERT, an effective machine learning method for automatic short answer grading when training data, i.e., graded answers, is limited. Our method combines BERT (Bidirectional Representation of the Transformer), the state-of-the-art model for learning textual data representations, with meta-learning, a training framework that leverages additional data and learning tasks to improve model performance when labeled data is limited. Our intuition is to use meta-learning to help us learn an initialization of the BERT parameters in a speciﬁc target subject domain using unlabeled data, thus fully leveraging the limited labeled training data for the grading task. Experiments on a real-world student answer dataset demonstrate the promise of ml-BERT method for automatic short answer grading.
Learning user interface element interactions

When generating tests for graphical user interfaces, one central problem is to identify how individual UI elements can be interacted with—clicking, long- or right-clicking, swiping, dragging, typing, or more. We present an approach based on reinforcement learning that automatically learns which interactions can be used for which elements, and uses this information to guide test generation. We model the problem as an instance of the multi-armed bandit problem (MAB problem) from probability theory, and show how its traditional solutions work on test generation, with and without relying on previous knowledge. The resulting guidance yields higher coverage. In our evaluation, our approach shows improvements in statement coverage between 18% (when not using any previous knowledge) and 20% (when reusing previously generated models).
Evaluation Criterion for Quality Assessment of E-Learning Content

Research trends related to e-learning systems are oriented towards increasing the efficiency and capacity of the systems, thus they reflect a large variance in performance when considering content conformity and quality standards. The Framework related to standardisation of digital content for e-learning systems is likely to play a significant role in the coming years. Specifically in formal e-learning systems, delivery of technically apt and qualified content is of utmost importance and must undergo scrutiny from experts in the related fields. In this research, a framework is proposed in the form of an instrument survey to evaluate the quality of digital content and its performance in an e-learning platform. Evaluation is made under seven primary criteria with multiple item questions to assess the performance of subject e-content in an e-learning environment. The survey is primarily designed to be incorporated to assess the quality of electronic education in higher education courses in Saudi Arabia. The evaluation framework is developed using an expert-oriented approach using reviews from a panel of 42 experts in the higher education field. This instrument survey presents a basic structure for evaluating and assessing potential of an e-learning digital course. This structure will facilitate standardisation and classification of e-content.
INFORMAL CAREGIVERS OF PEOPLE UNDERGOING HAEMODIALYSIS: ASSOCIATIONS BETWEEN ACTIVITIES AND BURDEN.

BACKGROUND Recent studies indicate that informal caregivers provide support to family members who are undergoing haemodialysis although the amount and type of activities provided and the burden of this is not well-known. OBJECTIVES To analyse the burden and support activities of informal caregivers caring for adults receiving haemodialysis. METHODS A cross-sectional study recruited 178 adult informal caregivers (family members or close friends) of patients who had been undergoing haemodialysis for more than three months. Caregiver burden was measured by the Zarit Caregiver Burden Scale (ZCBS). Difficulty and time-spent on caring tasks were assessed using the Oberst Caregiver Burden Scale (OCBS). Demographic characteristics were also collected. RESULTS Most informal caregivers were female (55.6%) between 30 and 60 years of age (66%) and were a spouse of the person receiving haemodialysis. Overall, informal caregivers reported a high level of burden in caring (M = 40.15, standard deviation [SD] = 10.46) with 80.9% identifying the level as severe. However, the caring activities were perceived as slightly difficult (M = 24.5, SD = 8.81) and not requiring much time (M = 29.47, SD = 8.75). Predictors of greater informal caregiver burden were having their own health problems, greater time required to provide care, and doing more difficult tasks (R2 = 0.43, p < 0.01). CONCLUSION While the caregiving activities were not reported to take much time, there was a high level of burden on informal caregivers. Renal nurses are ideally placed to assess for caregiver burden and to provide education and support to them.