Miracle-dz/UniFAR-FTUs huggingface dataset:
Acquiring Clinical Knowledge from an Online Video Platform: A Randomized Controlled Experiment on the Relevance of Integrating Anatomical Information and Clinical Practice

Basic subjects in medical education, such as anatomy, are often taught through teaching formats that do not always sufficiently demonstrate the relevance of this basic information for clinical practice. Accordingly, it is a recent trend in anatomy education to link anatomical information more explicitly to clinical practice. This article presents an online video platform (Tuebingen’s Sectio Chirurgica [TSC]) as one means of explicitly integrating preclinical anatomical knowledge and clinical application. The purpose of the study presented here was to examine the effects of videos through which medical students were educated about Anterior Cruciate Ligament reconstruction. A TSC video about this surgical procedure was compared to a video with a traditional lecture providing the identical information. Participants (n = 114) perceived the TSC video to be superior in comprehensibility of the presentation (P = 0.003) and conceivability of the surgical procedure (P = 0.027), and to be more entertaining (P < 0.001). Moreover, participants in the TSC condition acquired more clinical knowledge than in the lecture condition (P = 0.043) but did not differ in their acquisition of anatomical knowledge. Mediation analyses indicated that the effect on the acquisition of clinical knowledge was mediated by comprehensibility, conceivability, and entertainment. These findings are discussed regarding their implications for medical education in terms of contributing to the general trend of linking preclinical anatomical knowledge to clinical application. A discussion about the limitations of the study and suggestions for future research are also provided.
Training Dynamic Exponential Family Models with Causal and Lateral Dependencies for Generalized Neuromorphic Computing

Neuromorphic hardware platforms, such as Intel’s Loihi chip, support the implementation of Spiking Neural Networks (SNNs) as an energy-efficient alternative to Artificial Neural Networks (ANNs). SNNs are networks of neurons with internal analogue dynamics that communicate by means of binary time series. In this work, a probabilistic model is introduced for a generalized set-up in which the synaptic time series can take values in an arbitrary alphabet and are characterized by both causal and instantaneous statistical dependencies. The model, which can be considered as an extension of exponential family harmoniums to time series, is introduced by means of a hybrid directed-undirected graphical representation. Furthermore, distributed learning rules are derived for Maximum Likelihood and Bayesian criteria under the assumption of fully observed time series in the training set.
Optimizing Personalized Interaction Experience in Crowd-Interactive Livecast: A Cloud-Edge Approach

Enabling users to interact with broadcasters and audience, the crowd-interactive livecast greatly improves viewer's quality of experience (QoE) and attracts millions of daily active users recently. In addition to striking the balance between resource utilization and viewers' QoE met in the traditional video streaming service, this novel service needs to take supererogatory efforts to improve the interaction QoE, which reflects the viewer interaction experience. To tackle this issue, we conduct measurement studies over a large-scale dataset crawled from a representative livecast service provider. We observe that the individual's interaction pattern is quite heterogeneous: only 10% viewers proactively participate in the interaction, and the rest viewers usually watch passively. Incorporating the insight into the emerging cloud-edge architecture, we propose a framework PIECE, which optimizes the Personalized Interaction Experience with Cloud-Edge architecture (PIECE) for intelligent user access control and livecast distribution. In particular, we first devise a novel deep neural network based algorithm to predict users' interaction intensity using the historical viewer pattern. We then design an algorithm to maximize the individual's QoE, by strategically matching viewer sessions and transcoding-delivery paths over cloud-edge infrastructure. Finally, we use trace-driven experiments to verify the effectiveness of PIECE. Our results show that our prediction algorithm outperforms the state-of-the-art algorithms with a much smaller mean absolute error (40% reduction). Furthermore, in comparison with the cloud-based video delivery strategy, the proposed framework can simultaneously improve the average viewers QoE (26% improvement) and interaction QoE (21% improvement), while maintaining a high streaming bitrate.
Studying Topical Relevance with Evidence-based Crowdsourcing

Information Retrieval systems rely on large test collections to measure their effectiveness in retrieving relevant documents. While the demand is high, the task of creating such test collections is laborious due to the large amounts of data that need to be annotated, and due to the intrinsic subjectivity of the task itself. In this paper we study the topical relevance from a user perspective by addressing the problems of subjectivity and ambiguity. We compare our approach and results with the established TREC annotation guidelines and results. The comparison is based on a series of crowdsourcing pilots experimenting with variables, such as relevance scale, document granularity, annotation template and the number of workers. Our results show correlation between relevance assessment accuracy and smaller document granularity, i.e., aggregation of relevance on paragraph level results in a better relevance accuracy, compared to assessment done at the level of the full document. As expected, our results also show that collecting binary relevance judgments results in a higher accuracy compared to the ternary scale used in the TREC annotation guidelines. Finally, the crowdsourced annotation tasks provided a more accurate document relevance ranking than a single assessor relevance label. This work resulted is a reliable test collection around the TREC Common Core track.
Three weeks of respiratory muscle endurance training improve the O2 cost of walking and exercise tolerance in obese adolescents

Obese adolescents (OB) have an increased O2 cost of exercise, attributable in part to an increased O2 cost of breathing. In a previous work a short (3‐week) program of respiratory muscle endurance training (RMET) slightly reduced in OB the O2 cost of high‐intensity cycling and improved exercise tolerance. We hypothesized that during treadmill walking the effects of RMET would be more pronounced than those observed during cycling. Sixteen OB (age 16.0 ± 0.8 years; body mass [BM] 127.7 ± 14.2 kg; body mass index 40.7 ± 4.0 kg/m2) underwent to 3‐week RMET (n = 8) superimposed to a multidisciplinary BM reduction program, or (CTRL, n = 8) only to the latter. Heart rate (HR) and pulmonary O2 uptake ( V˙ O2) were measured during incremental exercise and 12‐min constant work rate (CWR) walking at 60% (moderate‐intensity, MOD) and 120% (heavy‐intensity, HEAVY) of the gas exchange threshold (GET). The O2 cost of walking (aerobic energy expenditure per unit of covered distance) was calculated as V˙ O2/velocity. BM decreased (~4–5 kg) both in CTRL and in RMET. V˙ O2peak and GET were not affected by both interventions; the time to exhaustion increased following RMET. During MOD and HEAVY RMET decreased V˙ O2, the O2 cost of walking (MOD: 0.130 ± 0.033 mL/kg/m [before] vs. 0.109 ± 0.027 [after], P = 0.03; HEAVY: 0.196 ± 0.031 [before] vs. 0.180 ± 0.025 [after], P = 0.02), HR and rates of perceived exertion; no significant changes were observed in CTRL. In OB a short RMET program lowered the O2 cost of MOD and HEAVY walking and improved exercise tolerance. RMET could represent a useful adjunct in the control of obesity.
The MAP3K-Coding QUI-GON JINN (QGJ) Gene Is Essential to the Formation of Unreduced Embryo Sacs in Paspalum

Apomixis is a clonal mode of reproduction via seeds, which results from the failure of meiosis and fertilization in the sexual female reproductive pathway. In previous transcriptomic surveys, we identified a mitogen-activated protein kinase kinase kinase (N46) displaying differential representation in florets of sexual and apomictic Paspalum notatum genotypes. Here, we retrieved and characterized the N46 full cDNA sequence from sexual and apomictic floral transcriptomes. Phylogenetic analyses showed that N46 was a member of the YODA family, which was re-named QUI-GON JINN (QGJ). Differential expression in florets of sexual and apomictic plants was confirmed by qPCR. In situ hybridization experiments revealed expression in the nucellus of aposporous plants’ ovules, which was absent in sexual plants. RNAi inhibition of QGJ expression in two apomictic genotypes resulted in significantly reduced rates of aposporous embryo sac formation, with respect to the level detected in wild type aposporous plants and transformation controls. The QGJ locus segregated independently of apospory. However, a probe derived from a related long non-coding RNA sequence (PN_LNC_QGJ) revealed RFLP bands cosegregating with the Paspalum apospory-controlling region (ACR). PN_LNC_QGJ is expressed in florets of apomictic plants only. Our results indicate that the activity of QGJ in the nucellus of apomictic plants is necessary to form non-reduced embryo sacs and that a long non-coding sequence with regulatory potential is similar to sequences located within the ACR.
SlideWiki - Towards a Collaborative and Accessible Platform for Slide Presentations

OpenCourseWare platforms for educational resources have the potential to open new horizons for knowledge sharing and e-learning by reaching learners beyond the constraints of traditional learning systems. SlideWiki is a crowd-sourcing platform that aims to rethink the creation and sharing of knowledge by providing an environment where authors can collaborate, reuse, adapt and share slide contents for educational purposes. As an OpenCourseWare platform, SlideWiki intends to make Open Educational Resources more accessible for all users, including those with disabilities, within formal and informal learning settings. Moreover, the platform offers collaborative tools that enable authors and contributors to translate the slide content. To address the implementation, scalability, usability, and adoption of the platform, it has been designed and deployed in many different learning settings with large-scale trials across Europe. At the time of writing, 56 trials have taken place in different geographical and cultural regions, organizational units, and institutions, covering various teaching and learning scenarios. The experiences and feedback from the trials have influenced the redesign of SlideWiki in terms of accessibility and openness. This paper discusses the findings of the large-scale trials and how they influenced the technical redesign of the platform. It also shows how incorporating user feedback into the technical development process can improve accessibility and collaboration.
Smart Handbag for Remembering Keys

In this paper, we present a design and prototype of a smart handbag, which detects the presence of the user's keys in the bag and shows it with an ambient display, consisting of LEDs embedded to the design. The handbag prototype is an example of a smart accessory, which has been designed for both aesthetics and usefulness. The handbag was evaluated in an in-the-wild user study, where five participants used it for a period of one day each. The salient findings from the user study emphasize the perceived usefulness of the concept, successful adaptation of the prototype in everyday life, and aesthetic design. The sensing mechanics and implementation need careful design to achieve a reliable detection accuracy. In addition, the in-the-wild study provoked concerns related to the potential mismatch between the user's personal style or outfit and the handbag's appearance.
Sparse DNNs with Improved Adversarial Robustness

Deep neural networks (DNNs) are computationally/memory-intensive and vulnerable to adversarial attacks, making them prohibitive in some real-world applications. By converting dense models into sparse ones, pruning appears to be a promising solution to reducing the computation/memory cost. This paper studies classification models, especially DNN-based ones, to demonstrate that there exists intrinsic relationships between their sparsity and adversarial robustness. Our analyses reveal, both theoretically and empirically, that nonlinear DNN-based classifiers behave differently under $l_2$ attacks from some linear ones. We further demonstrate that an appropriately higher model sparsity implies better robustness of nonlinear DNNs, whereas over-sparsified models can be more difficult to resist adversarial examples.
Frailty is strongly associated with increased risk of recurrent falls among older HIV-infected adults.

OBJECTIVE Both frailty and falls occur at earlier-than-expected ages among HIV-infected individuals, but the contribution of frailty-to-fall risk in this population is not well understood. We examined this association among participants enrolled in AIDS Clinical Trials Group (ACTG) A5322. DESIGN A prospective, multicenter cohort study of HIV-infected men and women aged at least 40 years. METHODS Frailty assessment included a 4-m walk, grip strength, and self-reported weight loss, exhaustion, and low physical activity. Multinomial logistic regression assessed the association between baseline frailty, grip, and 4-m walk, and single and recurrent (2+) falls over the next 12 months; logistic regression assessed effect modification by several factors on association between frailty and any (1+) falls. RESULTS Of 967 individuals, 6% were frail, 39% prefrail, and 55% nonfrail. Eighteen percent had at least one fall, and 7% had recurrent falls. In multivariable models, recurrent falls were more likely among frail (odds ratio 17.3, 95% confidence interval 7.03-42.6) and prefrail (odds ratio 3.80, 95% CI 1.87-7.72) than nonfrail individuals. Significant associations were also seen with recurrent falls and slow walk and weak grip. The association between frailty and any falls was substantially stronger among individuals with peripheral neuropathy. CONCLUSION Aging HIV-infected prefrail and frail individuals are at significantly increased risk of falls. Incorporation of frailty assessments or simple evaluations of walk speed or grip strength in clinical care may help identify individuals at greatest risk for falls. Peripheral neuropathy further increases fall risk among frail persons, defining a potential target population for closer fall surveillance, prevention, and treatment.
Taiwan IT Engineers' Perceptions toward Organizational Justice and Individual Work Performance

The purpose of this study was to investigate IT engineers' perceptions toward organizational justice and individual work performance. The study conducted a quantitative study, and the participants were IT engineers. Random sampling was used. The questionnaires were conducted online. The cover of the questionnaire explained the purposes of the study. 161 copies of questionnaires were sent and viewed, and 84 copies were returned. One participant indicated that she was in charge of the financial consultant, and therefore was excluded from analysis, resulting in a final sample of 83 engineers. All respondents completed the questionnaire on their personal computers, and returned their responses via the online survey system. The response rate was 51.5%. The data was analyzed by descriptive statistics, One-way ANOVA and Pearson's Correlation Coefficient in IBM SPSS 22. The findings of the study show that (1) position grade and profession have a significant influence on individual work performance, (2) distributive justice is positively associated with individual work performance, (3) procedural justice is positively associated with individual work performance, and (4) informational justice is positively associated with individual work performance.
Multi-Path Convolutional Neural Network for Distant Supervised Relation Extraction

Distant1 supervised relation extraction is a commonly adopted technique to extract relations from unstructured text. In distant supervision, an existing database is heuristically aligned to text and the alignment results are processed as labeled data. Comparing to hand-labeled data, distant supervision data is available in large quantity, but contains much more errors. In the past few years, Convolutional Neural Networks (CNNs) are applied to distant relation extraction and achieved remarkable results. However, standard CNN architectures do not reflect the multi-granularity nature of linguistic data, and have difficulties capturing long-term dependencies. We propose an architecture named Multi-Path Convolutional Network to address this problem. Inspired by the Inception module used in image recognition systems, we apply multiple convolution layers with different kernel size in parallel and combine the output with another deeper CNN sub-network. In addition, we utilize two levels of attention to discern valuable information from irrelevant context: a word-level attention to capture important parts of a sentence, and a sentence-level attention to improve classification from multiple sentences. By experiments, we show that our approach achieves outstanding results on the NYT-Freebase dataset and outperforms several existing approaches.
Dopaminergic genes are associated with both directed and random exploration

In order to maximize long-term rewards, agents must balance exploitation (choosing the option with the highest payoff) and exploration (gathering information about options that might have higher payoffs). Although the optimal solution to this trade-off is intractable, humans make use of two effective strategies: selectively exploring options with high uncertainty (directed exploration), and increasing the randomness of their choices when they are more uncertain (random exploration). Using a task that independently manipulates these two forms of exploration, we show that single nucleotide polymorphisms related to dopamine are associated with individual differences in exploration strategies. Variation in a gene linked to prefrontal dopamine (COMT) predicted the degree of directed exploration, as well as the overall randomness of responding. Variation in a gene linked to striatal dopamine (DARPP-32) predicted the degree of both directed and random exploration. These findings suggest that dopamine makes multiple contributions to exploration, depending on its afferent target.
A New Highly Thyrotropin Receptor-Selective Small-Molecule Antagonist with Potential for the Treatment of Graves' Orbitopathy.

BACKGROUND The thyrotropin receptor (TSHR) is the target for autoimmune thyroid stimulating antibodies (TSAb) triggering hyperthyroidism. Whereas elevated thyroid hormone synthesis by the thyroid in Graves' disease can be treated by antithyroid agents, for the pathogenic activation of TSHR in retro-orbital fibroblasts of the eye, leading to Graves' orbitopathy (GO), no causal TSHR directed therapy is available. METHODS Due to the therapeutic gap for severe GO, TSHR inhibitors were identified by high-throughput screening in Chinese hamster ovary cells expressing the TSHR. Stereo-selective synthesis of the screening hits led to the molecule S37, which contains seven chiral centers. Enantiomeric separation of the molecule S37 resulted in the enantiopure molecule S37a-a micro-molar antagonist of thyrotropin-induced cyclic adenosine monophosphate accumulation in HEK 293 cells expressing the TSHR. RESULTS The unique rigid bent shape of molecule S37a may mediate the observed high TSHR selectivity. Most importantly, the closely related follitropin and lutropin receptors were not affected by this compound. S37a not only inhibits the TSHR activation by thyrotropin itself but also activation by monoclonal TSAb M22 (human), KSAb1 (murine), and the allosteric small-molecule agonist C2. Disease-related ex vivo studies in HEK 293 cells expressing the TSHR showed that S37a also inhibits cyclic adenosine monophosphate formation by oligoclonal TSAb, which are highly enriched in GO patients' sera. Initial in vivo pharmacokinetic studies revealed no toxicity of S37a and a remarkable 53% oral bioavailability in mice. CONCLUSION In summary, a novel highly selective inhibitor for the TSHR is presented, which has promising potential for further development for the treatment of GO.
Construction and Application of Upper Country Ontology Based on OWL and SKOS

The concept1 of country has been widely used in all kinds of databases or systems. A commonly understood and unified schema for country entity is needed to knowledge organization, semantic association and deeply integration of the multi-source and heterogeneous data resources about country concepts. This paper proposes a computer understandable and computable Upper Country Ontology based on OWL and SKOS, with instances of 195 countries, 7 continents, 38 regions and more than 10 international organizations. The proposed ontology is expected to be used as knowledge middleware, supports identifying and indexing country entities, publishing open linked dataset, referencing country instance, SPARQL endpoint query and semantic reasoning and so on.
Exploring Diversified Similarity with Kundaha

Exploring large medical image sets by means of traditional similarity query criteria (e.g., neighborhood) can be fruitless if retrieved images are too similar among themselves. This demonstration introduces Kundaha, an exploration tool that assists experts in retrieving and navigating on results from a diversified similarity perspective of user-posed queries. Its implementation includes a wide set of metrics, descriptors, and indexes for enhancing query execution. Users can combine such features with diversified similarity criteria for the organized exploration of result sets and also employ relevance feedback cycles for finding new query-based viewpoints.
Quantified Factory Worker - Expert Evaluation and Ethical Considerations of Wearable Self-tracking Devices

Following the Quantified Self trend, everyday self-tracking practices have become common. Still, self-monitoring of people at work is a rather new research topic. Self-tracking of employees' activities, mental state and emotions enables data-based feedback, which could improve the employees' awareness of issues influencing their well-being and performance. We contribute to this topic from two perspectives. First, we explored the potential of wearable self-tracking devices for providing personal feedback to machine operators working in a factory. We used the expert evaluation method to lay ground to the user perspective of self-tracking at work. User experience experts evaluated five tracking devices for their user experience, perceived accuracy and fit to factory workers. Second, we conducted a workshop with the experts to systematically assess the ethical considerations that may arise when adopting self-tracking at work. The results provide insights into the potential of the use of self-tracking devices in a factory context.