Miracle-dz/UniFAR-FTUs huggingface dataset:
Knowledge graph of alpine skiing events: A focus on meteorological conditions

Alpine skiing, as an outdoor winter sport, is particularly vulnerable to the variation of meteorological conditions. Scattered and multi-source big data cannot be fully utilized to conduct effective decision analyses by conventional data analysis methods. Presently, knowledge graphs are the most advanced organization form of knowledge base, which can make explicit the complex relationships among different objects. Thus, introducing knowledge graph to the event management of alpine skiing is significant to improve the ability of risk prediction and decision-making. In this research, we analyze the components and dynamic characteristics of alpine skiing, and construct an “Object-Characteristic-Relation” representation model to express multi-level knowledge. Moreover, we propose a “Characteristic-value- Relationship” representation method based on the multi-source data, to construct the knowledge graph of alpine skiing. With the proposed method, comprehensive relationships between meteorological conditions and alpine skiing can be represented clearly, and support further knowledge reasoning for the event management under meteorological conditions. We have tested the utility of the proposed method in a case study of 2018 Winter Olympics in PyeongChang. The case study realizes an semi-automatic construction of knowledge graph for alpine skiing, provides decision supports for event risk managements, according to different meteorological conditions, and grounds a foundation for future knowledge graph construction of other large-scale sport events.
Investigation of cannabidiol’s potential targets in limbic seizures. In-silico approach

Abstract Even though the vast armamentarium of FDA-approved antiepileptic drugs is currently available, over one-third of patients do not respond to medication, which arises a need for alternative medicine. In clinical and preclinical studies, various investigations have shown the advantage of specific plant-based cannabidiol (CBD) products in treating certain groups of people with limbic epilepsy who have failed to respond to conventional therapies. This work aims to investigate possible mechanisms by which CBD possesses its anticonvulsant properties. Molecular targets for CBD’s treatment of limbic epilepsy, including hyperpolarization-activated cyclic nucleotide-gated channel 1 (HCN1), gamma-aminobutyric acid aminotransferase (GABA-AT), and gamma-aminobutyric acid type A receptor (GABAA), were used to evaluate its binding affinity. Interactions with the CB1 receptor were initially modeled as a benchmark, which further proved the efficiency of proposed here approach. Considering the successful benchmark, we further used the same concept for in silico investigation, targeting proteins of interest. As a result of molecular docking, molecular mechanics, and molecular dynamics simulations models of CBD-receptor complexes were proposed and evaluated. While CBD possessed decently high affinity and stability within the binding pockets of GABA-AT and some binding sites of GABAA, the most effective binding was observed in the CBD complex with HCN1 receptor. 100 ns molecular dynamics simulation revealed that CBD binds the open pore of HCN1 receptor, forming a similar pattern of interactions as potent Lamotrigine. Therefore, we can propose that HCN1 can serve as a most potent target for cannabinoid antiepileptic treatment. Communicated by Ramaswamy H. Sarma
A new cure rate regression framework for bivariate data based on the Chen distribution

The present study introduces a new multivariate mixture cure rate model based on the Chen probability distribution to model recurrent event data in the presence of cure fraction. In this context, we provide an alternative for the use of some usual modeling approaches as the semiparametric Cox proportional hazards model commonly used in lifetime data analysis, considering a new bivariate parametric model to be used in the data analysis of bivariate lifetime data assuming a mixture structure for the bivariate data in presence of covariates, censored data and cure fraction. Under a Bayesian setting, the proposed methodology was considered to analyze two real medical datasets from a retrospective cohort study related to leukemia and diabetic retinopathy diseases. The model validation process was addressed by using the Cox-Snell residuals, which allowed us to identify the suitability of the new proposed mixture cure rate model.
Sleep exerts lasting effects on hematopoietic stem cell function and diversity

Sleep interruption restructures the epigenome of hematopoietic stem and progenitor cells (HSPCs) and increases their proliferation, priming them for exaggerated inflammatory bursts and reducing hematopoietic clonal diversity through accelerated genetic drift. In humans, sleep restriction alters the HSPC epigenome and activates hematopoiesis.
Pass 8: Toward the Full Realization of the Fermi-LAT Scientific Potential

The event selection developed for the Fermi Large Area Telescope before launch has been periodically updated to reflect the constantly improving knowledge of the detector and the environment in which it operates. Pass 7, released to the public in August 2011, represents the most recent major iteration of this incremental process. In parallel, the LAT team has undertaken a coherent long-term effort aimed at a radical revision of the entire event-level analysis, based on the experience gained in the prime phase of the mission. This includes virtually every aspect of the data reduction process, from the simulation of the detector to the event reconstruction and the background rejection. The potential improvements include (but are not limited to) a significant reduction in background contamination coupled with an increased effective area, a better point-spread function, a better understanding of the systematic uncertainties and an extension of the energy reach for the photon analysis below 100 MeV and above a few hundred GeV. We present an overview of the work that has been done or is ongoing and the prospects for the near future.
Multiscale engineered human skeletal muscles with perfusable vasculature and microvascular network recapitulating the fluid compartments

Creating a vasculature in engineered human skeletal muscle tissues (ehSMTs) enables us to create thick tissues, increase cell survival in implantation, provide models of blood-organ barriers for drug testing, and enhance muscle differentiation through paracrine signaling. Here, contractile ehSMTs with a central perfusable vascular channel and microvascular networks growing from this central vasculature into the surrounding skeletal muscle tissue were newly demonstrated. Because coculturing muscle cells and endothelial cells requires incompatible media, we recapitulated the in vivo extracellular fluid compartments between blood plasma and interstitial fluid by creating an in vitro perfusable vasculature running through skeletal muscle tissue with a physiologic cell density. By using this model, we constructed large vascularized ehSMTs and showed the potential to be utilized for drug testing platforms. Also, we found that coculturing with two separate media from an early stage of muscle differentiation led to increased contractile force, thicker myotubes, and improved muscle differentiation.
Liver transplantation in rare late‐onset ornithine transcarbamylase deficiency with central nervous system injury: A case report and review of the literature

Ornithine transcarbamylase deficiency (OTCD) is a genetic metabolic disease. Its clinical manifestations are mainly central nervous system dysfunction caused by high blood ammonia. Late‐onset OTCD combined with central nervous system injury has a poor therapeutic response, which is one of the main factors affecting the prognosis and quality of life of patients. liver transplantation (LT) has gradually become a radical treatment for OTCD, which has achieved good results. However, there is no consensus on the timing of LT and problems of nervous system damage and repair.
Evaluation of genetic polymorphisms at 21 autosomal STR loci in Ramgharia Sikh population of Punjab, India

Abstract Background STR (Short Tandem Repeat) markers are highly polymorphic markers, which are widely used in forensics DNA analysis and aid to ascertain unique genotype profiles of individuals and determine the genetic diversity of the given population. Aim In the present study, an attempt has been made to evaluate the population genetic diversity of the Ramgharia Sikh population of Punjab, India, using 21 autosomal STR loci (D3S1358, vWA, D16S539, CSF1PO, TPOX, D8S1179, D21S11, D18S51, D2S441, D19S433, TH01, FGA, D22S1045, D5S818, D13S317, D7S820, SE33, D10S1248, D1S1656, D12S391, and D2S1338) to augment the emerging forensic database related to the indigenous population of India. Subjects and methods For generation of the database, 200 (blood on FTA card) samples were obtained from genetically unrelated Ramgharia Sikhs residing in the state of Punjab. Twenty-one autosomal STR markers were analysed using the Globalfiler® PCR amplification kit. Results With the help of various statistical tools, a total of 232 alleles were observed and 11.048 ± 1.284 (mean ± standard deviation) alleles per locus were recorded. No locus deviated from Hardy Weinberg Equilibrium. SE33 locus was found to be the most polymorphic and exhibited the highest discrimination power, that is, 0.99. Moreover, results further indicated that Ramgharia Sikhs of Punjab showed a high affinity with Bhils of Madhya Pradesh (India). Thus, the studied population showed genetic proximity with the geographically close populations of India and showed significant genetic variations with distant populations, which was evident from the UPGMA tree and Principal Component Analysis plot. Conclusion Overall, the 21 autosomal STRs were found to be polymorphic in the Ramgharia population and suitable for forensic casework and studies on population genetics.
The Structure of Nash Equilibrium in Repeated Games with Finite Automata (Now published in Econometrica, 56 (1988), pp.1259-1282.)

The authors study the Nash equilibria of a two-person, infinitely-repeated game in which players' preferences depend on repeated game payoffs and the complexity of the strategies they use. The model considered is that of A. Rubinstein (1986). Necessary conditions on the structure of the equilibria are derived. These results place significant restrictions on equilibrium payoffs. The results suggest that the introduction of implementation costs results in a striking discontinuity in the Nash equilibrium sets in terms of strategies and payoffs. Copyright 1988 by The Econometric Society.
Clinical characteristics and outcomes of acute pancreatitis following spinal surgery: a systematic review

Objective This study reviewed the current evidence on the clinical characteristics and outcome of acute pancreatitis (AP) following spinal surgery. Methods A systematic search was performed to identify English articles published through May 2020 in PubMed, Scopus, EMBASE, Latin American & Caribbean Health Sciences Literature, and Cochrane Library. Data on clinical characteristics, risk factors, and outcomes were analyzed. Results Eleven papers (including six case reports) were included, with 306 patients (incidence, 23.0%) developing AP after spinal surgery (mean age, 14.2 years). Of the studies that specified symptoms (55 patients), abdominal pain (43.6%), nausea and vomiting (32.7%), and abdominal distension (7.27%) were most prevalent. The mean duration from surgery to symptom onset was 6.15 days (range, 1–7). The most common complications of AP were glucose intolerance (25%), peritonitis (2%), pseudocyst formation (2%), and fluid collection (2%) were most prevalent. Prolonged fasting time (13.6%), intraoperative blood loss (9.09%), gastroesophageal reflux disease (9.1%), age >14 years (9.1%), and low BMI (9.1%) were most commonly associated with AP. Two deaths (0.6%) were reported. Conclusion AP remains an important complication of spinal surgery because of its morbidity and mortality. Avoiding major risk factors can reduce the incidence of AP following spinal surgery.
Development and validation of interpretable machine learning models for inpatient fall events and electronic medical record integration

Objective Falls are one of the most frequently occurring adverse events among hospitalized patients. The Morse Fall Scale, which has been widely used for fall risk assessment, has the two limitations of low specificity and difficulty in practical implementation. The aim of this study was to develop and validate an interpretable machine learning model for prediction of falls to be integrated in an electronic medical record (EMR) system. Methods This was a retrospective study involving a tertiary teaching hospital in Seoul, Korea. Based on the literature, 83 known predictors were grouped into seven categories. Interpretable fall event prediction models were developed using multiple machine learning models including gradient boosting and Shapley values. Results Overall, 191,778 cases with 272 fall events (0.1%) were included in the analysis. With the validation cohort of 2020, the area under the receiver operating curve (AUROC) of the gradient boosting model was 0.817 (95% confidence interval [CI], 0.720–0.904), better performance than random forest (AUROC, 0.801; 95% CI, 0.708–0.890), logistic regression (AUROC, 0.802; 95% CI, 0.721–0.878), artificial neural net (AUROC, 0.736; 95% CI, 0.650–0.821), and conventional Morse fall score (AUROC, 0.652; 95% CI, 0.570–0.715). The model’s interpretability was enhanced at both the population and patient levels. The algorithm was later integrated into the current EMR system. Conclusion We developed an interpretable machine learning prediction model for inpatient fall events using EMR integration formats.
Validation of a Deep Learning–based Automatic Detection Algorithm for Measurement of Endotracheal Tube–to–Carina Distance on Chest Radiographs

Background: Improper endotracheal tube (ETT) positioning is frequently observed and potentially hazardous in the intensive care unit. The authors developed a deep learning–based automatic detection algorithm detecting the ETT tip and carina on portable supine chest radiographs to measure the ETT–carina distance. This study investigated the hypothesis that the algorithm might be more accurate than frontline critical care clinicians in ETT tip detection, carina detection, and ETT–carina distance measurement. Methods: A deep learning–based automatic detection algorithm was developed using 1,842 portable supine chest radiographs of 1,842 adult intubated patients, where two board-certified intensivists worked together to annotate the distal ETT end and tracheal bifurcation. The performance of the deep learning–based algorithm was assessed in 4-fold cross-validation (1,842 radiographs), external validation (216 radiographs), and an observer performance test (462 radiographs) involving 11 critical care clinicians. The performance metrics included the errors from the ground truth in ETT tip detection, carina detection, and ETT–carina distance measurement. Results: During 4-fold cross-validation and external validation, the median errors (interquartile range) of the algorithm in ETT–carina distance measurement were 3.9 (1.8 to 7.1) mm and 4.2 (1.7 to 7.8) mm, respectively. During the observer performance test, the median errors (interquartile range) of the algorithm were 2.6 (1.6 to 4.8) mm, 3.6 (2.1 to 5.9) mm, and 4.0 (1.7 to 7.2) mm in ETT tip detection, carina detection, and ETT–carina distance measurement, significantly superior to that of 6, 10, and 7 clinicians (all P < 0.05), respectively. The algorithm outperformed 7, 3, and 0, 9, 6, and 4, and 5, 5, and 3 clinicians (all P < 0.005) regarding the proportions of chest radiographs within 5 mm, 10 mm, and 15 mm error in ETT tip detection, carina detection, and ETT–carina distance measurement, respectively. No clinician was significantly more accurate than the algorithm in any comparison. Conclusions: A deep learning–based algorithm can match or even outperform frontline critical care clinicians in ETT tip detection, carina detection, and ETT–carina distance measurement. A deep learning–based algorithm developed using portable chest radiographs from 1,842 adult intubated patients can identify the endotracheal tube tip, carina, and endotracheal tube tip to carina distance with a measurement error of 2.6 mm, 3.6 mm, and 4.0 mm, respectively. The algorithm performed as well as, if not better than, 11 critical care clinicians in identifying these portable chest radiograph landmarks.
Regulation of cohesin‐mediated chromosome folding by PDS5 in mammals

Cohesin regulates sister chromatid cohesion but also contributes to chromosome folding by promoting the formation of chromatin loops, a process mediated by loop extrusion. Although PDS5 regulates cohesin dynamics on chromatin, the exact function of PDS5 in cohesin‐mediated chromatin looping remains unclear. Two paralogs of PDS5 exist in vertebrates, PDS5A and PDS5B. Here we show that PDS5A and PDS5B co‐localize with RAD21 and CTCF at loop anchors. Rapid PDS5A or PDS5B degradation in liver cancer cells using an inducible degron system reduces chromatin loops and increases loop size. RAD21 enrichment at loop anchors is decreased upon depletion of PDS5A or PDS5B. PDS5B loss also reduces CTCF signals at loop anchors and has a stronger effect on loop enlargement compared with PDS5A. Co‐depletion of PDS5A and PDS5B reduces RAD21 levels at loop anchors although the amount of cohesin on chromatin is increased. Our study provides insight into how PDS5 proteins regulate cohesin‐mediated chromatin looping.
Research on the Influencing Factors of Herd Consumption Behavior in the Context of E-Commerce Live Streaming

This paper explores the influencing factors of herd consumption behavior in the context of e-commerce live streaming, and studies the mechanism of herd consumption behavior. Based on the theory of herd behavior, reference groups theory, and social presence theory, this paper constructed a model of the influencing factors of herd consumption behavior in the context of e-commerce live streaming, collected 236 valid samples through questionnaires, and used SPSS and AMOS to measure the reliability and validity of the table were tested. This research hypothesis was tested with the empirical method of structural equation model. We found that group identification positively affects normal herd motivation in e-commerce live streaming, reference groups positively affect informative herd motivation in e-commerce live streaming, and social presence and product involvement negatively affect informative herd motivation in e-commerce live streaming. Additionally, normative herd motivation and informative herd motivation positively affect herd consumption behavior in e-commerce live streaming. These results will help consumers make more rational decisions in e-commerce live shopping, optimize the interaction mechanism of the e-commerce platform, and improve the professionalism of the anchors.
Partial federated learning based network intrusion system for mobile devices: poster

We propose a partial federated learning based network intrusion system to utilize the limited communication resources of mobile devices. The key to our algorithm is that we share only part of the model to enable efficient communication and strengthen data privacy. The proposed method is evaluated using two network traffic datasets and three performance measures. Our simulation results confirm that the model can reach the performance of centralized learning, although the ratio of a shared layer is reduced up to 25%.
Ranked Document Retrieval in External Memory

The ranked (or top-k) document retrieval problem is defined as follows: preprocess a collection {T1,T2,… ,Td} of d strings (called documents) of total length n into a data structure, such that for any given query (P,k), where P is a string (called pattern) of length p ≥ 1 and k ∈ [1,d] is an integer, the identifiers of those k documents that are most relevant to P can be reported, ideally in the sorted order of their relevance. The seminal work by Hon et al. [FOCS 2009 and Journal of the ACM 2014] presented an O(n)-space (in words) data structure with O(p+k log k) query time. The query time was later improved to O(p+k) [SODA 2012] and further to O(p/ log σn+k) [SIAM Journal on Computing 2017] by Navarro and Nekrich, where σ is the alphabet size. We revisit this problem in the external memory model and present three data structures. The first one takes O(n)-space and answer queries in O(p/B + log B n + k/B+ log * (n/B)) I/Os, where B is the block size. The second one takes O(n log * (n/B)) space and answer queries in optimal O(p/B + log B n + k/B) I/Os. In both cases, the answers are reported in the unsorted order of relevance. To handle sorted top-k document retrieval, we present an O(n log (d/B)) space data structure with optimal query cost.
Learning Explicit and Implicit Dual Common Subspaces for Audio-visual Cross-modal Retrieval

Audio-visual tracks in video contain rich semantic information with potential in many applications and research. Since the audio-visual data have inconsistent distributions and because of the heterogeneous nature of representations, the heterogeneous gap between modalities makes them impossible to compare directly. To bridge the modality gap, a frequently adopted approach is to simultaneously project audio-visual data into a common subspace to capture the commonalities and characteristics of modalities for measurement, which has been extensively studied in relation to the issues of modality-common and modality-specific feature learning in previous research. However, it is difficult for existing methods to address the tradeoff between both issues; e.g., the modality-common feature is learned from the latent commonalities of audio-visual data or the correlated features as aligned projections, in which the modality-specific feature can be lost. To solve the tradeoff, we propose a novel end-to-end architecture, which synchronously projects audio-visual data into the explicit and the implicit dual common subspaces. The explicit subspace is used to learn modality-common features and reduce the modality gap of explicitly paired audio-visual data, where the representation-specific details are abandoned to retain the common underlying structure of audio-visual data. The implicit subspace is used to learn modality-specific features, where each modality privately pulls apart the feature distances between different categories to maintain the category-based distinctions, by minimizing the distance between audio-visual features and corresponding labels. The comprehensive experimental results on two audio-visual datasets, VEGAS and AVE, demonstrate that our proposed model for using two different common subspaces for audio-visual cross-modal learning is effective and significantly outperforms the state-of-the-art cross-modal models that learn features from a single common subspace by 4.30% and 2.30% in terms of average MAP on the VEGAS and AVE datasets, respectively.