Miracle-dz/UniFAR-FTUs huggingface dataset:
ONTOPRIMA: A Prototype for Automating Ontology Population

Ontology Population supports the process of building ontologies in the complex task of instantiating ontology. Performing this process manually is both expensive and time consuming; this logically leads to attempts of fully or partially automating the process of acquisition and absorption of knowledge in general and the process of Ontology Population in particular. This paper presents OntoPRiMa (Ontology Population for PRIMA Project), a prototype based on a semiautomatic approach for ontology population in the domain of risk management. OntoPRiMa demonstrates the feasibility of the proposed approach in populating generic and small domain ontologies from textual corpus with many potentials and possible instances that are unknown and not realized. The prototype is based on Natural Language Processing (NLP) techniques for language processing, semantic web techniques (RDFS, RDF, Jena APIs) for knowledge modeling and representation and on domain expert’s intervention for validating extracted instances. Furthermore, this paper presents an experiment for populating the ontology of the PRIMA project using a text corpus which is consist of chemical fact sheets issued by the U.S. Environmental Protection Agency. We have achieved high precision (above 80%) of knowledge acquisition in this experiment.
Intelligent Heart Disease Prediction System using Machine Learning : A Review

Heart disease is a major life threatening disease that can cause either death or a serious long term disability. However, there is lack of effective tools to discover hidden relationships and trends in e-health data. Medical diagnosis is a complicated task and plays a vital role in saving human lives so it needs to be executed accurately and efficiently. An appropriate and accurate computer based automated decision support system is required to reduce cost for achieving clinical tests. This paper provides an insight into machine learning techniques used in diagnosing various diseases. Various data mining classifiers have been discussed which has emerged in recent years for efficient and effective disease diagnosis.
Compressive Video Coding: A Review of the State-Of-The-Art

Video coding and its related applications have advanced quite substantially in recent years. Major coding standards such as MPEG [1] and H.26x [2] are well developed and widely deployed. These standards are developed mainly for applications such as DVDs where the compressed video is played over many times by the consumer. Since compression only needs to be performed once while decompression (playback) is performed many times, it is desirable that the decoding/decompression process can be done as simply and quickly as possible. Therefore, essentially all current video compression schemes, such as the various MPEG standards as well as H.264 [1, 2] involve a complex encoder and a simple decoder. The exploitation of spatial and temporal redundancies for data compression at the encoder causes the encoding process to be typically 5 to 10 times more complex computationally than the decoder [3]. In order that video encoding can be performed in real time at frame rates of 30 frames per second or more, the encoding process has to be performed by specially designed hardware, thus increasing the cost of cameras.
The influence of a first-order antedependence model and hyperparameters in BayesCπ for genomic prediction

Objective The Bayesian first-order antedependence models, which specified single nucleotide polymorphisms (SNP) effects as being spatially correlated in the conventional BayesA/B, had more accurate genomic prediction than their corresponding classical counterparts. Given advantages of BayesCπ over BayesA/B, we have developed hyper-BayesCπ, ante-BayesCπ, and ante-hyper-BayesCπ to evaluate influences of the antedependence model and hyperparameters for vg and sg2 on BayesCπ. Methods Three public data (two simulated data and one mouse data) were used to validate our proposed methods. Genomic prediction performance of proposed methods was compared to traditional BayesCπ, ante-BayesA and ante-BayesB. Results Through both simulation and real data analyses, we found that hyper-BayesCπ, ante-BayesCπ and ante-hyper-BayesCπ were comparable with BayesCπ, ante-BayesB, and ante-BayesA regarding the prediction accuracy and bias, except the situation in which ante-BayesB performed significantly worse when using a few SNPs and π = 0.95. Conclusion Hyper-BayesCπ is recommended because it avoids pre-estimated total genetic variance of a trait compared with BayesCπ and shortens computing time compared with ante-BayesB. Although the antedependence model in BayesCπ did not show the advantages in our study, larger real data with high density chip may be used to validate it again in the future.
Embedded Implementation of a Deep Learning Smile Detector

In this paper we study the real time deployment of deep learning algorithms in low resource computational environments. As the use case, we compare the accuracy and speed of neural networks for smile detection using different neural network architectures and their system level implementation on NVidia Jetson embedded platform. We also propose an asynchronous multithreading scheme for parallelizing the pipeline. Within this framework, we experimentally compare thirteen widely used network topologies. The experiments show that low complexity architectures can achieve almost equal performance as larger ones, with a fraction of computation required.
Phylogenetic comparative methods: Harnessing the power of species diversity to investigate welfare issues in captive wild animals.

This paper reviews a way of investigating health and welfare problems in captive wild animals (e.g., those in zoos, aviaries, aquaria, or aquaculture systems) that has great potential, but to date has been little used: systematically comparing species with few or no health and welfare issues to those more prone to problems. Doing so empirically pinpoints species-typical welfare risk and protective factors (such as aspects of their natural behavioral biology): information which can then be used to help prevent or remedy problems by suggesting new ways to improve housing and husbandry, and by identifying species intrinsically best suited to captivity. We provide a detailed, step-by-step "how to" guide for researchers interested in using these techniques, including guidance on how to statistically control for the inherent similarities shared by related species: an important concern because simple, cross-species comparisons that do not do this may well fail to meet statistical assumptions of non-independence. The few relevant studies that have investigated captive wild animals' welfare problems using this method are described. Overall, such approaches reap value from the great number and diversity of species held in captivity (e.g., the many thousands of species held in zoos); can yield new insights from existing data and published results; render previously intractable welfare questions (such as "do birds need to fly? " or "do Carnivora need to hunt? ") amenable to study; and generate evidence-based principles for integrating animal welfare into collection planning.
Deep PDF: Probabilistic Surface Optimization and Density Estimation

A probability density function (pdf) encodes the entire stochastic knowledge about data distribution, where data may represent stochastic observations in robotics, transition state pairs in reinforcement learning or any other empirically acquired modality. Inferring data pdf is of prime importance, allowing to analyze various model hypotheses and perform smart decision making. However, most density estimation techniques are limited in their representation expressiveness to specific kernel type or predetermined distribution family, and have other restrictions. For example, kernel density estimation (KDE) methods require meticulous parameter search and are extremely slow at querying new points. In this paper we present a novel non-parametric density estimation approach, DeepPDF, that uses a neural network to approximate a target pdf given samples from thereof. Such a representation provides high inference accuracy for a wide range of target pdfs using a relatively simple network structure, making our method highly statistically robust. This is done via a new stochastic optimization algorithm, \emph{Probabilistic Surface Optimization} (PSO), that turns to advantage the stochastic nature of sample points in order to force network output to be identical to the output of a target pdf. Once trained, query point evaluation can be efficiently done in DeepPDF by a simple network forward pass, with linear complexity in the number of query points. Moreover, the PSO algorithm is capable of inferring the frequency of data samples and may also be used in other statistical tasks such as conditional estimation and distribution transformation. We compare the derived approach with KDE methods showing its superior performance and accuracy.
Non-Hodgkin’s lymphoma of the oral cavity and maxillofacial region: a pathologist viewpoint

ABSTRACT Introduction: Non-Hodgkin lymphomas of the oral and maxillofacial regions are uncommon malignancies. They usually have progressive course and dismal outcome. Areas covered: This review provides an update about the clinicopathologic, immunophenotypic, and molecular features of these lymphomas. Some relevant case reports are presented. Expert commentary: The lymphomas of the oral and maxillofacial regions involve the tongue, gingiva, hard palate, maxilla, mandible, Waldeyer’s ring, palatine, and lingual tonsils. They include diffuse large B-cell lymphoma (DLBCL), T-cell lymphoma, Burkitt lymphoma (BL), natural killer/T-cell lymphoma (NKTCL), marginal zone lymphoma, plasmablastic lymphoma (PBL), mantle cell lymphoma, and lymphoblastic lymphoma. They are more common in males than in females. They commonly present as mass lesions covered by an intact or ulcerated mucosa. Some lymphomas are associated with viral infections (Epstein–Barr virus or HIV) and specific genetic alterations. Each subtype has its specific clinicopathologic features and the most common subtypes include DLBCL, BL, NKTCL, and PBL. DLBCL is the most common oral lymphoma. BL is a rapidly proliferating mature B cell neoplasm almost always presenting in extranodal sites. Nasal-type NKTCL presents as a locally destructive, midfacial necrotizing lesion. PBL is a rare, highly aggressive disease that is commonly associated with immunodeficiency conditions.
Variational Option Discovery Algorithms

We explore methods for option discovery based on variational inference and make two algorithmic contributions. First: we highlight a tight connection between variational option discovery methods and variational autoencoders, and introduce Variational Autoencoding Learning of Options by Reinforcement (VALOR), a new method derived from the connection. In VALOR, the policy encodes contexts from a noise distribution into trajectories, and the decoder recovers the contexts from the complete trajectories. Second: we propose a curriculum learning approach where the number of contexts seen by the agent increases whenever the agent's performance is strong enough (as measured by the decoder) on the current set of contexts. We show that this simple trick stabilizes training for VALOR and prior variational option discovery methods, allowing a single agent to learn many more modes of behavior than it could with a fixed context distribution. Finally, we investigate other topics related to variational option discovery, including fundamental limitations of the general approach and the applicability of learned options to downstream tasks.
The Implementation of Extended Examination Duration for Students With ADHD in Higher Education

Objective: Recently, research indicated that extended examination duration was perceived as effective by students with ADHD. However, the objective effectiveness of this accommodation has not been researched extensively. This study addresses this gap in literature. Method: A total of 30 students with ADHD and 30 matched typically developing controls (TDC) participated in a simulation of a real-life examination. They filled in three parallel versions of a paper-and-pencil test in three time conditions: 1 hr (+0%), 1 hr 20 min (+33%), and 1 hr 30 min (+50%). Results: When granted extended examination duration, students with ADHD and TDC use extra time but their performance does not increase. In addition, test-taking strategies were altered to a limited extent, and strategies that are perceived as effective (e.g., marking key words) were rarely used by the students. Conclusion: It is concluded that extended examination duration is not objectively effective in an experimental design. Limitations and guidelines for future research are formulated.
Developing a new clinical governance framework for chronic diseases in primary care: an umbrella review

Objectives Our goal is to conceptualise a clinical governance framework for the effective management of chronic diseases in the primary care setting, which will facilitate a reorganisation of healthcare services that systematically improves their performance. Setting Primary care. Participants Chronic Care Model by Wagner et aland Clinical Governance statement by Scally et alwere taken for reference. Each was reviewed, including their various components. We then conceptualised a new framework, merging the relevant aspects of both. Interventions We conducted an umbrella review of all systematic reviews published by the Cochrane Effective Practice and Organisation of Care Group to identify organisational interventions in primary care with demonstrated evidence of efficacy. Results All primary healthcare systems should be patient-centred. Interventions for patients and their families should focus on their values; on clinical, professional and institutional integration and finally on accountability to patients, peers and society at large. These interventions should be shaped by an approach to their clinical management that achieves the best clinical governance, which includes quality assurance, risk management, technology assessment, management of patient satisfaction and patient empowerment and engagement. This approach demands the implementation of a system of organisational, functional and professional management based on a population health needs assessment, resource management, evidence-based and patient-oriented research, professional education, team building and information and communication technologies that support the delivery system. All primary care should be embedded in and founded on an active partnership with the society it serves. Conclusions A framework for clinical governance will promote an integrated effort to bring together all related activities, melding environmental, administrative, support and clinical elements to ensure a coordinated and integrated approach that sustains the provision of better care for chronic conditions in primary care setting.
Vitamin D-binding protein is inversely associated with the incidence of gastrointestinal and ear infections in school-age children

Abstract Circulating 25-hydroxy vitamin D (25(OH)D) is related to decreased rates of gastrointestinal and ear infections in school-age children. Vitamin D-binding protein (DBP) transports 25(OH)D and exerts immunological functions; however, it is unknown whether DBP is associated with infectious morbidity in children. We quantified plasma DBP concentrations in 540 school-age children at the time of recruitment into a cohort study in Bogotá, Colombia and obtained daily information on infectious morbidity symptoms and doctor visits during the school year. We compared the incidence rates of gastrointestinal and respiratory symptoms across quartiles of DBP concentration by estimating adjusted incidence rate ratios (IRRs) with 95% confidence interval (CI). We also estimated the per cent of the associations between DBP and morbidity that were mediated through 25(OH)D using a counterfactual frame. Mean ± s.d. DBP concentration was 2650 ± 1145 nmol/l. DBP was inversely associated with the rates of diarrhoea with vomiting (IRR for quartiles 2–4 vs. 1 = 0.48; 95% CI 0.25–0.92; P = 0.03) and earache/ear discharge with fever (IRR for quartiles 2–4 vs. 1 = 0.29; 95% CI 0.12–0.71; P = 0.006). The DBP–morbidity associations were not mediated through 25(OH)D. We conclude that plasma DBP predicts lower incidence of gastrointestinal and ear infections in school-age children independent of 25(OH)D.
Chronic hypoxia‐induced slug promotes invasive behavior of prostate cancer cells by activating expression of ephrin‐B1

Advanced solid tumors are exposed to hypoxic conditions over longer periods of time as they grow. Tumor hypoxia is a major factor that induces malignant progression, but most previous studies on tumor hypoxia were performed under short‐term hypoxia for up to 72 hours and few studies have focused on tumor response to chronic hypoxic conditions. Here we show a molecular mechanism by which chronic hypoxia promotes invasive behavior in prostate cancer cells. We found that an epithelial‐mesenchymal transition (EMT)‐driving transcription factor, slug, is specifically upregulated under chronic hypoxia and promotes tumor cell migration and invasion. Unexpectedly, processes associated with EMT, such as loss of E‐cadherin, are not observed under chronic hypoxia. Instead, expression of ephrin‐B1, a ligand of Eph‐related receptor tyrosine kinases, is markedly induced by slug through E‐box motifs and promotes cell migration and invasion. Furthermore, slug and ephrin‐B1 are highly coexpressed in chronic hypoxic cells of human prostate adenocarcinoma tissues after androgen deprivation, which is known to cause tumor hypoxia. Taken together, these results indicate that chronic hypoxia‐induced slug promotes invasive behavior of prostate cancer cells by activating the expression of ephrin‐B1. In addition, ephrin‐B1 may be a novel therapeutic target in combination with androgen deprivation therapy for aggressive prostate cancer.
SUMO-defective c-Maf preferentially transactivates Il21 to exacerbate autoimmune diabetes

SUMOylation is involved in the development of several inflammatory diseases, but the physiological significance of SUMO-modulated c-Maf in autoimmune diabetes is not completely understood. Here, we report that an age-dependent attenuation of c-Maf SUMOylation in CD4+ T cells is positively correlated with the IL-21–mediated diabetogenesis in NOD mice. Using 2 strains of T cell–specific transgenic NOD mice overexpressing wild-type c-Maf (Tg-WTc) or SUMOylation site–mutated c-Maf (Tg-KRc), we demonstrated that Tg-KRc mice developed diabetes more rapidly than Tg-WTc mice in a CD4+ T cell–autonomous manner. Moreover, SUMO-defective c-Maf preferentially transactivated Il21 to promote the development of CD4+ T cells with an extrafollicular helper T cell phenotype and expand the numbers of granzyme B–producing effector/memory CD8+ T cells. Furthermore, SUMO-defective c-Maf selectively inhibited recruitment of Daxx/HDAC2 to the Il21 promoter and enhanced histone acetylation mediated by CREB-binding protein (CBP) and p300. Using pharmacological interference with CBP/p300, we illustrated that CBP30 treatment ameliorated c-Maf–mediated/IL-21–based diabetogenesis. Taken together, our results show that the SUMOylation status of c-Maf has a stronger regulatory effect on IL-21 than the level of c-Maf expression, through an epigenetic mechanism. These findings provide new insights into how SUMOylation modulates the pathogenesis of autoimmune diabetes in a T cell–restricted manner and on the basis of a single transcription factor.
Novel treatment options for anaplastic thyroid cancer

ABSTRACT Introduction: Several genetic alterations have been identified in different molecular pathways ofanaplastic thyroid cancer (ATC) and associated with tumor aggressiveness and progression (BRAF, p53,RAS, EGFR, VEGFR-1, VEGFR-2, etc). New drugs targeting these molecular pathways have beenrecently evaluated in ATC. Areas covered: We review the new targeted therapies of ATC. Interesting results have been reported with molecules targeting different pathways, as: a-BRAF (dabrafenib/trametinib, vemurafenib); b-angiogenesis (sorafenib, combretastatin, vandetanib, sunitinib, lenvatinib, CLM3, etc); c-EGFR (gefitinib); d- PPARγ agonists (rosiglitazone, pioglitazone, efatutazone). In patients with ATC treated with lenvatinib, a median overall survival of 10.6 (3.8–19.8) months was reported. In order to bypass the resistance to the single drug, the capability of targeted drugs to synergize with radiation, or chemotherapy, or other targeted drugs is explored. Expert commentary: New, affordable and individual genomic analysis combined with the opportunity to test these new treatments in primary cell cultures from every ATC patient in vitro, may permit the personalization of therapy. Increasing the therapeutic effectiveness and avoiding the use of ineffective drugs. The identification of new treatments is necessary, to extend life duration guaranteing a good quality of life. To bypass the resistance to asingle drug, the capability of targeted drugs to synergize with radiation, or chemotherapy, or othertargeted drugs is explored. Moreover, new affordable individual genomic analysis and the opportunity totest these novel treatments in primary cell cultures from every ATC patient in vitro, might permit topersonalize the therapy, increasing the therapeutic effectiveness and avoiding the use of ineffectivedrugs.
Spatial modeling of cutaneous leishmaniasis in Iranian army units during 2014-2017 using a hierarchical Bayesian method and the spatial scan statistic

OBJECTIVES This study aimed to map the incidence of cutaneous leishmaniasis (CL) in Iranian army units (IAUs) and to identify possible spatial clusters. METHODS This ecological study investigated incident cases of CL between 2014 and 2017. CL data were extracted from the CL registry maintained by the deputy of health of AJA University of Medical Sciences. The standardized incidence ratio (SIR) of CL was computed with a Besag, York, and Mollié model. The purely spatial scan statistic was employed to detect the most likely high-and low-rate clusters and to obtain the observed-to-expected (O/E) ratio for each detected cluster. The statistical significance of the clusters was assessed using the log likelihood ratio (LLR) test and Monte Carlo hypothesis testing. RESULTS A total of 1,144 new CL cases occurred in IAUs from 2014 to 2017, with an incidence rate of 260 per 100,000. Isfahan and Khuzestan Provinces were found to have more CL cases than expected in all studied years (SIR>1), while Kermanshah, Kerman, and Fars Provinces were observed to have been high-risk areas in only some years of the study period. The most significant CL cluster was in Kermanshah Province (O/E, 67.88; LLR, 1,200.62; p<0.001), followed by clusters in Isfahan Province (O/E, 6.02; LLR, 513.24; p<0.001) and Khuzestan Province (O/E, 2.35; LLR, 73.71; p<0.001), while low-rate clusters were located in the northeast areas, including Razavi Khorasan, North Khorasan, Semnan, and Golestan Provinces (O/E, 0.03; LLR, 95.11; p<0.001). CONCLUSIONS This study identified high-risk areas for CL. These findings have public health implications and should be considered when planning control interventions among IAUs.