Miracle-dz/UniFAR-FTUs huggingface dataset:
Transfer Learning for Biomedical Question Answering

Deep Neural Network (DNN) based Machine Learning models achieved remarkable success in many ﬁelds of research. Yet, many recent studies show the limitations of these approaches to generalize to unseen examples and to new domains such as the biomedical domain. Besides, supervised-learning based DNN models require a substantial amount of labeled data which is not readily available for many tasks such as the biomedical question answering task. Transfer Learning is shown to mitigate these challenges by transferring information from auxiliary tasks to improve the performance on a source task, and shown to be especially useful for low-resource tasks. These observations and ﬁndings motivated us to investigate the eﬀect of transfer learning and multi-task learning on the biomedical question answering task. We proposed a novel multi-task learning model to learn biomedical entities and questions simultaneously. In this work, we explain the three diﬀerent neural models we used to participate for the BioASQ 8B challenge. Our initial results showed that transferring information from the biomedical entity recognition task brings improvement for the biomedical question answering task.
Creating an Argument Search Engine for Online Debates

Consulting web search engines has become an everyday procedure for many internet users. One speciﬁc task that gained attention in recent work is the retrieval of arguments for controversial topics. Most of the preexisting diﬃculties that search engines have to face also apply for this task. However, certain challenges become even more important, such as providing an appropriate heterogeneity in the result set. We present an argument search engine for the argsme corpus. Our focus is on pre-processing the corpus while also addressing the heterogeneity problem and implementing a query expansion feature. Furthermore, we provide a brief evaluation of our retrieval results.
Extended Gauss-Newton and ADMM-Gauss-Newton algorithms for low-rank matrix optimization

In this paper, we develop a variant of the well-known Gauss-Newton (GN) method to solve a class of nonconvex optimization problems involving low-rank matrix variables. As opposed to the standard GN method, our algorithm allows one to handle general smooth convex objective function. We show, under mild conditions, that the proposed algorithm globally and locally converges to a stationary point of the original problem. We also show empirically that the GN algorithm achieves higher accurate solutions than the alternating minimization algorithm (AMA). Then, we specify our GN scheme to handle the symmetric case and prove its convergence, where AMA is not applicable. Next, we incorporate our GN scheme into the alternating direction method of multipliers (ADMM) to develop an ADMM-GN algorithm. We prove that, under mild conditions and a proper choice of the penalty parameter, our ADMM-GN globally converges to a stationary point of the original problem. Finally, we provide several numerical experiments to illustrate the proposed algorithms. Our results show that the new algorithms have encouraging performance compared to existing methods.
Effects of Huangjiu, Baijiu and Red Wine Combined With High-Fat Diet on Glucose and Lipid Metabolism: Aggravate or Alleviate?

AIM To compare effects on certain health indices in rodents of different doses of alcoholic beverages, huangjiu (Chinese yellow wine), red wine and baijiu (Chinese liquor) combined with high-fat diet (HFD) and the pure HFD. METHODS A total of 80 rats were randomly divided into eight groups and treated with (a) basal diet (3.5 kcal/g); (b) HFD (19.5% w/w lard, 4.5 kcal/g) and (c) HFD with low or high doses of separate alcoholic beverages (2.5 and 5 g/kg ethanol, respectively) for 28 weeks. RESULTS Chronic drinking when combined with HFD was associated with reduced body weight, fat accumulation and serum TNF-α level, serum TG, TC and LDL-C levels, and improved glucose tolerance (OGTT) and insulin sensitivity (ITT), hepatic enzymes; elevated levels or activities of the antioxidant enzymes like superoxide dismutase, catalase and glutathione reductase, reduced the content of lipid peroxidation productions such as malondialdehyde, in comparison with the pure HFD intake. In addition, compared with HFD, drinking plus HFD improved microbiota dysbiosis, down-regulated the ratio of Firmicutes/Bacteroidetes and promoted the growth of some probiotics including Prevotellaceae_UCG-001 and norank_f__Bacteroidales_S24-7_group. CONCLUSION Overall, the three beverages showed different impacts on indicators but red wine showed the most 'beneficial' effects. Of course, higher ethanol dosages can be expected to cause overall negative health effects, and harms of high fat intake can be prevented by healthier diet.
Effect of Ultra-high-definition Television on Ocular Surface and Fatigue

Purpose To investigate the effects of watching ultra-high-definition television (UHD TV) on subjective ocular fatigue and objective ocular surface indices. Methods A total of 76 volunteers were recruited for this study. Subjects watched UHD TV for 10 minutes at a viewing distance of 110 cm. Best-corrected visual acuity, refractive errors, tear break-up time, corneal staining grading, conjunctival redness grading by slit-lamp examination, tear meniscus measurement by anterior segment optical coherence tomography, and ocular symptom scores were obtained before and immediately after watching UHD TV. Subgroup analyses were conducted according to participant age (old age group >50 years vs. young age group ≤50 years) and dry-eye syndrome (presence vs. absence). The relationship between subjective and objective indices was evaluated. Results The mean age of subjects was 39.6 ± 12.4 years. Watching UHD TV induced a decrease in tear break-up time in the non-dry-eye group (p < 0.001) but not in the dry-eye group (p = 0.726). Corneal staining grades increased in all subgroups, and the changes were particularly larger in the older group (p = 0.038). The increase in ocular symptom scores was larger in the dry-eye group (p = 0.08) and in the older group (p = 0.016). The decrease in tear break-up time and ocular symptom scores after watching UHD TV was significantly correlated with tear break-up time. Conclusions Tear break-up time significantly decreased in non-dry-eye subjects after watching UHD TV. Subjective ocular discomfort increased significantly in subjects over the age of 50 and in participants with dry-eye syndrome.
The Relationship Between Anxiety and Incident Agitation in Alzheimer's Disease.

BACKGROUND Agitation in Alzheimer's disease (AD) has been hypothesized to be an expression of anxiety, but whether anxiety early in the course of dementia could be a risk factor for developing later agitation is unknown. OBJECTIVE We used the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to examine the longitudinal relationship between anxiety and incident agitation in individuals with a diagnosis of AD at baseline or during follow-up. METHODS Longitudinal neuropsychiatric symptom data from AD individuals who were agitation-free at study baseline (N = 272) were analyzed using mixed effects regression models to test the longitudinal relationship between baseline and incident anxiety with incident agitation. RESULTS Anxiety at baseline was not associated with subsequent agitation, but there was a positive linear relationship between incident anxiety and agitation over the study duration. Baseline apathy and delusions were consistently associated with subsequent agitation and greater disease severity and illness duration also appeared to be risk factors for agitation. CONCLUSION Our findings support the concept that anxiety and agitation are likely to be distinct rather than equivalent constructs in mild-moderate AD. Future longitudinal cohort studies are needed to replicate these findings and further characterize potential risk factors for agitation, such as apathy and delusions.
Environmental stressors affect intestinal permeability and repair responses in a pig intestinal ischemia model

ABSTRACT The pig is a powerful model for intestinal barrier studies, and it is important to carefully plan animal care and handling for optimal study design as psychological and physiological stressors significantly impact intestinal mucosal barrier function. Here, we report the effects of a period of environmental acclimation versus acute transport stress on mucosal barrier repair after intestinal ischemic injury. Jejunal ischemia was induced in young pigs which had been allowed to acclimate to a biomedical research housing environment or had been transported immediately prior to experimental injury (non-acclimated). Mucosa was then incubated ex vivo on Ussing chambers. In uninjured mucosa, there was no difference in transepithelial electrical resistance (TEER) or epithelial integrity between groups. However, acclimated pigs had increased macromolecular flux as compared to non-acclimated pigs during the first hour of ex vivo incubation. Ischemia induced greater epithelial loss in non-acclimated pigs as compared to acclimated pigs, yet this group achieved greater wound healing during recovery. Non-acclimated pigs had more robust TEER recovery ex vivo following injury versus acclimated pigs. The expression pattern of the tight junction protein claudin-4 was disrupted in acclimated pigs following recovery but showed enhanced localization to the apical membrane in non-acclimated pigs following recovery. Acute transport stress increases mucosal susceptibility to epithelial loss but also primes the tissue for a more robust barrier repair response. Alternatively, environmental acclimation increases leak pathway and diminishes barrier repair responses after ischemic injury.
Optimized deep belief network and entropy-based hybrid bounding model for incremental text categorization

This paper aims to model a technique that categorizes the texts from huge documents. The progression in internet technologies has raised the count of document accessibility, and thus the documents available online become countless. The text documents comprise of research article, journal papers, newspaper, technical reports and blogs. These large documents are useful and valuable for processing real-time applications. Also, these massive documents are used in several retrieval methods. Text classification plays a vital role in information retrieval technologies and is considered as an active field for processing massive applications. The aim of text classification is to categorize the large-sized documents into different categories on the basis of its contents. There exist numerous methods for performing text-related tasks such as profiling users, sentiment analysis and identification of spams, which is considered as a supervised learning issue and is addressed with text classifier. ,At first, the input documents are pre-processed using the stop word removal and stemming technique such that the input is made effective and capable for feature extraction. In the feature extraction process, the features are extracted using the vector space model (VSM) and then, the feature selection is done for selecting the highly relevant features to perform text categorization. Once the features are selected, the text categorization is progressed using the deep belief network (DBN). The training of the DBN is performed using the proposed grasshopper crow optimization algorithm (GCOA) that is the integration of the grasshopper optimization algorithm (GOA) and Crow search algorithm (CSA). Moreover, the hybrid weight bounding model is devised using the proposed GCOA and range degree. Thus, the proposed GCOA + DBN is used for classifying the text documents. ,The performance of the proposed technique is evaluated using accuracy, precision and recall is compared with existing techniques such as naive bayes, k-nearest neighbors, support vector machine and deep convolutional neural network (DCNN) and Stochastic Gradient-CAViaR + DCNN. Here, the proposed GCOA + DBN has improved performance with the values of 0.959, 0.959 and 0.96 for precision, recall and accuracy, respectively. ,This paper proposes a technique that categorizes the texts from massive sized documents. From the findings, it can be shown that the proposed GCOA-based DBN effectively classifies the text documents.
Anti-Virulence Activity of Selected Natural Products against Pseudomonas aeruginosa

Pseudomonas aeruginosa is a serious opportunistic pathogen in hospitalized patients. The ability of P. aeruginosa to cause a wide range of infections is due to the production of a large array of virulence factors (Balasubramanian et al., 2013). Pseudomonas aeruginosa is highly adaptable and have diverse phenotypic characteristics, to inhabit various environmental conditions (Yosuke and Yutaka, 2013). Plant extracts of many plants exhibit antibacterial, antifungal and insecticidal properties under laboratory trails (Okigbo and Ogbonnaya, 2006 and Shariff et al., 2006). The selection of crude plant extracts for screening programs has the potential of being more successful in its initial steps than the screening of pure individual compounds (Uniyal et al., 2006). Inhibition of bacterial QS can prevent the development of bacterial virulence and successful establishment of infections (Schauder and Bassler, 2001 and Robert et al., 2005). Selected natural products which characterized by QSI in their components will be prepared, extracted and investigated their anti pseudomonal activity against MDR isolates. Furthermore, the effect of the tested natural products/extracts on International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 6 Number 1 (2017) pp. 396-410 Journal homepage: http://www.ijcmas. com
Using N-grams to detect Fake News Spreaders on Twitter

This paper synthesizes our participation in the CLEF conference 2020 regarding the Proﬁling Fake News Spreaders on Twitter task, organized at the PAN lab on digital text forensics and stylome-try. The models that we suggested obtained one of the two best results, based on an average accuracy of 0.7775 –0.7350 for English and 0.8200 for Spanish. In summary, we propose a Support Vector Machine (SVM) classiﬁer with character and word n-gram features to determine whether the author of a Twitter feed is keen to be a spreader of fake news.
In Vitro Effects of the Phytocomplex TrichoTech TM on Human Fibroblasts: Proliferative Potential and Effects on Gene Expression of FGF-7 and FGF-10

The human hair follicle, a mini-organ formed with neuroectodermal-meso-dermal interaction, is a complex structure, in the active steady state (anagen) the dermal papilla can be considered as a ball of extracellular matrix, surrounding specialized fibroblasts. The cross-talk of dermal papilla with neighbouring matrix cells results in the maintenance of hair fibre production. This study aimed to investigate the proliferative potential of the compound TrichotechTM, a phytocomplex obtained from a mixture of essential oils, on cultured human fibroblasts and its ability to modulate the gene expression of FGF-7 and FGF-10. TrichotechTM was shown to enhance fibroblasts proliferation in concentrations of 0.5% to 2.0%, and also increase the percentage of cells in the S/G2/M phases of the cell cycle. TrichotechTM at both 1.0% and 2.0% induced a statistically significant effect on wound healing assay compared to the untreated control. We examined the interaction between cell survival (PI3K/Akt) and mitogenic (Ras/MAPK) signal transduction pathways after TrichotechTM treatment (1.0% and 2.0%) on the fibroblast cell line. TrichotechTM caused phosphorylation of ERK1/2, as well as greater phosphorylation of MEK in comparison with both the untreated control and ERK1/2. PI3K and AKT, however, were not shown to be significantly more phosphorylated following TrichotechTM exposure. To verify the relative expression of mRNA for FGF-7 and FGF-10 genes, a real-time polymerase chain reaction (qPCR) protocol was used. Results show the increase in mRNA expression by fibroblasts after treatment with TrichotechTM. In both concentrations tested, TrichotechTM was found to increase the expression of FGF-7 and FGF-10. Sirius red staining allows for rapid assessment of collagen content, it showed a significant increase in collagen content in treated fibroblasts. Further investigation concerning TrichotechTM could be helpful towards the development of new bioactive phytocomplexes for dermatological and trichological use.
Sequence properties underlying gene regulatory enhancers are conserved across mammals

Gene expression patterns and transcription factor DNA binding preferences are largely conserved across mammals; however, there is substantial turnover in active regulatory enhancers between closely related species. We investigated this seeming contradiction by quantifying the conservation of sequence patterns underlying histone-mark defined enhancers across six diverse mammalian species (human, macaque, mouse, dog, cow, and opossum). In each species, we found that machine-learning classifiers based on short DNA sequence patterns could accurately identify many adult liver and developing limb enhancers. We applied these classifiers across species and found that classifiers trained in different species performed nearly as well as classifiers trained on the target species, indicating that the underlying sequence properties predictive of enhancers are largely conserved. We also observed similar cross-species conservation in classifiers trained on human and mouse enhancers validated in transgenic reporter assays, and these classifiers learned predictive sequence properties similar to the classifiers trained on histone-mark defined enhancers. The sequence patterns most predictive of enhancers in each species matched the binding motifs for a common set of TFs enriched for expression in relevant tissues, which supports the biological relevance of the learned features. These results suggest that, though the genomic regions with enhancer activity change rapidly between species, many of the sequence properties encoding enhancer activity have been maintained across more than 180 million years of mammalian evolution.
Dowsing for Math Answers with Tangent-L

We present our application of the math-aware search engine Tangent-L to the ARQMath Community Question Answering (CQA) task. Our approach performs well, placing in the top three positions out of all 23 submissions, including the baseline runs. Tangent-L, built on the text search platform Lucene, handles math formulae by ﬁrst converting a formula’s Presentation MathML representation into a Symbol Layout Tree, followed by extraction of math tuples from the tree that serve as search terms. It applies BM25 + ranking to all math tuples and natural language terms in a document during searching. For the CQA task, we index all question-answer pairs in the Math Stack Exchange corpus. At query time, we ﬁrst convert a topic question into a bag of formulae and keywords that serves as a formal query. We then execute the queries using Tangent-L to ﬁnd the best matches. Finally, we re-rank the matches by a regression model that was trained on metadata attributes from the corpus. Our primary run produces an nDCG (cid:48) value of 0.278 and MAP (cid:48) value of 0.063, where these are two common measures of quality for ranked retrieval. However, our best performance, an nDCG (cid:48) value of 0.345 and MAP (cid:48) value of 0.139, is achieved by an alternate run without re-ranking. Follow-up experiments help to explain which aspects of our approach lead to our success.
Teacher perceptions of feedback in high school programming education: a thematic analysis

Fundamental principles of programming are being added to many school curricula world-wide. In New Zealand, digital technologies progress outcomes and assessment standards have recently been added to the national school curriculum. Among other things, the curriculum and assessment standards relate to aspects of code quality and decomposition as well as producing the logic and syntax of functional programs. To understand how teachers provide feedback to support student learning in these areas we conducted a survey relating to feedback on programming in the classroom. The survey focused on subcategories of feedback relating to the syntax, style and semantics of the code. We present the results of the survey which showed several themes that relate to categories of feedback. The paper outlines implications for what further support and research could be valuable.
Programming in primary education: towards a research based assessment framework

In March 2017, the Swedish government decided to introduce digital competence - including programming - in primary school. As a consequence, every math and technology teacher in grades 1-9 in Sweden is expected to integrate programming in their teaching. Furthermore, the Swedish school law requires that teaching is based on scientific evidence and proven experience. In addition to professional development for teachers, it is hence crucial to also conduct research on different aspects of programming in the classroom. In this paper, we describe the process of developing a scientifically grounded instrument for assessing students' programming skills, as part of a longitudinal research project investigating how students in primary school learn programming. We also present the main findings related to the suitability of the instrument based on a pilot study conducted in spring 2019, collecting data from 310 students.
Association of antimalarial drugs with decreased overall and cause specific mortality in systemic lupus erythematosus.

OBJECTIVE To evaluate the association and dose-response pattern between antimalarial drugs and overall and cause specific mortality in SLE patients. METHODS Medical records including information on HCQ/chloroquine (CQ) prescription were extracted from Jiangsu Lupus database. The database was designed to collect data from SLE patients that first-hospitalized during 1999-2009 in Jiangsu province, China, and a follow-up for survival status was performed in 2010 and 2015. Cox and restricted cubic spline models were used to estimate the hazard ratio and 95% CI. RESULTS We identified 221 deaths among 2446 SLE patients in total. Compared with non-users, decreased overall mortality was associated with either HCQ or CQ users, with adjusted hazard ratio (95% CI) of 0.49 (0.35, 0.67) and 0.49 (0.27, 0.87), respectively. The association between HCQ/CQ and overall mortality was similar across subgroups, such as patients with comorbidities and organ involvements. Interestingly, both the time and the daily dosage of HCQ/CQ use were related to decreased mortality of SLE in a linear dose-response relationship. In cause specific analyses, HCQ/CQ was inversely associated with death from renal insufficiency and other organ (cardiopulmonary, gastrointestinal and haematological) involvements, with adjusted hazard ratio (95% CI) of 0.23 (0.09, 0.55) and 0.25 (0.10, 0.62), respectively, yet it was not significantly associated with mortality from infection and neuropsychiatric involvements. CONCLUSION Antimalarial drugs were associated with lower risk of SLE mortality, especially renal insufficiency- and other organ involvement-related death. The protective effects for survival might be augmented by adherence and full dosage of these drugs.