Miracle-dz/UniFAR-FTUs huggingface dataset:
Therapeutic Functions of Stem Cells from Oral Cavity: An Update

Adult stem cells have been developed as therapeutics for tissue regeneration and immune regulation due to their self-renewing, differentiating, and paracrine functions. Recently, a variety of adult stem cells from the oral cavity have been discovered, and these dental stem cells mostly exhibit the characteristics of mesenchymal stem cells (MSCs). Dental MSCs can be applied for the replacement of dental and oral tissues against various tissue-damaging conditions including dental caries, periodontitis, and oral cancers, as well as for systemic regulation of excessive inflammation in immune disorders, such as autoimmune diseases and hypersensitivity. Therefore, in this review, we summarized and updated the types of dental stem cells and their functions to exert therapeutic efficacy against diseases.
Higher Risk Taking and Impaired Probability Judgment in Behavioral Addiction

Abstract Background Accumulating evidence suggests that deficits in decision-making and judgment may be involved in several psychiatric disorders, including addiction. Behavioral addiction is a conceptually new psychiatric condition, raising a debate of what criteria define behavioral addiction, and several impulse control disorders are equivalently considered as types of behavioral addiction. In this preliminary study with a relatively small sample size, we investigated how decision-making and judgment were compromised in behavioral addiction to further characterize this psychiatric condition. Method Healthy control subjects (n = 31) and patients with kleptomania and paraphilia as behavioral addictions (n = 16) were recruited. A battery of questionnaires for assessments of cognitive biases and economic decision-making were conducted, as was a psychological test for the assessment of the jumping-to-conclusions bias, using functional near-infrared spectroscopy recordings of prefrontal cortical (PFC) activity. Results Although behavioral addicts exhibited stronger cognitive biases than controls in the questionnaire, the difference was primarily due to lower intelligence in the patients. Behavioral addicts also exhibited higher risk taking and worse performance in economic decision-making, indicating compromised probability judgment, along with diminished PFC activity in the right hemisphere. Conclusion Our study suggests that behavioral addiction may involve impairments of probability judgment associated with attenuated PFC activity, which consequently leads to higher risk taking in decision-making.
Comparing Monitoring Results of Two Motion Trackers for Geriatric Patients

BACKGROUND Fitbit and Garmin motion tracker devices are highly used in research. The validity and reliability of these devices is proven for healthy adults between 18 and 64. OBJECTIVES Comparing data output of two devices. METHODS Observational case study on a test track and in the domestic environment of a 80- year-old female multimorbide geriatric patient. RESULTS High significant correlation of the devices on the test track [r=. 776, p≤. 001, Bca-CI-95% (.618;.874), N=33], but significant different in the domestic environment over time (z=4.840, p≤.001). CONCLUSION The dominant/non-dominant body side and further sources of error may play a role in monitoring steps with these devices.
Building a User-Generated Content North-African Arabizi Treebank: Tackling Hell

We introduce the first treebank for a romanized user-generated content variety of Algerian, a North-African Arabic dialect known for its frequent usage of code-switching. Made of 1500 sentences, fully annotated in morpho-syntax and Universal Dependency syntax, with full translation at both the word and the sentence levels, this treebank is made freely available. It is supplemented with 50k unlabeled sentences collected from Common Crawl and web-crawled data using intensive data-mining techniques. Preliminary experiments demonstrate its usefulness for POS tagging and dependency parsing. We believe that what we present in this paper is useful beyond the low-resource language community. This is the first time that enough unlabeled and annotated data is provided for an emerging user-generated content dialectal language with rich morphology and code switching, making it an challenging test-bed for most recent NLP approaches.
Jointly Learning to Align and Summarize for Neural Cross-Lingual Summarization

Cross-lingual summarization is the task of generating a summary in one language given a text in a different language. Previous works on cross-lingual summarization mainly focus on using pipeline methods or training an end-to-end model using the translated parallel data. However, it is a big challenge for the model to directly learn cross-lingual summarization as it requires learning to understand different languages and learning how to summarize at the same time. In this paper, we propose to ease the cross-lingual summarization training by jointly learning to align and summarize. We design relevant loss functions to train this framework and propose several methods to enhance the isomorphism and cross-lingual transfer between languages. Experimental results show that our model can outperform competitive models in most cases. In addition, we show that our model even has the ability to generate cross-lingual summaries without access to any cross-lingual corpus.
Structural Information Preserving for Graph-to-Text Generation

The task of graph-to-text generation aims at producing sentences that preserve the meaning of input graphs. As a crucial defect, the current state-of-the-art models may mess up or even drop the core structural information of input graphs when generating outputs. We propose to tackle this problem by leveraging richer training signals that can guide our model for preserving input information. In particular, we introduce two types of autoencoding losses, each individually focusing on different aspects (a.k.a. views) of input graphs. The losses are then back-propagated to better calibrate our model via multi-task training. Experiments on two benchmarks for graph-to-text generation show the effectiveness of our approach over a state-of-the-art baseline.
Modelling Context and Syntactical Features for Aspect-based Sentiment Analysis

The aspect-based sentiment analysis (ABSA) consists of two conceptual tasks, namely an aspect extraction and an aspect sentiment classification. Rather than considering the tasks separately, we build an end-to-end ABSA solution. Previous works in ABSA tasks did not fully leverage the importance of syntactical information. Hence, the aspect extraction model often failed to detect the boundaries of multi-word aspect terms. On the other hand, the aspect sentiment classifier was unable to account for the syntactical correlation between aspect terms and the context words. This paper explores the grammatical aspect of the sentence and employs the self-attention mechanism for syntactical learning. We combine part-of-speech embeddings, dependency-based embeddings and contextualized embeddings (e.g. BERT, RoBERTa) to enhance the performance of the aspect extractor. We also propose the syntactic relative distance to de-emphasize the adverse effects of unrelated words, having weak syntactic connection with the aspect terms. This increases the accuracy of the aspect sentiment classifier. Our solutions outperform the state-of-the-art models on SemEval-2014 dataset in both two subtasks.
TETRAD BASAL BODY‐AXONEME COMPLEXES IN THE PRIMARY SPERMATOCYTE OF THE SILKWORM, BOMBYX MORI

Abstract Using cell whole mount preparation, tetrad basal boby‐axoneme complexes in the primary spermatocyte from testicular cysts of fourth instar larvae of Bombyx mori are examined by transmission electron microscopy. There exist two paired basal body‐axoneme complexes and the two orthogonally oriented basal bodies are linked together with distal and proximal linking fibers. The growing complex displays voluminous distal swelling. At this stage, the axoneme consists of nine microtubular doublets. A connecting nodule is found at the juncture of basal body's triplet and axoneme's doublet, and the A and B tubules of the former continue through the nodule to become the ones of the latter.
Tree-Structured Neural Topic Model

This paper presents a tree-structured neural topic model, which has a topic distribution over a tree with an infinite number of branches. Our model parameterizes an unbounded ancestral and fraternal topic distribution by applying doubly-recurrent neural networks. With the help of autoencoding variational Bayes, our model improves data scalability and achieves competitive performance when inducing latent topics and tree structures, as compared to a prior tree-structured topic model (Blei et al., 2010). This work extends the tree-structured topic model such that it can be incorporated with neural models for downstream tasks.
Fatality Killed the Cat or: BabelPic, a Multimodal Dataset for Non-Concrete Concepts

Thanks to the wealth of high-quality annotated images available in popular repositories such as ImageNet, multimodal language-vision research is in full bloom. However, events, feelings and many other kinds of concepts which can be visually grounded are not well represented in current datasets. Nevertheless, we would expect a wide-coverage language understanding system to be able to classify images depicting recess and remorse, not just cats, dogs and bridges. We fill this gap by presenting BabelPic, a hand-labeled dataset built by cleaning the image-synset association found within the BabelNet Lexical Knowledge Base (LKB). BabelPic explicitly targets non-concrete concepts, thus providing refreshing new data for the community. We also show that pre-trained language-vision systems can be used to further expand the resource by exploiting natural language knowledge available in the LKB. BabelPic is available for download at http://babelpic.org.
HOME RANGE CHARACTERISTICS OF GREAT GRAY OWLS IN YOSEMITE NATIONAL PARK, CALIFORNIA

ABSTRACT We studied home range and habitat use of radio-tagged Great Gray Owls (Strix nebulosa) in Yosemite, California. From 1986–90 we made 5338 relocations on nine adult and three juvenile owls. Home-range size was not correlated with number of locations and was significantly different between breeding and nonbreeding periods. Breeding female summer home range averaged 61.47 ha and during the winter 2457.27 ha, while males average 19.89 and 2112.87 ha, respectively. Juveniles and nonbreeding birds had home-range sizes intermediate between seasonal values of breeding owls. Home ranges for California Great Gray Owls were larger than has been recorded for all studies in North America, but smaller than in Europe. All owls were found to have intensive high-use activity centers (x ¯ = 17.56 ha) in summer, with use patterns influenced primarily by meadows. Over 60% of all relocations occurred within 100 m of a meadow. Great Gray Owls habitat usage during summer was concentrated in fir (Abies spp.) and lodge.
Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning

Pneumonia causes the death of around 700,000 children every year and affects 7% of the global population. Chest X-rays are primarily used for the diagnosis of this disease. However, even for a trained radiologist, it is a challenging task to examine chest X-rays. There is a need to improve the diagnosis accuracy. In this work, an efficient model for the detection of pneumonia trained on digital chest X-ray images is proposed, which could aid the radiologists in their decision making process. A novel approach based on a weighted classifier is introduced, which combines the weighted predictions from the state-of-the-art deep learning models such as ResNet18, Xception, InceptionV3, DenseNet121, and MobileNetV3 in an optimal way. This approach is a supervised learning approach in which the network predicts the result based on the quality of the dataset used. Transfer learning is used to fine-tune the deep learning models to obtain higher training and validation accuracy. Partial data augmentation techniques are employed to increase the training dataset in a balanced way. The proposed weighted classifier is able to outperform all the individual models. Finally, the model is evaluated, not only in terms of test accuracy, but also in the AUC score. The final proposed weighted classifier model is able to achieve a test accuracy of 98.43% and an AUC score of 99.76 on the unseen data from the Guangzhou Women and Children’s Medical Center pneumonia dataset. Hence, the proposed model can be used for a quick diagnosis of pneumonia and can aid the radiologists in the diagnosis process.
Bicelles Rich in both Sphingolipids and Cholesterol and Their Use in Studies of Membrane Proteins.

How the distinctive lipid composition of mammalian plasma membranes impacts membrane protein structure is largely unexplored, partly because of the dearth of isotropic model membrane systems that contain abundant sphingolipids and cholesterol. This gap is addressed by showing that sphingomyelin and cholesterol-rich (SCOR) lipid mixtures with phosphatidylcholine can be co-solubilized by n-dodecyl-β-melibioside to form bicelles. Small angle X-ray and neutron scattering, as well as cryo-electron microscopy demonstrate that these assemblies are stable over a wide range of conditions and exhibit the bilayered-disc morphology of ideal bicelles even at low lipid-to-detergent mole ratios. SCOR bicelles are shown to be compatible with a wide array of experimental techniques, as applied to the transmembrane human amyloid precursor C99 protein in this medium. These studies reveal an equilibrium between low order oligomer structures that differ significantly from previous experimental structures of C99, providing an example of how ordered membranes alter membrane protein structure.
Dry Panels Supporting External Quality Assessment Programs for Next Generation Sequencing-Based HIV Drug Resistance Testing

External quality assessment (EQA) is a keystone element in the validation and implementation of next generation sequencing (NGS)-based HIV drug resistance testing (DRT). Software validation and evaluation is a critical element in NGS EQA programs. While the development, sharing, and adoption of wet lab protocols is coupled with the increasing access to NGS technology worldwide, rendering it easy to produce NGS data for HIV-DRT, bioinformatic data analysis remains a bottleneck for most of the diagnostic laboratories. Several computational tools have been made available, via free or commercial sources, to automate the conversion of raw NGS data into an actionable clinical report. Although different software platforms yield equivalent results when identical raw NGS datasets are analyzed for variations at higher abundance, discrepancies arise when variations at lower frequencies are considered. This implies that validation and performance assessment of the bioinformatics tools applied in NGS HIV-DRT is critical, and the origins of the observed discrepancies should be determined. Well-characterized reference NGS datasets with ground truth on the genotype composition at all examined loci and the exact frequencies of HIV variations they may harbor, so-called dry panels, would be essential in such cases. The strategic design and construction of such panels are challenging but imperative tasks in support of EQA programs for NGS-based HIV-DRT and the validation of relevant bioinformatics tools. Here, we present criteria that can guide the design of such dry panels, which were discussed in the Second International Winnipeg Symposium themed for EQA strategies for NGS HIVDR assays.
Cucumber Fruit Size and Shape Variations Explored from the Aspects of Morphology, Histology, and Endogenous Hormones

Fruit size and shape are important qualities and yield traits in cucumber (Cucumis sativus L.), but the factors that influence fruit size and shape remain to be explored. In this study, we investigated the dynamic changes of fruit size and shape from the aspects of morphology, cellular levels and endogenous hormones for nine typical cucumber inbred lines. The results show that fruit length had a strong positive correlation to the cell number in the longitudinal section of fruit throughout the four stages of 0, 6, 12, and 30 DAA (days after anthesis). However, the significant negative correlations were found between fruit length and the fruit cell size at 12 and 30 DAA. Furthermore, fruit diameter was positively correlated to the cell number in the cross section at all the investigated fruit growth stages. The indole-3-acetic acid (IAA) content showed significant positive correlations to the fruit length at all fruit growth stages of −6, −3, 0, 3, 6, 9 and 12 DAA, but IAA content and fruit diameter showed significant negative correlations for all the stages except for at −6 DAA. The trans-zeatin riboside (tZR), zeatin (ZT), gibberellic acid (GA3) and jasmonic acid (JA) content had a positive or negative correlation with fruit length or diameter only at certain stages. Neither fruit length nor diameter had significant correlations to abscisic acid (ABA) content. These results indicate that variations in fruit size and shape of different cucumber inbred lines mainly result from the differences in fruit cell number and endogenous IAA content. The present work is the first to propose cucumber fruit size and shape changes from the combined aspects of morphology, cellular levels, and endogenous hormones.
Blueberry Counteracts BV-2 Microglia Morphological and Functional Switch after LPS Challenge

Microglia, the innate immune cells of the CNS, respond to brain injury by activating and modifying their morphology. Our study arises from the great interest that has been focused on blueberry (BB) for the antioxidant and pharmacological properties displayed by its components. We analyzed the influence of hydroalcoholic BB extract in resting or lipopolysaccharide (LPS)-stimulated microglia BV-2 cells. BB exerted a protective effect against LPS-induced cytotoxicity, as indicated by cell viability. BB was also able to influence the actin cytoskeleton organization, to recover the control phenotype after LPS insult, and also to reduce LPS-driven migration. We evaluated the activity of Rho and Rac1 GTPases, which regulate both actin cytoskeletal organization and migratory capacity. LPS caused an increase in Rac1 activity, which was counteracted by BB extract. Furthermore, we demonstrated that, in the presence of BB, mRNA expression of pro-inflammatory cytokines IL-1β, IL-6 and TNF-α decreased, as did the immunofluorescence signal of iNOS, whereas that of Arg-1 was increased. Taken together, our results show that, during the inflammatory response, BB extract shifts the M1 polarization towards the M2 phenotype through an actin cytoskeletal rearrangement. Based on that, we might consider BB as a nutraceutical with anti-inflammatory activities.
Coupled dynamics of behaviour and disease contagion among antagonistic groups

Abstract Abstract Disease transmission and behaviour change are both fundamentally social phenomena. Behaviour change can have profound consequences for disease transmission, and epidemic conditions can favour the more rapid adoption of behavioural innovations. We analyse a simple model of coupled behaviour change and infection in a structured population characterised by homophily and outgroup aversion. Outgroup aversion slows the rate of adoption and can lead to lower rates of adoption in the later-adopting group or even behavioural divergence between groups when outgroup aversion exceeds positive ingroup influence. When disease dynamics are coupled to the behaviour-adoption model, a wide variety of outcomes are possible. Homophily can either increase or decrease the final size of the epidemic depending on its relative strength in the two groups and on R0 for the infection. For example, if the first group is homophilous and the second is not, the second group will have a larger epidemic. Homophily and outgroup aversion can also produce dynamics suggestive of a ‘second wave’ in the first group that follows the peak of the epidemic in the second group. Our simple model reveals dynamics that are suggestive of the processes currently observed under pandemic conditions in culturally and/or politically polarised populations such as the USA.
Urgent Need for Adolescent Physical Activity Policies and Promotion: Lessons from “Jeeluna”

Physical inactivity is a growing concern in Kingdom of Saudi Arabia (KSA) and globally. Data on physical activity (PA) trends, barriers, and facilitators among adolescents in KSA are scarce. This study aims to identify PA trends amongst adolescents in KSA and associated health and lifestyle behaviors. Data from “Jeeluna”, a national study in KSA involving around 12,500 adolescents, were utilized. School students were invited to participate, and a multistage sampling procedure was used. Data collection included a self-administered questionnaire, anthropometric measurements, and blood sampling. Adolescents who performed PA for at least one day per week for >30 min each day were considered to “engage in PA”. Mean age of the participants was 15.8 ± 0.8 years, and 51.3% were male. Forty-four percent did not engage in PA regularly. Only 35% engaged in PA at school, while 40% were not offered PA at school. Significantly more 10–14-year old than 15–19-year-old adolescents and more males than females engaged in PA (<0.01). Mental health was better in adolescents who engaged in PA (<0.01). Adolescents who engaged in PA were more likely to eat healthy food and less likely to live a sedentary lifestyle (<0.01). It is imperative that socio-cultural and demographic factors be taken into consideration during program and policy development. This study highlights the urgent need for promoting PA among adolescents in KSA and addressing perceived barriers, while offering a treasure of information to policy and decision makers.
ZPR2: Joint Zero Pronoun Recovery and Resolution using Multi-Task Learning and BERT

Zero pronoun recovery and resolution aim at recovering the dropped pronoun and pointing out its anaphoric mentions, respectively. We propose to better explore their interaction by solving both tasks together, while the previous work treats them separately. For zero pronoun resolution, we study this task in a more realistic setting, where no parsing trees or only automatic trees are available, while most previous work assumes gold trees. Experiments on two benchmarks show that joint modeling significantly outperforms our baseline that already beats the previous state of the arts.
Breaking Through the 80% Glass Ceiling: Raising the State of the Art in Word Sense Disambiguation by Incorporating Knowledge Graph Information

Neural architectures are the current state of the art in Word Sense Disambiguation (WSD). However, they make limited use of the vast amount of relational information encoded in Lexical Knowledge Bases (LKB). We present Enhanced WSD Integrating Synset Embeddings and Relations (EWISER), a neural supervised architecture that is able to tap into this wealth of knowledge by embedding information from the LKB graph within the neural architecture, and to exploit pretrained synset embeddings, enabling the network to predict synsets that are not in the training set. As a result, we set a new state of the art on almost all the evaluation settings considered, also breaking through, for the first time, the 80% ceiling on the concatenation of all the standard all-words English WSD evaluation benchmarks. On multilingual all-words WSD, we report state-of-the-art results by training on nothing but English.