Miracle-dz/UniFAR-FTUs huggingface dataset:
Interrater reliability of quantitative ultrasound measures of muscle in critically ill patients

Quantitative neuromuscular ultrasound is increasingly used to study muscle pathology and changes in muscle mass during critical illness. Advantages of ultrasound are high axial resolution, low procedural risks, no ionizing radiation, and ease of use early in the course of disease. However, ultrasound is known to be an operator dependent imaging modality and the intensive care unit setting poses additional challenges to obtaining reliable measurements. There is limited evidence validating the feasibility and reliability of its application in this setting.
ORTHOSKIM: In silico sequence capture from genomic and transcriptomic libraries for phylogenomic and barcoding applications

Low‐coverage whole genome shotgun sequencing (or genome skimming) has emerged as a cost‐effective method for acquiring genomic data in nonmodel organisms. This method provides sequence information on chloroplast genome (cpDNA), mitochondrial genome (mtDNA) and nuclear ribosomal regions (rDNA), which are over‐represented within cells. However, numerous bioinformatic challenges remain to accurately and rapidly obtain such data in organisms with complex genomic structures and rearrangements, in particular for mtDNA in plants or for cpDNA in some plant families. Here we introduce the pipeline ORTHOSKIM, which performs in silico capture of targeted sequences from genomic and transcriptomic libraries without assembling whole organelle genomes. ORTHOSKIM proceeds in three steps: (i) global sequence assembly, (ii) mapping against reference sequences and (iii) target sequence extraction; importantly it also includes a range of quality control tests. Different modes are implemented to capture both coding and noncoding regions of cpDNA, mtDNA and rDNA sequences, along with predefined nuclear sequences (e.g., ultraconserved elements) or collections of single‐copy orthologue genes. Moreover, aligned DNA matrices are produced for phylogenetic reconstructions, by performing multiple alignments of the captured sequences. While ORTHOSKIM is suitable for any eukaryote, a case study is presented here, using 114 genome‐skimming libraries and four RNA sequencing libraries obtained for two plant families, Primulaceae and Ericaceae, the latter being a well‐known problematic family for cpDNA assemblies. ORTHOSKIM recovered with high success rates cpDNA, mtDNA and rDNA sequences, well suited to accurately infer evolutionary relationships within these families. ORTHOSKIM is released under a GPL‐3 licence and is available at: https://github.com/cpouchon/ORTHOSKIM.
Polyphenol Contents, Potential Antioxidant, Anticholinergic and Antidiabetic Properties of Mountain Mint (Cyclotrichium leucotrichum)

In the present work, antioxidant and antidiabetic potentials of mountain mint [Cyclotrichium leu‐cotrichum (Stapf ex Rech. Fil.) Leblebici] was the first time appraised. In this sense, methanol (MECL) and water (WECL) extracts were obtained from aerial parts of mountain mint (Cyclotrichium leucotrichum) and studied for their antioxidant ability by several bioanalytical assays. Also, their inhibition profiles were realized toward several metabolic enzymes connected to some diseases, including butyrylcholinesterase (BChE), α‐glycosidase, acetylcholinesterase (AChE), and α‐amylase enzymes. Additionally, their phenolic contents were determined by putative chromatographic method of LC/MS/MS. Consequently, nineteen phenolic molecules were identified in MECL and fifteen phenolic molecules were found in WECL. Also, antioxidant effects of both extracts were studied using by the methods of 1,1‐diphenyl‐2‐picryl‐hydrazyl (DPPH⋅), 2,2'‐azinobis(3‐ethylbenzthiazoline‐6‐sulfonic acid) (ABTS.+) and N,N‐dimethyl‐p‐phenylenediamine (DMPD.+) scavenging activities, ferric (Fe3+) and cupric (Cu2+) ions and Fe3+‐2,4,6‐tri(2‐pyridyl)‐s‐triazine (TPTZ) reducing capacities. MECL and WECL were found as powerful DPPH⋅ (IC50: 23.74 and 28.85 μg/mL), ABTS. + (IC50: 12.53 and 14.05 μg/mL) and DMPD. + scavenging effects (IC50: 43.52 and 54.80 μg/mL). Also, both extracts demonstrated the effective inhibition on AChE (IC50: 69.31 and 115.51 μg/mL), BChE (IC50: 57.75 and 86.62 μg/mL), α‐glycosidase (IC50: 36.47 and 62.94 μg/mL) and α‐amylase (IC50: 1.01 and 3.43 μg/mL). This study will be useful for future studies to determine the antioxidant properties and enzyme inhibition profile of food, medical and industrially important plants.
Dualized Labor Market and Polarized Health: A Longitudinal Perspective on the Association between Precarious Employment and Mental and Physical Health in Germany

This study analyzes the longitudinal association between precarious employment and physical and mental health in a dualized labor market by disaggregating between-employee and within-employee effects and considering mobility in precariousness of employment. Analyses were based on the German Socio-Economic Panel from 2002 to 2018 considering all employees ages 18 to 67 years (n = 38,551). Precariousness of employment was measured as an additive index considering working poverty, nonstandard working time arrangements, perceived job insecurity, and low social rights. Health outcomes were mental and physical health. Random effects models were used and controlled for sociodemographic and socioeconomic variables. Results indicated that the association between precariousness of employment and mental and physical health is mainly based on between-employee differences and that prolonged precariousness of employment or upward or downward mobility are associated with poor health. We found evidence of polarization in health by precariousness of employment within a dualized labor market.
Macrophage Polarity and Disease Control

Macrophages are present in most human tissues and have very diverse functions. Activated macrophages are usually divided into two phenotypes, M1 macrophages and M2 macrophages, which are altered by various factors such as microorganisms, tissue microenvironment, and cytokine signals. Macrophage polarity is very important for infections, inflammatory diseases, and malignancies; its management can be key in the prevention and treatment of diseases. In this review, we assess the current state of knowledge on macrophage polarity and report on its prospects as a therapeutic target.
Maternal Stressful Life Events During the Periconceptional Period and Orofacial Clefts: A Systematic Review and Meta-Analysis

Objective To assess whether women who experience stressful life events during the periconceptional period are at higher risk of giving birth to a baby with an orofacial cleft (OFC). Design Systematic review and meta-analysis of studies reporting the proportion of babies born with OFC to mothers exposed and unexposed to population-level or personal-level stressful life events during the periconceptional period. Six electronic databases were searched from inception to August 2020. Risk of bias was assessed using the Newcastle-Ottawa scale. Odds ratios (ORs) for the odds of OFC in babies of exposed mothers relative to unexposed controls were extracted and/or calculated. Random effects meta-analysis was undertaken, stratified by cleft subtype. Results Of 12 eligible studies, 8 examined experience of personal events and 4 examined population-level events. Studies demonstrated low-moderate risk of bias and there was indication of publication bias. There was some evidence that personal stressful life events were associated with greater odds of cleft lip and/or palate (six studies, OR 1.63, 95% confidence interval (CI) 1.16, 2.30, P = 0.001) and cleft palate only (six studies, OR 1.45, 95% CI 1.02, 2.06, P = 0.04). Population-level events were associated with higher odds of OFC in studies that did not specify subtype (three studies, OR 1.64, 95% CI 1.19, 2.25, P = 0.002), but subtype stratified analyses were underpowered. Heterogeneity was high. Conclusions Limited evidence indicated a weak positive association between maternal stressful life events during the periconceptional period and risk of OFC in the offspring, but further studies with greater consistency in research design are needed.
High Frequent In-domain Words Segmentation and Forward Translation for the WMT21 Biomedical Task

This paper reports the optimization of using the out-of-domain data in the Biomedical translation task. We firstly optimized our parallel training dataset using the BabelNet in-domain terminology words. Afterward, to increase the training set, we studied the effects of the out-of-domain data on biomedical translation tasks, and we created a mixture of in-domain and out-of-domain training sets and added more in-domain data using forward translation in the English-Spanish task. Finally, with a simple bpe optimization method, we increased the number of in-domain sub-words in our mixed training set and trained the Transformer model on the generated data. Results show improvements using our proposed method.
Cross-Lingual Named Entity Recognition via FastAlign: a Case Study

Named Entity Recognition is an essential task in natural language processing to detect entities and classify them into predetermined categories. An entity is a meaningful word, or phrase that refers to proper nouns. Named Entities play an important role in different NLP tasks such as Information Extraction, Question Answering and Machine Translation. In Machine Translation, named entities often cause translation failures regardless of local context, affecting the output quality of translation. Annotating named entities is a time-consuming and expensive process especially for low-resource languages. One solution for this problem is to use word alignment methods in bilingual parallel corpora in which just one side has been annotated. The goal is to extract named entities in the target language by using the annotated corpus of the source language. In this paper, we compare the performance of two alignment methods, Grow-diag-final-and and Intersect Symmetrisation heuristics, to exploit the annotation projection of English-Brazilian Portuguese bilingual corpus to detect named entities in Brazilian Portuguese. A NER model that is trained on annotated data extracted from the alignment methods, is used to evaluate the performance of aligners. Experimental results show the Intersect Symmetrisation is able to achieve superior performance scores compared to the Grow-diag-final-and heuristic in Brazilian Portuguese.
Language Model Pretraining and Transfer Learning for Very Low Resource Languages

This paper describes our submission for the shared task on Unsupervised MT and Very Low Resource Supervised MT at WMT 2021. We submitted systems for two language pairs: German ↔ Upper Sorbian (de ↔ hsb) and German-Lower Sorbian (de ↔ dsb). For de ↔ hsb, we pretrain our system using MASS (Masked Sequence to Sequence) objective and then finetune using iterative back-translation. Final finetunng is performed using the parallel data provided for translation objective. For de ↔ dsb, no parallel data is provided in the task, we use final de ↔ hsb model as initialization of the de ↔ dsb model and train it further using iterative back-translation, using the same vocabulary as used in the de ↔ hsb model.
Localization of the Epileptic Focus using Multi-scale Deep Learning

PET imaging is considered as one of safest and most significant methods for localizing the epileptic focus in the field of brain neuroscience. With the advent of the era of Artificial Intelligence, it becomes one of hot research topic that neurologist and radiologist are assisted to locate the epileptic focus by using Computer-aided Diagnosis method. In this study, we propose a novel method for localization of epileptic focus in PET imaging using Multi-scale Deep Learning method. Firstly, three different Mask Region-based Convolutional Neural Network models were used to extract the candidate of epileptic focus. The three models were produced by transferring learning towards Mask R-CNN utilizing training images that made up of PET scanning from three different scales. Each training image set contained 375 brain PET axial slices of epilepsy. Then three Deep Learning models were combined using the ensemble learning to reduce false positive results and to localize the epileptic focus. 48 PET axial slices were utilized as test set for this research. The sensitivity and specificity of this new method were 0.80 and 0.8125 respectively. The experimental results show the effectiveness of this method in locating epileptic focus.
Supporting Personalized Health Care With Social Media Analytics: An Application to Hypothyroidism

Social media analytics can considerably contribute to understanding health conditions beyond clinical practice, by capturing patients’ discussions and feelings about their quality of life in relation to disease treatments. In this article, we propose a methodology to support a detailed analysis of the therapeutic experience in patients affected by a specific disease, as it emerges from health forums. As a use case to test the proposed methodology, we analyze the experience of patients affected by hypothyroidism and their reactions to standard therapies. Our approach is based on a data extraction and filtering pipeline, a novel topic detection model named Generative Text Compression with Agglomerative Clustering Summarization (GTCACS), and an in-depth data analytic process. We advance the state of the art on automated detection of adverse drug reactions (ADRs) since, rather than simply detecting and classifying positive or negative reactions to a therapy, we are capable of providing a fine characterization of patients along different dimensions, such as co-morbidities, symptoms, and emotional states.
Economical defence of resources structures territorial space use in a cooperative carnivore

Ecologists have long sought to understand space use and mechanisms underlying patterns observed in nature. We developed an optimality landscape and mechanistic territory model to understand mechanisms driving space use and compared model predictions to empirical reality. We demonstrate our approach using grey wolves (Canis lupus). In the model, simulated animals selected territories to economically acquire resources by selecting patches with greatest value, accounting for benefits, costs and trade-offs of defending and using space on the optimality landscape. Our approach successfully predicted and explained first- and second-order space use of wolves, including the population's distribution, territories of individual packs, and influences of prey density, competitor density, human-caused mortality risk and seasonality. It accomplished this using simple behavioural rules and limited data to inform the optimality landscape. Results contribute evidence that economical territory selection is a mechanistic bridge between space use and animal distribution on the landscape. This approach and resulting gains in knowledge enable predicting effects of a wide range of environmental conditions, contributing to both basic ecological understanding of natural systems and conservation. We expect this approach will demonstrate applicability across diverse habitats and species, and that its foundation can help continue to advance understanding of spatial behaviour.
Papago’s Submission for the WMT21 Quality Estimation Shared Task

This paper describes Papago submission to the WMT 2021 Quality Estimation Task 1: Sentence-level Direct Assessment. Our multilingual Quality Estimation system explores the combination of Pretrained Language Models and Multi-task Learning architectures. We propose an iterative training pipeline based on pretraining with large amounts of in-domain synthetic data and finetuning with gold (labeled) data. We then compress our system via knowledge distillation in order to reduce parameters yet maintain strong performance. Our submitted multilingual systems perform competitively in multilingual and all 11 individual language pair settings including zero-shot.
Driver's Fixation Transition Mode during Lane Changing Process

In order to research drivers' fixation transition characteristics on highway, real road lane changing experiments are conducted on highway. A kind of non-contact eye movement equipment faceLAB 5 is used to monitor and record the eye movement data of 13 drivers. Firstly, by statistic analysis of the eye movement data, it confirms the time length lane change intent stage. Then, the Markov Chain Theory is employed to analyze fixation transition mode as well as fixation steady state distribution characteristics. Finally, differences of the drivers' fixation situation are analyzed during lane change intent stage and lane change implementation stage. The study shows that during lane change intent stage the transition of driver's visual line between the original lane and target lane is more frequent than normal driving, with wider horizontal visual angle. The research results can provide a theoretical basis to determine whether a driver has lane changing intent.
Quality Estimation Using Dual Encoders with Transfer Learning

This paper describes POSTECH’s quality estimation systems submitted to Task 2 of the WMT 2021 quality estimation shared task: Word and Sentence-Level Post-editing Effort. We notice that it is possible to improve the stability of the latest quality estimation models that have only one encoder based on the self-attention mechanism to simultaneously process both of the two input data, a source sequence and its machine translation, in that such models have neglected to take advantage of pre-trained monolingual representations, which are generally accepted as reliable representations for various natural language processing tasks. Therefore, our model uses two pre-trained monolingual encoders and then exchanges the information of two encoded representations through two additional cross attention networks. According to the official leaderboard, our systems outperform the baseline systems in terms of the Matthews correlation coefficient for machine translations’ word-level quality estimation and in terms of the Pearson’s correlation coefficient for sentence-level quality estimation by 0.4126 and 0.5497 respectively.
Low Resource Similar Language Neural Machine Translation for Tamil-Telugu

This paper describes the participation of team oneNLP (LTRC, IIIT-Hyderabad) for the WMT 2021 task, similar language translation. We experimented with transformer based Neural Machine Translation and explored the use of language similarity for Tamil-Telugu and Telugu-Tamil. We incorporated use of different subword configurations, script conversion and single model training for both directions as exploratory experiments.
Small Model and In-Domain Data Are All You Need

I participated in the WMT shared news translation task and focus on one high resource language pair: English and Chinese (two directions, Chinese to English and English to Chinese). The submitted systems (ZengHuiMT) focus on data cleaning, data selection, back translation and model ensemble. The techniques I used for data filtering and selection include filtering by rules, language model and word alignment. I used a base translation model trained on initial corpus to obtain the target versions of the WMT21 test sets, then I used language models to find out the monolingual data that is most similar to the target version of test set, such monolingual data was then used to do back translation. On the test set, my best submitted systems achieve 35.9 and 32.2 BLEU for English to Chinese and Chinese to English directions respectively, which are quite high for a small model.
Kakao Enterprise’s WMT21 Machine Translation Using Terminologies Task Submission

This paper describes Kakao Enterprise’s submission to the WMT21 shared Machine Translation using Terminologies task. We integrate terminology constraints by pre-training with target lemma annotations and fine-tuning with exact target annotations utilizing the given terminology dataset. This approach yields a model that achieves outstanding results in terms of both translation quality and term consistency, ranking first based on COMET in the En→Fr language direction. Furthermore, we explore various methods such as back-translation, explicitly training terminologies as additional parallel data, and in-domain data selection.
Neural Machine Translation for Tamil–Telugu Pair

The neural machine translation approach has gained popularity in machine translation because of its context analysing ability and its handling of long-term dependency issues. We have participated in the WMT21 shared task of similar language translation on a Tamil-Telugu pair with the team name: CNLP-NITS. In this task, we utilized monolingual data via pre-train word embeddings in transformer model based neural machine translation to tackle the limitation of parallel corpus. Our model has achieved a bilingual evaluation understudy (BLEU) score of 4.05, rank-based intuitive bilingual evaluation score (RIBES) score of 24.80 and translation edit rate (TER) score of 97.24 for both Tamil-to-Telugu and Telugu-to-Tamil translations respectively.
Building a Corpus for Corporate Websites Machine Translation Evaluation. A Step by Step Methodological Approach

The aim of this paper is to describe the process carried out to develop a paral-lel corpus comprised of texts extracted from the corporate websites of south-ern Spanish SMEs from the sanitary sector which will serve as the basis for MT quality assessment. The stages for compiling the parallel corpora were: (i) selection of websites with content translated in English and Spanish, (ii) downloading of the HTML files of the selected websites, (iii) files filtering and pairing of English files with their Spanish equivalents, (iv) compilation of individual corpora (EN and ES) for each of the selected websites, (v) merging of the individual corpora into a two general corpus one in English and the other in Spanish, (vi) selection a representative sample of segments to be used as original (ES) and reference translations (EN), (vii) building of the parallel corpus intended for MT evaluation. The parallel corpus generated will serve to future Machine Translation quality assessment. In addition, the monolingual corpora generated during the process could as a base to carry out research focused on linguistic – bilingual or monolingual − analysis.
How Do the Beaver Home Ranges Vary during the Range Expansion?

Reoccupation of European landscapes by native species causes changes in their population densities and home ranges. To test whether an increase in population density affects home range sizes, we studied the strictly territorial herbivore Eurasian beaver (Castor fiber). Twenty-four beavers were radio-tracked to reveal their home ranges under varying population densities. The mean length of occupied shorelines was 2648.0 ± 1530.6 m. We did not find any linkage between home range sizes and population densities. Our results showed that the availability of resources was significantly associated with the duration of site occupation. Thus, the space use was primarily determined by the availability of food resources, rather than by rising population density.