Miracle-dz/UniFAR-FTUs huggingface dataset:
Trait-specific Selection Signature Detection Reveals Novel Loci of Meat Quality in Large White Pigs

In past decades, meat quality traits have been shaped by human-driven selection in the process of genetic improvement programs. Exploring the potential genetic basis of artificial selection and mapping functional candidate genes for economic traits are of great significance in genetic improvement of pigs. In this study, we focus on investigating the genetic basis of five meat quality traits, including intramuscular fat content (IMF), drip loss, water binding capacity, pH at 45 min (pH45min), and ultimate pH (pH24h). Through making phenotypic gradient differential population pairs, Wright’s fixation index (FST) and the cross-population extended haplotype homozogysity (XPEHH) were applied to detect selection signatures for these five traits. Finally, a total of 427 and 307 trait-specific selection signatures were revealed by FST and XPEHH, respectively. Further bioinformatics analysis indicates that some genes, such as USF1, NDUFS2, PIGM, IGSF8, CASQ1, and ACBD6, overlapping with the trait-specific selection signatures are responsible for the phenotypes including fat metabolism and muscle development. Among them, a series of promising trait-specific selection signatures that were detected in the high IMF subpopulation are located in the region of 93544042-95179724bp on SSC4, and the genes harboring in this region are all related to lipids and muscle development. Overall, these candidate genes of meat quality traits identified in this analysis may provide some fundamental information for further exploring the genetic basis of this complex trait.
Combining Statistical Analysis and Machine Learning for EEG Scalp Topograms Classification

Incorporating brain-computer interfaces (BCIs) into daily life requires reducing the reliance of decoding algorithms on the calibration or enabling calibration with the minimal burden on the user. A potential solution could be a pre-trained decoder demonstrating a reasonable accuracy on the naive operators. Addressing this issue, we considered ambiguous stimuli classification tasks and trained an artificial neural network to classify brain responses to the stimuli of low and high ambiguity. We built a pre-trained classifier utilizing time-frequency features corresponding to the fundamental neurophysiological processes shared between subjects. To extract these features, we statistically contrasted electroencephalographic (EEG) spectral power between the classes in the representative group of subjects. As a result, the pre-trained classifier achieved 74% accuracy on the data of newly recruited subjects. Analysis of the literature suggested that a pre-trained classifier could help naive users to start using BCI bypassing training and further increased accuracy during the feedback session. Thus, our results contribute to using BCI during paralysis or limb amputation when there is no explicit user-generated kinematic output to properly train a decoder. In machine learning, our approach may facilitate the development of transfer learning (TL) methods for addressing the cross-subject problem. It allows extracting the interpretable feature subspace from the source data (the representative group of subjects) related to the target data (a naive user), preventing the negative transfer in the cross-subject tasks.
Do Words Matter? Detecting Social Isolation and Loneliness in Older Adults Using Natural Language Processing

Introduction: Social isolation and loneliness (SI/L) are growing problems with serious health implications for older adults, especially in light of the COVID-19 pandemic. We examined transcripts from semi-structured interviews with 97 older adults (mean age 83 years) to identify linguistic features of SI/L. Methods: Natural Language Processing (NLP) methods were used to identify relevant interview segments (responses to specific questions), extract the type and number of social contacts and linguistic features such as sentiment, parts-of-speech, and syntactic complexity. We examined: (1) associations of NLP-derived assessments of social relationships and linguistic features with validated self-report assessments of social support and loneliness; and (2) important linguistic features for detecting individuals with higher level of SI/L by using machine learning (ML) models. Results: NLP-derived assessments of social relationships were associated with self-reported assessments of social support and loneliness, though these associations were stronger in women than in men. Usage of first-person plural pronouns was negatively associated with loneliness in women and positively associated with emotional support in men. ML analysis using leave-one-out methodology showed good performance (F1 = 0.73, AUC = 0.75, specificity = 0.76, and sensitivity = 0.69) of the binary classification models in detecting individuals with higher level of SI/L. Comparable performance were also observed when classifying social and emotional support measures. Using ML models, we identified several linguistic features (including use of first-person plural pronouns, sentiment, sentence complexity, and sentence similarity) that most strongly predicted scores on scales for loneliness and social support. Discussion: Linguistic data can provide unique insights into SI/L among older adults beyond scale-based assessments, though there are consistent gender differences. Future research studies that incorporate diverse linguistic features as well as other behavioral data-streams may be better able to capture the complexity of social functioning in older adults and identification of target subpopulations for future interventions. Given the novelty, use of NLP should include prospective consideration of bias, fairness, accountability, and related ethical and social implications.
Glucosamine/β-Alanine Carbon Dots Use as DNA Carriers Into E. coli Cells

Introducing foreign DNA into bacterial cells is essential in functional genomics and molecular research. Currently, heat shock and electroporation are the two major techniques of gene delivery in bacterial cells. However, both the techniques are time and resource consuming and are limited to a few species or strains of bacteria and there is a need to develop new transformation alternatives. Carbon dots with unique features such as facile synthesis, ease of functionalization, nontoxicity, and biocompatibility are considered novel biomolecule nanocarriers. In this study, we synthesized and evaluated DNA delivery potential of four carbon dots including: 1) amine-coated carbon dots (NH2-FCDs); 2) carboxylate carbon dots (COOH-FCDs); 3) L-arginine and glucose carbon dots (N-CDs), and 4) citric acid and polyethyleneimine (PEI) carbon dots into Escherichia. coli cells. We evaluated the minimum incubation time required for the plasmid DNA delivery and the maximum plasmid size that can be delivered into E. coli cells using these CDs. Bacteria were incubated with carbon dots solution for different lengths of time and plated on selection media. Transformed colonies were counted and data were analyzed to identify the optimum incubation time and measure DNA delivery of these CDs with plasmids of different sizes. Our study demonstrated that among all these CDs, only carboxylate carbon dots (COOH-FCDs) prepared from glucosamine and β-alanine were able to deliver plasmid DNA into E. coli cells and the best incubation time was between 30 and 60 min. The maximum plasmid size that could be delivered using these CDs was approximately 10 kb and transformation efficiency decreased with larger plasmids. This study shows the capacity of COOH-CDs to deliver plasmid DNA into bacteria with an immense potential to combine with modern genome-editing tools. However, further studies are needed to evaluate their potential in DNA delivery in other bacterial strains.
Teeport: Break the Wall Between the Optimization Algorithms and Problems

Optimization algorithms/techniques such as genetic algorithm, particle swarm optimization, and Gaussian process have been widely used in the accelerator field to tackle complex design/online optimization problems. However, connecting the algorithm with the optimization problem can be difficult, as the algorithms and the problems may be implemented in different languages, or they may require specific resources. We introduce an optimization platform named Teeport that is developed to address the above issues. This real-time communication-based platform is designed to minimize the effort of integrating the algorithms and problems. Once integrated, the users are granted a rich feature set, such as monitoring, controlling, and benchmarking. Some real-life applications of the platform are also discussed.
The Molecular Effects of Dietary Acid Load on Metabolic Disease (The Cellular PasaDoble: The Fast-Paced Dance of pH Regulation)

Metabolic diseases are becoming more common and more severe in populations adhering to western lifestyle. Since metabolic conditions are highly diet and lifestyle dependent, it is suggested that certain diets are the cause for a wide range of metabolic dysfunctions. Oxidative stress, excess calcium excretion, inflammation, and metabolic acidosis are common features in the origins of most metabolic disease. These primary manifestations of “metabolic syndrome” can lead to insulin resistance, diabetes, obesity, and hypertension. Further complications of the conditions involve kidney disease, cardiovascular disease, osteoporosis, and cancers. Dietary analysis shows that a modern “Western-style” diet may facilitate a disruption in pH homeostasis and drive disease progression through high consumption of exogenous acids. Because so many physiological and cellular functions rely on acid-base reactions and pH equilibrium, prolonged exposure of the body to more acids than can effectively be buffered, by chronic adherence to poor diet, may result in metabolic stress followed by disease. This review addresses relevant molecular pathways in mammalian cells discovered to be sensitive to acid - base equilibria, their cellular effects, and how they can cascade into an organism-level manifestation of Metabolic Syndromes. We will also discuss potential ways to help mitigate this digestive disruption of pH and metabolic homeostasis through dietary change.
Connecting the Dots Between Mindset and Impostor Phenomenon, via Fear of Failure and Goal Orientation, in Working Adults

This study aims to investigate the relationship between mindset and impostor phenomenon, via the explanatory role of fear of failure and goal orientation in the work domain. Only one known study has previously connected mindset and impostor phenomenon in the scientific literature among females in a university setting. Data was collected from 201 working adults, with a roughly equal male-female ratio, from a range of sectors in the United Kingdom, Ireland, and United States. Participants completed an online survey comprising the Implicit Theories of Intelligence Scale, the Performance Failure Appraisal Inventory, Work Domain Goal Orientation Instrument, and the Clance Impostor Phenomenon Scale (CIPS). We tested a serial-parallel mediation model using structural equation modeling. The results suggest that people with a fixed mindset tend to experience more impostor phenomenon at work and this relationship is predominantly explained by their fear of failure. Further, when employees are also motivated by a performance avoid goal orientation, the relationship increases in strength. This indirect relationship suggests that staff training, and coaching interventions designed to increase people’s belief that they can develop their abilities results in a reduction of their fear of failure and in their motivation to want to avoid showing their inability at work. The results also suggest cultivating environments that promote a growth mindset and learning goal orientation, alongside the safety to fail, could lessen the negative effects of having a fixed mindset, reduce fear of failure, and alleviate impostor phenomenon’s negative impact on employee career development and wellbeing.
Fabrication and Mechanical Performance of Graphene Nanoplatelet/Glass Fiber Reinforced Polymer Hybrid Composites

Glass fiber reinforced polymer (GFRP) composites are promising alternatives for the traditional carbon steel pipes used in the oil and gas industry due to their corrosion and chemical resistance. However, the out-of-plane mechanical properties of GFRPs still need further improvement to achieve this goal. Hence, in this work, two methods combining either vacuum mixing or spray coating with vacuum-assisted resin infusion were studied to fabricate graphene nanoplatelet (GNP)/GFRP hybrid composites. The former method resulted in a severe filtering effect, where the GNPs were not evenly distributed throughout the final composite, whereas the latter process resulted in a uniform GNP distribution on the glass fabrics. The addition of GNPs showed no modest contribution to the tensile performance of the GFRP composites due to the relatively high volume and in-plane alignment of the glass fibers. However, the GNPs did improve the flexural properties of GFRP with an optimal loading of 0.15 wt% GNPs, resulting in flexural strength and modulus increases of 6.8 and 1.6%, respectively. This work indicates how GNPs can be advantageous for out-of-plane mechanical reinforcement in fiber-reinforced composites.
On Refining BERT Contextualized Embeddings using Semantic Lexicons

Word vector representations play a fundamental role in many NLP applications. Exploiting human-curated knowledge was proven to improve the quality of word embeddings and their performance on many downstream tasks. Retroﬁtting is a simple and popular technique for reﬁning distributional word embeddings based on relations coming from a semantic lexicon. Inspired by this technique, we present two methods for incorporating knowledge into contextualized embeddings. We evaluate these methods with BERT embeddings on three biomedical datasets for relation extraction and one movie review dataset for sentiment analysis. We demonstrate that the retroﬁtted vectors do not substantially impact the performance for these tasks, and conduct a qualitative analysis to provide further insights on this negative result.
Characterization of Host lncRNAs in Response to Vibrio splendidus Infection and Function as Efficient miRNA Sponges in Sea Cucumber

Long non-coding RNAs (lncRNAs) have been reported to play critical roles during pathogen infection and innate immune response in mammals. Such observation inspired us to explore the expression profiles and functions of lncRNAs in invertebrates upon bacterial infection. Here, the lncRNAs of sea cucumber (Apostichopus japonicus) involved in Vibrio splendidus infection were characterized. RNA-seq obtained 2897 differentially expressed lncRNAs from Vibrio splendidus infected coelomocytes of sea cucumbers. The potential functions of the significant differentially expressed lncRNAs were related to immunity and metabolic process based on the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Moreover, we identify a lncRNA (XLOC_028509), which is downregulated with Vibrio splendidus challenged, further study indicated that XLOC_028509 adsorb miR-2008 and miR-31 as competing endogenous RNAs (ceRNAs) through base complementarity, which in turn decreased the amount of miRNAs (microRNAs) bound to the 3’UTRs (untranslated regions) of mRNAs to reduce their inhibition of target gene translation. These data demonstrated that the lncRNAs of invertebrates might be important regulators in pathogen-host interactions by sponging miRNAs.
Quantification of Myocyte Disarray in Human Cardiac Tissue

Proper three-dimensional (3D)-cardiomyocyte orientation is important for an effective tension production in cardiac muscle. Cardiac diseases can cause severe remodeling processes in the heart, such as cellular misalignment, that can affect both the electrical and mechanical functions of the organ. To date, a proven methodology to map and quantify myocytes disarray in massive samples is missing. In this study, we present an experimental pipeline to reconstruct and analyze the 3D cardiomyocyte architecture in massive samples. We employed tissue clearing, staining, and advanced microscopy techniques to detect sarcomeres in relatively large human myocardial strips with micrometric resolution. Z-bands periodicity was exploited in a frequency analysis approach to extract the 3D myofilament orientation, providing an orientation map used to characterize the tissue organization at different spatial scales. As a proof-of-principle, we applied the proposed method to healthy and pathologically remodeled human cardiac tissue strips. Preliminary results suggest the reliability of the method: strips from a healthy donor are characterized by a well-organized tissue, where the local disarray is log-normally distributed and slightly depends on the spatial scale of analysis; on the contrary, pathological strips show pronounced tissue disorganization, characterized by local disarray significantly dependent on the spatial scale of analysis. A virtual sample generator is developed to link this multi-scale disarray analysis with the underlying cellular architecture. This approach allowed us to quantitatively assess tissue organization in terms of 3D myocyte angular dispersion and may pave the way for developing novel predictive models based on structural data at cellular resolution.
CroAno : A Crowd Annotation Platform for Improving Label Consistency of Chinese NER Dataset

In this paper, we introduce CroAno, a web-based crowd annotation platform for the Chinese named entity recognition (NER). Besides some basic features for crowd annotation like fast tagging and data management, CroAno provides a systematic solution for improving label consistency of Chinese NER dataset. 1) Disagreement Adjudicator: CroAno uses a multi-dimensional highlight mode to visualize instance-level inconsistent entities and makes the revision process user-friendly. 2) Inconsistency Detector: CroAno employs a detector to locate corpus-level label inconsistency and provides users an interface to correct inconsistent entities in batches. 3) Prediction Error Analyzer: We deconstruct the entity prediction error of the model to six fine-grained entity error types. Users can employ this error system to detect corpus-level inconsistency from a model perspective. To validate the effectiveness of our platform, we use CroAno to revise two public datasets. In the two revised datasets, we get an improvement of +1.96% and +2.57% F1 respectively in model performance.
Effectiveness of a Non-pharmacological Intervention to Control Diabetes Mellitus in a Primary Care Setting in Kerala: A Cluster-Randomized Controlled Trial

Background: Despite being the first Indian state with a dedicated Non-Communicable Disease (NCD) program, glycemic control among a large proportion of patients is low in Kerala. This study tries to find evidence for a standardized non-pharmacological strategy delivered through Junior Public Health Nurses (JPHNs) in achieving and maintaining glycemic control among diabetic patients registered with NCD clinics of primary health care settings. Design: A cluster randomized controlled trial was conducted among adult patients with Diabetes Mellitus attending NCD clinics of primary care settings of South Kerala, India. JPHNs of the intervention group received additional module-based training while standard management continued in the control group. Sequence generation was done by random permuted blocks method and a cluster of 12 patients was selected from each of the 11 settings by computer-generated random numbers. Patients were followed up for 6 months with monthly monitoring of Fasting Blood Sugar (FBS), Post-Prandial Blood Sugar (PPBS), blood pressure, Body Mass Index (BMI), and health-related behaviors. Knowledge and skills/practice of JPHNs were also evaluated. Analysis of Covariance was done to study the final outcome adjusting for the baseline values and a model for glycemic control was predicted using multilevel modeling. Results: We analyzed 72 participants in the intervention group and 60 participants in the control group according to the intention-to-treat principle. The intervention was associated with a significant reduction in FBS (p < 0.001) and PPBS (p < 0.001) adjusting for the baseline values. The achievement of glycemic control was 1.5 (95% CI: 1.05–2.3) times better with intervention and they showed a better trend of maintenance of glycemic control (FBS, p = 0.003 and PPBS, p = 0.039). Adjusting for clustering and the baseline values, the intervention showed a significant effect on FBS (B = −3.1, SE = 0.57; p < 0.001) and PPBS (B = −0.81, SE = 0.3; p < 0.001) with time. Drug adherence score (p < 0.001), hours of physical activity (p < 0.001), BMI (p = 0.002), fruit intake (p = 0.004), and green leafy vegetable intake (p = 0.01) were the major predictors of FBS control. The practice/skills score of the JPHNs significantly improved with intervention (p < 0.001) adjusting for baseline values. Conclusion: A well-designed health worker intervention package incorporated into the existing health system can translate into attitude change and skill development in the health workers which can reflect in the improvement of glycemic control among the patients. Trial registration: [URL: http://www.ctri. nic.in], identifier [CTRI/2017/11/010622].
Risk Factors for Invasive Aspergillosis in Patients Admitted to the Intensive Care Unit With Coronavirus Disease 2019: A Multicenter Retrospective Study

Background: Invasive pulmonary aspergillosis (IPA) is a life-threatening complication in coronavirus disease 2019 (COVID-19) patients admitted to intensive care units (ICUs), but risk factors for COVID-19-associated IPA (CAPA) have not been fully characterized. The aim of the current study was to identify factors associated with CAPA, and assess long-term mortality. Methods: A retrospective cohort study of adult COVID-19 patients admitted to ICUs from six hospitals was conducted in Hubei, China. CAPA was diagnosed via composite clinical criteria. Demographic information, clinical variables, and 180-day outcomes after the diagnosis of CAPA were analyzed. Results: Of 335 critically ill patients with COVID-19, 78 (23.3%) developed CAPA within a median of 20.5 days (range 13.0–42.0 days) after symptom onset. Compared to those without CAPA, CAPA patients were more likely to have thrombocytopenia (50 vs. 19.5%, p < 0.001) and secondary bacterial infection prior to being diagnosed with CAPA (15.4 vs. 6.2%, p = 0.013), and to receive vasopressors (37.2 vs. 8.6%, p < 0.001), higher steroid dosages (53.9 vs. 34.2%, p = 0.002), renal replacement therapy (37.2 vs. 13.6%, p < 0.001), and invasive mechanical ventilation (57.7 vs. 35.8%, p < 0.001). In multivariate analysis incorporating hazard ratios (HRs) and confidence intervals (CIs), thrombocytopenia (HR 1.98, 95% CI 1.16–3.37, p = 0.012), vasopressor use (HR 3.57, 95% CI 1.80–7.06, p < 0.001), and methylprednisolone use at a daily dose ≥ 40 mg (HR 1.69, 95% CI 1.02–2.79, p = 1.02–2.79) before CAPA diagnosis were independently associated with CAPA. Patients with CAPA had longer median ICU stays (17 days vs. 12 days, p = 0.007), and higher 180-day mortality (65.4 vs. 33.5%, p < 0.001) than those without CAPA. Conclusions: Thrombocytopenia, vasopressor use, and corticosteroid treatment were significantly associated with increased risk of incident IPA in COVID-19 patients admitted to ICUs. The occurrence of CAPA may increase the likelihood of long-term COVID-19 mortality.