Miracle-dz/UniFAR-FTUs huggingface dataset:
Cardiac sympathovagal activity initiates a functional brain-body response to emotional processing

A century-long debate on bodily states and emotions persists. While the involvement of bodily activity in emotion physiology is widely recognized, the specificity and causal role of such activity related to brain dynamics has not yet been demonstrated. We hypothesize that the peripheral neural monitoring and control of cardiovascular activity prompts and sustains brain dynamics during an emotional experience, so these afferent inputs are processed by the brain by triggering a concurrent efferent information transfer to the body. To this end, we investigated the functional brain-heart interplay under emotion elicitation in publicly available data from 62 healthy participants using a computational model based on synthetic data generation of EEG and ECG signals. Our findings show that sympathovagal activity plays a leading and causal role in initiating the emotional response, in which ascending modulations from vagal activity precede neural dynamics and correlate to the reported level of arousal. The subsequent dynamic interplay observed between the central and autonomic nervous systems sustains emotional processing. These findings should be particularly revealing for the psychophysiology and neuroscience of emotions. Significance We investigate the temporal dynamics of brain and cardiac activities in healthy subjects who underwent an emotional elicitation through videos. We demonstrate that, within the first few seconds, emotional stimuli modulate the heart activity, which in turn stimulate an emotion-specific cortical response in the brain. Then, the conscious emotional experience is sustained by a bidirectional brain-heart interplay and information exchange. Moreover, the perceived intensity of an emotional stimulus is predicted by the intensity of neural control regulating the heart activity. These findings may constitute the fundamental knowledge linking neurophysiology and psychiatric disorders, including the link between depressive symptoms and cardiovascular disorders.
Risk of bias assessment in preclinical literature using natural language processing

Objective We sought to apply natural language processing to the task of automatic risk of bias assessment in preclinical literature, which could speed the process of systematic review, provide information to guide research improvement activity, and support translation from preclinical to clinical research. Materials and Methods We use 7,840 full-text publications describing animal experiments with yes/no annotations for five risk of bias items. We implement a series of models including baselines (support vector machine, logistic regression, random forest), neural models (convolutional neural network, recurrent neural network with attention, hierarchical neural network) and models using BERT with two strategies (document chunk pooling and sentence extraction). We tune hyperparameters to obtain the highest F1 scores for each risk of bias item on the validation set and compare evaluation results on the test set to our previous regular expression approach. Results The F1 scores of best models on test set are 82.0% for random allocation, 81.6% for blinded assessment of outcome, 82.6% for conflict of interests, 91.4% for compliance with animal welfare regulations and 46.6% for reporting animals excluded from analysis. Our models significantly outperform regular expressions for four risk of bias items. Conclusion For random allocation, blinded assessment of outcome, conflict of interests and animal exclusions, neural models achieve good performance, and for animal welfare regulations, BERT model with sentence extraction strategy works better.
Aging leads to DNA methylation alterations associated with loss of lineage fidelity and breast cancer in mammary luminal epithelial cells

DNA methylation in mammalian genomes is critical for repression of transposable elements (TEs) and regulation of transcription factor (TF) binding. Methylation loss in cancer cells results in widespread TE activation and altered TF binding that subsequently impact transcriptional networks. While the aging process is similarly characterized by changes to the methylome, the extent and its functional consequences have remained unclear. We report here that aging leads to distinct methylation changes in luminal epithelial cells, a key cell lineage implicated in age-related breast cancers. Regulatory elements and evolutionarily old TEs display consistent methylation changes at lineage-specific TF binding sites. Evolutionarily younger TEs, however, exhibit stochastic methylation loss that corresponds with increased methylation entropy. Each of these classes of methylation change impact the regulation of genes associated with luminal breast cancer. Altogether, our results indicate that aging leads to distinct DNA methylation changes that can rewire gene networks important for breast cancer.
Missing Link Prediction Using Non-Overlapped Features and Multiple Sources of Social Networks

The current methods for missing link prediction in social networks focus on using data from overlapping users from two social network sources to recommend links between unconnected users. To improve prediction of the missing link, this paper presents the use of information from non-overlapping users as additional features in training a prediction model using a machine-learning approach. The proposed features are designed to use together with the common features as extra features to help in tuning up for a better classification model. The social network data sources used in this paper are Twitter and Facebook where Twitter is a main data for prediction and Facebook is a supporting data. For evaluations, a comparison using different machine-learning techniques, feature settings, and different network-density level of data source is studied. The experimental results can be concluded that the prediction model using a combination of the proposed features and the common features with Random Forest technique gained the best efficiency using percentage amount of recovering missing links and F1 score. The model of combined features yields higher percentage of recovering link by an average of 23.25% and the F1-measure by an average of 19.80% than the baseline of multi-social network source.
Ontology-Based Approach to Semantically Enhanced Question Answering for Closed Domain: A Review

For many users of natural language processing (NLP), it can be challenging to obtain concise, accurate and precise answers to a question. Systems such as question answering (QA) enable users to ask questions and receive feedback in the form of quick answers to questions posed in natural language, rather than in the form of lists of documents delivered by search engines. This task is challenging and involves complex semantic annotation and knowledge representation. This study reviews the literature detailing ontology-based methods that semantically enhance QA for a closed domain, by presenting a literature review of the relevant studies published between 2000 and 2020. The review reports that 83 of the 124 papers considered acknowledge the QA approach, and recommend its development and evaluation using different methods. These methods are evaluated according to accuracy, precision, and recall. An ontological approach to semantically enhancing QA is found to be adopted in a limited way, as many of the studies reviewed concentrated instead on NLP and information retrieval (IR) processing. While the majority of the studies reviewed focus on open domains, this study investigates the closed domain.
Multi-Task Learning for Sentiment Analysis with Hard-Sharing and Task Recognition Mechanisms

: In the era of big data, multi-task learning has become one of the crucial technologies for sentiment analysis and classiﬁcation. Most of the existing multi-task learning models for sentiment analysis are developed based on the soft-sharing mechanism that has less interference between different tasks than the hard-sharing mechanism. However, there are also fewer essential features that the model can extract with the soft-sharing method, resulting in unsatisfactory classiﬁcation performance. In this paper, we propose a multi-task learning framework based on a hard-sharing mechanism for sentiment analysis in various ﬁelds. The hard-sharing mechanism is achieved by a shared layer to build the interrelationship among multiple tasks. Then, we design a task recognition mechanism to reduce the interference of the hard-shared feature space and also to enhance the correlation between multiple tasks. Experiments on two real-world sentiment classiﬁcation datasets show that our approach achieves the best results and improves the classiﬁcation accuracy over the existing methods signiﬁcantly. The task recognition training process enables a unique representation of the features of different tasks in the shared feature space, providing a new solution reducing interference in the shared feature space for sentiment analysis.
Mitigating Information Asymmetry to Achieve Crowdfunding Success: Signaling and Online Communication

This paper examines how signals and the herding factor originating from multiple sources complement or substitute for each other’s effects in the crowdfunding context. Drawing on the elaboration likelihood model, we propose that signals from campaigns (videos) and creators (experience) can mitigate information asymmetry concerns about project quality and creator credibility and that creator-originating signals offset the effect of campaign-originating signals on crowdfunding success. Further, we posit that online communication between creators and backers (backer comments and creator replies) complements or substitutes for the effects of signals originating from the campaign or creator. We collected and analyzed objective data on 9,884 crowdfunding projects from a major reward-based crowdfunding platform in China and were able to confirm most of our hypotheses.
What’s a Biofilm?—How the Choice of the Biofilm Model Impacts the Protein Inventory of Clostridioides difficile

The anaerobic pathogen Clostridioides difficile is perfectly equipped to survive and persist inside the mammalian intestine. When facing unfavorable conditions C. difficile is able to form highly resistant endospores. Likewise, biofilms are currently discussed as form of persistence. Here a comprehensive proteomics approach was applied to investigate the molecular processes of C. difficile strain 630Δerm underlying biofilm formation. The comparison of the proteome from two different forms of biofilm-like growth, namely aggregate biofilms and colonies on agar plates, revealed major differences in the formation of cell surface proteins, as well as enzymes of its energy and stress metabolism. For instance, while the obtained data suggest that aggregate biofilm cells express both flagella, type IV pili and enzymes required for biosynthesis of cell-surface polysaccharides, the S-layer protein SlpA and most cell wall proteins (CWPs) encoded adjacent to SlpA were detected in significantly lower amounts in aggregate biofilm cells than in colony biofilms. Moreover, the obtained data suggested that aggregate biofilm cells are rather actively growing cells while colony biofilm cells most likely severely suffer from a lack of reductive equivalents what requires induction of the Wood-Ljungdahl pathway and C. difficile’s V-type ATPase to maintain cell homeostasis. In agreement with this, aggregate biofilm cells, in contrast to colony biofilm cells, neither induced toxin nor spore production. Finally, the data revealed that the sigma factor SigL/RpoN and its dependent regulators are noticeably induced in aggregate biofilms suggesting an important role of SigL/RpoN in aggregate biofilm formation.
Identification of DNA Methylation and Transcriptomic Profiles Associated With Fruit Mealiness in Prunus persica (L.) Batsch

Peach (Prunus persica) fruits have a fast ripening process and a shelf-life of days, presenting a challenge for long-distance consuming markets. To prolong shelf-life, peach fruits are stored at low temperatures (0 to 7 °C) for at least two weeks, which can lead to the development of mealiness, a physiological disorder that reduces fruit quality and decreases consumer acceptance. Several studies have been made to understand this disorder, however, the molecular mechanisms underlying mealiness are not fully understood. Epigenetic factors, such as DNA methylation, modulate gene expression according to the genetic background and environmental conditions. In this sense, the aim of this work was to identify differentially methylated regions (DMRs) that could affect gene expression in contrasting individuals for mealiness. Peach flesh was studied at harvest time (E1 stage) and after cold storage (E3 stage) for 30 days. The distribution of DNA methylations within the eight chromosomes of P. persica showed higher methylation levels in pericentromeric regions and most differences between mealy and normal fruits were at Chr1, Chr4, and Chr8. Notably, differences in Chr4 co-localized with previous QTLs associated with mealiness. Additionally, the number of DMRs was higher in CHH cytosines of normal and mealy fruits at E3; however, most DMRs were attributed to mealy fruits from E1, increasing at E3. From RNA-Seq data, we observed that differentially expressed genes (DEGs) between normal and mealy fruits were associated with ethylene signaling, cell wall modification, lipid metabolism, oxidative stress and iron homeostasis. When integrating the annotation of DMRs and DEGs, we identified a CYP450 82A and an UDP-ARABINOSE 4 EPIMERASE 1 gene that were downregulated and hypermethylated in mealy fruits, coinciding with the co-localization of a transposable element (TE). Altogether, this study indicates that genetic differences between tolerant and susceptible individuals is predominantly affecting epigenetic regulation over gene expression, which could contribute to a metabolic alteration from earlier stages of development, resulting in mealiness at later stages. Finally, this epigenetic mark should be further studied for the development of new molecular tools in support of breeding programs.
Globus Pallidus Internus Deep Brain Stimulation for Dystonic Opisthotonus in Adult-Onset Dystonia: A Personalized Approach

Introduction Dystonic opisthotonus is defined as a backward arching of the neck and trunk, which ranges in severity from mild backward jerks to life-threatening prolonged severe muscular spasms. It can be associated with generalized dystonic syndromes or, rarely, present as a form of axial truncal dystonia. The etiologies vary from idiopathic, genetic, tardive, hereditary-degenerative, or associated with parkinsonism. We report clinical cases of dystonic opisthotonus associated with adult-onset dystonic syndromes, that benefitted from globus pallidus internus (GPi) deep brain stimulation (DBS). Methods Clinical data from patients with dystonic syndromes who underwent comprehensive medical review, multidisciplinary assessment, and tailored medical and neurosurgical managements were prospectively analyzed. Quantification of dystonia severity pre- and postoperatively was performed using the Burke-Fahn-Marsden Dystonia Rating Scale and quantification of overall pain severity was performed using the Visual Analog Scale. Results Three male patients, with age of onset of the dystonic symptoms ranging from 32 to 51 years old, were included. Tardive dystonia, adult-onset dystonia-parkinsonism and adult-onset idiopathic axial dystonia were the etiologies identified. Clinical investigation and management were tailored according to the complexity of the individual presentations. Although they shared common clinical features of adult-onset dystonia, disabling dystonic opisthotonus, refractory to medical management, was the main indication for GPi-DBS in all patients presented. The severity of axial dystonia ranged from disturbance of daily function to life-threatening truncal distortion. All three patients underwent bilateral GPi DBS at a mean age of 52 years (range 48–55 years), after mean duration of symptoms prior to DBS of 10.7 years (range 4–16 years). All patients showed a rapid and sustained clinical improvement of their symptoms, notably of the dystonic opisthotonos, at postoperative follow-up ranging from 20 to 175 months. In some, the ability to resume activities of daily living and reintegration into the society was remarkable. Conclusion Adult-onset dystonic syndromes predominantly presenting with dystonic opisthotonus are relatively rare. The specific nature of dystonic opisthotonus remains a treatment challenge, and thorough investigation of this highly disabling condition with varying etiologies is often necessary. Although patients may be refractory to medical management and botulinum toxin injection, Globus pallidus stimulation timed and tailored provided symptomatic control in this cohort and may be considered in other carefully selected cases.
Gait Analysis in Progressive Supranuclear Palsy Phenotypes

The objective of the present study was to describe gait parameters of progressive supranuclear palsy (PSP) phenotypes at early stage verifying the ability of gait analysis in discriminating between disease phenotypes and between the other variant syndromes of PSP (vPSP) and Parkinson's disease (PD). Nineteen PSP (10 PSP-Richardson's syndrome, five PSP-parkinsonism, and four PSP-progressive gait freezing) and nine PD patients performed gait analysis in single and dual tasks. Although phenotypes showed similar demographic and clinical variables, Richardson's syndrome presented worse cognitive functions. Gait analysis demonstrated worse parameters in Richardson's syndrome compared with the vPSP. The overall diagnostic accuracy of the statistical model during dual task was almost 90%. The correlation analysis showed a significant relationship between gait parameters and visuo-spatial, praxic, and attention abilities in PSP-Richardson's syndrome only. vPSP presented worse gait parameters than PD. Richardson's syndrome presents greater gait dynamic instability since the earliest stages than other phenotypes. Computerized gait analysis can differentiate between PSP phenotypes and between vPSP and PD.
What Intervention Techniques Are Effective in Changing Positive Affective Variables and Physical Activity? A Systematic Review and Meta-Analysis

A recent meta-analysis has demonstrated that positive affective variables (PAVs) partially mediate physical activity (PA) interventions. However, the effectiveness of each intervention technique on PAVs and PA is still unknown. Thus, this meta-analytic review included two primary objectives: (1) to summarize intervention effects on PA and PAVs; (2) to examine each behavior change technique's effectiveness in modifying PAVs and PA. Following PRISMA protocols, we had searched five electronic databases by April 1, 2020. The random-effect model in the Comprehensive Meta-Analysis Version 3 was adopted to perform these meta-analytic analyses. The search identified 1,742 articles, and 37 studies (49 datasets) met our inclusion criteria. Finally, inferential statistics yielded that: the utilization of “teach to use prompts/cues,” “facilitate social comparison,” and “provide information on consequences of behavior in general” had positive effects on PA or PAVs outcomes; the utilization of “barrier identification/problem solving” and “plan social support/social change” negatively affected on PA or PAVs outcomes. However, there was considerable heterogeneity in the findings. Nonetheless, this paper has considerable implications for guiding future comparative intervention studies to achieve more reliable outcomes.
Quality Improvement to Increase Breastfeeding in Preterm Infants: Systematic Review and Meta-Analysis

Background and Objective: Due to its numerous health benefits, breast milk (BM) is recommended for preterm infants. Despite such recommendations, the rates of breastfeeding in preterm infants are lower than that in term infants. Quality improvement (QI) bundles increase breastfeeding in preterm infants, but their replication in neonatal intensive care units has had inconsistent outcomes. Methods: We used the Population or Problem, Intervention, Comparison, and Outcomes (PICO) framework to develop our search strategy, and searched MEDLINE, Embase, and the Cochrane Library from inception through January 15, 2021. Studies describing any active QI intervention to increase BM use in preterm infants were included. The primary outcome measure was the rate of any breastfeeding or exclusive mother's own milk (MOM) at discharge or during hospitalization. Results: Sixteen studies were eligible for inclusion and showed an acceptable risk of bias, and included 1 interrupted time series, study 3 controlled before-and-after studies, and 12 uncontrolled before-and-after studies; of these, 3 studies were excluded due to insufficient dichotomous data, 13 were included in the meta-analysis. In the meta-analysis, the rate of any breastfeeding was significantly improved at discharge and during hospitalization after QI [risk ratio (RR) = 1.23, 95% confidence interval (CI): 1.14–1.32, P < 0.00001 and RR = 1.89, 95% CI: 1.09–3.29, P = 0.02, respectively]. The rate of exclusive MOM after QI was also significantly increased at discharge (RR = 1.51, 95% CI: 1.04–2.18, P = 0.03), but not during hospitalization (RR = 1.53, 95% CI: 0.78–2.98, P = 0.22). However, after sensitivity analysis, the comprehensive results still suggested that QI could significantly improve the rate of exclusive MOM during hospitalization (RR = 1.21, 95% CI: 1.08–1.35, P = 0.001). Funnel plots and Egger's test indicated publication bias in the rate of any BF at discharge. We corrected publication bias by trim and fill analysis, and corrected RR to 1.272, 95% CI: (1.175, 1.369), which was consistent with the results of the initial model. Conclusions: A QI bundle appears to be effective for promoting BM use in preterm infants at discharge or during hospitalization.