Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural Machine Translation with Decoding History Enhanced Attention

Neural machine translation with source-side attention have achieved remarkable performance. however, there has been little work exploring to attend to the target-side which can potentially enhance the memory capbility of NMT. We reformulate a Decoding History Enhanced Attention mechanism (DHEA) to render NMT model better at selecting both source-side and target-side information. DHA enables dynamic control of the ratios at which source and target contexts contribute to the generation of target words, offering a way to weakly induce structure relations among both source and target tokens. It also allows training errors to be directly back-propagated through short-cut connections and effectively alleviates the gradient vanishing problem. The empirical study on Chinese-English translation shows that our model with proper configuration can improve by 0:9 BLEU upon Transformer and the best reported results in the dataset. On WMT14 English-German task and a larger WMT14 English-French task, our model achieves comparable results with the state-of-the-art.
Cooperative Denoising for Distantly Supervised Relation Extraction

Distantly supervised relation extraction greatly reduces human efforts in extracting relational facts from unstructured texts. However, it suffers from noisy labeling problem, which can degrade its performance. Meanwhile, the useful information expressed in knowledge graph is still underutilized in the state-of-the-art methods for distantly supervised relation extraction. In the light of these challenges, we propose CORD, a novelCOopeRativeDenoising framework, which consists two base networks leveraging text corpus and knowledge graph respectively, and a cooperative module involving their mutual learning by the adaptive bi-directional knowledge distillation and dynamic ensemble with noisy-varying instances. Experimental results on a real-world dataset demonstrate that the proposed method reduces the noisy labels and achieves substantial improvement over the state-of-the-art methods.
Longitudinal associations of lymphocyte subsets with clinical outcomes in chronic fatigue syndrome

ABSTRACT Background: Chronic fatigue syndrome (CFS) is characterized by prolonged fatigue and other physical and neurocognitive symptoms. Some studies suggest that CFS is accompanied by disruptions in the number and function of various lymphocytes. However, it is not clear which lymphocytes might influence CFS symptoms. Purpose: To determine if patient reported fatigue symptoms and physical functioning scores significantly changed across time with lymphocyte counts as evidence of a relation among chronic fatigue symptoms and the immune response. Methods: The current longitudinal, naturalistic study assessed the cellular expression of three lymphocyte subtypes – natural killer (NK) cells (CD3 − CD16+ and CD3 − CD56+) and naïve T cells (CD4 + CD45RA+) – to determine whether changes in lymphocytes at 4 time points across 18 months were associated with clinical outcomes, including CFS symptoms, physical functioning, and vitality, among patients with chronic fatigue. Latent growth curve models were used to examine the longitudinal relationship between lymphocytes and clinical outcomes. Results: Ninety-three patients with Fukuda-based CFS and seven with non-CFS fatigue provided study data. Results indicated that higher proportions of naïve T cells and lower proportions of NK cells were associated with worse physical functioning, whereas higher proportions of NK cells (CD3 − CD16+) and lower proportions of naïve T cells were associated with fewer CFS symptoms. Conclusion: These findings suggest that lymphocytes are modestly related to clinical outcomes over time.
Incorporating Deep Visual Features into Multiobjective based Multi-view Search Results Clustering

Current paper explores the use of multi-view learning for search result clustering. A web-snippet can be represented using multiple views. Apart from textual view cued by both the semantic and syntactic information, a complimentary view extracted from images contained in the web-snippets is also utilized in the current framework. A single consensus partitioning is finally obtained after consulting these two individual views by the deployment of a multiobjective based clustering technique. Several objective functions including the values of a cluster quality measure measuring the goodness of partitionings obtained using different views and an agreement-disagreement index, quantifying the amount of oneness among multiple views in generating partitionings are optimized simultaneously using AMOSA. In order to detect the number of clusters automatically, concepts of variable length solutions and a vast range of permutation operators are introduced in the clustering process. Finally, a set of alternative partitioning are obtained on the final Pareto front by the proposed multi-view based multiobjective technique. Experimental results by the proposed approach on several benchmark test datasets of SRC with respect to different performance metrics evidently establish the power of visual and text-based views in achieving better search result clustering.
Primary Vasculitis in Childhood: GPA and MPA in Childhood

Childhood onset anti-neutrophilic cytoplasmic antibody (ANCA) associated vasculitis (AAV) is a rare group of primary systemic vasculitides affecting medium and small blood vessels. AAV includes granulomatosis with polyangiitis (GPA), microscopic polyangiitis (MPA), eosinophilic granulomatosis with polyangiitis (EGPA), and renal limited ANCA vasculitis. These disorders are associated with severe clinical manifestations, frequent relapses and a high cumulative morbidity, and often present with multisystem involvement. Renal involvement is common in the pediatric age group, characterized by pauci-immune necrotizing and crescentic glomerulonephritis which frequently progresses to chronic kidney disease in adulthood. ANCAs against proteinase 3 (PR3-ANCA) or myeloperoxidase (MPO) (MPO-ANCA) remain the hallmark of AAV and are integral to the disease pathogenesis. Newer understanding of neutrophil extracellular traps and complement activation have provided better insights into disease pathogenesis. A pediatric vasculitis working group has developed and validated childhood vasculitis classification criteria and disease activity and damage scores. No specific pediatric treatment recommendations exist due to rare nature of the illness in pediatric population. Smaller case series have been published on the efficacy of adult treatment regimens in pediatric patients. The prognosis often remains guarded with frequent relapses and a high cumulative morbidity. The aim of this article is to provide a comprehensive review on pediatric AAV with a focus on recent observations regarding epidemiology, disease pathogenesis, treatment, and prognosis.
Human acellular amniotic membrane is adopted to treat venous ulcers

Venous lower limb ulcers are very common and affect ~1% of the general population. The human acellular amniotic membrane (HAAM), which is isolated from the amniotic membrane (AM) via excluding the majority of cellular components, has lower antigenicity than the AM. The aim of the present study was to evaluate the effectiveness and safety of the HAAM, adopted to treat venous ulcers (VUs) of the lower extremities. The HAMM was isolated from the AM by the Stem Cells and Tissue Engineering laboratory at the West China Hospital of Sichuan University (Chengdu, China). HAMMs were grafted onto VUs in 4 patients, with follow-up evaluations performed on the 3rd day and at the end of the 1st, 2nd and 3rd week, and 2nd, 3rd and 6th month after the HAAMs were applied. The size and depth of the VU (determined based on whether the depth of VU reaches the tibial plane), the proportion of granulation tissue (whether >50%) and the degree of secretion (measured by asessing the degree of satuation in the outer gauze) and infection (assessed qualitatively via the appearance of purulence or peripheral swelling) were assessed. Pain score was monitored at the same intervals using a visual analog scale. Complete epithelialization (healed tissue) occurred in 2 cases: The first at the end of the 3rd week and the second, at the 2nd month following HAAM induction. In one of the remaining cases, ulcer size was reduced by >60%; however the ulcer size of the remaining case only reduced by <20%. Overall, the size of ulcer in cases 1, 2, 3 and 4 decreased to 1.2×1.1 and 1.4×0.4 cm, 1.3×1.8 and 2.3×1.4, respectively, with evident decreases in ulcer depth. The proportion of granulation tissue in each case was >50%. Furthermore, purulence and secretion completely disappeared in all 4 cases. Additionally, the medical cost of HAAM treatment is substantially lower than that of AM treatment, skin autografts and biomaterial transplantation, thus alleviating the patients' financial burden. These findings suggest that HAMM was highly effective in treating VUs in patients.
Decellularized Tissue for Muscle Regeneration

Several acquired or congenital pathological conditions can affect skeletal muscle leading to volumetric muscle loss (VML), i.e., an irreversible loss of muscle mass and function. Decellularized tissues are natural scaffolds derived from tissues or organs, in which the cellular and nuclear contents are eliminated, but the tridimensional (3D) structure and composition of the extracellular matrix (ECM) are preserved. Such scaffolds retain biological activity, are biocompatible and do not show immune rejection upon allogeneic or xenogeneic transplantation. An increase number of reports suggest that decellularized tissues/organs are promising candidates for clinical application in patients affected by VML. Here we explore the different strategies used to generate decellularized matrix and their therapeutic outcome when applied to treat VML conditions, both in patients and in animal models. The wide variety of VML models, source of tissue and methods of decellularization have led to discrepant results. Our review study evaluates the biological and clinical significance of reported studies, with the final aim to clarify the main aspects that should be taken into consideration for the future application of decellularized tissues in the treatment of VML conditions.
Coupling between conformational dynamics and catalytic function at the active site of the lead-dependent ribozyme

In common with other self-cleaving RNAs, the lead-dependent ribozyme (leadzyme) undergoes dynamic fluctuations to a chemically activated conformation. We explored the connection between conformational dynamics and self-cleavage function in the leadzyme using a combination of NMR spin-relaxation analysis of ribose groups and conformational restriction via chemical modification. The functional studies were performed with a North-methanocarbacytidine modification that prevents fluctuations to C2′-endo conformations while maintaining an intact 2′-hydroxyl nucleophile. Spin-relaxation data demonstrate that the active-site Cyt-6 undergoes conformational exchange attributed to sampling of a minor C2′-endo state with an exchange lifetime on the order of microseconds to tens of microseconds. A conformationally restricted species in which the fluctuations to the minor species are interrupted shows a drastic decrease in self-cleavage activity. Taken together, these data indicate that dynamic sampling of a minor species at the active site of this ribozyme, and likely of related naturally occurring motifs, is strongly coupled to catalytic function. The combination of NMR dynamics analysis with functional probing via conformational restriction is a general methodology for dissecting dynamics-function relationships in RNA.
Learning Target-Specific Representations of Financial News Documents For Cumulative Abnormal Return Prediction

Texts from the Internet serve as important data sources for financial market modeling. Early statistical approaches rely on manually defined features to capture lexical, sentiment and event information, which suffers from feature sparsity. Recent work has considered learning dense representations for news titles and abstracts. Compared to news titles, full documents can contain more potentially helpful information, but also noise compared to events and sentences, which has been less investigated in previous work. To fill this gap, we propose a novel target-specific abstract-guided news document representation model. The model uses a target-sensitive representation of the news abstract to weigh sentences in the news content, so as to select and combine the most informative sentences for market modeling. Results show that document representations can give better performance for estimating cumulative abnormal returns of companies when compared to titles and abstracts. Our model is especially effective when it used to combine information from multiple document sources compared to the sentence-level baselines.
Executive Functions and Their Relation to Sleep Following Mild Traumatic Brain Injury in Preschoolers

Abstract Objectives: Traumatic brain injury (TBI) sustained during childhood is known to impact children’s executive functioning. However, few studies have focused specifically on executive functioning after preschool TBI. TBI has also been associated with sleep disturbances, which are known to impair executive functions in healthy children. The aim of this study was to investigate executive functions in preschoolers with mild TBI, and to determine the role of sleep in the links between TBI and executive functioning. Methods: The sample was drawn from a longitudinal study and included 167 children, aged 18 to 60 months, divided into 2 groups: children with accidental mild TBI (n=84) and typically developing children (n=83). Children were assessed 6 months post-injury on executive function measures (inhibition and cognitive flexibility) and sleep measures (actigraphy data and parental rating of sleep problems). Results: The two groups did not differ in their executive abilities. However, relative to controls, children with mild TBI and shorter nighttime sleep duration or increased sleep problems exhibited poorer executive functions. Conclusions: These results support a “double hazard” effect, whereby the combination of sleep disturbances and mild TBI results in poorer executive functions. The findings highlight the importance of assessing and monitoring the quality of sleep even after mild head injuries. Poor sleep may place children at risk for increased cognitive difficulties. (JINS, 2018, 24, 769–780)
Applied diagnostics in liver cancer. Efficient combinations of sorafenib with targeted inhibitors blocking AKT/mTOR

Hepatocellular carcinoma (HCC) is the third most common cause of cancer-related deaths worldwide. There is increasing interest in developing specific markers to serve as predictors of response to sorafenib and to guide targeted therapy. Using a sequencing platform designed to study somatic mutations in a selection of 112 genes (HepatoExome), we aimed to characterize lesions from HCC patients and cell lines, and to use the data to study the biological and mechanistic effects of case-specific targeted therapies used alone or in combination with sorafenib. We characterized 331 HCC cases in silico and 32 paired samples obtained prospectively from primary tumors of HCC patients. Each case was analyzed in a time compatible with the requirements of the clinic (within 15 days). In 53% of the discovery cohort cases, we detected unique mutational signatures, with up to 34% of them carrying mutated genes with the potential to guide therapy. In a panel of HCC cell lines, each characterized by a specific mutational signature, sorafenib elicited heterogeneous mechanistic and biological responses, whereas targeted therapy provoked the robust inhibition of cell proliferation and DNA synthesis along with the blockage of AKT/mTOR signaling. The combination of sorafenib with targeted therapies exhibited synergistic anti-HCC biological activity concomitantly with highly effective inhibition of MAPK and AKT/mTOR signaling. Thus, somatic mutations may lead to identify case-specific mechanisms of disease in HCC lesions arising from multiple etiologies. Moreover, targeted therapies guided by molecular characterization, used alone or in combination with sorafenib, can effectively block important HCC disease mechanisms.
A Systematic Review of the Technology Acceptance Model in Health Informatics

Abstract Background One common model utilized to understand clinical staff and patients' technology adoption is the technology acceptance model (TAM). Objective This article reviews published research on TAM use in health information systems development and implementation with regard to application areas and model extensions after its initial introduction. Method An electronic literature search supplemented by citation searching was conducted on February 2017 of the Web of Science, PubMed, and Scopus databases, yielding a total of 492 references. Upon eliminating duplicates and applying inclusion and exclusion criteria, 134 articles were retained. These articles were appraised and divided into three categories according to research topic: studies using the original TAM, studies using an extended TAM, and acceptance model comparisons including the TAM. Results The review identified three main information and communication technology (ICT) application areas for the TAM in health services: telemedicine, electronic health records, and mobile applications. The original TAM was found to have been extended to fit dynamic health service environments by integration of components from theoretical frameworks such as the theory of planned behavior and unified theory of acceptance and use of technology, as well as by adding variables in specific contextual settings. These variables frequently reflected the concepts subjective norm and self-efficacy, but also compatibility, experience, training, anxiety, habit, and facilitators were considered. Conclusion Telemedicine applications were between 1999 and 2017, the ICT application area most frequently studied using the TAM, implying that acceptance of this technology was a major challenge when exploiting ICT to develop health service organizations during this period. A majority of the reviewed articles reported extensions of the original TAM, suggesting that no optimal TAM version for use in health services has been established. Although the review results indicate a continuous progress, there are still areas that can be expanded and improved to increase the predictive performance of the TAM.
Model-Free Context-Aware Word Composition

Word composition is a promising technique for representation learning of large linguistic units (e.g., phrases, sentences and documents). However, most of the current composition models do not take the ambiguity of words and the context outside of a linguistic unit into consideration for learning representations, and consequently suffer from the inaccurate representation of semantics. To address this issue, we propose a model-free context-aware word composition model, which employs the latent semantic information as global context for learning representations. The proposed model attempts to resolve the word sense disambiguation and word composition in a unified framework. Extensive evaluation shows consistent improvements over various strong word representation/composition models at different granularities (including word, phrase and sentence), demonstrating the effectiveness of our proposed method.
Towards a unified framework for bilingual terminology extraction of single-word and multi-word terms

Extracting a bilingual terminology for multi-word terms from comparable corpora has not been widely researched. In this work we propose a unified framework for aligning bilingual terms independently of the term lengths. We also introduce some enhancements to the context-based and the neural network based approaches. Our experiments show the effectiveness of our enhancements of previous works and the system can be adapted in specialized domains.
A Novel Strategy to Identify Placebo Responders: Prediction Index of Clinical and Biological Markers in the EMBARC Trial

Background: One in three clinical trial patients with major depressive disorder report symptomatic improvement with placebo. Strategies to mitigate the effect of placebo responses have focused on modifying study design with variable success. Identifying and excluding or controlling for individuals with a high likelihood of responding to placebo may improve clinical trial efficiency and avoid unnecessary medication trials. Methods: Participants included those assigned to the placebo arm (n = 141) of the Establishing Moderators and Biosignatures for Antidepressant Response in Clinical Care (EMBARC) trial. The elastic net was used to evaluate 283 baseline clinical, behavioral, imaging, and electrophysiological variables to identify the most robust yet parsimonious features that predicted depression severity at the end of the double-blind 8-week trial. Variables retained in at least 50% of the 100 imputed data sets were used in a Bayesian multiple linear regression model to simultaneously predict the probabilities of response and remission. Results: Lower baseline depression severity, younger age, absence of melancholic features or history of physical abuse, less anxious arousal, less anhedonia, less neuroticism, and higher average theta current density in the rostral anterior cingulate predicted a higher likelihood of improvement with placebo. The Bayesian model predicted remission and response with an actionable degree of accuracy (both AUC > 0.73). An interactive calculator was developed predicting the likelihood of placebo response at the individual level. Conclusion: Easy-to-measure clinical, behavioral, and electrophysiological assessments can be used to identify placebo responders with a high degree of accuracy. Development of this calculator based on these findings can be used to identify potential placebo responders.
Predictors of Problematic Smartphone Use: An Examination of the Integrative Pathways Model and the Role of Age, Gender, Impulsiveness, Excessive Reassurance Seeking, Extraversion, and Depression

Background: The progression of mobile phone technology has led to the development of multi-functional smartphones providing access to features such as social media, e-mail, and videos alongside the basic functions of a mobile phone. Increasing amounts of research has explored the potential addictive nature of smartphones to develop a theoretical framework that describes personality factors related to problematic use. The present study examined the Integrative Pathways Model and the effect of age, gender, impulsiveness, excessive reassurance seeking, extraversion, and depression on problematic smartphone use. Method: A total of 147 smartphone users (mean age = 30.96, SD = 12.97, 69.4% female) completed an online survey comprising of measures of problematic smartphone use, excessive reassurance seeking, extraversion, depression, and impulsiveness. Results: Age, impulsiveness, excessive reassurance seeking, and depression were all significantly related to problematic smartphone use, however extraversion was not significantly related. Furthermore, age and impulsiveness were significant independent predictors of problematic smartphone use. No gender differences were found. Conclusions: The findings presented several factors that predict problematic smartphone use, implications and suggestions for future research are discussed.