Miracle-dz/UniFAR-FTUs huggingface dataset:
Semantic Oppositeness Assisted Deep Contextual Modeling for Automatic Rumor Detection in Social Networks

Social networks face a major challenge in the form of rumors and fake news, due to their intrinsic nature of connecting users to millions of others, and of giving any individual the power to post anything. Given the rapid, widespread dissemination of information in social networks, manually detecting suspicious news is sub-optimal. Thus, research on automatic rumor detection has become a necessity. Previous works in the domain have utilized the reply relations between posts, as well as the semantic similarity between the main post and its context, consisting of replies, in order to obtain state-of-the-art performance. In this work, we demonstrate that semantic oppositeness can improve the performance on the task of rumor detection. We show that semantic oppositeness captures elements of discord, which are not properly covered by previous efforts, which only utilize semantic similarity or reply structure. We show, with extensive experiments on recent data sets for this problem, that our proposed model achieves state-of-the-art performance. Further, we show that our model is more resistant to the variances in performance introduced by randomness.
Predicting Treatment Outcome from Patient Texts:The Case of Internet-Based Cognitive Behavioural Therapy

We investigate the feasibility of applying standard text categorisation methods to patient text in order to predict treatment outcome in Internet-based cognitive behavioural therapy. The data set is unique in its detail and size for regular care for depression, social anxiety, and panic disorder. Our results indicate that there is a signal in the depression data, albeit a weak one. We also perform terminological and sentiment analysis, which confirm those results.
Enhancing Aspect-level Sentiment Analysis with Word Dependencies

Aspect-level sentiment analysis (ASA) has received much attention in recent years. Most existing approaches tried to leverage syntactic information, such as the dependency parsing results of the input text, to improve sentiment analysis on different aspects. Although these approaches achieved satisfying results, their main focus is to leverage the dependency arcs among words where the dependency type information is omitted; and they model different dependencies equally where the noisy dependency results may hurt model performance. In this paper, we propose an approach to enhance aspect-level sentiment analysis with word dependencies, where the type information is modeled by key-value memory networks and different dependency results are selectively leveraged. Experimental results on five benchmark datasets demonstrate the effectiveness of our approach, where it outperforms baseline models on all datasets and achieves state-of-the-art performance on three of them.
Quantifying Appropriateness of Summarization Data for Curriculum Learning

Much research has reported the training data of summarization models are noisy; summaries often do not reflect what is written in the source texts. We propose an effective method of curriculum learning to train summarization models from such noisy data. Curriculum learning is used to train sequence-to-sequence models with noisy data. In translation tasks, previous research quantified noise of the training data using two models trained with noisy and clean corpora. Because such corpora do not exist in summarization fields, we propose a model that can quantify noise from a single noisy corpus. We conduct experiments on three summarization models; one pretrained model and two non-pretrained models, and verify our method improves the performance. Furthermore, we analyze how different curricula affect the performance of pretrained and non-pretrained summarization models. Our result on human evaluation also shows our method improves the performance of summarization models.
ENPAR:Enhancing Entity and Entity Pair Representations for Joint Entity Relation Extraction

Current state-of-the-art systems for joint entity relation extraction (Luan et al., 2019; Wad-den et al., 2019) usually adopt the multi-task learning framework. However, annotations for these additional tasks such as coreference resolution and event extraction are always equally hard (or even harder) to obtain. In this work, we propose a pre-training method ENPAR to improve the joint extraction performance. ENPAR requires only the additional entity annotations that are much easier to collect. Unlike most existing works that only consider incorporating entity information into the sentence encoder, we further utilize the entity pair information. Specifically, we devise four novel objectives,i.e., masked entity typing, masked entity prediction, adversarial context discrimination, and permutation prediction, to pre-train an entity encoder and an entity pair encoder. Comprehensive experiments show that the proposed pre-training method achieves significant improvement over BERT on ACE05, SciERC, and NYT, and outperforms current state-of-the-art on ACE05.
NLQuAD: A Non-Factoid Long Question Answering Data Set

We introduce NLQuAD, the first data set with baseline methods for non-factoid long question answering, a task requiring document-level language understanding. In contrast to existing span detection question answering data sets, NLQuAD has non-factoid questions that are not answerable by a short span of text and demanding multiple-sentence descriptive answers and opinions. We show the limitation of the F1 score for evaluation of long answers and introduce Intersection over Union (IoU), which measures position-sensitive overlap between the predicted and the target answer spans. To establish baseline performances, we compare BERT, RoBERTa, and Longformer models. Experimental results and human evaluations show that Longformer outperforms the other architectures, but results are still far behind a human upper bound, leaving substantial room for improvements. NLQuAD’s samples exceed the input limitation of most pre-trained Transformer-based models, encouraging future research on long sequence language models.
Maximal Multiverse Learning for Promoting Cross-Task Generalization of Fine-Tuned Language Models

Language modeling with BERT consists of two phases of (i) unsupervised pre-training on unlabeled text, and (ii) fine-tuning for a specific supervised task. We present a method that leverages the second phase to its fullest, by applying an extensive number of parallel classifier heads, which are enforced to be orthogonal, while adaptively eliminating the weaker heads during training. We conduct an extensive inter- and intra-dataset evaluation, showing that our method improves the generalization ability of BERT, sometimes leading to a +9% gain in accuracy. These results highlight the importance of a proper fine-tuning procedure, especially for relatively smaller-sized datasets. Our code is attached as supplementary.
Dysregulated Free Fatty Acid Receptor 2 Exacerbates Colonic Adenoma Formation in ApcMin/+ Mice: Relation to Metabolism and Gut Microbiota Composition

Free fatty acid receptor 2 (FFAR2) has been reported as a tumor suppressor in colon cancer development. The current study investigated the effects of FFAR2 signaling on energy metabolism and gut microbiota profiling in a colorectal cancer mouse model (ApcMin/+). Ffar2 deficiency promoted colonic polyp development and enhanced fatty acid oxidation and bile acid metabolism. Gut microbiome sequencing analysis showed distinct clustering among wild-type, ApcMin/+, and ApcMin/+-Ffar2-/- mice. The relative abundance of Flavobacteriaceae and Verrucomicrobiaceae was significantly increased in the ApcMin/+-Ffar2-/- mice compared to the ApcMin/+ mice. In addition, knocking-down FFAR2 in the human colon cancer cell lines (SW480 and HT29) resulted in increased expression of several key enzymes in fatty acid oxidation, such as carnitine palmitoyltransferase 2, acyl-CoA dehydrogenase, long-chain acyl-CoA dehydrogenase, C-2 to C-3 short chain, and hydroxyacyl-CoA dehydrogenase/3-ketoacyl-CoA thiolase/enoyl-CoA hydratase, alpha subunit. Collectively, these results demonstrated that Ffar2 deficiency significantly altered profiles of fatty acid metabolites and gut microbiome, which might promote colorectal cancer development.
Sudden Cardiac Death and Sudden Cardiac Arrest in Patients with Human Immunodeficiency Virus: A Systematic Review

The importance of this review lies in its study of the risk of sudden cardiac death (SCD) and sudden cardiac arrest (SCA) in people living with the human immunodeficiency virus (PLWH). To the best of our knowledge, this is the first review investigating the effect of the human immunodeficiency virus (HIV) on SCD and SCA. The review's objective was to determine the risk of SCD and SCA in PLWH. To do this, the electronic databases Ovid MEDLINE, EMBASE, Cochrane Central, Scopus, and Google Scholar were systematically searched to identify eligible studies published before January 31, 2021. Reference lists of the included studies were searched for further identification of relevant studies. The search terms included: "Sudden Cardiac Death," "Sudden Cardiac Arrest," "Human Immunodeficiency virus," "HIV," "Acquired immunodeficiency syndrome," and "AIDS. " Only observational studies that assessed the association between SCD and SCA in PWLH were selected. Data were extracted by two independent authors who screened titles, abstracts, and articles to meet the inclusion criterion. Quality assessment was done by using modified Downs and Black checklist. A total of seven studies were included in this review. Five studies revealed a higher incidence of SCD in PLWH, two of which focused on patients with HIV and low left ventricular ejection fraction (LVEF). The other two studies were about the association of HIV and SCA. Studies reported that PLWH had a three- to five-fold higher incidence of SCD as compared to non-HIV patients. HIV patients with low LVEF had a higher incidence of SCD than HIV patients with normal LVEF. PLWH had a higher incidence of SCA and less successful cardiopulmonary resuscitation (CPR) as compared to patients without HIV. After adjusting for various confounders in multiple studies, all the studies reported a higher incidence of SCD in PLWH. To conclude, PLWH is at an increased risk of SCD and SCA. Some risk factors for this include LVEF, viral load (VL), and the cluster of differentiation 4 (CD4) count. There is a paucity of data on the mechanisms involved, although a higher prevalence of cardiac fibrosis and interstitial fibrosis in PLWH may play a role. Because of the general suboptimal quality of the heterogeneous nature of the current evidence, further, rigorous studies are needed to determine the association of increased risk of SCD and SCA in PLWH.
Reduced Fluoroscopy Time With Physician-Controlled Fluoroscopy During Endoscopic Retrograde Cholangiopancreatography: A Community Hospital Experience

Background and objective Fluoroscopy during endoscopic retrograde cholangiopancreatography (ERCP) is associated with radiation exposure and related health risks. Either the physician or the radiology technologist can activate fluoroscopy during ERCP. The aim of this study was to determine if physician-controlled fluoroscopy is associated with decreased fluoroscopy time, which may correspond to less radiation exposure to patients and staff. Methods We conducted a single-center, retrospective study; data were collected on ERCP performed using physician-controlled and technologist-controlled fluoroscopy. Fluoroscopy time, procedure complexity level, and Stanford Fluoroscopy Score were compared between the two groups. Results The median fluoroscopy time significantly differed between the two groups with 108 seconds for physician-controlled and 146 seconds for technologist-controlled procedures (p=0.004). The ratio of median fluoroscopy time to procedure complexity level was significantly lower in the physician-controlled group at 73.0 seconds compared to 97.0 seconds in the technologist-controlled group (p=0.002). The ratio of median fluoroscopy time to Stanford Fluoroscopy Score was 25.5 seconds in the physician-controlled group compared to 39.3 seconds in the technologist-controlled group, which was also statistically significant (p<0.001). A subgroup analysis of physicians with advanced training in ERCP also showed a significantly reduced median fluoroscopy time to Stanford Fluoroscopy Complexity Score ratio: 25.5 seconds for physician-controlled versus 35.0 seconds for technologist-controlled (p=0.001). Conclusion The ERCP technique with physician-controlled fluoroscopy may be associated with shorter fluoroscopy time. This may correspond to decreased radiation exposure to patients compared to radiology technologist-controlled fluoroscopy. Further investigations with larger, prospective studies are warranted.
Crayfish Eating in Snakes: Testing How Anatomy and Behavior Affect Prey Size and Feeding Performance

Synopsis Quantifying the performance of animals is a powerful methodology for determining the functional consequences of morphological variation. For example, snakes consume prey whole, and variation in the anatomy of their trophic apparatus directly affects gape and limits maximal prey size. However, for the foraging ecology of snakes and other systems, scant data exist regarding how often maximal capacities are taxed in nature. Hence, we quantified: (1) maximal gape, (2) the size of prey relative to maximal gape, and (3) how the type and relative size of prey affected behavior and prey handling times (HTs) for two species of natricine snakes that primarily eat soft- (Regina septemvittata) or hard-shelled (Liodytes alleni) crayfish. Liodytes alleni had significantly larger maximal gape than R. septemvittata with equal snout–vent length. The percentages of large prey (>60% maximal gape area) consumed in the field were low in both R. septemvittata (22%) and L. alleni (2%). However, R. septemvittata, especially juveniles, ate relatively larger prey than L. alleni. Strategies for dealing with the seasonal scarcity of small crayfish differed as juvenile R. septemvittata commonly removed and ate only chelipeds from crayfish too large to swallow whole, whereas juvenile L. alleni ate many small odonate nymphs. During laboratory trials, unlike R. septemvittata, L. alleni usually used its body to restrain prey with behaviors that depended on relative prey size and prey hardness. Liodytes alleni consumed soft-shelled crayfish significantly faster than R. septemvittata and significantly faster than hard-shelled crayfish. Several of the differences in gape, prey size, and prey HTs and behavior between the crayfish-eating snakes resemble those between two phylogenetically distant species of homalopsid snakes that consume either hard- or soft-shelled crabs. In both groups of crustacean-eating snakes, the decreased capture success in captivity and the rare consumption of relatively large hard-shelled crustaceans in the field suggest that the ability to capture this type of prey constrains prey size more commonly than maximal gape. Based on data integrating snake size and gape with the relative mass of intact prey, the predicted potential feeding performance R. septemvittata consuming intact prey exceeded that of the other three species.
Overview of MR Image Segmentation Strategies in Neuromuscular Disorders

Neuromuscular disorders are rare diseases for which few therapeutic strategies currently exist. Assessment of therapeutic strategies efficiency is limited by the lack of biomarkers sensitive to the slow progression of neuromuscular diseases (NMD). Magnetic resonance imaging (MRI) has emerged as a tool of choice for the development of qualitative scores for the study of NMD. The recent emergence of quantitative MRI has enabled to provide quantitative biomarkers more sensitive to the evaluation of pathological changes in muscle tissue. However, in order to extract these biomarkers from specific regions of interest, muscle segmentation is mandatory. The time-consuming aspect of manual segmentation has limited the evaluation of these biomarkers on large cohorts. In recent years, several methods have been proposed to make the segmentation step automatic or semi-automatic. The purpose of this study was to review these methods and discuss their reliability, reproducibility, and limitations in the context of NMD. A particular attention has been paid to recent deep learning methods, as they have emerged as an effective method of image segmentation in many other clinical contexts.
Alzheimer’s Disease and Its Association With Bone Health: A Case-Control Study

Introduction Alzheimer’s disease is associated with low bone mineral density. Various studies have linked early-onset Alzheimer's disease with bone health. In this study, we will determine the association between bone health and recently diagnosed Alzheimer's disease in the local population. Methods This case-control study was conducted at the neurology unit from April 2019 to Sept 2019. One hundred and fifty (150) Alzheimer’s patients with recently (within the last six months) confirmed diagnoses, based on clinical symptoms, mental status, and computed tomography (CT) scan, were included from the neurology outpatient department. The gender and age-matched 150 healthy participants were included in the study as the reference group. Various parameters of bone health and mental status were measured. Results Participants with Alzheimer’s had a significantly lower level of serum vitamin D (15.2 ± 4.2 ng/mL vs. 27.5 ± 8.1 ng/mL, p-value: < 0.0001) and lower level of serum osteocalcin (4.3 ± 1.7 ng/L vs. 5.6 ± 2.0 ng/L, p-value: < 0.0001). Participants with Alzheimer’s disease had more people with T-score ≤ -2.5 as compared to the general population (52.0% vs. 16.6%, p-value <0.0001). Conclusion Alzheimer’s disease is associated with poor bone health as compared to the general population of the same age. Bone health can be an important parameter to screen patients at risk of Alzheimer’s disease. The management of Alzheimer’s disease should include a regular assessment of bone health, and the treatment plan should include therapies to improve bone health.
Li6SiO4Cl2: A Hexagonal Argyrodite Based on Antiperovskite Layer Stacking

A hexagonal analogue, Li6SiO4Cl2, of the cubic lithium argyrodite family of solid electrolytes is isolated by a computation–experiment approach. We show that the argyrodite structure is equivalent to the cubic antiperovskite solid electrolyte structure through anion site and vacancy ordering within a cubic stacking of two close-packed layers. Construction of models that assemble these layers with the combination of hexagonal and cubic stacking motifs, both well known in the large family of perovskite structural variants, followed by energy minimization identifies Li6SiO4Cl2 as a stable candidate composition. Synthesis and structure determination demonstrate that the material adopts the predicted lithium site-ordered structure with a low lithium conductivity of ∼10–10 S cm–1 at room temperature and the predicted hexagonal argyrodite structure above an order–disorder transition at 469.3(1) K. This transition establishes dynamic Li site disorder analogous to that of cubic argyrodite solid electrolytes in hexagonal argyrodite Li6SiO4Cl2 and increases Li-ion mobility observed via NMR and AC impedance spectroscopy. The compositional flexibility of both argyrodite and perovskite alongside this newly established structural connection, which enables the use of hexagonal and cubic stacking motifs, identifies a wealth of unexplored chemistry significant to the field of solid electrolytes.
Patient attitudes towards faecal sampling for gut microbiome studies and clinical care reveal positive engagement and room for improvement

Faecal sample collection is crucial for gut microbiome research and its clinical applications. However, while patients and healthy volunteers are routinely asked to provide stool samples, their attitudes towards sampling remain largely unknown. Here, we investigate the attitudes of 780 Dutch patients, including participants in a large Inflammatory Bowel Disease (IBD) gut microbiome cohort and population controls, in order to identify barriers to sample collection and provide recommendations for gut microbiome researchers and clinicians. We sent questionnaires to 660 IBD patients and 112 patients with other disorders who had previously been approached to participate in gut microbiome studies. We also conducted 478 brief interviews with participants in our general population cohort who had collected stool samples. Statistical analysis of the data was performed using R. 97.4% of respondents reported that they had willingly participated in stool sample collection for gut microbiome research, and most respondents (82.9%) and interviewees (95.6%) indicated willingness to participate again, with their motivations for participating being mainly altruistic (57.0%). Responses indicated that storing stool samples in the home freezer for a prolonged time was the main barrier to participation (52.6%), but clear explanations of the sampling procedures and their purpose increased participant willingness to collect and freeze samples (P = 0.046, P = 0.003). To account for participant concerns, gut microbiome researchers establishing cohorts and clinicians trying new faecal tests should provide clear instructions, explain the rationale behind their protocol, consider providing a small freezer and inform patients about study outcomes. By assessing the attitudes, motives and barriers surrounding participation in faecal sample collection, we provide important information that will contribute to the success of gut microbiome research and its near-future clinical applications.
Vibrio parahaemolyticus CadC regulates acid tolerance response to enhance bacterial motility and cytotoxicity

Abstract Pathogens adapted to sub‐lethal acidic conditions could increase the virulence and survival ability under lethal conditions. In the aquaculture industry, feed acidifiers have been used to increase the growth of aquatic animals. However, there is limited study on the effects of acidic condition on the virulence and survival of pathogens in aquaculture. In this study, we investigated the survival ability of Vibrio parahaemolyticus at lethal acidic pH (4.0) after adapted the bacteria to sub‐lethal acidic pH (5.5) for 1 hr. Our results indicated that the adapted strain increased the survival ability at lethal acidic pH invoked by an inorganic (HCl) or organic (citric) acid. RNA‐sequencing (RNA‐seq) results revealed that 321 genes were differentially expressed at the sub‐lethal acidic pH including cadC, cadBA and groES/groEL relating to acid tolerance response (ATR), as well as genes relating to outer membrane, heat‐shock proteins, phosphotransferase system and flagella system. Quantitative real‐time polymerase chain reaction (qRT‐PCR) confirmed that cadC and cadBA were upregulated under sub‐lethal acidic conditions. The CadC protein could directly regulate the expression of cadBA to modulate the ATR in V. parahaemolyticus. RNA‐seq data also indicated that 113 genes in the CadC‐dependent way and 208 genes in the CadC‐independent way were differentially expressed, which were related to the regulation of ATR. Finally, the motility and cytotoxicity of the sub‐lethal acidic adapted wild type (WT) were significantly increased compared with the unadapted strain. Our results demonstrated that the dietary acidifiers may increase the virulence and survival of V. parahaemolyticus in aquaculture.