Miracle-dz/UniFAR-FTUs huggingface dataset:
DNA-induced 2′3′-cGAMP enhances haplotype-specific human STING cleavage by dengue protease

Significance Dengue virus (DENV) antagonizes the DNA sensing cGAS-STING pathway to subvert innate immunity, but how DENV protease-mediated human STING cleavage contributes to DENV pathogenesis remains obscure. Here, we found that STING haplotype frequency varies among different subhuman populations, and different haplotypes respond differently to DENV protease. The cleavage of a DENV protease-sensitive STING can be further enhanced by coculture with neighboring cells producing 2′3′-cGAMP, either by DNA transfection of cGAS or by reactivating Epstein–Barr virus from latent infection. Thus, DENV infection trims down human STING-mediated innate immunity in a haplotype-specific manner. The genetic background of host STING and bystander coinfection of pathogens triggering 2′3′-cGAMP production may be the missing link between STING cleavage and DENV pathogenesis. The cytosolic DNA sensor cGMP-AMP synthase (cGAS) synthesizes the noncanonical cyclic dinucleotide 2′3′-cGAMP to activate the adaptor protein stimulator of IFN genes (STING), thus awakening host immunity in response to DNA pathogen infection. However, dengue virus (DENV), an RNA virus without a DNA stage in its life cycle, also manipulates cGAS-STING–mediated innate immunity by proteolytic degradation of STING. Here, we found that the sensitivity of STING to DENV protease varied with different human STING haplotypes. Exogenous DNA further enhanced DENV protease’s ability to interact and cleave protease-sensitive STING. DNA-enhanced STING cleavage was reduced in cGAS-knockdown cells and triggered by the cGAS product 2′3′-cGAMP. The source of DNA may not be endogenous mitochondrial DNA but rather exogenous reactivated viral DNA. Cells producing 2′3′-cGAMP by overexpressing cGAS or with DNA virus reactivation enhanced STING cleavage in neighboring cells harboring DENV protease. DENV infection reduced host innate immunity in cells with the protease-sensitive STING haplotype, whose homozygote genotype frequency was found significantly reduced in Taiwanese people with dengue fever. Therefore, the human STING genetic background and DNA pathogen coinfection may be the missing links contributing to DENV pathogenesis.
Automatic Construction of a Depression-Domain Lexicon Based on Microblogs: Text Mining Study

Background According to a World Health Organization report in 2017, there was almost one patient with depression among every 20 people in China. However, the diagnosis of depression is usually difficult in terms of clinical detection owing to slow observation, high cost, and patient resistance. Meanwhile, with the rapid emergence of social networking sites, people tend to share their daily life and disclose inner feelings online frequently, making it possible to effectively identify mental conditions using the rich text information. There are many achievements regarding an English web-based corpus, but for research in China so far, the extraction of language features from web-related depression signals is still in a relatively primary stage. Objective The purpose of this study was to propose an effective approach for constructing a depression-domain lexicon. This lexicon will contain language features that could help identify social media users who potentially have depression. Our study also compared the performance of detection with and without our lexicon. Methods We autoconstructed a depression-domain lexicon using Word2Vec, a semantic relationship graph, and the label propagation algorithm. These two methods combined performed well in a specific corpus during construction. The lexicon was obtained based on 111,052 Weibo microblogs from 1868 users who were depressed or nondepressed. During depression detection, we considered six features, and we used five classification methods to test the detection performance. Results The experiment results showed that in terms of the F1 value, our autoconstruction method performed 1% to 6% better than baseline approaches and was more effective and steadier. When applied to detection models like logistic regression and support vector machine, our lexicon helped the models outperform by 2% to 9% and was able to improve the final accuracy of potential depression detection. Conclusions Our depression-domain lexicon was proven to be a meaningful input for classification algorithms, providing linguistic insights on the depressive status of test subjects. We believe that this lexicon will enhance early depression detection in people on social media. Future work will need to be carried out on a larger corpus and with more complex methods.
Multiple paternity in the freshwater pearl mussel Hyriopsis cumingii (Lea, 1852)

Hyriopsis cumingii (Lea, 1852) is a freshwater mussel of major commercial importance for pearl culture in China. In this study, five polymorphic microsatellites from H. cumingii were identified and employed to investigate the occurrence and frequency of multiple paternity in 15 H. cumingii broods caught from Poyang Lake, China. The allele number of each locus ranged from 12 to 25, with a mean of 16.8 Â± 5.4 alleles per locus. Multiple paternity was discovered in 10 broods by genotyping the dam mussel and the offspring from each brood. The sire number inferred by COLONY software in the 10 broods ranged from 2 to 4 with a mean of 2.7 Â± 0.26 sires per brood. In 6 of the 10 broods, sires contributed skewed numbers of offspring (P < 0.05). These factors may be important for avoidance of inbreeding, optimization of genetic variation in offspring and for the persistence and evolution of H. cumingii.
Efficacy and safety of endoscopic submucosal tunnel dissection vs endoscopic submucosal dissection for early superficial upper gastrointestinal precancerous lesions and tumors: A meta‐analysis

Endoscopic submucosal tunnel dissection (ESTD) is a new treatment for superficial upper gastrointestinal precancerous lesions and tumors. However, its safety and efficacy for treating these lesions is still uncertain. This meta‐analysis aimed to compare the efficacy and safety of ESTD with those of endoscopic submucosal dissection (ESD) in treating early superficial upper gastrointestinal precancerous lesions and tumors.
TransS-Driven Joint Learning Architecture for Implicit Discourse Relation Recognition

Implicit discourse relation recognition is a challenging task due to the lack of connectives as strong linguistic clues. Previous methods primarily encode two arguments separately or extract the specific interaction patterns for the task, which have not fully exploited the annotated relation signal. Therefore, we propose a novel TransS-driven joint learning architecture to address the issues. Specifically, based on the multi-level encoder, we 1) translate discourse relations in low-dimensional embedding space (called TransS), which could mine the latent geometric structure information of argument-relation instances; 2) further exploit the semantic features of arguments to assist discourse understanding; 3) jointly learn 1) and 2) to mutually reinforce each other to obtain the better argument representations, so as to improve the performance of the task. Extensive experimental results on the Penn Discourse TreeBank (PDTB) show that our model achieves competitive results against several state-of-the-art systems.
Contribution a la connaissance des Araneidae Gasteracanthinae du sud-est africain: les Gasteracanthes du Natal Museum

L'auteur a trouvA© dans les collections du Natal Museum 12 espA¨ces d'Araneidae Gasteracanthinae dont deux sont nouvelles (Gastroxya benoiti et Isoxya yatesi). La variabilitA© des femelles est prA©cisA©e quantiÂ­tativement, et plusieurs mA¢les sont dA©crits pour la premiA¨re fois (Isoxya tabulata, Isoxya cicatricosa, HypsaÂ­cantha crucimaculata). La prA©sence de formes phylogA©nA¨tiquement primitives dans le Sud-est africain soulA¨ve le problA¨me de l'origine des GastA©racanthes africaines, et en particulier des Isoxya.
Improving Neural Machine Translation with Soft Template Prediction

Although neural machine translation (NMT) has achieved significant progress in recent years, most previous NMT models only depend on the source text to generate translation. Inspired by the success of template-based and syntax-based approaches in other fields, we propose to use extracted templates from tree structures as soft target templates to guide the translation procedure. In order to learn the syntactic structure of the target sentences, we adopt constituency-based parse tree to generate candidate templates. We incorporate the template information into the encoder-decoder framework to jointly utilize the templates and source text. Experiments show that our model significantly outperforms the baseline models on four benchmarks and demonstrates the effectiveness of soft target templates.
Does masting scale with plant size? High reproductive variability and low synchrony in small and unproductive individuals.

BACKGROUND AND AIMS In a range of plant species, the distribution of individual mean fecundity is skewed and dominated by a few highly fecund individuals. Larger plants produce greater seed crops, but the exact nature of the relationship between size and reproductive patterns are poorly understood. This is especially clear in plants that reproduce by exhibiting synchronized quasi-periodic variation in fruit production: a process which is called masting. METHODS We investigated covariation of plant size and fecundity with individual-plant level masting patterns and seed predation in twelve mast seeding species: Pinus pinea, Astragalus scaphoides, Sorbus aucuparia, Quercus ilex, Q. humilis, Q. rubra, Q. alba, Q. montana, Chionochloa pallens, C. macra, Celmisia lyallii, and Phormium tenax. KEY RESULTS Fecundity was non-linearly related to masting patterns. Small and unproductive plants frequently failed to produce any seeds, which elevated their annual variation and decreased synchrony. Above a low fecundity threshold, plants had similar variability and synchrony, regardless of their size and productivity. CONCLUSIONS Our study shows that within-species variation in masting patterns is correlated with variation in fecundity, which in turn is related to plant size. Low synchrony of low-fertility plants shows that the failure years were idiosyncratic to each small plant, which in turn implies that the small plants fail to reproduce because of plant-specific factors (e.g. internal resource limits). Thus, the behavior of these sub-producers is apparently the result of trade-offs in resource allocation and environmental limits with which the small plants cannot cope. Plant size, and especially fecundity and propensity for mast failure years, play a major role in determining the variability and synchrony of reproduction in plants.
Allosteric Integrase Inhibitors Reveal a Role for Integrase During HIV-1 Maturation

Integration of the DNA copy of the HIV-1 genome is an essential step for virus replication and is mediated by a homotetrameric complex of the viral protein integrase (IN) in association with the ends of linear viral DNA (vDNA). HIV-1 integrates into actively transcribed genes, a trait mediated by cellular host cofactor LEDGF/p75. LEDGF/p75 engages IN in a pocket formed by dimerization of the IN catalytic core domain, a region that has been validated as a drug target for allosteric IN inhibitors (ALLINIs). Previous in vitro work suggested that ALLINIs function through disruption of two integration-associated functions: IN-vDNA complex formation and the IN-LEDGF/p75 interaction. We now demonstrate that ALLINI potency is accounted for during the late phase of HIV-1 replication where the inhibitors block the formation of the viral core, converting the normally electron-dense conical core to an eccentric phenotype where the electron-density exists as a condensate situated between a translucent core and the viral membrane. We have further elucidated the eccentric condensates to represent non-packaged viral ribonucleoprotein (vRNP) complexes and that either genetic or pharmacological inhibition of IN can impair vRNP encapsidation. Supplying IN in trans as part of a Vpr-IN fusion protein partially restored the formation of conical cores with the internal electron density. Moreover the ability of ALLINIs to induce eccentric condensate formation required both IN and viral RNA. Based on these observations, we propose an active role for IN during HIV-1 maturation that involves initiating core morphogenesis and vRNP encapsidation.
Position-Dependent Effect of Guanine Base Damage and Mutations on Telomeric G-quadruplex and Telomerase Extension.

Telomeres are hot spots for mutagenic oxidative and methylation base damage due to their high guanine content. We used single-molecule FRET detection and biochemical assays to determine how different positions and types of guanine damage and mutations alter telomeric G-quadruplex structure and telomerase activity. We compared fifteen modifications including 8-oxoguanine (8oxoG), O-6-methylguanine (O6mG), and all three possible point mutations (G to A, T, and C) at the 3' three terminal guanine positions of a telomeric G-quadruplex, which is the critical access point for telomerase. We found that G-quadruplex structural instability was induced in the order of C<T<A≤8oxoG<O6mG with the O6mG far exceeding the per-turbation caused by other base alterations. For all base modifications, the central G position was the most destabilizing among the three terminal guanines. While the structural disruption by 8oxoG and O6mG led to concomitant increases in telomerase binding and extension activity, the structural perturbation by point mutations (A, T and C) did not, due to dis-rupted annealing between the telomeric overhang and the telomerase RNA template. Repositioning the same mutations away from the terminal guanines caused both G-quadruplex structural instability and elevated telomerase activity. Our find-ings demonstrate how a single base modification drives structural alterations and telomere lengthening in a position-dependent manner. Furthermore, our results suggest a long-term and inheritable effect of telomeric DNA damage that can lead to telomere lengthening, which potentially contributes to oncogenesis.
Maternal influences on offspring phenotypes and sex ratios in a multi-clutching lizard with environmental sex determination

Maternal and environmental factors are important sources of phenotypic variation because both factors influence offspring traits in ways that impact offspring and maternal fitness. The present study explored the effects of maternal factors (maternal body size, egg size, yolk-steroid allocation, and oviposition-site choice) and seasonally-variable environmental factors on offspring phenotypes and sex ratios in a multi-clutching lizard with environmental sex determination (Amphibolurus muricatus). Maternal identity had strong effects on offspring morphology, but the nature of maternal effects differed among successive clutches produced by females throughout the reproductive season (i.e. maternal identity by environment interactions). The among-female and among-clutch variation in offspring traits (including sex ratios) was not mediated through maternal body size, egg size, or variation in yolk steroid hormones. This lack of nongenetic maternal effects suggests that phenotypic variation may be generated by gene by environment interactions. These results demonstrate a significant genetic component to variation in offspring phenotypes, including sex ratios, even in species with environmental sex determination. © 2008 The Linnean Society of London, Biological Journal of the Linnean Society, 2008, 95, 256–266.
CluBERT: A Cluster-Based Approach for Learning Sense Distributions in Multiple Languages

Knowing the Most Frequent Sense (MFS) of a word has been proved to help Word Sense Disambiguation (WSD) models significantly. However, the scarcity of sense-annotated data makes it difficult to induce a reliable and high-coverage distribution of the meanings in a language vocabulary. To address this issue, in this paper we present CluBERT, an automatic and multilingual approach for inducing the distributions of word senses from a corpus of raw sentences. Our experiments show that CluBERT learns distributions over English senses that are of higher quality than those extracted by alternative approaches. When used to induce the MFS of a lemma, CluBERT attains state-of-the-art results on the English Word Sense Disambiguation tasks and helps to improve the disambiguation performance of two off-the-shelf WSD models. Moreover, our distributions also prove to be effective in other languages, beating all their alternatives for computing the MFS on the multilingual WSD tasks. We release our sense distributions in five different languages at https://github.com/SapienzaNLP/clubert.
Roles and Utilization of Attention Heads in Transformer-based Neural Language Models

Sentence encoders based on the transformer architecture have shown promising results on various natural language tasks. The main impetus lies in the pre-trained neural language models that capture long-range dependencies among words, owing to multi-head attention that is unique in the architecture. However, little is known for how linguistic properties are processed, represented, and utilized for downstream tasks among hundreds of attention heads inside the pre-trained transformer-based model. For the initial goal of examining the roles of attention heads in handling a set of linguistic features, we conducted a set of experiments with ten probing tasks and three downstream tasks on four pre-trained transformer families (GPT, GPT2, BERT, and ELECTRA). Meaningful insights are shown through the lens of heat map visualization and utilized to propose a relatively simple sentence representation method that takes advantage of most influential attention heads, resulting in additional performance improvements on the downstream tasks.
SeqVAT: Virtual Adversarial Training for Semi-Supervised Sequence Labeling

Virtual adversarial training (VAT) is a powerful technique to improve model robustness in both supervised and semi-supervised settings. It is effective and can be easily adopted on lots of image classification and text classification tasks. However, its benefits to sequence labeling tasks such as named entity recognition (NER) have not been shown as significant, mostly, because the previous approach can not combine VAT with the conditional random field (CRF). CRF can significantly boost accuracy for sequence models by putting constraints on label transitions, which makes it an essential component in most state-of-the-art sequence labeling model architectures. In this paper, we propose SeqVAT, a method which naturally applies VAT to sequence labeling models with CRF. Empirical studies show that SeqVAT not only significantly improves the sequence labeling performance over baselines under supervised settings, but also outperforms state-of-the-art approaches under semi-supervised settings.
Multi-source Meta Transfer for Low Resource Multiple-Choice Question Answering

Multiple-choice question answering (MCQA) is one of the most challenging tasks in machine reading comprehension since it requires more advanced reading comprehension skills such as logical reasoning, summarization, and arithmetic operations. Unfortunately, most existing MCQA datasets are small in size, which increases the difficulty of model learning and generalization. To address this challenge, we propose a multi-source meta transfer (MMT) for low-resource MCQA. In this framework, we first extend meta learning by incorporating multiple training sources to learn a generalized feature representation across domains. To bridge the distribution gap between training sources and the target, we further introduce the meta transfer that can be integrated into the multi-source meta training. More importantly, the proposed MMT is independent of backbone language models. Extensive experiments demonstrate the superiority of MMT over state-of-the-arts, and continuous improvements can be achieved on different backbone networks on both supervised and unsupervised domain adaptation settings.
High-Dose Inactivated Influenza Vaccine Quadrivalent for Older Adults

Objective: To review the efficacy and safety of the high-dose inactivated influenza vaccine quadrivalent (HD-IIV4) in the prevention of influenza in older adults. Data Sources: A literature search was performed using PubMed and Google Scholar with the search terms high-dose, influenza vaccine, and quadrivalent. Other resources included the Centers for Disease Control and Prevention (CDC), the prescribing information, and the manufacturer’s website. Study Selection and Data Extraction: All relevant English-language articles of studies assessing the efficacy and safety of HD-IIV4 were included. Data Synthesis: HD-IIV4 is licensed by the Food and Drug Administration for the prevention of influenza in adults aged 65 years and older. The safety and immunogenicity of HD-IIV4 was demonstrated in a phase 3 trial, and the efficacy of the trivalent formulation (HD-IIV3) was demonstrated in a phase 3b-4 trial. HD-IIV4 carries a warning regarding the occurrence of Guillain-Barré syndrome. Adverse reactions, including injection-site pain and myalgia, were reported more frequently with HD-IIV4 than with HD-IIV3. Relevance to Patient Care and Clinical Practice: Although the CDC recommends any age-appropriate influenza vaccine for adults aged 65 years and older, HD-IIV4 was associated with improved immunogenicity against the added B strain and HD-IIV3 provided better protection against influenza than the standard-dose vaccine. Other influenza vaccines have weaker evidence of efficacy in older adults. Therefore, HDIIV4 should be recommended as the vaccine of choice in adults aged 65 years and older. Conclusion: HD-IIV4 has proven immunogenic, safe, and effective in preventing influenza in older adults and should be recommended as the vaccine of choice in this patient population.
Improving Low-Resource Named Entity Recognition using Joint Sentence and Token Labeling

Exploiting sentence-level labels, which are easy to obtain, is one of the plausible methods to improve low-resource named entity recognition (NER), where token-level labels are costly to annotate. Current models for jointly learning sentence and token labeling are limited to binary classification. We present a joint model that supports multi-class classification and introduce a simple variant of self-attention that allows the model to learn scaling factors. Our model produces 3.78%, 4.20%, 2.08% improvements in F1 over the BiLSTM-CRF baseline on e-commerce product titles in three different low-resource languages: Vietnamese, Thai, and Indonesian, respectively.
Fact-based Text Editing

We propose a novel text editing task, referred to as fact-based text editing, in which the goal is to revise a given document to better describe the facts in a knowledge base (e.g., several triples). The task is important in practice because reflecting the truth is a common requirement in text editing. First, we propose a method for automatically generating a dataset for research on fact-based text editing, where each instance consists of a draft text, a revised text, and several facts represented in triples. We apply the method into two public table-to-text datasets, obtaining two new datasets consisting of 233k and 37k instances, respectively. Next, we propose a new neural network architecture for fact-based text editing, called FactEditor, which edits a draft text by referring to given facts using a buffer, a stream, and a memory. A straightforward approach to address the problem would be to employ an encoder-decoder model. Our experimental results on the two datasets show that FactEditor outperforms the encoder-decoder approach in terms of fidelity and fluency. The results also show that FactEditor conducts inference faster than the encoder-decoder approach.