Miracle-dz/UniFAR-FTUs huggingface dataset:
Region-Specific Loss of Two-Headed Ciliary Dyneins in Ascidian Endostyle

A mucous secreting organ in ascidians, the endostyle, consists of several epithelial zones with different ciliary length, density, and beating direction. Here we found by transmission electron microscopy that long cilia in endostyle zone 1 showed 9 + 2 axonemal structures but completely lacked the outer arm dynein. In contrast, cilia in other zones bore both outer and inner dynein arms. Western blotting and immunofluorescence microscopy further revealed that zone 1 appeared to lack not only outer arm dynein but also two-headed inner arm dynein. These results suggest a mechanism for a region-specific gene suppression that causes the limited loss of two-headed axonemal dyneins in the endostyle epithelium. The loss of these dyneins in zone 1 is considered to contribute to the generation of undulating ciliary movement that is essential for a unique circuit of mucus flow in the endostyle.
Vesicular Release of GABA by Mammalian Horizontal Cells Mediates Inhibitory Output to Photoreceptors

Feedback inhibition by horizontal cells regulates rod and cone photoreceptor calcium channels that control their release of the neurotransmitter glutamate. This inhibition contributes to synaptic gain control and the formation of the center-surround antagonistic receptive fields passed on to all downstream neurons, which is important for contrast sensitivity and color opponency in vision. In contrast to the plasmalemmal GABA transporter found in non-mammalian horizontal cells, there is evidence that the mechanism by which mammalian horizontal cells inhibit photoreceptors involves the vesicular release of the inhibitory neurotransmitter GABA. Historically, inconsistent findings of GABA and its biosynthetic enzyme, L-glutamate decarboxylase (GAD) in horizontal cells, and the apparent lack of surround response block by GABAergic agents diminished support for GABA's role in feedback inhibition. However, the immunolocalization of the vesicular GABA transporter (VGAT) in the dendritic and axonal endings of horizontal cells that innervate photoreceptor terminals suggested GABA was released via vesicular exocytosis. To test the idea that GABA is released from vesicles, we localized GABA and GAD, multiple SNARE complex proteins, synaptic vesicle proteins, and Cav channels that mediate exocytosis to horizontal cell dendritic tips and axonal terminals. To address the perceived relative paucity of synaptic vesicles in horizontal cell endings, we used conical electron tomography on mouse and guinea pig retinas that revealed small, clear-core vesicles, along with a few clathrin-coated vesicles and endosomes in horizontal cell processes within photoreceptor terminals. Some small-diameter vesicles were adjacent to the plasma membrane and plasma membrane specializations. To assess vesicular release, a functional assay involving incubation of retinal slices in luminal VGAT-C antibodies demonstrated vesicles fused with the membrane in a depolarization- and calcium-dependent manner, and these labeled vesicles can fuse multiple times. Finally, targeted elimination of VGAT in horizontal cells resulted in a loss of tonic, autaptic GABA currents, and of inhibitory feedback modulation of the cone photoreceptor Cai, consistent with the elimination of GABA release from horizontal cell endings. These results in mammalian retina identify the central role of vesicular release of GABA from horizontal cells in the feedback inhibition of photoreceptors.
Lymphocyte Changes in Severe COVID-19: Delayed Over-Activation of STING?

Upon recognition of microbial DNA or self-DNA, the cyclic-GMP-AMP synthase (cGAS) of the host catalyzes the production of the cyclic dinucleotide cGAMP. cGAMP is the main activator of STING, stimulator of interferon genes, leading to interferon synthesis through the STING-TBK1-IRF3 pathway. STING is also a hub for activation of NF-κB and autophagy. The present review details the striking similarities between T and B cell responses in severe coronavirus disease 2019 (COVID-19) and both animal or human models of STING gain of function (SAVI syndromes: STING-associated vasculopathy with onset in infancy). Those similarities may be further clues for a delayed activation of STING in severe COVID-19 patients, due to DNA damages following severe acute respiratory syndrome coronaviruses (SARS-CoV-2) infection and unusual role of STING in SARS-CoV-2 control. In early stages, Th2 differentiation are noticed in both severe COVID-19 and SAVI syndromes; then, CD4+ and CD8+ T cells functional exhaustion/senescent patterns due to TCR hyper-responsiveness are observed. T cell delayed over-responses can contribute to pneumonitis and delayed cytokine secretion with over-production of IL-6. Last, STING over-activation induces progressive CD4+ and CD8+ T lymphopenia in SAVI syndromes, which parallels what is observed in severe COVID-19. ACE2, the main receptor of SARS-CoV-2, is rarely expressed in immune cells, and it has not been yet proven that some human lymphocytes could be infected by SARS-CoV-2 through CD147 or CD26. However, STING, expressed in humans T cells, might be triggered following excessive transfer of cGAMP from infected antigen presenting cells into activated CD4+ and CD8+ T cells lymphocytes. Indeed, those lymphocytes highly express the cGAMP importer SLC19A1. Whereas STING is not expressed in human B cells, B cells counts are much less affected, either in COVID-19 or SAVI syndromes. The recognition of delayed STING over-activation in severe COVID-19 patients could prompt to target STING with specific small molecules inhibitors already designed and/or aspirin, which inhibits cGAS.
Consolidation Immunotherapy After Platinum-Based Chemoradiotherapy in Patients With Unresectable Stage III Non-Small Cell Lung Cancer—Cross-Sectional Study of Eligibility and Administration Rates

Introduction The PACIFC trial demonstrated a significant benefit of durvalumab consolidation immunotherapy (CIT) after definitive platinum-based chemoradiotherapy (P-CRT) for survival in stage III non-small cell lung cancer (NSCLC). It is unknown how many patients are eligible in clinical practice to receive CIT according to PACIFIC criteria compared to real administration rates and what influencing factors are. Patients and Methods We analyzed 442 patients with unresectable stage III NSCLC who received P-CRT between 2009 and 2019 regarding CIT eligibility rates according to PACIFIC criteria and administration rates since drug approval. Results Sixty-four percent of 437 patients were male, median age was 63 years [interquartile range (IQR): 57–69]. The most common histologic subtypes were adenocarcinoma (42.8%) and squamous cell carcinoma (41.1%), most tumors were in stage IIIB (56.8%). Mean PD-L1 tumor proportion score (TPS) was 29.8% (IQR: 1–60). The median total RT dose was 60 Gy (IQR: 60–66). Platinum component of P-CRT was evenly distributed between cisplatin (51.4%) and carboplatin (48.6%). 50.3% of patients were eligible for CIT according to PACIFIC criteria. Observed contraindications were progressive disease according to RECIST (32.4%), followed by a PD-L1 TPS < 1% (22.3%), pneumonitis CTCAE ≥ 2 (12.6%) and others (4.9%). One year after drug approval, 85.6% of patients who were eligible according to PACIFIC criteria actually received CIT. Time interval between chemotherapy start and radiation therapy start (OR 0.9, 95% CI: [0.9; 1.0] p = 0.009) and probably cisplatin as platinum-component of P-CRT (OR 1.5, 95% CI: [1.0; 2.4] p < 0.061) influence CIT eligibility. Highly positive PD-L1 TPS (≥50%; (OR 2.4, 95% CI: [1.3; 4.5] p = 0.004) was associated to a better chance for CIT eligibility. Conclusion Eighty-five percent of potentially eligible patients received CIT one year after drug approval. Fifty percent of patients did not meet PACIFIC criteria for durvalumab eligibility, this was mainly caused by disease progression during platinum-based CRT, followed by therapy-related pneumonitis and PD-L1 TPS < 1% (in view of the EMA drug approval).
Stroke Mortality Attributable to Low Fruit Intake in China: A Joinpoint and Age-Period-Cohort Analysis

Stroke is the first leading cause of death in China, and low fruit intake is suggested to be one of the most important risk factors for stroke mortality. However, the trends of stroke mortality attributable to low fruit intake remain unclear in China. Therefore, this study aimed to investigate the long-term trends of stroke mortality attributable to low fruit intake by sex in China during 1990–2017. Data were obtained from the Global Burden of Disease 2017 study; the annual percentage change (APC) and average annual percentage change (AAPC) were estimated by joinpoint regression analysis, and the net age, period, and cohort effects were estimated using the age–period–cohort model with an intrinsic estimator algorithm (APC-IE). The crude mortality rates (CMRs) increased for males and decreased for females from 1990 to 2017. The age-standardized mortality rates (ASMRs) for both males and females showed consecutive significant declines from 1990 to 2017. By APC analysis, substantially increasing age effects were presented from 25 to 79 years for both sexes. The independent period and cohort effects progressively decreased during the entire period for both sexes, with a faster decrease for females than for males. Males and elder groups were the high-risk population for stroke mortality caused by low fruit intake. Although the mortality risk showed a decreasing trend, the fruit intake was still low for the Chinese population. Therefore, effective strategies and global awareness are essential to improve the current situation of low fruit intake, thereby preventing and reducing the stroke mortality risk caused by low fruit intake in China.
High frequency transformer-assisted passive soft switching PWM DC-DC converter with energy recovery for compact auxiliary power supply in rolling stock transportation

In this paper, a two-switch high frequency flyback type zero voltage soft switching PWM DC-DC converter using IGBTs is proposed. Effective applications for this power converter can be found in auxiliary power supplies of rolling stock transportation and electric vehicles. This power converter is basically composed of two active power switches and a flyback high frequency transformer. In addition to these, two passive lossless snubbers with power regeneration loops for energy recovery, consisting of a three winding auxiliary high frequency transformer, auxiliary capacitors and diodes are introduced to achieve zero voltage soft switching from light to full load conditions. Furthermore, this power converter has some advantages such as low cost circuit configuration, simple control scheme and high efficiency. Its operating principle is described and to determine circuit parameters, some practical design considerations are discussed. The effectiveness of the proposed power converter is evaluated and compared with the hard switching PWM DC-DC converter from an experimental point of view.
The Impact of External and Internal Incentives on Work Engagement: Evidence from China

The purpose of this paper is to study the Influence Mechanism and Promotion Strategies of Base Official, and to explore the Role of Organizational Identity in this Path. Based on the survey and interview of 350 grass-roots civil servants from China, this paper analyses the relationship among internal and external incentive mechanism, organizational identity and work engagement. The research results show that: the intrinsic and extrinsic motivation have a significant positive impact on work engagement, and the positive influence of intrinsic motive is greater than extrinsic motivation. Both intrinsic and extrinsic motivations have positive influence on organizational identification, And in the process of influence, the external incentive has a certain "crowding effect" on the internal incentive. Organizational identification plays a mediating role between extrinsic motivation and work engagement, but the mediating effect of organizational identification on intrinsic motivation and work engagement is not significant. The research results have certain reference value for improving the work input of grass-roots civil servants.
Approach and Avoidance Behavior in Female Patients With Borderline Personality Disorder

Objectives Borderline personality disorder (BPD) is portrayed by unstable relationships, fears of abandonment and heightened sensitivity to social rejection. Research has shown that these characteristics may lead to inappropriate social behavior including altered approach-avoidance behavior. However, it has remained unclear how social exclusion may affect approach-avoidance behavior in patients with BPD. Design We assessed social approach-avoidance behavior and the impact of social exclusion in a sample of 38 patients with BPD and 40 healthy control participants. Methods We used an explicit joystick-based approach-avoidance task (AAT) after playing a virtual ball-tossing game (Cyberball), which simulates the exclusion of the participant by two other players. In the AAT, participants were required to push or pull emotional stimuli, more specifically happy and angry facial expressions, with either direct or averted gaze direction. Results Patients with BPD approached happy stimuli less and showed overall less differential approach-avoidance behavior toward individuals expressing positive or negative facial emotions compared to healthy participants, who showed more approach behavior for happy compared to angry facial expressions. Moreover, borderline symptom severity correlated inversely with the AAT score for happy facial expressions and positively with subjective unpleasantness during social exclusion as well as rejection sensitivity. However, social exclusion did not influence approach-avoidance tendencies. Conclusion Patients with BPD showed altered approach-avoidance behavior, which might affect social interactions in the patient’s everyday lives and may therefore impede social interaction.
Noise Isn’t Always Negative: Countering Exposure Bias in Sequence-to-Sequence Inflection Models

Morphological inflection, like many sequence-to-sequence tasks, sees great performance from recurrent neural architectures when data is plentiful, but performance falls off sharply in lower-data settings. We investigate one aspect of neural seq2seq models that we hypothesize contributes to overfitting - teacher forcing. By creating different training and test conditions, exposure bias increases the likelihood that a system too closely models its training data. Experiments show that teacher-forced models struggle to recover when they enter unknown territory. However, a simple modification to the training algorithm to more closely mimic test conditions creates models that are better able to generalize to unseen environments.
Annotating Topics, Stance, Argumentativeness and Claims in Dutch Social Media Comments: A Pilot Study

One of the major challenges currently facing the field of argumentation mining is the lack of consensus on how to analyse argumentative user-generated texts such as online comments. The theoretical motivations underlying the annotation guidelines used to generate labelled corpora rarely include motivation for the use of a particular theoretical basis. This pilot study reports on the annotation of a corpus of 100 Dutch user comments made in response to politically-themed news articles on Facebook. The annotation covers topic and aspect labelling, stance labelling, argumentativeness detection and claim identification. Our IAA study reports substantial agreement scores for argumentativeness detection (0.76 Fleiss’ kappa) and moderate agreement for claim labelling (0.45 Fleiss’ kappa). We provide a clear justification of the theories and definitions underlying the design of our guidelines. Our analysis of the annotations signal the importance of adjusting our guidelines to include allowances for missing context information and defining the concept of argumentativeness in connection with stance. Our annotated corpus and associated guidelines are made publicly available.
TIMBERT: Toponym Identifier For The Medical Domain Based on BERT

In this paper, we propose an approach to automate the process of place name detection in the medical domain to enable epidemiologists to better study and model the spread of viruses. We created a family of Toponym Identification Models based on BERT (TIMBERT), in order to learn in an end-to-end fashion the mapping from an input sentence to the associated sentence labeled with toponyms. When evaluated with the SemEval 2019 task 12 test set (Weissenbacher et al., 2019), our best TIMBERT model achieves an F1 score of 90.85%, a significant improvement compared to the state-of-the-art of 89.13% (Wang et al., 2019).
Multimodal Topic-Enriched Auxiliary Learning for Depression Detection

From the perspective of health psychology, human beings with long-term and sustained negativity are highly possible to be diagnosed with depression. Inspired by this, we argue that the global topic information derived from user-generated contents (e.g., texts and images) is crucial to boost the performance of the depression detection task, though this information has been neglected by almost all previous studies on depression detection. To this end, we propose a new Multimodal Topic-enriched Auxiliary Learning (MTAL) approach, aiming at capturing the topic information inside different modalities (i.e., texts and images) for depression detection. Especially, in our approach, a modality-agnostic topic model is proposed to be capable of mining the topical clues from either the discrete textual signals or the continuous visual signals. On this basis, the topic modeling w.r. t. the two modalities are cast as two auxiliary tasks for improving the performance of the primary task (i.e., depression detection). Finally, the detailed evaluation demonstrates the great advantage of our MTAL approach to depression detection over the state-of-the-art baselines. This justifies the importance of the multimodal topic information to depression detection and the effectiveness of our approach in capturing such information.
Image Caption Generation for News Articles

In this paper, we address the task of news-image captioning, which generates a description of an image given the image and its article body as input. This task is more challenging than the conventional image captioning, because it requires a joint understanding of image and text. We present a Transformer model that integrates text and image modalities and attends to textual features from visual features in generating a caption. Experiments based on automatic evaluation metrics and human evaluation show that an article text provides primary information to reproduce news-image captions written by journalists. The results also demonstrate that the proposed model outperforms the state-of-the-art model. In addition, we also confirm that visual features contribute to improving the quality of news-image captions.
SLICE: Supersense-based Lightweight Interpretable Contextual Embeddings

Contextualised embeddings such as BERT have become de facto state-of-the-art references in many NLP applications, thanks to their impressive performances. However, their opaqueness makes it hard to interpret their behaviour. SLICE is a hybrid model that combines supersense labels with contextual embeddings. We introduce a weakly supervised method to learn interpretable embeddings from raw corpora and small lists of seed words. Our model is able to represent both a word and its context as embeddings into the same compact space, whose dimensions correspond to interpretable supersenses. We assess the model in a task of supersense tagging for French nouns. The little amount of supervision required makes it particularly well suited for low-resourced scenarios. Thanks to its interpretability, we perform linguistic analyses about the predicted supersenses in terms of input word and context representations.
Graph-based Aspect Representation Learning for Entity Resolution

Entity Resolution (ER) identifies records that refer to the same real-world entity. Deep learning approaches improved the generalization ability of entity matching models, but hardly overcame the impact of noisy or incomplete data sources. In real scenes, an entity usually consists of multiple semantic facets, called aspects. In this paper, we focus on entity augmentation, namely retrieving the values of missing aspects. The relationship between aspects is naturally suitable to be represented by a knowledge graph, where entity augmentation can be modeled as a link prediction problem. Our paper proposes a novel graph-based approach to solve entity augmentation. Specifically, we apply a dedicated random walk algorithm, which uses node types to limit the traversal length, and encodes graph structure into low-dimensional embeddings. Thus, the missing aspects could be retrieved by a link prediction model. Furthermore, the augmented aspects with fixed orders are served as the input of a deep Siamese BiLSTM network for entity matching. We compared our method with state-of-the-art methods through extensive experiments on downstream ER tasks. According to the experiment results, our model outperforms other methods on evaluation metrics (accuracy, precision, recall, and f1-score) to a large extent, which demonstrates the effectiveness of our method.
Named Entity Recognition for Chinese biomedical patents

There is a large body of work on Biomedical Entity Recognition (Bio-NER) for English but there have only been a few attempts addressing NER for Chinese biomedical texts. Because of the growing amount of Chinese biomedical discoveries being patented, and lack of NER models for patent data, we train and evaluate NER models for the analysis of Chinese biomedical patent data, based on BERT. By doing so, we show the value and potential of this domain-specific NER task. For the evaluation of our methods we built our own Chinese biomedical patents NER dataset, and our optimized model achieved an F1 score of 0.54±0.15. Further biomedical analysis indicates that our solution can help detecting meaningful biomedical entities and novel gene-gene interactions, with limited labeled data, training time and computing power.
Homonym normalisation by word sense clustering: a case in Japanese

This work presents a method of word sense clustering that differentiates homonyms and merge homophones, taking Japanese as an example, where orthographical variation causes problem for language processing. It uses contextualised embeddings (BERT) to cluster tokens into distinct sense groups, and we use these groups to normalise synonymous instances to a single representative form. We see the benefit of this normalisation in language model, as well as in transliteration.