Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Grammatical Error Correction Models with Purpose-Built Adversarial Examples

A sequence-to-sequence (seq2seq) learning with neural networks empirically shows to be an effective framework for grammatical error correction (GEC), which takes a sentence with errors as input and outputs the corrected one. However, the performance of GEC models with the seq2seq framework heavily relies on the size and quality of the corpus on hand. We propose a method inspired by adversarial training to generate more meaningful and valuable training examples by continually identifying the weak spots of a model, and to enhance the model by gradually adding the generated adversarial examples to the training set. Extensive experimental results show that such adversarial training can improve both the generalization and robustness of GEC models.
Sentiment Analysis of Tweets Using Heterogeneous Multi-layer Network Representation and Embedding

Sentiment classification on tweets often needs to deal with the problems of under-specificity, noise, and multilingual content. This study proposes a heterogeneous multi-layer network-based representation of tweets to generate multiple representations of a tweet and address the above issues. The generated representations are further ensembled and classified using a neural-based early fusion approach. Further, we propose a centrality aware random-walk for node embedding and tweet representations suitable for the multi-layer network. From various experimental analysis, it is evident that the proposed method can address the problem of under-specificity, noisy text, and multilingual content present in a tweet and provides better classification performance than the text-based counterparts. Further, the proposed centrality aware based random walk provides better representations than unbiased and other biased counterparts.
Towards Medical Machine Reading Comprehension with Structural Knowledge and Plain Text

Machine reading comprehension (MRC) has achieved significant progress on the open domain in recent years, mainly due to large-scale pre-trained language models. However, it performs much worse in specific domains such as the medical field due to the lack of extensive training data and professional structural knowledge neglect. As an effort, we first collect a large scale medical multi-choice question dataset (more than 21k instances) for the National Licensed Pharmacist Examination in China. It is a challenging medical examination with a passing rate of less than 14.2% in 2018. Then we propose a novel reading comprehension model KMQA, which can fully exploit the structural medical knowledge (i.e., medical knowledge graph) and the reference medical plain text (i.e., text snippets retrieved from reference books). The experimental results indicate that the KMQA outperforms existing competitive models with a large margin and passes the exam with 61.8% accuracy rate on the test set.
Routing Enforced Generative Model for Recipe Generation

One of the most challenging part of recipe generation is to deal with the complex restrictions among the input ingredients. Previous researches simplify the problem by treating the inputs independently and generating recipes containing as much information as possible. In this work, we propose a routing method to dive into the content selection under the internal restrictions. The routing enforced generative model (RGM) can generate appropriate recipes according to the given ingredients and user preferences. Our model yields new state-of-the-art results on the recipe generation task with significant improvements on BLEU, F1 and human evaluation.
Weakly Supervised Subevent Knowledge Acquisition

Subevents elaborate an event and widely exist in event descriptions. Subevent knowledge is useful for discourse analysis and event-centric applications. Acknowledging the scarcity of subevent knowledge, we propose a weakly supervised approach to extract subevent relation tuples from text and build the first large scale subevent knowledge base. We first obtain the initial set of event pairs that are likely to have the subevent relation, by exploiting two observations that 1) subevents are temporally contained by the parent event, and 2) the definitions of the parent event can be used to further guide the identification of subevents. Then, we collect rich weak supervision using the initial seed subevent pairs to train a contextual classifier using BERT and apply the classifier to identify new subevent pairs. The evaluation showed that the acquired subevent tuples (239K) are of high quality (90.1% accuracy) and cover a wide range of event types. The acquired subevent knowledge has been shown useful for discourse analysis and identifying a range of event-event relations.
Doc2Dial: A Goal-Oriented Document-Grounded Dialogue Dataset

We introduce doc2dial, a new dataset of goal-oriented dialogues that are grounded in the associated documents. Inspired by how the authors compose documents for guiding end users, we first construct dialogue flows based on the content elements that corresponds to higher-level relations across text sections as well as lower-level relations between discourse units within a section. Then we present these dialogue flows to crowd contributors to create conversational utterances. The dataset includes over 4500 annotated conversations with an average of 14 turns that are grounded in over 450 documents from four domains. Compared to the prior document-grounded dialogue datasets, this dataset covers a variety of dialogue scenes in information-seeking conversations. For evaluating the versatility of the dataset, we introduce multiple dialogue modeling tasks and present baseline approaches.
Biomedical Event Extraction as Sequence Labeling

We introduce Biomedical Event Extraction as Sequence Labeling (BeeSL), a joint end-to-end neural information extraction model. BeeSL recasts the task as sequence labeling, taking advantage of a multi-label aware encoding strategy and jointly modeling the intermediate tasks via multi-task learning. BeeSL is fast, accurate, end-to-end, and unlike current methods does not require any external knowledge base or preprocessing tools. BeeSL outperforms the current best system (Li et al., 2019) on the Genia 2011 benchmark by 1.57% absolute F1 score reaching 60.22% F1, establishing a new state of the art for the task. Importantly, we also provide first results on biomedical event extraction without gold entity information. Empirical results show that BeeSL’s speed and accuracy makes it a viable approach for large-scale real-world scenarios.
Bioinspired DNase‐I‐Coated Melanin‐Like Nanospheres for Modulation of Infection‐Associated NETosis Dysregulation

The current outbreak of the beta‐coronavirus (beta‐Cov) severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) began in December 2019. No specific antiviral treatments or vaccines are currently available. A recent study has reported that coronavirus disease 2019 (COVID‐19), the disease caused by SARS‐CoV‐2 infection, is associated with neutrophil‐specific plasma membrane rupture, and release excessive neutrophil extracellular traps (NETs) and extracellular DNAs (eDNAs). This mechanism involves the activation of NETosis, a neutrophil‐specific programmed cell death, which is believed to play a crucial role in COVID‐19 pathogenesis. Further progression of the disease can cause uncontrolled inflammation, leading to the initiation of cytokine storms, acute respiratory distress syndrome (ARDS), and sepsis. Herein, it is reported that DNase‐I‐coated melanin‐like nanospheres (DNase‐I pMNSs) mitigate sepsis‐associated NETosis dysregulation, thereby preventing further progression of the disease. Recombinant DNase‐I and poly(ethylene glycol) (PEG) are used as coatings to promote the lengthy circulation and dissolution of NET structure. The data indicate that the application of bioinspired DNase‐I pMNSs reduce neutrophil counts and NETosis‐related factors in the plasma of SARS‐CoV‐2 sepsis patients, alleviates systemic inflammation, and attenuates mortality in a septic mouse model. Altogether, the findings suggest that these nanoparticles have potential applications in the treatment of SARS‐CoV‐2‐related illnesses and other beta‐CoV‐related diseases.
Welfare of cattle at slaughter

Abstract The killing of cattle for human consumption (slaughtering) can take place in a slaughterhouse or on farm. The processes of slaughtering that were assessed for welfare, from the arrival of cattle until their death (including slaughtering without stunning), were grouped into three main phases: pre‐stunning (including arrival, unloading from the truck, lairage, handling and moving of cattle); stunning (including restraint); and bleeding. Stunning methods were grouped into two categories: mechanical and electrical. Twelve welfare consequences that cattle may be exposed to during slaughter were identified: heat stress, cold stress, fatigue, prolonged thirst, prolonged hunger, impeded movement, restriction of movements, resting problems (inability to rest or discomfort during resting), social stress, pain, fear and distress. Welfare consequences and their relevant animal‐based measures are described. In total, 40 welfare hazards that could occur during slaughter were identified and characterised, most of them related to stunning and bleeding. Staff were identified as the origin of 39 hazards, which were attributed to the lack of appropriate skill sets needed to perform tasks or to fatigue. Measures to prevent and correct hazards were identified, and structural and managerial measures were identified as those with a crucial role in prevention. Outcome tables linking hazards, welfare consequences, animal‐based measures, origin of hazards, and preventive and corrective measures were developed for each process. Mitigation measures to minimise welfare consequences are proposed.
Naringin in Combination with Isothiocyanates as Liposomal Formulations Potentiates the Anti-inflammatory Activity in Different Acute and Chronic Animal Models of Rheumatoid Arthritis

Combination of drugs is extensively used to treat chronic inflammatory disease. Naringin (NAR), sulforaphane (SFN), and phenethyl isothiocyanate (PEITC) are nutraceuticals with promising anti-inflammatory properties. However, their clinical effectiveness gets hindered because of low aqueous solubility and poor bioavailability. In the current study, two combinations of liposome (NAR + SFN and NAR + PEITC) were prepared and studied thoroughly in different in vivo models of acute and chronic models of inflammation. The encapsulation efficiency of NAR, SFN, and PEITC in the combination liposomal formulations (CLFs) prepared with 1,2-dipalmitoyl-sn-glycero-3-phosphocholine/cholesterol/1,2-distearoyl-sn-glycero-3-phosphoethanolamine -020CN (15:4:1 M ratio) was determined to be 79.8 ± 4.2, 46.5 ± 3.6, and 78.5 ± 3.2%, respectively. The CLFs were characterized by differential scanning calorimetry, X-ray diffraction, dynamic light scattering, and Fourier transform infrared spectroscopy. The physicochemical results showed that the preparations were monodisperse (PDI 0.062–0.248) in water with an average size from 140.5 to 165.6 nm and a zeta potential of −47.3 to −53.3 mV. Dissolution studies in vitro showed a slower release of PEITC (>90%, 6 h) in comparison to that of SFN (3 h). Here, we are the first to report the antiarthritic activity of CLF of NAR + SFN and NAR + PEITC in the Freund’s complete adjuvant (FCA)-induced arthritic model. At an intraperitoneal dose (375 + 375 μg/mL) for 3 weeks, the NAR + PEITC liposome significantly improves both % paw edema and arthritic score compared to their free drug combinations in FCA rats. Most importantly, hematological and biochemical results showed improved anemic conditions with significant changes in the SGOT, SGPT, and ALP levels. The ELISA results showed similar trends of increased cytokine (IL-10) and decreased inflammation markers (TNF-α, IL-6, IFN-γ). Histological evaluations showing reduction in cell infiltration, pannus formation, and bone and cartilage destruction further confirm and validate the antiarthritic activity of the CLF. This comprehensive study reveals the effectiveness of combination liposomes of poorly soluble anti-inflammatory molecules (NAR, SFN, PEITC) in the treatment of arthritis.
Amylotrophic Lateral Sclerosis-Like Motor Impairment in Prion Diseases

Neurodegenerative diseases are collective diseases that affect different parts of the brain with common or distinct disease phenotype. In almost all of the Prion diseases, motor impairments that are characterized by motor derangement, apathy, ataxia, and myoclonus are documented and again are shared by motor neuron diseases (MND). Proteins such as; B-Cell lymphoma 2 (BCL2), Copper chaperone for superoxide dismutase (CCS), Amyloid beta precursor protein (APP), Amyloid Precursor-Like Protein1/2 (APLP1/2), Catalase (CAT), and Stress induced phosphoprotein 1 (STIP1), are common interactomes of Prion and superoxide dismutase 1 (SOD1). Although there is no strong evidence to show the interaction of SOD1 and Prion, the implicated common interacting proteins indicate the potential bilateral interaction of those proteins in health and disease. For example, down-regulation of Heat shock protein A (HSPA5), a Prion interactome, increases accumulation of misfolded SOD1 leading to MND. Loss of Cu uptake function disturbs normal function of CCS. Over-expressed proteasome subunit alpha 3 (PSMA3) could fatigue its normal function of removing misfolded proteins. Studies showed the increase in CAT and lipid oxidation both in Prion-knocked out animal and in catalase deficiency cases. Up regulation, down regulation or direct interaction with their interactomes are predicted molecular mechanisms by which Prion and SOD exert their effect. The loss of protective function or the gain of a novel toxic property by the principal proteins is shared in Prion and MND. Thus, it might be possible to conclude that the interplay of proteins displayed in both diseases could be a key phenomenon in motor dysfunction development.
Characterizing the Dynamical Complexity Underlying Meditation

Over the past 2,500 years, contemplative traditions have explored the nature of the mind using meditation. More recently, neuroimaging research on meditation has revealed differences in brain function and structure in meditators. Nevertheless, the underlying neural mechanisms are still unclear. In order to understand how meditation shapes global activity through the brain, we investigated the spatiotemporal dynamics across the whole-brain functional network using the Intrinsic Ignition Framework. Recent neuroimaging studies have demonstrated that different states of consciousness differ in their underlying dynamical complexity, i.e., how the broadness of communication is elicited and distributed through the brain over time and space. In this work, controls and experienced meditators were scanned using functional magnetic resonance imaging (fMRI) during resting-state and meditation (focused attention on breathing). Our results evidenced that the dynamical complexity underlying meditation shows less complexity than during resting-state in the meditator group but not in the control group. Furthermore, we report that during resting-state, the brain activity of experienced meditators showed higher metastability (i.e., a wider dynamical regime over time) than the one observed in the control group. Overall, these results indicate that the meditation state operates in a different dynamical regime than the resting-state.
Towards connecting biodiversity and geodiversity across scales with satellite remote sensing

Abstract Issue Geodiversity (i.e., the variation in Earth's abiotic processes and features) has strong effects on biodiversity patterns. However, major gaps remain in our understanding of how relationships between biodiversity and geodiversity vary over space and time. Biodiversity data are globally sparse and concentrated in particular regions. In contrast, many forms of geodiversity can be measured continuously across the globe with satellite remote sensing. Satellite remote sensing directly measures environmental variables with grain sizes as small as tens of metres and can therefore elucidate biodiversity–geodiversity relationships across scales. Evidence We show how one important geodiversity variable, elevation, relates to alpha, beta and gamma taxonomic diversity of trees across spatial scales. We use elevation from NASA's Shuttle Radar Topography Mission (SRTM) and c. 16,000 Forest Inventory and Analysis plots to quantify spatial scaling relationships between biodiversity and geodiversity with generalized linear models (for alpha and gamma diversity) and beta regression (for beta diversity) across five spatial grains ranging from 5 to 100 km. We illustrate different relationships depending on the form of diversity; beta and gamma diversity show the strongest relationship with variation in elevation. Conclusion With the onset of climate change, it is more important than ever to examine geodiversity for its potential to foster biodiversity. Widely available satellite remotely sensed geodiversity data offer an important and expanding suite of measurements for understanding and predicting changes in different forms of biodiversity across scales. Interdisciplinary research teams spanning biodiversity, geoscience and remote sensing are well poised to advance understanding of biodiversity–geodiversity relationships across scales and guide the conservation of nature.
Clinical efficacy of three types of autogenous bone grafts in treatment of single-segment thoracic tuberculosis: A retrospective cohort study

A retrospective study investigated and compared the results of lamina with spinous process (LSP), transverse process strut (TPS) and iliac graft (IG) as bone graft in thoracic single-segment spinal tuberculosis(TB) with the one-stage posterior approach of debridement, fusion and internal instrumentation. 99 patients treated from January 2012 to December 2015 were reviewed. LSP was performed in 35 patients (group A), TPS was undertaken in 33 patients (group B), and IG was carried out in 31 patients (group C). Surgical time, blood loss, hospitalization time, drainage volume, and follow-up (FU) duration were recorded. The visual analog scale (VAS), Oswestry Disability Index (ODI), erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), American Spinal Injury Association (ASIA) grade, segmental angle, intervertebral height and bone fusion time were compared between preoperative and final FU. All the patients were followed up for a mean 43.90±10.39 months in group A, 45.30±6.20 months in group B, 44.32±7.17 months in group C without difference(P>0.05). The mean age was younger, the blood loss was less, the hospitalization time and the surgical time were shorter in group A than those in group B and C (P<0.05). The drainage volume was less in group A than that in group B and group C. The CRP, ESR, VAS, and ODI were significantly decreased and there were no significant difference among the groups at the final FU. The neurological function after surgery was improved compared with preoperation among the groups. The bony fusion at a mean time 12.90±3.91 months in group A was longer than that in group B (6.75±1.55 months) and group C (5.52±1.64 months) (P<0.05). No significant difference was found at the mean segmental angle, mean intervetebral height of preoperation and final FU among the groups (P>0.05). In conclusion, the LSP and TPS as bone graft are reliable, safe, and effective for single-segment stability reconstruction for surgical management of thoracic TB and TPS could be new bone graft methods.
Towards an optimised processing pipeline for diffusion MRI data: Effects of artefact corrections on diffusion metrics and their age associations in UK Biobank

Increasing interest in the structural and functional organization of the human brain in health and disease encourages the acquisition of big datasets consisting of multiple neuroimaging modalities accompanied by additional information obtained from health records, cognitive tests, biomarkers and genotypes. Diffusion weighted magnetic resonance imaging data enables a range of promising imaging phenotypes probing structural connections as well as macroanatomical and microstructural properties of the brain. The reliability and biological sensitivity and specificity of diffusion data depend on processing pipeline. A state-of-the-art framework for data processing facilitates crossstudy harmonisation and reduces pipeline-related variability. Using diffusion data from the UK Biobank we provide a comprehensive evaluation of different processing steps that have been suggested to reduce imaging artefacts and improve reliability of diffusion metrics. We consider a general pipeline comprising 7 post-processing blocks: noise correction; Gibbs ringing correction; evaluation of field distortions; susceptibility, eddy-current and motion-induced distortion corrections; bias field correction; spatial smoothing; and final diffusion metric estimations. Based on this evaluation, we suggest an optimised diffusion pipeline for processing of diffusion weighted imaging data.
Clarifying the merchandising function: analysis of merchandising roles and responsibilities in the South African apparel retail industry

ABSTRACT The purpose of this study was to examine merchandising roles and responsibilities from the perspective of professionals employed in the South African apparel retail industry. Data were collected through semi-structured, individual in-depth interviews. A total of 16 merchandising professionals were recruited to participate in the study. A phenomenological research design was followed. Atlas.ti software was used to analyze the transcribed interviews. The findings showed that merchandising departments in the South African retail tend to employ professionals in four distinct positions of buyers, planners, product developers, and sourcing coordinators. These merchandising roles, that make up the merchandising function, are essential in delivery products which are profitable and will satisfy consumers needs. The four positions also have distinct responsibilities that are organised according to the product lifecycle. Merchandising professionals rely on frequent daily interactions with each other through shared responsibilities and decision-making for some of the stages of the product lifecycle.
F-actin disassembly factor MICAL1 binding to Myosin Va mediates cargo unloading during cytokinesis

Cellular, biochemical, and structural investigation of the MyoVa/MICAL1 complex reveals an intracellular cargo-unloading mechanism. Motor-mediated intracellular trafficking requires motors to position cargoes at proper locations. Myosin Va (MyoVa), an actin-based motor, is a classic model for studying cargo transport. However, the molecular basis underlying cargo unloading in MyoVa-mediated transport has remained enigmatic. We have identified MICAL1, an F-actin disassembly regulator, as a binding partner of MyoVa and shown that MICAL1-MyoVa interaction is critical for localization of MyoVa at the midbody. By binding to MICAL1, MyoVa-mediated transport is terminated, resulting in vesicle unloading at the midbody for efficient cytokinesis. The MyoVa/MICAL1 complex structure reveals that MICAL1 and F-actin assembly factors, Spires, share an overlapped binding surface on MyoVa, suggesting a regulatory role of F-actin dynamics in cargo unloading. Down-regulating F-actin disassembly by a MICAL1 mutant significantly reduces MyoVa and vesicles accumulating at the midbody. Collectively, our findings demonstrate that MyoVa binds to MICAL1 at the midbody destination and triggers F-actin disassembly to unload the vesicle cargo.