Miracle-dz/UniFAR-FTUs huggingface dataset:
Influences of healthcare workers' behaviours towards infection prevention and control practices in the clinical setting: A systematic review

Abstract Aim To systematically evaluate empirical studies investigating the influences of healthcare workers' behaviours towards infection prevention and control practices in the Coronavirus clinical space, and to appraise and synthesise these findings. Design A systematic review of the literature. Methods The review used a five‐step framework described by Khan et al. (Journal of the Royal Society of Medicine, 2003, 96 and 118) of Framing questions for a review; Identifying relevant work; Assessing the quality of studies; Summarising the evidence; and Interpreting the findings. Searches were conducted in CINHAL, MEDLINE, PsychINFO, Scopus, and Google Scholar databases to retrieve relevant peer‐reviewed literature published in English between 2019 and 2023. Covidence and Joanna Briggs Quality appraisal tools were used for critical assessment. To improve transparent reporting, this review used a Synthesis Without Meta‐analysis (SWiM) in systematic review guidelines, as informed by Campbell et al. (BMJ, 2020, 368). Results Twenty studies were included in this review, identifying nine themes describing factors influencing HCWs' behaviours towards IPC practices in the coronavirus environment. The overarching influences emerged as knowledge‐oriented, person‐oriented, and environment‐oriented. Conclusion Healthcare workers' responsibilities at point‐of‐care involve providing direct care to patients with highly transmissible infections and working in clinical settings that may be ill‐designed for IPC practices, increasing the risk of transmission. Given the lack of a definitive solution to eradicate new mutant viruses and that IPC practices are the mainstay of prevention and control of transmissible, measures to improve are imperative. The identified HCWs' domains on behaviours towards IPC are critical in strategies to mitigate risks and further set an opportunity for developing an IPC model congruent with the rapid response required for HCWs during emerging or re‐merging mutant virus outbreaks. This is significant, given that HCWs' preparedness with IPC practices at point‐of‐care is central to patient care, the workforce and community safety.
Linking Microbial Community Structure to Methane Emissions from Stored Liquid Dairy Manure: Responses to Emission Mitigation Actions

LINKING MICROBIAL COMMUNITY STRUCTURE TO METHANE EMISSIONS FROM STORED LIQUID DAIRY MANURE: RESPONSES TO EMISSION MITIGATION ACTIONS Jemaneh Habtewold University of Guelph, 2018 Advisor(s): Professor Kari Dunfield Professor Robert Gordon Stored liquid dairy manure provides a conducive environment to microbial communities for methane (CH4) production. Significant reductions of emissions from these systems have been observed by using different management strategies and treatment technologies. However, knowledge on how microbial communities respond to the mitigation actions is lacking, and is important for developing and refining mitigation strategies. Using lab and meso-scale studies, the responses of microbial communities to varying levels of total solids content, residual slurry, pH, and chemical agents were assessed. Gas chromatography and laser-based trace gas analysis were used to monitor CH4 flux whereas molecular techniques including denaturing gradient gel electrophoresis, quantitative real-time PCR, and next generation sequencing of marker genes were used to study the microbes. With reduction of total solids content (9.5% to 0.3%), we found up to ~23% increases in the mean abundance and activity of methanogens. Cumulative CH4 emissions, however, decreased by ~70% as total solid levels decreased. These results indicated that available carbon substrate, and not methanogen abundance, may be limiting cumulative CH4 emissions at reduced total solids of dairy slurries. In a follow-up study, abundant bacterial and methanogenic communities were detected, and related to significant increases in cumulative CH4 emissions from stored dairy slurries with 10% and 20% residual slurry. The presence of residual slurry, did not alter the core methanogenic phylotypes, although, Methanocorpusculum predominated the methanogen population, RNA data indicated that methanogens related to Methanosarcina were the major players in CH4 production. Microbial communities in residual slurries may be reduced by chemical treatments. In a lab-scale study, significant reductions in the abundance of methanogens (up to ~28%) were observed when sodium persulfate, potassium permanganate, or their combination with sodium hypochlorite. In slurries treated with H2SO4, the abundance of methanogen populations was not impacted. In a subsequent pilot-scale study, the transcriptional activities of methanogen populations and CH4 production were significantly affected by acidification of manure. Taken together, the findings of these studies suggest that the activity responses of slurry microorganisms can help in the refinement and/or development of effective mitigation strategies.
Effects of microphysics parameterization on simulations of summer heavy precipitation in the Yangtze-Huaihe Region, China

It has been a longstanding problem for current weather/climate models to accurately predict summer heavy precipitation over the Yangtze-Huaihe Region (YHR) which is the key flood-prone area in China with intensive population and developed economy. Large uncertainty has been identified with model deficiencies in representing precipitation processes such as microphysics and cumulus parameterizations. This study focuses on examining the effects of microphysics parameterization on the simulation of different type of heavy precipitation over the YHR taking into account two different cumulus schemes. All regional persistent heavy precipitation events over the YHR during 2008-2012 are classified into three types according to their weather patterns: the type I associated with stationary front, the type II directly associated with typhoon or with its spiral rain band, and the type III associated with strong convection along the edge of the Subtropical High. Sixteen groups of experiments are conducted for three selected cases with different types and a local short-time rainstorm in Shanghai, using the WRF model with eight microphysics and two cumulus schemes. Results show that microphysics parameterization has large but different impacts on the location and intensity of regional heavy precipitation centers. The Ferrier (microphysics) –BMJ (cumulus) scheme and Thompson (microphysics) – KF (cumulus) scheme most realistically simulates the rain-bands with the center location and intensity for type I and II respectively. For type III, the Lin microphysics scheme shows advantages in regional persistent cases over YHR, while the WSM5 microphysics scheme is better in local short-term case, both with the BMJ cumulus scheme.
N95 respirators alter facial skin physiological functions and lipidome composition in health care personnel

Abstract Background During the coronavirus disease 2019 pandemic, wearing medical respirators and masks was essential to prevent transmission. Objective To quantify the effects of N95 mask usage by measuring facial skin biophysical characteristics and changes in the lipidome. Methods Sixty healthy volunteers wore N95 respirators for 3 or 6 h. Facial images were acquired and physiological parameters were measured in specific facial areas, before and after mask‐wearing. Lipidome analysis was also performed. Results After N95 respirator usage, facial erythema was observed in both the 3 and 6 h groups. Both sebum secretion and trans‐epidermal water loss increased significantly in mask‐covered cheeks and chins after 6 h of mask wearing compared with before mask wearing (p < 0.05). Principal component analysis revealed significant differences in lipid composition after mask wearing compared with before. The ceramide subclass NS exhibited a positive correlation with stratum corneum hydration, whereas the AP subclass was negatively correlated with trans‐epidermal water loss in the 6 h group. Conclusion Prolonged wear of N95 respirators may impair facial skin function and alter lipidome composition.
Evaluation of the effectiveness of surgical resection and ablation for the treatment of early‐stage hepatocellular carcinoma: A retrospective cohort study

Abstract Background The optimal treatment strategy for early‐stage hepatocellular carcinoma (HCC) remains controversial, specifically in regard to surgical resection (SR) and ablation. The aim of this study was to investigate the impact of SR and ablation on recurrence and prognosis in early‐stage HCC patients, to optimize treatment strategies and improve long‐term survival. Methods A retrospective analysis was conducted on 801 patients diagnosed with Barcelona Clinic Liver Cancer (BCLC) stage 0/A HCC and treated with SR or ablation between January 2015 and December 2019. The effectiveness and complications of both treatments were analyzed, and patients were followed up to measure recurrence and survival. Propensity score matching (PSM) was employed to increase comparability between the two groups. The Kaplan–Meier method was used to analyze recurrence and survival, and a Cox risk proportional hazard model was used to identify risk factors that affect recurrence and surviva. Results Before PSM, the overall survival (OS) rates were similar in both groups, with recurrence‐free survival (RFS) rates better in the SR group than in the ablation group. After PSM, there was no significant difference in OS between the two groups. However, the RFS rates were significantly better in the SR group than in the ablation group. The ablation group exhibited superior outcomes compared to the SR group, with shorter treatment times, reduced bleeding, shorter hospital stays, and lower hospital costs. Concerning the location of the HCC within the liver, comparable efficacy was observed between SR and ablation for disease located in the noncentral region or left lobe. However, for HCCs located in the central region or right lobe of the liver, SR was more effective than ablation. Conclusions This study revealed no significant difference in OS between SR and ablation for early‐stage HCC, with SR providing better RFS and ablation demonstrating better safety profiles and lower hospital costs. These findings offer valuable insights for clinicians in determining optimal treatment strategies for early‐stage HCC patients, particularly in terms of balancing efficacy, safety, and cost considerations.
Resolution enhancement of DEM using photometric stereo method in time-varying shadowed region

Generation of high-resolution Digital Elevation Model (DEM) is essential for space missions like an investigation of the topographic feature, a selection of landing site or a path planning of rover. Fusion of image which contains high-frequency information on the terrain surface and depth information which gives sparse information is an important topic in the generation of DEM. In this paper, the photometric stereo method is used to generate the surface normal information of shadowed region in the crater. Then, the surface normal is fused with interpolated DEM from ground truth DEM. The fusion enhances the lateral resolution of interpolated DEM and generates details of the ground truth DEM which is not shown in interpolated DEM. This method can be adapted to enhance DEM for near polar region, which has a large variation in shadow and has many image dataset due to orbiter's flight orbit.
Predrill pore-pressure prediction using seismic data

1A predrill estimate of pore pressure can be obtained from seismic velocities using a velocity‐to–pore‐pressure transform, but the seismic velocities need to be derived using methods having sufficient resolution for well planning purposes. For a deepwater Gulf of Mexico example, significant differences are found between the velocity field obtained using reflection tomography and that obtained using a conventional method based on the Dix equation. These lead to significant differences in the predicted pore pressure. Parameters in the velocity‐to–pore‐pressure transform are estimated using seismic interval velocities and pressure data from nearby calibration wells. The uncertainty in the pore pressure prediction is analyzed by examining the spread in the predicted pore pressure obtained using parameter combinations which sample the region of parameter space consistent with the available well data. If calibration wells are not available, the ideas proposed in this paper can be used with measurements made whi.
A comparison of the efficacy and safety of complementary and alternative therapies for the primary dysmenorrhea

Background: There are a number of complementary and alternative therapies for the primary dysmenorrhea (PD) and their efficacy has been assessed by several systematic reviews. But only pair-wised drugs have been evaluated in the traditional meta-analyses and conflicting interpretation of results also existed among different studies. Here, a protocol for a network meta-analysis will be presented aimed to compare the efficacy and safety of different complementary and alternative therapies for PD. Methods: All randomized controlled trials of complementary and alternative therapies for the PD will be included. The primary outcomes of our interest are pain intensity and pain duration and the secondary outcomes are quality of life, clinical effective rate, and adverse events. We will search relevant database, the ongoing trial, previous relevant reviews and reference lists, and so on. The identification and selection of studies and data extraction will be conducted by two independent reviewers. We will perform a battery of pairwise meta-analyses and Bayesian network meta-analyses to assess the relative outcomes of different complementary and alternative therapies. We will use the surface under the cumulative ranking curve values and the mean ranks to get the treatment hierarchy, and then use the node-splitting method to evaluate consistency. The softwares WinBUGS 1.4. 3 and STATA will be selected and the quality of the evidence will be evaluated using the Grading of Recommendations Assessment, Development and Evaluation instrument. Ethics and dissemination: This review does not require ethical approval. PROSPERO registration number: PROSPERO CRD42018107763.
Morphological and genetic verification of Ovophis tonkinensis (Bourret, 1934) in Hong Kong

Geographically, Hong Kong lies in the putative ranges of two Ovophis species— O. makazayazaya (Gunther, 1864) (Sichuan and Yunnan Provinces, Taiwan, and northern Vietnam) and O. tonkinensis (Bourret, 1934) (Guangxi, Guangdong, and Hainan Provinces, and northern Vietnam). It is unclear which Ovophis species is/are present. Previous studies identified O. tonkinensis using morphology, but no study has included morphological and molecular data of the same specimens. In this study, we use molecular (four mitocondrial DNA loci) and morphological data of two specimens to verify the presence of O. tonkinensis in Hong Kong.
RORγt up-regulates RAG gene expression in DP thymocytes to expand the Tcra repertoire

Recombination activating gene (RAG) expression increases as thymocytes transition from the CD4−CD8− double-negative (DN) to the CD4+CD8+ double-positive (DP) stage, but the physiological importance and mechanism of transcriptional up-regulation are unknown. Here, we show that a DP-specific component of the recombination activating genes antisilencer (DPASE) provokes elevated RAG expression in DP thymocytes. Mouse DP thymocytes lacking the DPASE display RAG expression equivalent to that in DN thymocytes, but this supports only a partial Tcra repertoire due to inefficient secondary Vα-Jα rearrangement. These data indicate that RAG up-regulation is required for a replete Tcra repertoire and that RAG expression is fine-tuned during lymphocyte development to meet the requirements of distinct antigen receptor loci. We further show that transcription factor RORγt directs RAG up-regulation in DP thymocytes by binding to the DPASE and that RORγt influences the Tcra repertoire by binding to the Tcra enhancer. These data, together with prior work showing RORγt to control Tcra rearrangement by regulating DP thymocyte proliferation and survival, reveal RORγt to orchestrate multiple pathways that support formation of the Tcra repertoire. RORγt directs elevated RAG expression in DP thymocytes that is essential to generate a replete Tcra repertoire. Editor’s summary Thymocytes show an increase in recombination activating gene (RAG) expression as they transition from CD4−CD8− double-negative (DN) to CD4+CD8+ double-positive (DP) cells. Naik et al. examined the mechanism and effects of increased RAG expression in thymocytes. The presence of a DP-specific component of the RAG antisilencer (DPASE) in the RAG locus was linked to higher RAG expression in DP thymocytes and was needed for efficient Vα-Jα rearrangement and a sufficient Tcra repertoire. The RORγt transcription factor binds to the DPASE to up-regulate RAG expression in DP thymocytes and binds to the Tcra enhancer as well, which affects the Tcra repertoire. These data deepen our understanding of mechanisms involved in DP thymocyte development and how this affects the Tcra repertoire. —Christiana N. Fogg
SSN-Nova@LT-EDI 2024: POS Tagging, Boosting Techniques and Voting Classifiers for Caste And Migration Hate Speech Detection

This paper presents our submission for the shared task on Caste and Migration Hate Speech Detection: LT-EDI@EACL 20241 . This text classification task aims to foster the creation of models capable of identifying hate speech related to caste and migration. The dataset comprises social media comments, and the goal is to categorize them into negative and positive sentiments. Our approach explores back-translation for data augmentation to address sparse datasets in low-resource Dravidian languages. While Part-of-Speech (POS) tagging is valuable in natural language processing, our work highlights its ineffectiveness in Dravidian languages, with model performance drastically reducing from 0.73 to 0.67 on application. In analyzing boosting and ensemble methods, the voting classifier with traditional models outperforms others and the boosting techniques, underscoring the efficacy of simper models on low-resource data despite augmentation.
The Future of Web Data Mining: Insights from Multimodal and Code-based Extraction Methods

The extraction of structured data from websites is critical for numerous Artificial Intelligence applications, but modern web design increasingly stores information visually in images rather than in text. This shift calls into question the optimal technique, as language-only models fail without textual cues while new multimodal models like GPT-4 promise image understanding abilities. We conduct the first rigorous comparison between text-based and vision-based models for extracting event metadata harvested from comic convention websites. Surprisingly, our results between GPT-4 Vision and GPT-4 Text uncover a significant accuracy advantage for vision-based methods in an applies-to-apples setting, indicating that vision models may be outpacing language-alone techniques in the task of information extraction from websites. We release our dataset and provide a qualitative analysis to guide further research in multi-modal models for web information extraction.
MUCS@LT-EDI-2024: Learning Approaches to Empower Homophobic/Transphobic Comment Identification

Homophobic/Transphobic (H/T) content includes hatred and discriminatory comments directed at Lesbian, Gay, Bisexual, Transgender, Queer (LGBTQ) individuals on social media platforms. As this unfavourable perception towards LGBTQ individuals may affect them physically and mentally, it is necessary to detect H/T content on social media. This demands automated tools to identify and address H/T content. In view of this, in this paper, we - team MUCS describe the learning models submitted to “Homophobia/Transphobia Detection in social media comments:LT-EDI@EACL 2024” shared task at European Chapter of the Association for Computational Linguistics (EACL) 2024. The learning models: i) Homo_Ensemble - an ensemble of Machine Learning (ML) algorithms trained with Term Frequency-Inverse Document Frequency (TFIDF) of syllable n-grams in the range (1, 3), ii) Homo_TL - a model based on Transfer Learning (TL) approach with Bidirectional Encoder Representations from Transformers (BERT) models, iii) Homo_probfuse - an ensemble of ML classifiers with soft voting trained using sentence embeddings (except for Hindi), and iv) Homo_FSL - Few-Shot Learning (FSL) models using Sentence Transformer (ST) (only for Tulu), are proposed to detect H/T content in the given languages. Among the models submitted to the shared task, the models that performed better for each language include: i) Homo_Ensemble model obtained macro F1 score of 0.95 securing 4th rank for Telugu language, ii) Homo_TL model obtained macro F1 scores of 0.49, 0.53, 0.45, 0.94, and 0.95 securing 2nd, 2nd, 1st, 1st, and 4th ranks for English, Marathi, Hindi, Kannada, and Gujarathi languages, respectively, iii) Homo_probfuse model obtained macro F1 scores of 0.86, 0.87, and 0.53 securing 2nd, 6th, and 2nd ranks for Tamil, Malayalam, and Spanish languages respectively, and iv) Homo_FSL model obtained a macro F1 score of 0.62 securing 2nd rank for Tulu dataset.
People and Places of the Past - Named Entity Recognition in Swedish Labour Movement Documents from Historical Sources

Named Entity Recognition (NER) is an important step in many Natural Language Processing tasks. The existing state-of-the-art NER systems are however typically developed based on contemporary data, and not very well suited for analyzing historical text. In this paper, we present a comparative analysis of the performance of several language models when applied to Named Entity Recognition for historical Swedish text. The source texts we work with are documents from Swedish labour unions from the 19th and 20th century. We experiment with three off-the-shelf models for contemporary Swedish text, and one language model built on historical Swedish text that we fine-tune with labelled data for adaptation to the NER task. Lastly, we propose a hybrid approach by combining the results of two models in order to maximize usability. We show that, even though historical Swedish is a low-resource language with data sparsity issues affecting overall performance, historical language models still show very promising results. Further contributions of our paper are the release of our newly trained model for NER of historical Swedish text, along with a manually annotated corpus of over 650 named entities.