Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of second-line anti-tuberculosis drugs on the intestinal microbiota of patients with rifampicin-resistant tuberculosis

Objective To determine the effects of second-line anti-tuberculosis (TB) drugs on the composition and functions of intestinal microbiota in patients with rifampicin-resistant TB (RR-TB). Methods In this cross-sectional study, stool samples and relevant clinical information were collected from patients with RR-TB admitted to the Drug-resistant Specialty Department at Hunan Chest Hospital (Hunan Institute For Tuberculosis Control). The composition and functions of intestinal microbiota were analyzed using metagenomic sequencing and bioinformatics methods. Results Altered structural composition of the intestinal microbiota was found when patients from the control, intensive phase treatment, and continuation phase treatment groups were compared (P<0.05). Second-line anti-TB treatment resulted in a decrease in the relative abundance of species, such as Prevotella copri, compared with control treatment. However, the relative abundance of Escherichia coli, Salmonella enterica, and 11 other conditionally pathogenic species increased significantly in the intensive phase treatment group. Based on differential functional analysis, some metabolism-related functions, such as the biosynthesises of phenylalanine, tyrosine, and tryptophan, were significantly inhibited during second-line anti-TB drug treatment, while other functions, such as phenylalanine metabolism, were significantly promoted during the intensive phase of treatment. Conclusion Second-line anti-TB drug treatment caused changes in the structural composition of the intestinal microbiota in patients with RR-TB. In particular, this treatment induced a significant increase in the relative abundance of 11 conditionally pathogenic species, including Escherichia coli. Functional analysis revealed significantly decreased biosynthesises of phenylalanine, tyrosine, and tryptophan and significantly increased phenylalanine metabolism.
Sphalerite as a non-traditional critical metal source: Correlative microscopy (EPMA, EBSD, and APT) of cobalt-enriched sulfides from the sediment-hosted copper-cobalt Dolostone Ore Formation deposit, Namibia

Sphalerite from the sediment-hosted Dolostone Ore Formation (DOF) Cu-Co-Zn deposit, in northwestern Namibia, has previously been shown to contain extremely high concentrations of the critical metal Co (up to 1 wt%). These concentrations are the highest reported in sphalerite to date, and the how and why of sphalerite being able to incorporate such high concentrations of Co are poorly understood. We use correlative electron probe microanalysis, electron backscattered diffraction, and atom probe to reconstruct the likely incorporation mechanisms and modes of occurrence of such high Co concentrations in natural sphalerite. While over twenty samples were studied, the comprehensive analytical workflow was executed on one representative sample to gain a detailed understanding of Co enrichment. The sulfides of the studied sample are Co-rich pyrite, chalcopyrite, Co-rich sphalerite, linnaeite, cobaltpentlandite, and cobaltite, mentioned in order of abundance. Detailed petrography of these sulfides indicates that they formed through three stages during the main Cu-Co-Zn ore stage of the DOF. Cobalt was initially contained in pyrite that grew during Ore Stage 1 and was later affected by oxidizing fluids (Ore Stage two). This led to remobilization and growth of linnaeite (Co2+Co3+ 2S4). A later change in fO2 (Ore Stage three) led to the breakdown of linnaeite and the further growth of accessory cobaltite along with the Co-rich sphalerite and chalcopyrite. The hyper-enriched Co-sphalerite then is the last major sink for Co in the DOF deposit. A low Fe and Co and high Zn sub-grain boundary network within the Co-rich sphalerite was identified by EPMA and EBSD. This sub-grain network is believed to have formed during a later, secondary metamorphic stage (Cu-Zn (-Pb) Ore Stage 4), which developed during ductile deformational mineralization styles such as pressure shadows and veins. Our APT data reconstructions show no evidence for Co-inclusions within the Co-sphalerite, and spatial ion correlation analyses of the data suggest that Co occurs in the sphalerite through simple substitution of Zn. This study demonstrates that sphalerite may contain significant concentrations of the Co through simple substitution, potentially representing an important non-traditional Co source in future critical metal exploration.
Multi-Lingual Multi-Partite Product Title Matching

In a globalized marketplace, one could access products or services from almost anywhere. However, resolving which product in one language corresponds to another product in a different language remains an under-explored problem. We explore this from two perspectives. First, given two products of different languages, how to assess their similarity that could signal a potential match. Second, given products from various languages, how to arrive at a multi-partite clustering that respects cardinality constraints efficiently. We describe algorithms for each perspective and integrate them into a promising solution validated on real-world datasets.
Oral health promotion in acute hospital setting: a quality improvement programme

Tooth extraction is the most common hospital procedure for children aged 6–10 years in England. Tooth decay is almost entirely preventable and is inequitably distributed across the population: it can cause pain, infection, school absences and undermine overall health status. An oral health programme (OHP) was delivered in a hospital setting, comprising: (1) health promotion activities; (2) targeted supervised toothbrushing (STB) and (3) staff training. Outcomes were measured using three key performance indicators (KPI1: percentage of children/families seeing promotional material; KPI2: number of children receiving STB; KPI3: number of staff trained) and relevant qualitative indicators. Data were collected between November 2019 and August 2021 using surveys and data from the online booking platform. OHP delivery was impacted by COVID-19, with interventions interrupted, reduced, eliminated or delivered differently (eg, in-person training moved online). Despite these challenges, progress against all KPIs was made. 93 posters were deployed across the hospital site, along with animated video 41% (233/565) of families recalled seeing OHP materials across the hospital site (KPI1). 737 children received STB (KPI2), averaging 35 children/month during the active project. Following STB, 96% participants stated they learnt something, and 94% committed to behaviour change. Finally, 73 staff members (KPI3) received oral health training. All people providing feedback (32/32) reported learning something new from the training session, with 84% (27/32) reporting that they would do things differently in the future. Results highlight the importance of flexibility and resilience when delivering QI projects under challenging conditions or unforeseen circumstances. While results suggest that hospital-based OHP is potentially an effective and equitable way to improve patient, family and staff knowledge of good oral health practices, future work is needed to understand if and how patients and staff put into practice the desired behaviour change and what impact this may have on oral health outcomes.
Changes in grit and psychological capital at the time of major crisis: nursing students’ perseverance, resources, and resilience

Abstract Objectives To examine changes in grit and psychological capital among nursing students prior to, during the height of the pandemic, and more than 12 months after the initial pandemic announcement. Methods A cross-sectional study design addressed the aim of the study. Nursing students undertaking a three-year baccalaureate degree between 2019 and 2021 were included. Results Mean grit levels among the n=818 unique student participants were significantly lower in 2020 than in 2019 and 2021; however, no significant difference was detected for psychological capital over the same period. Conclusions Although normative day-to-day challenges may aid grit development, a major event has a negative impact yet has a buffering effect of negative life events at the time of a crisis. The study further placates that psychological capital remains malleable and open to change at the time of a crisis and may be an essential mechanism to mediate grit and has the capacity to influence student performance over time. It remains essential to develop grit through the mediating elements of psychological capital to enable nursing student to undertake academic studies, particularly in the event of major challenges, such approaches may further enable students’ endurance to withstand major crises as they enter the workforce.
Free space material characterization of carbon nanotube thin films at sub-terahertz frequencies

Abstract A free space, non-destructive, vectorial continuous wave terahertz analysis method is employed to study the material properties of single-walled and multi-walled carbon nanotube thin films deposited on substrates of fused quartz and silicon. The electrical and optical properties of the thin films have been investigated using a vector network analyzer, in the frequency range of 220–325 GHz (WR 3.4) and 325–500 GHz (WR 2.2). The Nicolson–Ross–Weir method is used to extract the material parameters. The refractive index, absorption coefficient, and the complex conductivity of the single-walled and multi-walled carbon nanotube thin films are extracted and studied.
Construction of System Evaluation Index System and Grey Fuzzy Evaluation Model

For the most social,economic and other systems are grey system,and have the characteristic of significant levels complexity,fuzziness of structural relationship,randomness of dynamic varity,incompleteness and uncertainty of index data.With grey system theory to evaluate the system can expand the sources information,improve the credibility of the evaluation results.In this paper,study the two key issues of the grey evaluation.One is the system evaluation index construction,including the composition of index system,the index system structure model,the determination of index weights,the dimensionless of index value and the quantitative of qualitative index.The other is grey fuzzy evaluation model construction,study the single indicators fuzzy evaluation,first-order grey comprehensive evaluation,multi-level grey comprehensive evaluation by evaluation hierarchical order.
Human ovarian follicular granulosa cells isolated during ART procedure reflect substantial changes in activation of hormonal signaling pathways, during long-term in vitro conditions

Abstract The ovary is commonly known as an endocrine gland responsible for sex steroid production. One of the outstanding cells in ovarian microenvironment - granulosa cells (GCs) are responsible for converting the androgens to estrogens during follicular growth and secreting progesterone after ovulation. These secretory processes within the ovary are directly involved in hormonal signaling pathways, and they depend on different stages of cholesterol and lipid biosynthesis during the ovarian cycle. The understating of the regulation and further investigation into the processes taking part in ovary will expose new clinical advantages in detection and treatment of female reproductive system diseases associated with sex hormone abnormalities. The expression of genes belonging to ontology groups associated with steroid biosynthesis and metabolism, such as “cholesterol biosynthetic process” (GO:0006695, “regulation of lipid biosynthetic process” (GO:0046890), “regulation of lipid metabolic process” (GO:0019216), “response to insulin” (GO:0032868) and “response to lipopolysaccharide” (GO:0032496) were analyzed by using the microarray approach. The patterns of gene expression in human GCs at days 1-day, 7-day, 15-day, and 30-day of primary in vitro culture have been analyzed. Based on the microarray results, a group of upregulated genes have been selected: CCL20, CXCL5, STAR, MSMO1, and AADAC. The genes STAT5B, OPA3, PPARG, PROX1, and SEC14L2 were decreased across all the experimental groups during the 30-day cell cultivation period. These results suggest that, the GCs in cell culture under in vitro express steroidogenic markers and it is important to understand associations with lipid and liposaccharide synthesis relative to reproductive medicine.
Automatic adaptive weighted fusion of features-based approach for plant disease identification

Abstract With the rapid expansion in plant disease detection, there has been a progressive increase in the demand for more accurate systems. In this work, we propose a new method combining color information, edge information, and textural information to identify diseases in 14 different plants. A novel 3-branch architecture is proposed containing the color information branch, an edge information branch, and a textural information branch extracting the textural information with the help of the central difference convolution network (CDCN). ResNet-18 was chosen as the base architecture of the deep neural network (DNN). Unlike the traditional DNNs, the weights adjust automatically during the training phase and provide the best of all the ratios. The experiments were performed to determine individual and combinational features’ contribution to the classification process. Experimental results of the PlantVillage database with 38 classes show that the proposed method has higher accuracy, i.e., 99.23%, than the existing feature fusion methods for plant disease identification.
Nurses’ experiences of the social stigma caused by the COVID-19 pandemic: a qualitative study

Background: One of the psychological issues that health personnel face when treating COVID-19 patients is the societal stigma. This issue has not been addressed due to the disease’s devastating impact on numerous sectors of society. This study aimed to evaluate nurses’ experiences of social stigma caused by the COVID-19 pandemic with a qualitative approach. Methods: Inductive qualitative content analysis was used to conduct this qualitative research. COVID ward nurses who had been subjected to social stigma were chosen as the study sample. Data were collected through semi-structured and individual interviews and were concurrently analysed using the conventional content analysis approach recommended by Graneheim and Lundman. Results: There are three categories and six sub-categories of social stigma experienced by nurses: (1) paradoxical emotions (psychological stress and positive attitude), (2) abandonment (all-round rejection and isolation) and (3) coping strategies (self-awareness over time and the influential role of the media). Conclusions: The findings of this study can help healthcare managers to improve the quality of care by enhancing understanding of the psychological needs arising from social stigma. Based on these findings, designing psychosocial interventions related to stigma can promote the mental health of this group and their families.
Lessons learned from the impact of the COVID-19 pandemic in Brazil on primary-care workplace learning groups

Background: The COVID-19 pandemic in Brazil had a major impact on face-to-face permanent education for health (PEH) workplace learning groups in primary care. Aims: The aim of the study was to explore how PEH groups in primary care changed during the COVID-19 pandemic. Methods: A qualitative exploratory evaluation study was conducted with six key participants, who answered semi-structured questions in on-line interviews. Results: The PEH groups rapidly and successfully implemented an online approach using social media. The role of the coordinator changed from being a facilitator to an information broker for navigating the large volume of conflicting information and misinformation. Conclusions: The findings highlight the importance of being an agile organisation, with a ‘bottom-up’ approach to innovation, and the key role of having an information broker in rapidly changing and complex environments. An appreciation of the importance of the socio-technical system for technology implementation in an organisation was also highlighted, with the use of technologies that are familiar to members of the organisation.
Probing Power by Prompting: Harnessing Pre-trained Language Models for Power Connotation Framing

When describing actions, subtle changes in word choice can evoke very different associations with the involved entities. For instance, a company ‘{{it employing} workers’ evokes a more positive connotation than the one ‘{{it exploiting}’ them. This concept is called {{it connotation}. This paper investigates whether pre-trained language models (PLMs) encode such subtle connotative information about {{it power differentials} between involved entities. We design a probing framework for power connotation, building on~{citet{sap-etal-2017-connotation}’s operationalization of {{it connotation frames}. We show that zero-shot prompting of PLMs leads to above chance prediction of power connotation, however fine-tuning PLMs using our framework drastically improves their accuracy. Using our fine-tuned models, we present a case study of {{it power dynamics} in US news reporting on immigration, showing the potential of our framework as a tool for understanding subtle bias in the media.
ALAMBIC : Active Learning Automation Methods to Battle Inefficient Curation

In this paper, we present ALAMBIC, an open-source dockerized web-based platform for annotating text data through active learning for classification task. Active learning is known to reduce the need of labelling, a time-consuming task, by selecting the most informative instances among the unlabelled instances, reaching an optimal accuracy faster than by just randomly labelling data. ALAMBIC integrates all the steps from data import to customization of the (active) learning process and annotation of the data, with indications of the progress of the trained model that can be downloaded and used in downstream tasks. Its architecture also allows the easy integration of other types of model, features and active learning strategies.The code is available on https://github.com/Trusted-AI-Labs/ALAMBIC and a video demonstration is available on https://youtu.be/4oh8UADfEmY.
ALLECS: A Lightweight Language Error Correction System

In this paper, we present ALLECS, a lightweight web application to serve grammatical error correction (GEC) systems so that they can be easily used by the general public. We design ALLECS to be accessible to as many users as possible, including users who have a slow Internet connection and who use mobile phones as their main devices to connect to the Internet. ALLECS provides three state-of-the-art base GEC systems using two approaches (sequence-to-sequence generation and sequence tagging), as well as two state-of-the-art GEC system combination methods using two approaches (edit-based and text-based). ALLECS can be accessed at https://sterling8.d2. comp.nus. edu.sg/gec-demo/
jTLEX: a Java Library for TimeLine EXtraction

jTLEX is a programming library that provides a Java implementation of the TimeLine EXtraction algorithm (TLEX; Finlayson et al.,2021), along with utilities for programmatic manipulation of TimeML graphs. Timelines are useful for a number of natural language understanding tasks, such as question answering, cross-document event coreference, and summarization & visualization. jTLEX provides functionality for (1) parsing TimeML annotations into Java objects, (2) construction of TimeML graphs from scratch, (3) partitioning of TimeML graphs into temporally connected subgraphs, (4) transforming temporally connected subgraphs into point algebra (PA) graphs, (5) extracting exact timeline of TimeML graphs, (6) detecting inconsistent subgraphs, and (7) calculating indeterminate sections of the timeline. The library has been tested on the entire TimeBank corpus, and comes with a suite of unit tests. We release the software as open source with a free license for non-commercial use.
Investigating anatomical bias in clinical machine learning algorithms

Clinical machine learning algorithms have shown promising results and could potentially be implemented in clinical practice to provide diagnosis support and improve patient treatment. Barriers for realisation of the algorithms’ full potential include bias which is systematic and unfair discrimination against certain individuals in favor of others.The objective of this work is to measure anatomical bias in clinical text algorithms. We define anatomical bias as unfair algorithmic outcomes against patients with medical conditions in specific anatomical locations. We measure the degree of anatomical bias across two machine learning models and two Danish clinical text classification tasks, and find that clinical text algorithms are highly prone to anatomical bias. We argue that datasets for creating clinical text algorithms should be curated carefully to isolate the effect of anatomical location in order to avoid bias against patient subgroups.
Probabilistic Robustness for Data Filtering

We introduce our probabilistic robustness rewarded data optimization (PRoDO) approach as a framework to enhance the model’s generalization power by selecting training data that optimizes our probabilistic robustness metrics. We use proximal policy optimization (PPO) reinforcement learning to approximately solve the computationally intractable training subset selection problem. The PPO’s reward is defined as our (${alpha,{epsilon, {gamma$)-Robustness that measures performance consistency over multiple domains by simulating unknown test sets in real-world scenarios using a leaving-one-out strategy. We demonstrate that our PRoDO effectively filters data that lead to significantly higher prediction accuracy and robustness on unknown-domain test sets. Our experiments achieve up to +17.2{% increase of accuracy (+25.5{% relatively) in sentiment analysis, and -28.05 decrease of perplexity (-32.1{% relatively) in language modeling.In addition, our probabilistic (${alpha,{epsilon, {gamma$)-Robustness definition serves as an evaluation metric with higher levels of agreement with human annotations than typical performance-based metrics.
Bridging the Gap Between BabelNet and HowNet: Unsupervised Sense Alignment and Sememe Prediction

As the minimum semantic units of natural languages, sememes can provide precise representations of concepts. Despite the widespread utilization of lexical resources for semantic tasks, use of sememes is limited by a lack of available sememe knowledge bases. Recent efforts have been made to connect BabelNet with HowNet by automating sememe prediction. However, these methods depend on large manually annotated datasets. We propose to use sense alignment via a novel unsupervised and explainable method. Our method consists of four stages, each relaxing predefined constraints until a complete alignment of BabelNet synsets to HowNet senses is achieved. Experimental results demonstrate the superiority of our unsupervised method over previous supervised ones by an improvement of 12% overall F1 score, setting a new state of the art. Our work is grounded in an interpretable propagation of sememe information between lexical resources, and may benefit downstream applications which can incorporate sememe information.