bluuebunny/crossref_metadata_2025_split huggingface dataset:
A costing framework to compare tuberculosis infection tests
Authors: Brumwell Amanda , Tso Jade , Pingali Viswanath , Millones Ana Karina , Jimenez Judith , Calderon Roger I , Barreda Nadia , Lecca Leonid , Nicholson Tom , Brooks Meredith 
Published: November 2023

ObjectiveTo develop a framework to estimate the practical costs incurred from, and programmatic impact related to, tuberculosis (TB) infection testing—tuberculin skin tests (TST) versus interferon gamma release assay (IGRA)—in a densely populated high-burden TB area.MethodsWe developed a seven-step framework that can be tailored to individual TB programmes seeking to compare TB infection (TBI) diagnostics to inform decision-making. We present methodology to estimate (1) the prevalence of TBI, (2) true and false positives and negatives for each test, (3) the cost of test administration, (4) the cost of false negatives, (5) the cost of treating all that test positive, (6) the per-test cost incurred due to treatment and misdiagnosis and (7) the threshold at which laboratory infrastructure investments for IGRA are outweighed by system-wide savings incurred due to IGRA utilisation. We then applied this framework in a densely populated, peri-urban district in Lima, Peru with high rates of Bacillus Calmette–Guérin (BCG) vaccination.FindingsThe lower sensitivity of TST compared with IGRA is a major cost driver, leading to health system and societal costs due to misdiagnosis. Additionally, patient and staff productivity costs were greater for TST because it requires two patient visits compared with only one for IGRA testing. When the framework was applied to the Lima setting, we estimate that IGRA-associated benefits outweigh infrastructural costs after performing 672 tests.ConclusionsGiven global shortages of TST and concerns about costs of IGRA testing and laboratory capacity building, this costing framework can provide public health officials and TB programmes guidance for decision-making about TBI testing locally. This framework was designed to be adaptable for use in different settings with available data. Diagnostics that increase accuracy or mitigate time to treatment should be thought of as an investment instead of an expenditure.
Decomposing income-related inequality in health-related quality of life in mainland China: a national cross-sectional study
Authors: Yao Qiang , Zhang Xiaodan , Wu Yibo , Liu Chaojie 
Published: November 2023

IntroductionHealth equity is an important indicator measuring social development and solidarity. However, there is a paucity in nationwide studies into the inequity in health-related quality of life (HRQoL) in mainland China, in particular using the most recent data measuring HRQoL using the EuroQol 5-Dimension-5 Level (EQ-5D-5L). This study aimed to address the gap in the literature by estimating and decomposing income-related inequality of the utility index (UI) of EQ-5D-5L in mainland China.MethodsData were extracted from the Psychology and Behaviour Investigation of Chinese Residents (2022), including 19 738 respondents over the age of 18 years. HRQoL was assessed by the UI of the EQ-5D-5L. Concentration index (CI) was calculated to measure the degree of income-related inequality in the UI. The contributions of individual, behavioural and context characteristics to the CI were estimated using the Wagstaff decomposition method.ResultsThe CI of the EQ-5D-5L UI reached 0.0103, indicating pro-rich inequality in HRQoL. Individual characteristics made the greatest contribution to the CI (57.68%), followed by context characteristics (0.60%) and health behaviours (−3.28%). The contribution of individual characteristics was mainly attributable to disparities in the enabling (26.86%) and need factors (23.86%), with the chronic conditions (15.76%), health literacy (15.56%) and average household income (15.24%) as the top three contributors. Educational level (−5.24%) was the top negative contributor, followed by commercial (−1.43%) and basic medical insurance (−0.56%). Higher inequality was found in the least developed rural (CI=0.0140) and western regions (CI=0.0134).ConclusionPro-rich inequality in HRQoL is evident in mainland China. Targeted interventions need to prioritise measures that aim at reducing disparities in chronic conditions, health literacy and income.
Methylglyoxal from gut microbes boosts radiosensitivity and radioimmunotherapy in rectal cancer by triggering endoplasmic reticulum stress and cGAS-STING activation
Authors: Zhou Han , Wang Lei , Lin Zhiwen , Jiang Chenwei , Chen Xingte , Wang Kai , Liu Libin , Shao Lingdong , Pan Jianji , Li Jinluan , Zhang Da , Wu Junxin 
Published: November 2023

BackgroundPreoperative radiation therapy (preRT) is a fundamental aspect of neoadjuvant treatment for rectal cancer (RC), but the response to this treatment remains unsatisfactory. The combination of radiation therapy (RT) and immunotherapy (iRT) presents a promising approach to cancer treatment, though the underlying mechanisms are not yet fully understood. The gut microbiota may influence the response to RT and immunotherapy. Therefore, we aimed to identify the metabolism of gut microbiota to reverse radioresistance and enhance the efficacy of iRT.MethodsFecal and serum samples were prospectively collected from patients with locally advanced rectal cancer (LARC) who had undergone pre-RT treatment. Candidate gut microbiome-derived metabolites linked with radiosensitization were screened using 16s rRNA gene sequencing and ultrahigh-performance liquid chromatography-mass coupled with mass spectrometry. In vitro and in vivo studies were conducted to assess the radiosensitizing effects of the metabolites including the syngeneic CT26 tumor model and HCT116 xenograft tumor model, transcriptomics and immunofluorescence. The CT26 abscopal effect modeling was employed to evaluate the combined effects of metabolites on iRT.ResultsWe initially discovered the gut microbiota-associated metabolite, methylglyoxal (MG), which accurately predicts the response to preRT (Area Under Curve (AUC) value of 0.856) among patients with LARC. Subsequently, we observed that MG amplifies the RT response in RC by stimulating intracellular reactive oxygen species (ROS) and reducing hypoxia in the tumor in vitro and in vivo. Additionally, our study demonstrated that MG amplifies the RT-induced activation of the cyclic guanosine monophosphate AMP synthase-stimulator of interferon genes pathway by elevating DNA double-strand breaks. Moreover, it facilitates immunogenic cell death generated by ROS-mediated endoplasmic reticulum stress, consequently leading to an increase in CD8+T and natural killer cells infiltrated in the tumor immune microenvironment. Lastly, we discovered that the combination of anti-programmed cell death protein 1 (anti-PD1) therapy produced long-lasting complete responses in all irradiated tumor sites and half of the non-irradiated ones.ConclusionsOur research indicates that MG shows promise as a radiosensitizer and immunomodulator for RC. Furthermore, we propose that combining MG with iRT has great potential for clinical practice.
Effects of 7-day quercetin intervention on motor unit activity and muscle contractile properties before and after resistance exercise in young adults randomized controlled trials
Authors: Nishikawa Taichi , Hirono Tetsuya , Takeda Ryosuke , Okudaira Masamichi , Ohya Toshiyuki , Watanabe Kohei 
Published: November 2023

We investigated whether the alteration of the motor unit recruitment threshold (MURT) caused by quercetin ingestion intervention for 7 days modifies motor unit activation patterns before and after a single session of resistance exercise. Twenty young male and female adults were divided into two groups: ingestion of placebo (PLA) or quercetin glycosides at 200 mg/day (QUE). High-density surface electromyography during submaximal contractions was measured to assess the motor unit firing rate (MUFR) and MURT of the vastus lateralis muscle before (PRE) and after (POST) resistance exercise (DAY1). The same measurements were repeated after 7 days of placebo or quercetin glycoside ingestion (DAY8). In QUE, MURT decreased more from DAY1-PRE to DAY8-PRE (29.1 ± 9.1 to 27.1 ± 9.5% MVC, p < 0.001) but not in PLA (29.8 ± 10.4 to 28.9 ± 9.7% MVC, p < 0.167). For percentage change in MUFR following resistance exercise, there was a significant interaction (day × group, p < 0.001). The degree of changes in MURT from DAY1-PRE to DAY8-PRE was significantly correlated with the percentage change of MUFR from DAY8-PRE to DAY8-POST in QUE ( p = 0.014, r = −0.363) but not in PLA ( p = 0.518). The study suggests that 7-day quercetin ingestion alters the motor unit recruitment pattern, and this may induce changes in motor unit firing patterns during a single session of resistance training (Trial registration: UMIN000052255, R000059650).
Do environmental researchers from marginalized groups experience greater interference? Understanding scientists' perceptions
Authors: Chu Samantha M. , Robertson Manjulika E. , Cloutier Anika , Arif Suchinta , Westwood Alana R. 
Published: November 2023

Researchers in the environmental studies and sciences play a critical role in influencing real-world decision-making and policies. However, interference during research and sharing of results has been documented in Canada and around the world. Further, research has shown that workers from marginalized social identitie(s) experience discrimination in the workplace. Whether interference in research is related to social identity has never been examined. Using a mixed-methods design, we surveyed 741 environmental researchers in Canada to understand the relationship between social identity (gender, disability status, 2SLGBTQI+ status, race, and perception of racial identity) and reported experiences of interference. Results found that researchers with marginalized identities experienced worse outcomes across 11 of the 25 quantitative measures. For example, most marginalized groups experienced significantly greater fear of misrepresentation by media and (or) fear of negative career consequences due to public commentary, and racialized and disabled persons reported greater external interference in their work (e.g., from management and workplace policy). Given these findings, we express concern that the experience of interference in research can (1) threaten the personal well-being of marginalized researchers, (2) limit the representativeness of information disseminated, thererby impacting environmental decision-making and policy, and (3) contribute to inequities in representativeness of marginalized researchers in environmental sciences in Canada.
Depositional system and plant ecosystem responses to long-term low tempo volcanism, the Interbasaltic Formation, Antrim Lava Group
Authors: Beresford-Browne Adam , Jolley David , Millett John , Stevenson Carl , Watt Sebastian , Raine Rob , Carter Elliot 
Published: November 2023

Abstract
The Antrim Lava Group of NE Ireland comprises a volcanic sequence dominated by basaltic lava flows. Including subsidiary sedimentary interlayers and some evolved lavas and intrusions, the overall sequence reaches a cumulative thickness of ∼800 m. The tempo of eruption of the Antrim Lava Group is poorly constrained but can be evaluated via weathering patterns and environmental reconstructions derived from lava-flow interbeds. In this contribution, we present palynology from a newly identified and well-developed 2.0–2.5 m thick sedimentary sequence (interbed) at Ross's Quarry, Ballycastle, Co. Antrim, that helps elucidate the contemporary development of environments in a setting subject to periodic basaltic volcanism. The interbed is subdivided into geologically distinct subunits of cross-bedded and parallel-bedded sandstones and sandy siltstones, all rich in visible organic remains such as rootlets and fragments of wood and bark. A total of 19 samples was collected from the sequence and subsequently analysed for palynological content. The palynomorph data point toward a diversity of inputs ranging from estuaries, chalky soils, dry soils, swamps, lakes, floodplains, sand bars, wet soils, established bogs and fenlands. In contrast to current understanding, the palynological data and their inferred environments collectively reveal the presence of flora that favour a temperate climate rather than the subtropical climate that has previously been inferred from the lateritic interbeds of the Antrim Lava Group. By combining the Ross's Quarry observations with palynological data from other quarry sites and boreholes in Antrim, we provide new insights into the climate, weathering systems and eruptive history of the Antrim Lava Group.
Nudges to Mitigate Confirmation Bias during Web Search on Debated Topics: Support vs. Manipulation
Authors: Rieger Alisa , Draws Tim , Theune Mariët , Tintarev Nava 
Published: November 2023

When people use web search engines to find information on debated topics, the search results they encounter can influence opinion formation and practical decision-making with potentially far-reaching consequences for the individual and society. However, current web search engines lack support for information-seeking strategies that enable responsible opinion formation, e.g., by mitigating confirmation bias and motivating engagement with diverse viewpoints. We conducted two preregistered user studies to test the benefits and risks of an intervention aimed at confirmation bias mitigation. In the first study, we tested the effect of warning labels, warning of the risk of confirmation bias, combined with obfuscations, hiding selected search results per default. We observed that obfuscations with warning labels effectively reduce engagement with search results. These initial findings did not allow conclusions about the extent to which the reduced engagement was caused by the warning label (reflective nudging element) versus the obfuscation (automatic nudging element). If obfuscation was the primary cause, this would raise concerns about harming user autonomy. We thus conducted a follow-up study to test the effect of warning labels and obfuscations separately.
According to our findings, obfuscations run the risk of manipulating behavior instead of guiding it, while warning labels without obfuscations (purely reflective) do not exhaust processing capacities but encourage users to actively choose to decrease engagement with attitude-confirming search results. Therefore, given the risks and unclear benefits of obfuscations and potentially other automatic nudging elements to guide engagement with information, we call for prioritizing interventions that aim to enhance human cognitive skills and agency instead.
Comparison of Chest Computed Tomography (CT) Imaging Patterns and Severity among COVID-19 Patients during the First and Fourth Waves in Addis Ababa, Ethiopia
Authors: Baharu Lensa Million , Alwan Amir , Dellie Seife Teferi , Legesse Tesfaye Kebede , Abore Kibruyisfaw Weldeab 
Published: November 2023

Background. Studies done globally had shown that chest imaging patterns of Corona virus disease 2019 (COVID-19) infection varied depending on the strains of the virus and the waves of the pandemic. There is no published literature done in Ethiopia to examine whether there is any difference in chest computed tomography (CT) findings of COVID-19 patients during the first and fourth waves. Thus, this study tries to fill the gap of knowledge in that regard. Objective. To describe and compare chest CT scan imaging pattern and assess the predictors of chest CT severity of the first and fourth wave of COVID-19 infection. Methods. An institution-based cross-sectional study was conducted on 200 polymerase chain reaction test confirmed COVID-19 patients who underwent chest CT scan imaging in two diagnostic centers in Addis Ababa city. Pioneer and Wudassie diagnostic centers were selected due to the high case load and availability of well-experienced cardiothoracic radiologists. Data were collected from July 1 to August 3, 2022, using a structured Google form sheet questionnaire. Binary logistic regression was performed, and statistical significance was assessed at a level of significance α = 0.05. Results. Comparatively higher proportion of patients from the first wave had positive chest CT finding than fourth wave (99% vs. 69%). Bilateral lung involvement and lower lobe predilection were seen for both waves of COVID-19. Ground glass opacity and consolidation were the most common CT features for both waves. Delayed chest CT features such as traction bronchiectasis were primarily seen among first-wave patients. Mean global CT severity score was higher for the first-wave patients (13.18 vs. 8.31), and the mean difference is statistically significant (

p

< 0.001). Duration of symptoms was a statistically significant predictor of CT severity during the first wave of COVID-19, and patients that presented later than 14 days had 4.12 times higher odds of being in the severe CT score category than those that presented less than 7 days (AOR = 4.12,

p

= 0.011). There was no statistically significant predictor of CT severity for the fourth wave in this study. Conclusion. Chest CT positivity was comparatively higher for first wave patients. Common features included bilateral involvement, lower lobe involvement, ground glass opacity, and consolidation. Mean chest CT severity was comparatively higher for the first wave than the fourth wave, and the duration of symptoms was a statistically significant predictor of the CT severity for first wave.
Using deep neural networks in classifying electromyography signals for hand gestures
Authors: Al-Khazzar Ahmed M. , Altaweel Zainab , Hussain Jabbar S. 
Published: November 2023

<span lang="EN-US">Electromyography (EMG) signals are used for various applications, especially in smart prostheses. Recognizing various gestures (hand movements) in EMG systems introduces challenges. These challenges include the noise effect on EMG signals and the difficulty in identifying the exact movement from the collected EMG data amongst others. In this paper, three neural network models are trained using an open EMG dataset to classify and recognize seven different gestures based on the collected EMG data. The three implemented models are: a four-layer deep neural network (DNN), an eight-layer DNN, and a five-layer convolutional neural network (CNN). In addition, five optimizers are tested for each model, namely Adam, Adamax, Nadam, Adagrad, and AdaDelta. It has been found that four layers achieve respectable recognition accuracy of 95% in the proposed model. </span>
Model for autism disorder detection using deep learning
Authors: Sharma Anshu , Tanwar Poonam 
Published: November 2023

<span lang="EN-US">Autism spectrum disorder (ASD) is a neurodegenerative illness that impacts individuals' social abilities. The majority of available approaches rely on structural and resting-state functional magnetic resonance imaging (fMRI) to detect ASD with a small dataset, resulting in high accuracy but low generality. To detect ASD with a limited dataset, the bulk of known technologies involve Machine Learning, pattern recognition, and other techniques, leading to high accuracy but moderate generality. To address this constraint and improve the efficacy of the automated autism diagnosis model, an ASD detection model based on deep learning (DL) is provided in this work. The classification challenge is solved using a convolutional neural network classifier. The suggested model beats state-of-the-art methodologies in terms of accuracy, according to simulation findings. The proposed approach investigates how anatomical and functional connectivity indicators can be used to determine whether or not a person is autistic. The proposed method delivers state-of-the-art results, with the classification of Autistic patients achieving 93.41% accuracy and the localization of the classified data regressed to 0.29 mean absolute error (MAE). </span>
Optimizer algorithms and convolutional neural networks for text classification
Authors: Qorich Mohammed , Ouazzani Rajae El 
Published: November 2023

<span lang="EN-US">Lately, deep learning has improved the algorithms and the architectures of several natural language processing (NLP) tasks. In spite of that, the performance of any deep learning model is widely impacted by the used optimizer algorithm; which allows updating the model parameters, finding the optimal weights, and minimizing the value of the loss function. Thus, this paper proposes a new convolutional neural network (CNN) architecture for text classification (TC) and sentiment analysis and uses it with various optimizer algorithms in the literature. Actually, in NLP, and particularly for sentiment classification concerns, the need for more empirical experiments increases the probability of selecting the pertinent optimizer. Hence, we have evaluated various optimizers on three types of text review datasets: small, medium, and large. Thereby, we examined the optimizers regarding the data amount and we have implemented our CNN model on three different sentiment analysis datasets so as to binary label text reviews. The experimental results illustrate that the adaptive optimization algorithms Adam and root mean square propagation (RMSprop) have surpassed the other optimizers. Moreover, our best CNN model which employed the RMSprop optimizer has achieved 90.48% accuracy and surpassed the state-of-the-art CNN models for binary sentiment classification problems. </span>
Pneumonia prediction on chest x-ray images using deep learning approach
Authors: Puspita Rani , Rahayu Cindy 
Published: November 2023

<p>Coronavirus disease 2019 (COVID-19) is an infectious disease with first symptoms similar to the flu. In many cases, this disease causes pneumonia. Since pulmonary infections can be observed through radiography images, this paper investigates deep learning methods for automatically analyzing query chest x-ray images. In deep learning, computers can automatically identify useful features for the model, directly from the raw data, bypassing the difficult step of manual information refinement. The main part of the deep learning method is the focus on automatically learning data representations. Visual geometry group 16 (VGG16) and DenseNet121 are methods in deep learning. The data used is a chest x-ray of pneumonia. Data is divided into training, testing, and validation. The best method for this research case is VGG16 with 93% accuracy training and 90% accuracy testing. In this study, DenseNet121 obtained accuracy below VGG16, with 92% accuracy in training and 88% for accuracy testing. Parameters have a significant influence on the accuracy of each model, and with the parameters that have been used, the VGG16 is a method that has high accuracy and can be used to predict chest x-ray images aimed at checking pneumonia in patients. </p><p><span lang="EN-US"><br /></span></p>