bluuebunny/crossref_metadata_2025_split huggingface dataset:
An investigation of the effects of meteorological factors on the incidence of tuberculosis
Authors: Chang Minli , Emam Mawlanjan , Chen Xiaodie , Lu Dongmei , Zhang Liping , Zheng Yanling 
Published: January 2024

AbstractTo explore the influence of meteorological factors on the incidence of tuberculosis (TB) in Yingjisha County, Kashgar Region, Xinjiang, and to provide a scientific basis for the prevention and control of TB. The Spearman correlation analysis and distribution lag nonlinear model analysis were conducted on the number of daily reported cases of TB from 2016 to 2023 to study the association effect of various meteorological factors and the daily incidence number of TB in Yingjisha County. A total of 13,288 TB cases were reported from January 2016 to June 2023, and June to October is the peak period of annual TB incidence. Spearman correlation analysis revealed that average daily temperature (AT) and average daily wind speed (WS) were positively correlated with TB incidence (rAT = 0.110, rWS = 0.090); and average daily relative humidity (RH) and TB incidence was negatively correlated (rRH = − 0.093). When AT was − 15 °C, the RR reached a maximum of 2.20 (95% CI: 0.77–6.29) at a lag of 21 days. When RH was 92%, the RR reached a maximum of 1.05 (95% CI: 0.92–1.19) at a lag of 6 days. When WS was 5.2 m/s, the RR reached a maximum of 1.30 (95% CI: 0.78–2.16) at a lag of 16 days. There is a non-linearity and a certain lag between meteorological factors and the occurrence and prevalence of TB in the population, which is mainly manifested in the fact that the risk of incidence of TB decreases with the increase of the daily AT, has a hazardous effect within a certain range of humidity as the average daily RH rises, and gradually increases with the increase of the average daily WS. Local residents are advised to pay attention to climate change so as to take appropriate preventive measures, especially women and middle and old age group should pay close attention to climate change and add more clothes in time, minimise travelling in hazy weather and windy and sandy weather, maintain good nutrition, adequate sleep and moderate exercise in daily life to enhance their immunity, wash hands frequently and ventilate the air, and try to avoid staying in humid and confined spaces in order to reduce the risk of latent TB patients developing the disease.
Downstream network transformations dissociate neural activity from causal functional contributions
Authors: Fakhar Kayson , Dixit Shrey , Hadaeghi Fatemeh , Kording Konrad P. , Hilgetag Claus C. 
Published: January 2024

AbstractNeuroscientists rely on distributed spatio-temporal patterns of neural activity to understand how neural units contribute to cognitive functions and behavior. However, the extent to which neural activity reliably indicates a unit's causal contribution to the behavior is not well understood. To address this issue, we provide a systematic multi-site perturbation framework that captures time-varying causal contributions of elements to a collectively produced outcome. Applying our framework to intuitive toy examples and artificial neural networks revealed that recorded activity patterns of neural elements may not be generally informative of their causal contribution due to activity transformations within a network. Overall, our findings emphasize the limitations of inferring causal mechanisms from neural activities and offer a rigorous lesioning framework for elucidating causal neural contributions.
A phased genome assembly of a Colombian Trypanosoma cruzi TcI strain and the evolution of gene families
Authors: Hoyos Sanchez Maria Camila , Ospina Zapata Hader Sebastian , Suarez Brayhan Dario , Ospina Carlos , Barbosa Hamilton Julian , Carranza Martinez Julio Cesar , Vallejo Gustavo Adolfo , Urrea Montes Daniel , Duitama Jorge 
Published: January 2024

AbstractChagas is an endemic disease in tropical regions of Latin America, caused by the parasite Trypanosoma cruzi. High intraspecies variability and genome complexity have been challenges to assemble high quality genomes needed for studies in evolution, population genomics, diagnosis and drug development. Here we present a chromosome-level phased assembly of a TcI T. cruzi strain (Dm25). While 29 chromosomes show a large collinearity with the assembly of the Brazil A4 strain, three chromosomes show both large heterozygosity and large divergence, compared to previous assemblies of TcI T. cruzi strains. Nucleotide and protein evolution statistics indicate that T. cruzi Marinkellei separated before the diversification of T. cruzi in the known DTUs. Interchromosomal paralogs of dispersed gene families and histones appeared before but at the same time have a more strict purifying selection, compared to other repeat families. Previously unreported large tandem arrays of protein kinases and histones were identified in this assembly. Over one million variants obtained from Illumina reads aligned to the primary assembly clearly separate the main DTUs. We expect that this new assembly will be a valuable resource for further studies on evolution and functional genomics of Trypanosomatids.
Disentangling boredom from depression using the phenomenology and content of involuntary autobiographical memories
Authors: Yeung Ryan C. , Danckert James , van Tilburg Wijnand A. P. , Fernandes Myra A. 
Published: January 2024

AbstractRecurrent involuntary autobiographical memories (IAMs) are memories retrieved unintentionally and repetitively. We examined whether the phenomenology and content of recurrent IAMs could differentiate boredom and depression, both of which are characterized by affective dysregulation and spontaneous thought. Participants (n = 2484) described their most frequent IAM and rated its phenomenological properties (e.g., valence). Structural topic modeling, a method of unsupervised machine learning, identified coherent content within the described memories. Boredom proneness was positively correlated with depressive symptoms, and both boredom proneness and depressive symptoms were correlated with more negative recurrent IAMs. Boredom proneness predicted less vivid recurrent IAMs, whereas depressive symptoms predicted more vivid, negative, and emotionally intense ones. Memory content also diverged: topics such as relationship conflicts were positively predicted by depressive symptoms, but negatively predicted by boredom proneness. Phenomenology and content in recurrent IAMs can effectively disambiguate boredom proneness from depressive symptoms in a large sample of undergraduate students from a racially diverse university.
Efficacy of sodium glucose cotransporter 2 inhibitors on hepatic fibrosis and steatosis in non-alcoholic fatty liver disease: an updated systematic review and meta-analysis
Authors: Ong Lopez Albert Macaire C. , Pajimna Janine Audrei T. 
Published: January 2024

AbstractNon-alcoholic fatty liver disease (NAFLD) is a substantial contributor to liver-related morbidity worldwide, and yet, there are no standard, universal pharmacologic therapies approved for this indication. The aim of this systematic review and meta-analysis is to evaluate the effectiveness of SGLT-2 inhibitors in improving hepatic steatosis and hepatic fibrosis in patients with NAFLD. An extensive electronic database search was done to identify studies published from inception until December 2023, without any language restrictions. All randomized controlled trials (RCT) that evaluated the use of SGLT-2 inhibitors for patients with NAFLD, regardless of diabetes mellitus status, were included. The Cochrane Risk of Bias 2.0 tool was used to assess the risk of bias of each study included. Evidence from all studies were synthesized as mean differences for continuous data, and as risk ratio for dichotomous outcomes. An inverse variance or Mantel–Haenszel test was used in conjunction with a random-effects meta-analysis model, where necessary. 18 eligible RCTs involving 1330 participants were analyzed, all of which had risk of bias ranging from low to some concerns. Significant difference in means was observed for controlled attenuation parameter (6 trials, n = 372; MD: − 10.59 dB/m, 95% CI [− 18.25, − 2.92], p = 0.007, I2 = 0%); L/S ratio (3 trials, n = 163; MD: 0.11, 95% CI [0.01, 0.21], p = 0.04, I2 = 78%); LSM (7 trials, n = 447; MD: − 0.67 kPa, 95% CI [− 1.19, − 0.16], p = 0.010, I2 = 69%); MRI-PDFF (5 trials, n = 330; MD: − 2.61%, 95% CI [− 5.05, − 0.17], p = 0.04, I2 = 78%), and FIB-4 index (10 trials, n = 648; MD: − 0.12, 95% CI [− 0.21, − 0.04], p = 0.005, I2 = 16%) after SGLT-2 inhibitor treatment as compared to controls. In conclusion, the use of SGLT-2 inhibitors may lead to slight improvement of hepatic steatosis and/or fibrosis as compared to controls in patients with NAFLD and Type 2 diabetes mellitus based on imaging and histopathology biomarkers with low to moderate certainty of evidence.
Prediction of atmospheric PM2.5 level by machine learning techniques in Isfahan, Iran
Authors: Mohammadi Farzaneh , Teiri Hakimeh , Hajizadeh Yaghoub , Abdolahnejad Ali , Ebrahimi Afshin 
Published: January 2024

AbstractWith increasing levels of air pollution, air quality prediction has attracted more attention. Mathematical models are being developed by researchers to achieve precise predictions. Monitoring and prediction of atmospheric PM2.5 levels, as a predominant pollutant, is essential in emission mitigation programs. In this study, meteorological datasets from 9 years in Isfahan city, a large metropolis of Iran, were applied to predict the PM2.5 levels, using four machine learning algorithms including Artificial Neural |Networks (ANNs), K-Nearest-Neighbors (KNN), Support Vector |Machines (SVMs) and ensembles of classification trees Random Forest (RF). The data from 7 air quality monitoring stations located in Isfahan City were taken into consideration. The Confusion Matrix and Cross-Entropy Loss were used to analyze the performance of classification models. Several parameters, including sensitivity, specificity, accuracy, F1 score, precision, and the area under the curve (AUC), are computed to assess model performance. Finally, by introducing the predicted data for 2020 into ArcGIS software and using the IDW (Inverse Distance Weighting) method, interpolation was conducted for the area of Isfahan city and the pollution map was illustrated for each month of the year. The results showed that, based on the accuracy percentage, the ANN model has a better performance (90.1%) in predicting PM2.5 grades compared to the other models for the applied meteorological dataset, followed by RF (86.1%), SVM (84.6%) and KNN (82.2%) models, respectively. Therefore, ANN modelling provides a feasible procedure for the managerial planning of air pollution control.
Electric field-enhanced backscatter interferometry detection for capillary electrophoresis
Authors: De Silva Miyuru , Dunn Robert C. 
Published: January 2024

AbstractBackscatter interferometry (BSI) is a refractive index (RI) detection method that is easily integrated with capillary electrophoresis (CE) and is capable of detecting species ranging from inorganic ions to proteins without additional labels or contrast agents. The BSI signal changes linearly with the square of the separation voltage which has been used to quantify sample injection, but has not been explored as a potential signal enhancement mechanism in CE. Here we develop a mathematical model that predicts a signal enhancement at high field strengths, where the BSI signal is dominated by the voltage dependent mechanism. This is confirmed in both simulation and experiment, which show that the analyte peak area grows linearly with separation voltage at high field strengths. This effect can be exploited by adjusting the background electrolyte (BGE) to increase the conductivity difference between the BGE and analyte zones, which is shown to improve BSI performance. We also show that this approach has utility in small bore capillaries where larger separation fields can be applied before excess Joule heating degrades the separation. Unlike other optical detection methods that generally degrade as the optical pathlength is reduced, the BSI signal-to-noise can improve in small bore capillaries as the larger separation fields enhance the signal.
Identification of shared pathogenetic mechanisms between COVID-19 and IC through bioinformatics and system biology
Authors: Sun Zhenpeng , Zhang Li , Wang Ruihong , Wang Zheng , Liang Xin , Gao Jiangang 
Published: January 2024

AbstractCOVID-19 increased global mortality in 2019. Cystitis became a contributing factor in SARS-CoV-2 and COVID-19 complications. The complex molecular links between cystitis and COVID-19 are unclear. This study investigates COVID-19-associated cystitis (CAC) molecular mechanisms and drug candidates using bioinformatics and systems biology. Obtain the gene expression profiles of IC (GSE11783) and COVID-19 (GSE147507) from the Gene Expression Omnibus (GEO) database. Identified the common differentially expressed genes (DEGs) in both IC and COVID-19, and extracted a number of key genes from this group. Subsequently, conduct Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis on the DEGs. Additionally, design a protein–protein interaction (PPI) network, a transcription factor gene regulatory network, a TF miRNA regulatory network, and a gene disease association network using the DEGs. Identify and extract hub genes from the PPI network. Then construct Nomogram diagnostic prediction models based on the hub genes. The DSigDB database was used to forecast many potential molecular medicines that are associated with common DEGs. Assess the precision of hub genes and Nomogram models in diagnosing IC and COVID-19 by employing Receiver Operating Characteristic (ROC) curves. The IC dataset (GSE57560) and the COVID-19 dataset (GSE171110) were selected to validate the models' diagnostic accuracy. A grand total of 198 DEGs that overlapped were found and chosen for further research. FCER1G, ITGAM, LCP2, LILRB2, MNDA, SPI1, and TYROBP were screened as the hub genes. The Nomogram model, built using the seven hub genes, demonstrates significant utility as a diagnostic prediction model for both IC and COVID-19. Multiple potential molecular medicines associated with common DEGs have been discovered. These pathways, hub genes, and models may provide new perspectives for future research into mechanisms and guide personalised and effective therapeutics for IC patients infected with COVID-19.
Outcomes and predictive factors of vital pulp therapy in a large-scale retrospective cohort study over 10 years
Authors: Asgary Saeed , Roghanizadeh Leyla , Eghbal Mohammad Jafar , Akbarzadeh Baghban Alireza , Aminoshariae Anita , Nosrat Ali 
Published: January 2024

AbstractThis cohort study evaluated the long-term success/survival of vital pulp therapies (VPTs) after carious pulp exposure in adult teeth. Additionally, factors influencing long-term success were identified. Teeth treated during 2011–2022 in a private clinic were studied with clinical/radiographic follow-ups. Data included patient demographics, tooth specifics, and treatment details. Outcomes were classified as success/failure based on clinical/radiographic findings, with tooth functionality determining the survival rate. Encompassing 1149 patients and 1257 VPT-treated teeth, the average monitoring period was 42.2 months. Overall VPTs’ survival and success rates were 99.1% and 91.6%, respectively. Success rates for 768 direct pulp cappings, 217 miniature pulpotomies, and 272 full pulpotomies were 91.9%, 92.6%, and 90.1%, respectively (P > 0.05). Influencing factors included symptomatic irreversible pulpitis (SIP; HR 1.974, 95% CI 1.242–3.137; P = 0.004), radiographic signs of apical periodontitis (AP; HR 2.983, 95% CI 1.961–4.540; P < 0.001), restoration type (HR 2.263, 95%CI 1.423–3.600; P = 0.001), and restoration surfaces (HR 1.401, 95%CI 1.034–1.899; P = 0.030). This study concludes that VPT techniques consistently exhibit high long-term success/survival rates in treating carious pulp exposures. Critical predictors include initial clinical signs of SIP/AP, caries extent, and use of composite restorations.
A novel two-dimensional phantom for electrical impedance tomography using 3D printing
Authors: Creegan Andrew , Nielsen Poul M. F. , Tawhai Merryn H. 
Published: January 2024

AbstractElectrical impedance tomography (EIT) is an imaging method that can be used to image electrical impedance contrasts within various tissues of the body. To support development of EIT measurement systems, a phantom is required that represents the electrical characteristics of the imaging domain. No existing type of EIT phantom combines good performance in all three characteristics of resistivity resolution, spatial resolution, and stability. Here, a novel EIT phantom concept is proposed that uses 3D printed conductive material. Resistivity is controlled using the 3D printing infill percentage parameter, allowing arbitrary resistivity contrasts within the domain to be manufactured automatically. The concept of controlling resistivity through infill percentage is validated, and the manufacturing accuracy is quantified. A method for making electrical connections to the 3D printed material is developed. Finally, a prototype phantom is printed, and a sample EIT analysis is performed. The resulting phantom, printed with an Ultimaker S3, has high reported spatial resolution of 6.9 µm, 6.9 µm, and 2.5 µm for X, Y, and Z axis directions, respectively (X and Y being the horizontal axes, and Z the vertical). The number of resistivity levels that are manufacturable by varying infill percentage is 15 (calculated by dividing the available range of resistivities by two times the standard deviation of the manufacturing accuracy). This phantom construction technique will allow assessment of the performance of EIT devices under realistic physiological scenarios.
Examining the Relationship Between Multiple Tests of Receptive Vocabulary
Authors: Harel Daphna , Goudelias Deanna , Cheng Hung-Shao , Baese-Berk Melissa M. , Theodore Rachel M. , Levi Susannah V. 
Published: January 2024

Purpose:
Numerous tasks have been developed to measure receptive vocabulary, many of which were designed to be administered in person with a trained researcher or clinician. The purpose of the current study is to compare a common, in-person test of vocabulary with other vocabulary assessments that can be self-administered.

Method:
Fifty-three participants completed the Peabody Picture Vocabulary Test (PPVT) via online video call to mimic in-person administration, as well as four additional fully automated, self-administered measures of receptive vocabulary. Participants also completed three control tasks that do not measure receptive vocabulary.

Results:

Pearson correlations indicated moderate correlations among most of the receptive vocabulary measures (approximately
r
= .50–.70). As expected, the control tasks revealed only weak correlations to the vocabulary measures. However, subsets of items of the four self-administered measures of receptive vocabulary achieved high correlations with the PPVT (
r
> .80). These subsets were found through a repeated resampling approach.

Conclusions:
Measures of receptive vocabulary differ in which items are included and in the assessment task (e.g., lexical decision, picture matching, synonym matching). The results of the current study suggest that several self-administered tasks are able to achieve high correlations with the PPVT when a subset of items are scored, rather than the full set of items. These data provide evidence that subsets of items on one behavioral assessment can more highly correlate to another measure. In practical terms, these data demonstrate that self-administered, automated measures of receptive vocabulary can be used as reasonable substitutes of at least one test (PPVT) that requires human interaction. That several of the fully automated measures resulted in high correlations with the PPVT suggests that different tasks could be selected depending on the needs of the researcher. It is important to note the aim was not to establish clinical relevance of these measures, but establish whether researchers could use an experimental task of receptive vocabulary that probes a similar construct to what is captured by the PPVT, and use these measures of individual differences.
Smart meter privacy control strategy based on multi‐agent hidden Markov energy management model under low trust communication
Authors: Wang Qingchen , Xu Qing , Lei Xiyu , Ma Dazhong 
Published: January 2024

AbstractWith the popularity of smart meters, the frequent information exchange between smart grids and consumers leads to easy leakage of consumers' electricity consumption data. These leaked electricity consumption data are obtained by some malicious attackers and used to infer consumers' behavioural patterns by non‐intrusive load monitoring (NILM), which seriously threatens consumers' privacy. Therefore, the multi‐agent Hidden Markov energy management model is proposed in this paper to safeguard the privacy of consumers. First, a weighted Bayesian risk model is proposed, which combines privacy leakage risks and energy storage system (ESS) losses in a microgrid with multiple agents. Next, a three‐loop model for lithium batteries is constructed to quantify the capacity degradation and cost issues of the ESS. Finally, the multi‐objective optimization problem is resolved by integrating the Bayesian risk model with a hidden Markov model to simulate attackers. The proposed multi‐agent Markov decision process method is validated on Electricity Consumption and Occupancy (ECO) dataset, and control strategies are evaluated based on different weights in the Bayesian risk model. The results demonstrate that by incorporating the multi‐agent approach and energy storage system capacity degradation into the privacy protection strategy, the lifespan of the energy storage system can be significantly increased.
Novel rod‐like carbon nanomaterials as near infrared‐responsive drug delivery system for potential anticancer applications
Authors: Liu Mengru , Du Xiaoxue , Liu Jingyi , Wang Guangshuo , Yang Jiajia , Wang Xueling , Han Shuai , Huo Zhongchao 
Published: January 2024

AbstractThe combination of chemotherapy and photothermal therapy shows great potential in cancer treatment, and the carbon nanomaterials have been attracted great attention in biomedical technology. However, it is still a great challenge to design stimuli‐responsive nano‐carbon‐based drug release systems with integrated functions. In this paper, rod‐like carbon nanomaterials (RCNs) were prepared by the soft template hydrothermal method with glucose as raw materials, which were proved to have high photothermal efficiency (∼23.7%), as well as good biocompatibility and excellent drug‐loading capacity. After that, RCNs were used to load doxorubicin (DOX) as DOX@RCNs for integrated photothermal/chemotherapy towards cancer, which demonstrated 52.2% higher cancer cell killing efficiency than that of RCNs under near infrared (NIR) irradiation in vitro. The authors’ approach provided a novel NIR‐responsive nano platform for combined photothermal/chemotherapy towards cancer, which was considered to be of great potential in anticancer applications.