bluuebunny/crossref_metadata_2025_split huggingface dataset:
Real-time performance evaluation and optimization of electrical substation equipment inspection algorithm based on distributed computing
Authors: Zhang Guoliang , Du Zexu , Zhang Yi , Chen Jiangqi , Zhang Xi 
Published: August 2024

Abstract
Substation equipment inspection faces problems such as large amounts of data, various information types, complex analysis and calculations, and low resource integration. Distributed computing system uses networks to realize computer connectivity and collaboration, achieving in-depth sharing of computing resources, software resources, information resources, and communication resources. Therefore, firstly, this article introduces segmentation method for load balancing and recommendation calculation based on path search; secondly, an algorithm model for substation equipment inspection based on distributed computing is constructed; then, the article analyzes and discusses multi-core cluster task allocation, heterogeneous task scheduling framework, fragmented transmission mechanism, and vertical expansion mechanism; finally, case analysis and performance evaluation are carried out. Distributed computing has significant advantages in the inspection algorithm of power substation equipment. It can efficiently process large-scale data, improve computing power, ensure data redundancy and fault tolerance, provide good scalability, optimize resource utilization, and support remote data processing. By integrating multi-source data, distributed computing improves the accuracy and comprehensiveness of the algorithm, enhances security, supports complex algorithms such as machine learning and deep learning, and realizes real-time monitoring and early warning. The results show that the proposed algorithm can improve the efficiency of distributed parallel computing and analysis of substation equipment inspections and can simulate, evaluate, and screen substation equipment failure scenarios that may cause serious consequences within an effective time.
Bacterial growth and environmental adaptation via thiamine biosynthesis and thiamine-mediated metabolic interactions
Authors: Xu Xihui , Li Can , Cao Weimiao , Yan Lulu , Cao Lulu , Han Qi , Gao Minling , Chen Yahua , Shen Zhenguo , Jiang Jiandong , Chen Chen 
Published: August 2024

Abstract
Understanding the ancestral transition from anaerobic to aerobic lifestyles is essential for comprehending life’s early evolution. However, the biological adaptations occurring during this crucial transition remain largely unexplored. Thiamine is an important cofactor involved in central carbon metabolism and aerobic respiration. Here, we explored the phylogenetic and global distribution of thiamine-auxotrophic and thiamine-prototrophic bacteria based on the thiamine biosynthetic pathway in 154 838 bacterial genomes. We observed strong coincidences of the origin of thiamine-synthetic bacteria with the “Great Oxygenation Event,” indicating that thiamine biosynthesis in bacteria emerged as an adaptation to aerobic respiration. Furthermore, we demonstrated that thiamine-mediated metabolic interactions are fundamental factors influencing the assembly and diversity of bacterial communities by a global survey across 4245 soil samples. Through our newly established stable isotope probing–metabolic modeling method, we uncovered the active utilization of thiamine-mediated metabolic interactions by bacterial communities in response to changing environments, thus revealing an environmental adaptation strategy employed by bacteria at the community level. Our study demonstrates the widespread thiamine-mediated metabolic interactions in bacterial communities and their crucial roles in setting the stage for an evolutionary transition from anaerobic to aerobic lifestyles and subsequent environmental adaptation. These findings provide new insights into early bacterial evolution and their subsequent growth and adaptations to environments.
The relationship between treatment-related changes in total hip BMD measured after 12, 18, and 24 mo and fracture risk reduction in osteoporosis clinical trials: the FNIH-ASBMR-SABRE project
Authors: Vilaca Tatiane , Schini Marian , Lui Li-Yung , Ewing Susan K , Thompson Austin R , Vittinghoff Eric , Bauer Douglas C , Eastell Richard , Black Dennis M , Bouxsein Mary L 
Published: August 2024

Abstract
There is a strong association between total hip bone mineral density (THBMD) changes after 24 mo of treatment and reduced fracture risk. We examined whether changes in THBMD after 12 and 18 mo of treatment are also associated with fracture risk reduction. We used individual patient data (n = 122 235 participants) from 22 randomized, placebo-controlled, double-blind trials of osteoporosis medications. We calculated the difference in mean percent change in THBMD (active-placebo) at 12, 18, and 24 mo using data available for each trial. We determined the treatment-related fracture reductions for the entire follow-up period, using logistic regression for radiologic vertebral fractures and Cox regression for hip, non-vertebral, “all” (combination of non-vertebral, clinical vertebral, and radiologic vertebral) fractures and all clinical fractures (combination of non-vertebral and clinical vertebral). We performed meta-regression to estimate the study-level association (r2 and 95% confidence interval) between treatment-related differences in THBMD changes for each BMD measurement interval and fracture risk reduction. The meta-regression revealed that for vertebral fractures, the r2 (95% confidence interval) was 0.59 (0.19, 0.75), 0.69 (0.32, 0.82), and 0.73 (0.33, 0.84) for 12, 18, and 24 mo, respectively. Similar patterns were observed for hip: r2 = 0.27 (0.00, 0.54), 0.39 (0.02, 0.63), and 0.41 (0.02, 0.65); non-vertebral: r2 = 0.27 (0.01, 0.52), 0.49 (0.10, 0.69), and 0.53 (0.11, 0.72); all fractures: r2 = 0.44 (0.10, 0.64), 0.63 (0.24, 0.77), and 0.66 (0.25, 0.80); and all clinical fractures: r2 = 0.46 (0.11, 0.65), 0.64 (0.26, 0.78), and 0.71 (0.32, 0.83), for 12-, 18-, and 24-mo changes in THBMD, respectively. These findings demonstrate that treatment-related THBMD changes at 12, 18, and 24 mo are associated with fracture risk reductions across trials. We conclude that BMD measurement intervals as short as 12 mo could be used to assess fracture efficacy, but the association is stronger with longer BMD measurement intervals.
Reading metacomprehension of Spanish deaf and hard-of-hearing students
Authors: Rodríguez-Ortiz Isabel R , Moreno-Pérez Francisco J , Saldaña David 
Published: August 2024

Abstract
Difficulties in monitoring reading comprehension result in poor comprehension. One key aspect of monitoring is metacomprehension, which refers to one’s awareness of one’s own reading comprehension. Previous studies have observed difficulties in metacomprehension among the deaf or hard-of-hearing (DHH) population. This study aims to determine whether the metacomprehension of DHH students corresponds to their reading score and whether they are truly capable of adjusting their metacomprehension to the difficulty of the text. We evaluated 25 Spanish-speaking DHH students with reading scores approximately equivalent to Grades 5 or 6 of Primary School. Participants were asked to read a text and answer questions. The texts corresponded to three levels of difficulty (explicit, inferable, and noninferable). The results revealed that the metacomprehension of DHH students corresponded to their reading score. The DHH population may have better reading metacomprehension than is typically assumed, although the manifestation of this skill may depend on the type of task demanded of them (comprehension judgment or knowledge judgment).
Forecasting bilateral asylum seeker flows with high-dimensional data and machine learning techniques
Authors: Boss Konstantin , Groeger Andre , Heidland Tobias , Krueger Finja , Zheng Conghan 
Published: August 2024

Abstract
We develop monthly asylum seeker flow forecasting models for 157 origin countries to the EU27, using machine learning and high-dimensional data, including digital trace data from Google Trends. Comparing different models and forecasting horizons and validating out-of-sample, we find that an ensemble forecast combining Random Forest and Extreme Gradient Boosting algorithms outperforms the random walk over horizons between 3 and 12 months. For large corridors, this holds in a parsimonious model exclusively based on Google Trends variables, which has the advantage of near real-time availability. We provide practical recommendations how our approach can enable ahead-of-period asylum seeker flow forecasting applications.
Direct antiglobulin (Coombs) test in HIV-positive Talaromycosis marneffei patients
Authors: Wang Mengyan , Jin Yujiao , Zhu Biao 
Published: August 2024

Abstract
Talaromycosis marneffei (T.M) is the primary opportunistic infection of AIDS patients, and its morbidity and mortality are extremely high. To further clarify the disease characteristics of patients and provide a solid basis for in-depth exploration of their pathogenic mechanisms, we retrospectively summarized and analyzed their clinical data. We included all T.M patients tested for direct antiglobulin test (DAT) in the study. Interestingly, we found that AIDS-T.M patients had an extremely high rate of DAT positivity (92/127, 72.44%). In univariate analysis, a positive DAT was associated with blood culture of TM (P = .021), hypoproteinemia (P = .001), anemia (P = .001), thrombocytopenia (P = .003), sepsis (P = .007), and Sequential Organ Failure Assessment (SOFA) (P = .001). Hypoproteinemia, anemia, SOFA, APTT &gt; 32.6 s, and AST &gt; 40 U/l were studied by logistic regression. Logistic regression revealed that SOFA (OR = 1.311, P = .043), hypoproteinemia (OR = 0.308, P = .021), and anemia (OR = 0.19, P = .044) were associated with positive DAT. Positive DAT was associated with severe disease manifestations such as sepsis, and the DAT test is crucial in patients with fungemia.
Structure functions with higher-order stencils as a probe to separate small- and large-scale magnetic fields
Authors: Seta Amit , Federrath Christoph 
Published: August 2024

ABSTRACT
Magnetic fields are an energetically important component of star formation galaxies, but it is often difficult to measure their properties from observations. One of the complexities stems from the fact that the magnetic fields, especially in spiral galaxies, have a two-scale nature: a large-scale field, coherent over kpc scales, and a small-scale random field, with a scale of ${\lesssim} 100 \, {\rm pc}$. Moreover, it is known that the strength of small- and large-scale fields is comparable and this makes it even harder to find their imprints in radio polarization observations such as the Faraday rotation measure (RM), which is the integral over the path-length of the product of the thermal electron density and the parallel component of the magnetic field to the line of sight. Here, we propose and demonstrate the use of second-order structure functions of RM computed with multiple higher-order stencils as a powerful analysis to separate the small- and large-scale magnetic field components. In particular, we provide new methods and calibrations to compute the scale and the strength of the large-scale magnetic field in the presence of small-scale magnetic fluctuations. We then apply the method to find the scale of large-scale magnetic fields in the nearby galaxies M51 and NGC 6946, using archival data, and further discuss the need for computing the RM structure functions with higher-order stencils. With multiple modern radio polarization observatories and eventually the Square Kilometre Array, RM observations will significantly improve in quantity and quality, and the higher-order stencil structure function techniques developed here can be used to extract information about multiscale magnetic fields in galaxies.
Genomic Anatomy of Homozygous XX Females and YY Males Reveals Early Evolutionary Trajectory of Sex-determining Gene and Sex Chromosomes inSilurusFishes
Authors: Wang Tao , Gong Gaorui , Li Zhi , Niu Jun-Sheng , Du Wen-Xuan , Wang Zhong-Wei , Wang Yang , Zhou Li , Zhang Xiao-Juan , Lian Zong-Qiang , Mei Jie , Gui Jian-Fang , Li Xi-Yin 
Published: August 2024

AbstractSex chromosomes display remarkable diversity and variability among vertebrates. Compared with research on the X/Y and Z/W chromosomes, which have long evolutionary histories in mammals and birds, studies on the sex chromosomes at early evolutionary stages are limited. Here, we precisely assembled the genomes of homozygous XX female and YY male Lanzhou catfish (Silurus lanzhouensis) derived from an artificial gynogenetic family and a self-fertilized family, respectively. Chromosome 24 (Chr24) was identified as the sex chromosome based on resequencing data. Comparative analysis of the X and Y chromosomes showed an approximate 320 kb Y-specific region with a Y-specific duplicate of anti-Mullerian hormone type II receptor (amhr2y), which is consistent with findings in 2 other Silurus species but on different chromosomes (Chr24 of Silurus meridionalis and Chr5 of Silurus asotus). Deficiency of amhr2y resulted in male-to-female sex reversal, indicating that amhr2y plays a male-determining role in S. lanzhouensis. Phylogenetic analysis and comparative genomics revealed that the common sex-determining gene amhr2y was initially translocated to Chr24 of the Silurus ancestor along with the expansion of transposable elements. Chr24 was maintained as the sex chromosome in S. meridionalis and S. lanzhouensis, whereas a sex-determining region transition triggered sex chromosome turnover from Chr24 to Chr5 in S. asotus. Additionally, gene duplication, translocation, and degeneration were observed in the Y-specific regions of Silurus species. These findings present a clear case for the early evolutionary trajectory of sex chromosomes, including sex-determining gene origin, repeat sequence expansion, gene gathering and degeneration in sex-determining region, and sex chromosome turnover.
Genotype and X-chromosome inactivation are associated with disease severity in females with X-linked Alport syndrome
Authors: Suzuki Ryota , Sakakibara Nana , Murakami Sae , Ichikawa Yuta , Kitakado Hideaki , Ueda Chika , Tanaka Yu , Okada Eri , Kondo Atsushi , Aoto Yuya , Ishiko Shinya , Ishimori Shingo , Nagano China , Yamamura Tomohiko , Horinouchi Tomoko , Okamoto Takayuki , Nozu Kandai 
Published: August 2024

ABSTRACT

Background
Male patients with X-linked Alport syndrome (XLAS) generally develop end-stage kidney disease in early or middle adulthood and show distinct genotype–phenotype correlations. However, female patients show various phenotypes ranging from asymptomatic to severe with no genotype–phenotype correlations. The factors affecting the severity of XLAS in female patients are unclear. Since X-chromosome inactivation (XCI) affects the severity of certain female X-linked diseases, we investigated whether genotype and XCI were associated with XLAS severity in female patients in a large Japanese cohort.

Methods
Among 139 female patients with genetically diagnosed XLAS at our institution, we conducted XCI analysis on peripheral blood leucocytes using the human androgen receptor assay method and analysed two cohorts. In 74 adult female patients we evaluated the correlation between kidney function [creatinine estimated glomerular filtration rate (Cr-eGFR) optimized for Japanese individuals] and genotype/XCI using multivariable linear regression analysis and in 65 paediatric female patients we evaluated the correlation between kidney function (Cr-eGFR optimized for Japanese individuals) and genotype/XCI using multivariable linear regression analysis. We also investigated the correlation between the development of proteinuria (urine protein:creatinine ratio above normal for the patient's age) and genotype/XCI using multivariable Cox proportional hazards analysis.

Results
In adult female patients, the XCI pattern was significantly associated with Cr-eGFR (regression coefficient estimate −0.53, P = .004), whereas genotype was not (P = .892). In paediatric female patients, both genotype and XCI pattern were significant independent risk factors for the development of proteinuria {hazard ratio [HR] 3.702 [95% confidence interval (CI) 1.681–8.150], P = .001 and HR 1.043 [95% CI 1.061–1.070], P = .001, respectively}, whereas both genotype and XCI pattern were not associated with Cr-eGFR (P = .20 and P = .67, respectively).

Conclusion
Genotype and XCI are factors associated with severity in females with XLAS.
Prognostic impact of post-transplant diabetes mellitus in kidney allograft recipients: a meta-analysis
Authors: Kanbay Mehmet , Siriopol Dimitrie , Guldan Mustafa , Ozbek Lasin , Topcu Ahmet U , Siriopol Ianis , Tuttle Katherine 
Published: August 2024

ABSTRACT

Background
Post-transplant diabetes mellitus (PTDM) is a complex condition arising from various factors including immunosuppressive medications, insulin resistance, impaired insulin secretion and inflammatory processes. Its impact on patient and graft survival is a significant concern in kidney transplant recipients. PTDM's impact on kidney transplant recipients, including patient and graft survival and cardiovascular mortality, is a significant concern, given conflicting findings in previous studies. This meta-analysis was imperative not only to incorporate emerging evidence but also to delve into cause-specific mortality considerations. We aimed to comprehensively evaluate the association between PTDM and clinical outcomes, including all-cause and cardiovascular mortality, sepsis-related mortality, malignancy-related mortality and graft loss, in kidney transplant recipients.

Methods
PubMed, Ovid/Medline, Web of Science, Scopus and Cochrane Library databases were screened and studies evaluating the effect of PTDM on all-cause mortality, cardiovascular mortality, sepsis-related mortality, malignancy-related mortality and overall graft loss in adult kidney transplant recipients were included.

Results
Fifty-three studies, encompassing a total of 138 917 patients, evaluating the association between PTDM and clinical outcomes were included. Our analysis revealed a significant increase in all-cause mortality [risk ratio (RR) 1.70, 95% confidence interval (CI) 1.53 to 1.89, P &lt; .001] and cardiovascular mortality (RR 1.86, 95% CI 1.36 to 2.54, P &lt; .001) among individuals with PTDM. Moreover, PTDM was associated with a higher risk of sepsis-related mortality (RR 1.96, 95% CI 1.51 to 2.54, P &lt; .001) but showed no significant association with malignancy-related mortality (RR 1.20, 95% CI 0.76 to 1.88). Additionally, PTDM was linked to an increased risk of overall graft failure (RR 1.33, 95% CI 1.16 to 1.54, P &lt; .001).

Conclusion
These findings underscore the importance of comprehensive management strategies and the need for research targeting PTDM to improve outcomes in kidney transplant recipients.
Finding emergence in data by maximizing effective information
Authors: Yang Mingzhe , Wang Zhipeng , Liu Kaiwei , Rong Yingqi , Yuan Bing , Zhang Jiang 
Published: August 2024

ABSTRACT
Quantifying emergence and modeling emergent dynamics in a data-driven manner for complex dynamical systems is challenging due to the fact that emergent behaviors cannot be directly captured by micro-level observational data. Thus, it is crucial to develop a framework to identify emergent phenomena and capture emergent dynamics at the macro-level using available data. Inspired by the theory of causal emergence (CE), this paper introduces a machine learning framework to learn macro-dynamics in an emergent latent space and quantify the degree of CE. The framework maximizes effective information, resulting in a macro-dynamics model with enhanced causal effects. Experimental results on simulated and real data demonstrate the effectiveness of the proposed framework. It quantifies degrees of CE effectively under various conditions and reveals distinct influences of different noise types. It can learn a one-dimensional coarse-grained macro-state from functional magnetic resonance imaging data to represent complex neural activities during movie clip viewing. Furthermore, improved generalization to different test environments is observed across all simulation data.
Politicians, institutional incentives, and citizen welfare: evidence from a lab-in-the-field experiment in India
Authors: Banerjee Prasenjit , Iversen Vegard , Mitra Sandip , Sen Kunal 
Published: August 2024

Abstract
We examine how politicians and non-politicians in rural India respond to behavioural incentives. Using a modified dictator game, we vary treatments (and incentives) across the nature of interactions, the visibility of actions, and an upfront promise. Under anonymity, politicians and non-politicians behave selfishly: both become significantly more generous when interactions are personalized. However, while non-politicians respond to greater visibility more strongly than politicians, an upfront promise induces more pronounced politician responses. Whereas promise-breaking appears to be more costly for politicians, visibility, via social image concerns, appears to matter more for non-politicians. This mix of similarity and heterogeneity in response suggests that evidence about the behaviour of real-world politicians is more important for effective policy design than acknowledged so far.
18F-FDG-PET-based deep learning for predicting cognitive decline in non-demented elderly across the Alzheimer’s disease clinical spectrum
Authors: Sohn Beomseok , Chung Seok Jong , Lee Jeong Ryong , Hwang Dosik , Xie Wanying , Chan Ling Ling , Choi Yoon Seong ,   Harvard Brain Imaging Study,   Japanese Alzheimer’s Disease Neuroimaging Initiative,   Alzheimer’s Disease Neuroimaging Initiative
Published: August 2024

Abstract

Background
With disease-modifying treatments for Alzheimer's disease (AD), prognostic tools for the pre-dementia stage are needed. This study aimed to evaluate the prognostic value of an 18F-fluorodeoxyglucose-positron emission tomography (18F-FDG-PET)-based deep-learning (DL) model in the pre-dementia stage of mild cognitive impairment (MCI) and normal cognition (NC).

Materials and Methods
A 18F-FDG-PET-based DL model was developed to classify diagnosis of AD-dementia vs NC using AD Neuroimaging Initiative (ADNI) and Japanese-ADNI (J-ADNI) datasets (n = 756), which provided the degree of similarity to AD-dementia. The prognostic value of the DL output for cognitive decline was assessed in the ADNI MCI (n = 663), J-ADNI MCI (n = 129), and Harvard Aging Brain Study (HABS) NC (n = 274) participants using Cox regression and calculating the integrated area under the time-dependent ROC curves (iAUC), along with clinical information and 18F-FDG-PET standardized uptake value ratio (SUVR). Subgroup analysis in the amyloid-positive ADNI MCI participants was performed using Cox regression and calculating the area under the time-dependent ROC (tdAUC) curves at 4-year follow-up to assess prognostic value of DL output over clinical information, 18F-FDG-PET SUVR, and amyloid PET Centiloids.

Results
DL output remained independently prognostic among other factors in all three datasets (P &lt; .05 for all by Cox regression). By adding DL output to other prognostic factors, prediction significantly improved in ADNI-MCI (iAUC differences 0.020 [0.007-0.034] before and after adding DL output) and improved without statistical significance in J-ADNI (0.020 [−0.005 to 0.044], and HABS-NC sets (0.059 [−0.003 to 0.126]). DL output showed independent (P = .002 by Cox regression) and significant added prognostic value (tdROC difference 0.019 [&lt;0.001-0.036]) over clinical information, 18F-FDG-PET SUVR, and Centiloids in the amyloid-positive ADNI MCI participants.

Conclusion
The 18F-FDG-PET-based DL model demonstrated the potential to improve cognitive decline prediction beyond clinical information, and conventional measures from 18F-FDG-PET and amyloid PET and may prove useful for clinical trial recruitment and individualized management.