bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effects of C-reactive protein trajectories of critically ill patients with sepsis on in-hospital mortality rate
Authors: Jiang Xuandong , Zhang Chenlu , Pan Yuting , Cheng Xuping , Zhang Weimin 
Published: September 2023

AbstractSepsis, a life-threatening condition caused by an inflammatory response to systemic infection, results in a significant social burden and healthcare costs. This study aimed to investigate the relationship between the C-reactive protein (CRP) trajectories of patients with sepsis in the intensive care unit (ICU) and the in-hospital mortality rate. We reviewed 1464 patients with sepsis treated in the ICU of Dongyang People’s Hospital from 2010 to 2020 and used latent growth mixture modeling to divide the patients into four classes according to CRP trajectory (intermediate, gradually increasing, persistently high, and persistently low CRP levels). We found that patients with intermediate and persistently high CRP levels had the lowest (18.1%) and highest (32.6%) in-hospital mortality rates, respectively. Multiple logistic regression analysis showed that patients with persistently high (odds ratio [OR] = 2.19, 95% confidence interval [CI] = 1.55–3.11) and persistently low (OR = 1.41, 95% CI = 1.03–1.94) CRP levels had a higher risk of in-hospital mortality than patients with intermediate CRP levels. In conclusion, in-hospital mortality rates among patients with sepsis differ according to the CRP trajectory, with patients with intermediate CRP levels having the lowest mortality rate. Further research on the underlying mechanisms is warranted.
Environmental DNA detects biodiversity and ecological features of phytoplankton communities in Mediterranean transitional waters
Authors: Specchia Valeria , Zangaro Francesco , Tzafesta Eftychia , Saccomanno Benedetta , Vadrucci Maria Rosaria , Pinna Maurizio 
Published: September 2023

AbstractClimate changes and anthropogenic pressures are causing a biodiversity decline in terms of species number and genetic diversity, reducing the adaptability and evolvability of natural communities. Transitional water ecosystems are more sensitive to habitat reduction and degradation and, thus, are more exposed to biodiversity declines requiring biodiversity monitoring programs for their conservation. Environmental DNA (eDNA) metabarcoding represents a high-throughput tool for biodiversity assessment that is facilitating data collection for biodiversity monitoring. In this study, we applied, for the first time, eDNA metabarcoding in a Mediterranean coastal lagoon to assess the ecological features of eukaryotic phytoplankton communities. We sampled water in seven different lagoon sites and amplified the extracted DNA with primers targeting the variable region 4 (V4) of the 18S rRNA gene marker. The results demonstrated the validity of eDNA studies to provide insights into lagoon phytoplankton composition, establish the structure and spatial variation of phytoplankton communities, and evaluate its correlation to abiotic factors. Finally, the genetic distances analysis suggests that the different spatial distribution of OTUs, at least for the Tetraselmis genus, reflects the genetic background.
Interaction between genetic susceptibility to obesity and food intake on BMI in Finnish school-aged children
Authors: Viljakainen Heli , Sorlí Jose V. , Dahlström Emma , Agrawal Nitin , Portolés Olga , Corella Dolores 
Published: September 2023

AbstractDiet modulates the genetic risk of obesity, but the modulation has been rarely studied using genetic risk scores (GRSs) in children. Our objectives were to identify single nucleotide polymorphisms (SNPs) that drive the interaction of specific foods with obesity and combine these into GRSs. Genetic and food frequency data from Finnish Health in Teens study was utilized. In total, 1142 11-year-old subjects were genotyped on the Metabochip array. BMI-GRS with 30 well-known SNPs was computed and the interaction of individual SNPs with food items and their summary dietary scores were examined in relation to age- and sex-specific BMI z-score (BMIz). The whole BMI-GRS interacted with several foods on BMIz. We identified 7–11 SNPs responsible for each interaction and these were combined into food-specific GRS. The most predominant interaction was witnessed for pizza (p < 0.001): the effect on BMIz was b − 0.130 (95% CI − 0.23; − 0.031) in those with low-risk, and 0.153 (95% CI 0.072; 0.234) in high-risk. Corresponding, but weaker interactions were verified for sweets and chocolate, sugary juice drink, and hamburger and hotdog. In total 5 SNPs close to genes NEGR1, SEC16B, TMEM18, GNPDA2, and FTO were shared between these interactions. Our results suggested that children genetically prone to obesity showed a stronger association of unhealthy foods with BMIz than those with lower genetic susceptibility. Shared SNPs of the interactions suggest common differences in metabolic gene-diet interactions, which warrants further investigation.
RAB10 promotes breast cancer proliferation migration and invasion predicting a poor prognosis for breast cancer
Authors: Zhuo Jian , Han Jianjun , Zhao Yanchun , Hao Ruiying , shen Chong , Li He , Dai Luxian , Sheng Ankang , Yao Hanyu , Yang Xiaohong , Liu Weiguang 
Published: September 2023

AbstractRAB10, a member of the small GTPase family, has complex biological functions, but its role in breast cancer (BC) remains unclear. The aim of this study was to investigate the relationship between RAB10's role in BC, its biological functions, and BC prognosis. An online database was used to analyze the correlation between differential expression of RAB10 in BC and prognosis. The results of immunohistochemical assays in clinical cohorts were combined with the database analysis. The chi-square test and COX regression were employed to analyze the correlation between RAB10 and pathological features of BC. MTT, Transwell, and wound healing assays were conducted to detect BC cell proliferation, invasion, and metastatic ability. Bioinformatics techniques were employed to explore the correlation between RAB10 and BC tumor immune cell infiltration, and to speculate the biological function of RAB10 in BC and related signaling pathways. Our findings suggest that RAB10 expression is elevated in BC and is associated with HER2 status, indicating a poor prognosis for BC patients. RAB10 can promote the proliferation, migration, and invasion ability of BC cells in vitro. RAB10 is also associated with BC immune cell infiltration and interacts with multiple signaling pathways. RAB10 is a potential biomarker or molecular target for BC.
All-inorganic perovskite photovoltaics for power conversion efficiency of 31%
Authors: Karna Lipsa Rani , Upadhyay Rohitash , Ghosh Avijit 
Published: September 2023

AbstractThe lead-free perovskite halides emerge as the great alternative for highly efficient and environment friendly photovoltaics due to the inherent optoelectronic properties. In this paper, the numerical study of all-inorganic regular n–i–p structured perovskite photovoltaics using solar cells capacitance simulator (SCAPS-1D) has been performed. The optimised device structure using rGO provided best performance compared to the other hole transport layers (HTLs) like CuI, CuSCN, Cu2O, NiO, WSe2, MoO3 with CsSnI3 as an active material and TiO2 as electron transport layer (ETL). Furthermore, WS2 as an ETL compared to TiO2, Li-TiO2, ZnO, Al-ZnO, etc. provided the best performance with rGO as HTL and CsSnI3 as active material. Therefore, the optimized solar cell structure (FTO/WS2/CsSnI3/rGO/Pt) showed best photovoltaic performance with power conversion efficiency (PCE) of 31%, fill factor (FF) of 88.48%, open circuit voltage (VOC) of 1.15 V, and short circuit current density (JSC) of 30.47 mA/cm2, respectively. Consequently, the effect of variation of temperature, thickness, defect density, doping density of active layer and variation of illumination intensity on the photovoltaic performance of the optimised device are also analysed. Furthermore, this study is also focused on the analysis of photovoltaic parameters for the optimized structure using concept of ideality factor associated with the illumination intensity. Therefore, this analysis suggests a route for further development of all-inorganic, lead-free perovskite photovoltaics experimentally with improved photovoltaic performance.
Magnetization reversals in core–shell sphere clusters: finite-element micromagnetic simulation and machine learning analysis
Authors: Park Hyeon-Kyu , Kim Sang-Koog 
Published: September 2023

AbstractRecently developed permanent magnets, featuring specially engineered microstructures of inhomogeneous magnetic phases, are being considered as cost-effective alternatives to homogeneous single-main-phase hard magnets composed of Nd2Fe14B, without compromising performance. In this study, we conducted a comprehensive examination of a core–shell sphere cluster model of Ce-substituted inhomogeneous Nd2-δCeδFe14B phases versus homogeneous magnetic phases, utilizing finite-element micromagnetic simulation and machine learning methods. This involved a meticulous, sphere-by-sphere analysis of individual demagnetization curves calculated from the cluster model. The grain-by-grain analyses unveiled that these individual demagnetization curves can elucidate the overall magnetization reversal in terms of the nucleation and coercive fields for each sphere. Furthermore, it was observed that Nd-rich spheres exhibited much broader ranges of nucleation and coercive field distributions, while Nd-lean spheres showed relatively narrower ranges. To identify the key parameter responsible for the notable differences in the nucleation fields, we constructed a machine learning regression model. The model utilized numerous hyperparameter sets, optimized through the very fast simulated annealing algorithm, to ensure reliable training. Using the kernel SHapley Additive eXplanation (SHAP) technique, we inferred that stray fields among the 11 parameters were closely related to coercivity. We further substantiated the machine learning models’ inference by establishing an analytical model based on the eigenvalue problem in classical micromagnetic theory. Our grain-by-grain interpretation can guide the optimal design of granular hard magnets from Nd2Fe14B and other abundant rare earth transition elements, focusing on extraordinary performance through the careful adjustment of microstructures and elemental compositions.
Genetic atlas of hygro-and thermosensory cells in the vinegar fly Drosophila melanogaster
Authors: Corthals Kristina , Andersson Vilma , Churcher Allison , Reimegård Johan , Enjin Anders 
Published: September 2023

AbstractThe ability of animals to perceive and respond to sensory information is essential for their survival in diverse environments. While much progress has been made in understanding various sensory modalities, the sense of hygrosensation, which involves the detection and response to humidity, remains poorly understood. In this study, we focused on the hygrosensory, and closely related thermosensory, systems in the vinegar fly Drosophila melanogaster to unravel the molecular profile of the cells of these senses. Using a transcriptomic analysis of over 37,000 nuclei, we identified twelve distinct clusters of cells corresponding to temperature-sensing arista neurons, humidity-sensing sacculus neurons, and support cells relating to these neurons. By examining the expression of known and novel marker genes, we validated the identity of these clusters and characterized their gene expression profiles. We found that each cell type could be characterized by a unique expression profile of ion channels, GPCR signaling molecules, synaptic vesicle cycle proteins, and cell adhesion molecules. Our findings provide valuable insights into the molecular basis of hygro- and thermosensation. Understanding the mechanisms underlying hygro- and thermosensation may shed light on the broader understanding of sensory systems and their adaptation to different environmental conditions in animals.
Phase estimation of definite photon number states by using quantum circuits
Authors: Najafi Peyman , Naeimi Ghasem , Saeidian Shahpoor 
Published: September 2023

AbstractWe propose a method to map the conventional optical interferometry setup into quantum circuits. The unknown phase shift inside a Mach–Zehnder interferometer in the presence of photon loss is estimated by simulating the quantum circuits. For this aim, we use the Bayesian approach in which the likelihood functions are needed, and they are obtained by simulating the appropriate quantum circuits. The precision of four different definite photon-number states of light, which all possess six photons, is compared. The measurement scheme that we have considered is counting the number of photons detected after the final beam splitter of the interferometer, and photon loss is modeled by using fictitious beam splitters in the arms of the interferometer. Our results indicate that three of the four definite photon-number states considered can have better precision than the standard interferometry limit whenever the photon loss rate is in a specific range. In addition, the Fisher information for the four definite photon-number states in the setup is also estimated to check the optimality of the chosen measurement scheme.
Predicting lung adenocarcinoma prognosis, immune escape, and pharmacomic profile from arginine and proline-related genes
Authors: Wang Ziqiang , Zhang Jing , Shi Shuhua , Ma Hongyu , Wang Dongqin , Zuo Chao , Zhang Qiang , Lian Chaoqun 
Published: September 2023

AbstractLung adenocarcinoma (LUAD) is a highly heterogeneous disease that ranks first in morbidity and mortality. Abnormal arginine metabolism is associated with inflammatory lung disease and may influence alterations in the tumor immune microenvironment. However, the potential role of arginine and proline metabolic patterns and immune molecular markers in LUAD is unclear. Gene expression, somatic mutations, and clinicopathological information of LUAD were downloaded from The Cancer Genome Atlas (TCGA) database. Univariate Cox regression analysis was performed to identify metabolic genes associated with overall survival (OS). Unsupervised clustering divided the sample into two subtypes with different metabolic and immunological profiles. Gene set enrichment analysis (GESA) and gene set variation analysis (GSVA) were used to analyze the underlying biological processes of the two subtypes. Drug sensitivity between subtypes was also predicted; then prognostic features were developed by multivariate Cox regression analysis. In addition, validation was obtained in the GSE68465, and GSE50081 dataset. Then, gene expression, and clinical characterization of hub genes CPS1 and SMS were performed; finally, in vitro validation experiments for knockdown of SMS were performed in LUAD cell lines. In this study, we first identified 12 arginine and proline-related genes (APRGs) significantly associated with OS and characterized the clinicopathological features and tumor microenvironmental landscape of two different subtypes. Then, we established an arginine and proline metabolism-related scoring system and identified two hub genes highly associated with prognosis, namely CPS1, and SMS. In addition, we performed CCK8, transwell, and other functional experiments on SMS to obtain consistent results. Our comprehensive analysis revealed the potential molecular features and clinical applications of APRGs in LUAD. A model based on 2 APRGs can accurately predict survival outcomes in LUAD, improve our understanding of APRGs in LUAD, and pave a new pathway to guide risk stratification and treatment strategy development for LUAD patients.
Machine learning model for predicting late recurrence of atrial fibrillation after catheter ablation
Authors: Budzianowski Jan , Kaczmarek-Majer Katarzyna , Rzeźniczak Janusz , Słomczyński Marek , Wichrowski Filip , Hiczkiewicz Dariusz , Musielak Bogdan , Grydz Łukasz , Hiczkiewicz Jarosław , Burchardt Paweł 
Published: September 2023

AbstractLate recurrence of atrial fibrillation (LRAF) in the first year following catheter ablation is a common and significant clinical problem. Our study aimed to create a machine-learning model for predicting arrhythmic recurrence within the first year since catheter ablation. The study comprised 201 consecutive patients (age: 61.8 ± 8.1; women 36%) with paroxysmal, persistent, and long-standing persistent atrial fibrillation (AF) who underwent cryoballoon (61%) and radiofrequency ablation (39%). Five different supervised machine-learning models (decision tree, logistic regression, random forest, XGBoost, support vector machines) were developed for predicting AF recurrence. Further, SHapley Additive exPlanations were derived to explain the predictions using 82 parameters based on clinical, laboratory, and procedural variables collected from each patient. The models were trained and validated using a stratified fivefold cross-validation, and a feature selection was performed with permutation importance. The XGBoost model with 12 variables showed the best performance on the testing cohort, with the highest AUC of 0.75 [95% confidence interval 0.7395, 0.7653]. The machine-learned model, based on the easily available 12 clinical and laboratory variables, predicted LRAF with good performance, which may provide a valuable tool in clinical practice for better patient selection and personalized AF strategy following the procedure.
Calculation method of HJC constitutive model parameters of natural joint angle slate
Authors: Wan Antong , Tao Tiejun , Tian Xingchao , Xie Caijin , Jia Jian 
Published: September 2023

AbstractIn the course of underground engineering, layered slate is often encountered. Understanding the mechanical characteristics of layered slate is a prerequisite for engineering construction and disaster prevention and mitigation. As a result, at the beginning of a project, a large number of indoor tests are required, which are time-consuming and laborious. In addition, the natural joint angle of the layered slate is random, so it is extremely difficult to establish a database of the mechanical characteristics of layered slate. Hence, it is necessary to find a simple, feasible, and high-precision method to determine the Holmquist–Johnson–Cook (HJC) constitutive model parameters for naturally jointed layered slate with different dip angles. This study first determines the HJC constitutive model parameters of layered slate with five specific joint dip angles (0°, 30°, 45°, 60°, and 90°) through static tests and the split Hopkinson pressure bar (SHPB) test. Furthermore, by employing sensitivity analysis methods, the influence of key parameters of the HJC constitutive model on the dynamic peak stress of slate is determined. Among them, parameters A and B have the most significant impact on stress, exceeding 50%. Thirdly, a nonlinear fitting regression method is used to determine the HJC constitutive model parameters of naturally jointed angular slate. The relationship between the HJC model parameters and the inclination angle of slate joints is derived, and the accuracy of these parameters is verified through numerical simulation methods. The error between the numerical simulation and indoor experiments is within 10%, indicating a high level of simulation accuracy. The research findings provide a highly precise numerical simulation method for similar projects.
Exploring Cognitive-Pragmatic Heterogeneity Following Acquired Brain Injury: A Cluster Analysis of Hint Comprehension
Authors: Cordonier Natacha , Fossard Marion , Tillé Yves , Champagne-Lavau Maud 
Published: September 2023

Purpose:
Difficulties understanding nonliteral language (especially hints) are frequently reported following acquired brain injury (ABI). Several cognitive mechanisms, such as context processing, executive functions, and theory of mind (ToM), may underlie these disorders. However, their role remains controversial, mainly because of the characteristic heterogeneity of this population. Therefore, our study aimed to identify cognitive-pragmatic profiles in individuals with ABI.

Method:
A new task of hint comprehension, manipulating executive demand, markers of hints, and ToM and neuropsychological tests were administered to 33 participants with frontal ABI and 33 control participants. Cluster analysis, a method sensitive to profile heterogeneity, was applied and coupled with error analysis.

Results:
We highlighted two cognitive-pragmatic profiles. One subgroup of participants with ABI exhibited contextual insensitivity, leading them to infer the utterance meaning based on linguistic decoding alone—literal meaning. This difficulty in understanding hints was associated with deficits in working memory, inhibition, and ToM. The second subgroup of participants with ABI showed difficulty with literal statements, associated with impaired inhibition and ToM. In addition, the two subgroups differed only on the ToM task. This result suggests that various types of ToM deficit (misunderstanding vs. incorrect attribution of mental states) could contribute to the variability of the pragmatic profiles observed (difficulties in interpreting hints vs. literal statements).

Conclusion:
The experimental design adopted in this study provides valuable insight into the explanatory hypotheses of nonliteral language comprehension disorders and has important clinical implications.

Supplemental Material:

https://doi.org/10.23641/asha.24069516
Theoretical analysis of the electrical characteristics of lead‐free formamidinium tin iodide solar cell
Authors: Katunge Elizabeth K. , Njema George G. , Kibet Joshua K. 
Published: September 2023

AbstractGreen energy transition and climate change have gathered significant momentum in the world because of the rising population and increased clean energy demands. For this reason, renewable energy alternatives such as inexhaustible photo energy from the sun appear to be the ultimate solution to the world's energy needs. Formamidinium tin tri‐iodide (HC(NH2)2SnI3)‐based perovskites are found to be more efficient and stable than their methylammonium tin tri‐iodide (MASnI3) counterparts because of its wider bandgap and better temperature stability. A device simulation of FASnI3‐based solar cell is numerically performed using solar cell capacitance simulator (SCAPS‐1D). The focus is to investigate the effect of changing working temperature, metal back contact, absorber thickness, defect density, and doping concentration on the performance of the proposed solar cell device. The optimised solar cell parameters of the proposed solar cell were: short‐circuit current density (Jsc) of 28.45 mAcm−2, open‐circuit voltage (Voc) of 1.0042 V, fill factor of 63.73%, and power conversion efficiency of 18.21% at 300 K, thus, paving the way for novel perovskite solar cells which are environmentally benign because they are lead‐free, have better absorption efficiency, and can be injected into the production work flow for commercial applications.