bluuebunny/crossref_metadata_2025_split huggingface dataset:
Asymmetric analogous hyperbola model of overburden movement and its verification
Authors: Jiashun Liu , Yang Wang , Jianping Zuo , Zuoqi Wu , Yunjiang Sun , Zhiyong Zheng 
Published: July 2024

AbstractThe extraction of underground coal resources induces the fracture and movement of overlying strata, leading to geological hazards such as surface deformation, cracks, and even subsidence. Utilizing the analogous hyperbola model of overlying strata movement, we conducted a mechanical analysis to examine the asymmetric fracture mechanism resulting from coal seam mining in thick loose strata. An asymmetric analogous hyperbola model was established by introducing distinct virtual half-axis lengths (b). The thickness impact of thick loose layers (H) and bedrock layer (h) on the asymmetric movement of overlying rock during mining was also discussed. Similarity model tests were conducted to research the migration characteristics and surface subsidence patterns of overburdened rock and thick loose layers at different mining stages and validate the hypothesis of asymmetric overburdened rock migration. Additionally, the discrete element numerical model for thick and loose layers mining was established by using UDEC and discussed the asymmetric analogous hyperbola behaviour of overburden movement and surface subsidence. The comparison results show that the established asymmetric hyperbolic model can effectively predict the movement law of overlying strata and surface subsidence characteristics. Therefore, the proposed model can provide valuable theoretical support for predicting the movement patterns of overburden under thick loose layers and mitigating surface subsidence disasters.
AI models predicting breast cancer distant metastasis using LightGBM with clinical blood markers and ultrasound maximum diameter
Authors: Tan Yang , Zhang Wen-hai , Huang Zhen , Tan Qi-xing , Zhang Yue-mei , Wei Chang-yuan , Feng Zhen-Bo 
Published: July 2024

AbstractBreast cancer metastasis significantly impacts women's health globally. This study aimed to construct predictive models using clinical blood markers and ultrasound data to predict distant metastasis in breast cancer patients, ensuring clinical applicability, cost-effectiveness, relative non-invasiveness, and accessibility of these models. Analysis was conducted on data from 416 patients across two centers, focusing on clinical blood markers (tumor markers, liver and kidney function indicators, blood lipid markers, cardiovascular biomarkers) and maximum lesion diameter from ultrasound. Feature reduction was performed using Spearman correlation and LASSO regression. Two models were built using LightGBM: a clinical model (using clinical blood markers) and a combined model (incorporating clinical blood markers and ultrasound features), validated in training, internal test, and external validation (test1) cohorts. Feature importance analysis was conducted for both models, followed by univariate and multivariate regression analyses of these features. The AUC values of the clinical model in the training, internal test, and external validation (test1) cohorts were 0.950, 0.795, and 0.883, respectively. The combined model showed AUC values of 0.955, 0.835, and 0.918 in the training, internal test, and external validation (test1) cohorts, respectively. Clinical utility curve analysis indicated the combined model's superior net benefit in identifying breast cancer with distant metastasis across all cohorts. This suggests the combined model's superior discriminatory ability and strong generalization performance. Creatine kinase isoenzyme (CK-MB), CEA, CA153, albumin, creatine kinase, and maximum lesion diameter from ultrasound played significant roles in model prediction. CA153, CK-MB, lipoprotein (a), and maximum lesion diameter from ultrasound positively correlated with breast cancer distant metastasis, while indirect bilirubin and magnesium ions showed negative correlations. This study successfully utilized clinical blood markers and ultrasound data to develop AI models for predicting distant metastasis in breast cancer. The combined model, incorporating clinical blood markers and ultrasound features, exhibited higher accuracy, suggesting its potential clinical utility in predicting and identifying breast cancer distant metastasis. These findings highlight the potential prospects of developing cost-effective and accessible predictive tools in clinical oncology.
A novel optimization rainfall coupling model based on stepwise decomposition technique
Authors: Zheng Zhiwen , Zhang Xianqi , Yin Qiuwen , Liu Fang , Ren He , Zhao Ruichao 
Published: July 2024

AbstractTraditional decomposition integration models decompose the original sequence into subsequences, which are then proportionally divided into training and testing periods for modeling. Decomposition may cause data aliasing, then the decomposed training period may contain part of the test period data. A more effective method of sample construction is sought in order to accurately validate the model prediction accuracy. Semi-stepwise decomposition (SSD), full stepwise decomposition (FSD), single model semi-stepwise decomposition (SMSSD), and single model full stepwise decomposition (SMFSD) techniques were used to create the samples. This study integrates Variational Mode Decomposition (VMD), African Vulture Optimization Algorithm (AVOA), and Least Squares Support Vector Machine (LSSVM) to construct a coupled rainfall prediction model. The influence of different VMD parameters α is examined, and the most suitable stepwise decomposition machine learning coupled model algorithm for various stations in the North China Plain is selected. The results reveal that SMFSD is relatively the most suitable tool for monthly precipitation forecasting in the North China Plain. Among the predictions for the five stations, the best overall performance is observed at Huairou Station (RMSE of 18.37 mm, NSE of 0.86, MRE of 107.2%) and Jingxian Station (RMSE of 24.74 mm, NSE of 0.86, MRE of 51.71%), while Hekou Station exhibits the poorest performance (RMSE of 25.11 mm, NSE of 0.75, MRE of 173.75%).
Validation of the Hungarian version of the 6-item turnover intention scale among elderly care workers
Authors: Németh Zsanett , Deák Petra , Szűcs Réka , Makai Alexandra , Hock Márta 
Published: July 2024

AbstractThis research examines the psychometric characteristics and reliability of the 6-item turnover intention scale (TIS-6) by Bothma and Roodt (SA J Hum Resour Manag 11:a507, 2013) on a Hungarian sample. The internal validity of the TIS-6 was assessed using data from 269 Hungarian elderly care institution workers. Confirmatory factor analysis was performed to analyse the structural validity. Convergent and discriminant validity were examined with questions on job characteristics and using the Maslach Burnout Inventory and Effort-Reward Imbalance Scale. IBM SPSS 28.0 software was used for the statistical analysis, and the results were considered significant at p < 0.05. The internal consistency of the questionnaire's scale proved to be acceptable (α = 0.826). Convergent validity was confirmed by the relationships between the components of the questionnaire and burnout (rs = 0.512; p < 0.001; rs = 0.419; p < 0.001) and workplace stress (rs = 0.565; p < 0.001; rs = 0.310; p < 0.001). There were significant differences between the TIS-6 scores among the groups with different degrees of burnout (p < 0.001), which indicated adequate discriminant validity of the questionnaire. The structural validity of the questionnaire was acceptable, and the scale questions fit well. The Hungarian version of the TIS-6 scale is a valid and reliable tool for assessing turnover intention among elderly care institution workers in Hungary.
Quantifying impairment and disease severity using AI models trained on healthy subjects
Authors: Yu Boyang , Kaku Aakash , Liu Kangning , Parnandi Avinash , Fokas Emily , Venkatesan Anita , Pandit Natasha , Ranganath Rajesh , Schambra Heidi , Fernandez-Granda Carlos 
Published: July 2024

AbstractAutomatic assessment of impairment and disease severity is a key challenge in data-driven medicine. We propose a framework to address this challenge, which leverages AI models trained exclusively on healthy individuals. The COnfidence-Based chaRacterization of Anomalies (COBRA) score exploits the decrease in confidence of these models when presented with impaired or diseased patients to quantify their deviation from the healthy population. We applied the COBRA score to address a key limitation of current clinical evaluation of upper-body impairment in stroke patients. The gold-standard Fugl-Meyer Assessment (FMA) requires in-person administration by a trained assessor for 30-45 minutes, which restricts monitoring frequency and precludes physicians from adapting rehabilitation protocols to the progress of each patient. The COBRA score, computed automatically in under one minute, is shown to be strongly correlated with the FMA on an independent test cohort for two different data modalities: wearable sensors (ρ = 0.814, 95% CI [0.700,0.888]) and video (ρ = 0.736, 95% C.I [0.584, 0.838]). To demonstrate the generalizability of the approach to other conditions, the COBRA score was also applied to quantify severity of knee osteoarthritis from magnetic-resonance imaging scans, again achieving significant correlation with an independent clinical assessment (ρ = 0.644, 95% C.I [0.585,0.696]).
Deep learning for multi-type infectious keratitis diagnosis: A nationwide, cross-sectional, multicenter study
Authors: Li Zhongwen , Xie He , Wang Zhouqian , Li Daoyuan , Chen Kuan , Zong Xihang , Qiang Wei , Wen Feng , Deng Zhihong , Chen Limin , Li Huiping , Dong He , Wu Pengcheng , Sun Tao , Cheng Yan , Yang Yanning , Xue Jinsong , Zheng Qinxiang , Jiang Jiewei , Chen Wei 
Published: July 2024

AbstractThe main cause of corneal blindness worldwide is keratitis, especially the infectious form caused by bacteria, fungi, viruses, and Acanthamoeba. The key to effective management of infectious keratitis hinges on prompt and precise diagnosis. Nevertheless, the current gold standard, such as cultures of corneal scrapings, remains time-consuming and frequently yields false-negative results. Here, using 23,055 slit-lamp images collected from 12 clinical centers nationwide, this study constructed a clinically feasible deep learning system, DeepIK, that could emulate the diagnostic process of a human expert to identify and differentiate bacterial, fungal, viral, amebic, and noninfectious keratitis. DeepIK exhibited remarkable performance in internal, external, and prospective datasets (all areas under the receiver operating characteristic curves > 0.96) and outperformed three other state-of-the-art algorithms (DenseNet121, InceptionResNetV2, and Swin-Transformer). Our study indicates that DeepIK possesses the capability to assist ophthalmologists in accurately and swiftly identifying various infectious keratitis types from slit-lamp images, thereby facilitating timely and targeted treatment.
Accuracy of dental implant placement using different dynamic navigation and robotic systems: an in vitro study
Authors: Xu Zonghe , Zhou Lin , Han Bin , Wu Shuang , Xiao Yanjun , Zhang Sihui , Chen Jiang , Guo Jianbin , Wu Dong 
Published: July 2024

AbstractComputer-aided implant surgery has undergone continuous development in recent years. In this study, active and passive systems of dynamic navigation were divided into active dynamic navigation system group and passive dynamic navigation system group (ADG and PDG), respectively. Active, passive and semi-active implant robots were divided into active robot group, passive robot group and semi-active robot group (ARG, PRG and SRG), respectively. Each group placed two implants (FDI tooth positions 31 and 36) in a model 12 times. The accuracy of 216 implants in 108 models were analysed. The coronal deviations of ADG, PDG, ARG, PRG and SRG were 0.85 ± 0.17 mm, 1.05 ± 0.42 mm, 0.29 ± 0.15 mm, 0.40 ± 0.16 mm and 0.33 ± 0.14 mm, respectively. The apical deviations of the five groups were 1.11 ± 0.23 mm, 1.07 ± 0.38 mm, 0.29 ± 0.15 mm, 0.50 ± 0.19 mm and 0.36 ± 0.16 mm, respectively. The axial deviations of the five groups were 1.78 ± 0.73°, 1.99 ± 1.20°, 0.61 ± 0.25°, 1.04 ± 0.37° and 0.42 ± 0.18°, respectively. The coronal, apical and axial deviations of ADG were higher than those of ARG, PRG and SRG (all P < 0.001). Similarly, the coronal, apical and axial deviations of PDG were higher than those of ARG, PRG, and SRG (all P < 0.001). Dynamic and robotic computer-aided implant surgery may show good implant accuracy in vitro. However, the accuracy and stability of implant robots are higher than those of dynamic navigation systems.
A point cloud segmentation framework for image-based spatial transcriptomics
Authors: Defard Thomas , Laporte Hugo , Ayan Mallick , Soulier Juliette , Curras-Alonso Sandra , Weber Christian , Massip Florian , Londoño-Vallejo José-Arturo , Fouillade Charles , Mueller Florian , Walter Thomas 
Published: July 2024

AbstractRecent progress in image-based spatial RNA profiling enables to spatially resolve tens to hundreds of distinct RNA species with high spatial resolution. It presents new avenues for comprehending tissue organization. In this context, the ability to assign detected RNA transcripts to individual cells is crucial for downstream analyses, such as in-situ cell type calling. Yet, accurate cell segmentation can be challenging in tissue data, in particular in the absence of a high-quality membrane marker. To address this issue, we introduce ComSeg, a segmentation algorithm that operates directly on single RNA positions and that does not come with implicit or explicit priors on cell shape. ComSeg is applicable in complex tissues with arbitrary cell shapes. Through comprehensive evaluations on simulated and experimental datasets, we show that ComSeg outperforms existing state-of-the-art methods for in-situ single-cell RNA profiling and in-situ cell type calling. ComSeg is available as a documented and open source pip package at https://github.com/fish-quant/ComSeg.
Integrative analysis of genomic and epigenomic regulation reveals miRNA mediated tumor heterogeneity and immune evasion in lower grade glioma
Authors: Yang Zhen , Liu Xiaocen , Xu Hao , Teschendorff Andrew E. , Xu Lingjie , Li Jingyi , Fu Minjie , Liu Jun , Zhou Hanyu , Wang Yingying , Zhang Licheng , He Yungang , Lv Kun , Yang Hui 
Published: July 2024

AbstractThe expression dysregulation of microRNAs (miRNA) has been widely reported during cancer development, however, the underling mechanism remains largely unanswered. In the present work, we performed a systematic integrative study for genome-wide DNA methylation, copy number variation and miRNA expression data to identify mechanisms underlying miRNA dysregulation in lower grade glioma. We identify 719 miRNAs whose expression was associated with alterations of copy number variation or promoter methylation. Integrative multi-omics analysis revealed four subtypes with differing prognoses. These glioma subtypes exhibited distinct immune-related characteristics as well as clinical and genetic features. By construction of a miRNA regulatory network, we identified candidate miRNAs associated with immune evasion and response to immunotherapy. Finally, eight prognosis related miRNAs were validated to promote cell migration, invasion and proliferation through in vitro experiments. Our study reveals the crosstalk among DNA methylation, copy number variation and miRNA expression for immune regulation in glioma, and could have important implications for patient stratification and development of biomarkers for immunotherapy approaches.
RECORD, a high-throughput, customizable system that unveils behavioral strategies leveraged by rodents during foraging-like decision-making
Authors: Ibáñez Alcalá Raquel J. , Beck Dirk W. , Salcido Alexis A. , Davila Luis D. , Giri Atanu , Heaton Cory N. , Villarreal Rodriguez Kryssia , Rakocevic Lara I. , Hossain Safa B. , Reyes Neftali F. , Batson Serina A. , Macias Andrea Y. , Drammis Sabrina M. , Negishi Kenichiro , Zhang Qingyang , Umashankar Beck Shreeya , Vara Paulina , Joshi Arnav , Franco Austin J. , Hernandez Carbajal Bianca J. , Ordonez Miguel M. , Ramirez Felix Y. , Lopez Jonathan D. , Lozano Nayeli , Ramirez Abigail , Legaspy Linnete , Cruz Paulina L. , Armenta Abril A. , Viel Stephanie N. , Aguirre Jessica I. , Quintanar Odalys , Medina Fernanda , Ordonez Pablo M. , Munoz Alfonzo E. , Martínez Gaudier Gustavo E. , Naime Gabriela M. , Powers Rosalie E. , O’Dell Laura E. , Moschak Travis M. , Goosens Ki A. , Friedman Alexander 
Published: July 2024

AbstractTranslational studies benefit from experimental designs where laboratory organisms use human-relevant behaviors. One such behavior is decision-making, however studying complex decision-making in rodents is labor-intensive and typically restricted to two levels of cost/reward. We design a fully automated, inexpensive, high-throughput framework to study decision-making across multiple levels of rewards and costs: the REward-COst in Rodent Decision-making (RECORD) system. RECORD integrates three components: 1) 3D-printed arenas, 2) custom electronic hardware, and 3) software. We validated four behavioral protocols without employing any food or water restriction, highlighting the versatility of our system. RECORD data exposes heterogeneity in decision-making both within and across individuals that is quantifiably constrained. Using oxycodone self-administration and alcohol-consumption as test cases, we reveal how analytic approaches that incorporate behavioral heterogeneity are sensitive to detecting perturbations in decision-making. RECORD is a powerful approach to studying decision-making in rodents, with features that facilitate translational studies of decision-making in psychiatric disorders.
Low-dose ionizing radiation generates a hormetic response to modify lipid metabolism in Chlorella sorokiniana
Authors: Stanić Marina , Jevtović Mima , Kovačević Snežana , Dimitrijević Milena , Danilović Luković Jelena , McIntosh Owen A. , Zechmann Bernd , Lizzul Alessandro Marco , Spasojević Ivan , Pittman Jon K. 
Published: July 2024

AbstractAlgal biomass is a viable source of chemicals and metabolites for various energy, nutritional, medicinal and agricultural uses. While stresses have commonly been used to induce metabolite accumulation in microalgae in attempts to enhance high-value product yields, this is often very detrimental to growth. Therefore, understanding how to modify metabolism without deleterious consequences is highly beneficial. We demonstrate that low-doses (1–5 Gy) of ionizing radiation in the X-ray range induces a non-toxic, hormetic response in microalgae to promote metabolic activation. We identify specific radiation exposure parameters that give reproducible metabolic responses in Chlorella sorokiniana caused by transcriptional changes. This includes up-regulation of >30 lipid metabolism genes, such as genes encoding an acetyl-CoA carboxylase subunit, phosphatidic acid phosphatase, lysophosphatidic acid acyltransferase, and diacylglycerol acyltransferase. The outcome is an increased lipid yield in stationary phase cultures by 25% in just 24 hours, without any negative effects on cell viability or biomass.
Enhancing the expressivity of quantum neural networks with residual connections
Authors: Wen Jingwei , Huang Zhiguo , Cai Dunbo , Qian Ling 
Published: July 2024

AbstractIn noisy intermediate-scale quantum era, the research on the combination of artificial intelligence and quantum computing has been greatly developed. Here we propose a quantum circuit-based algorithm to implement quantum residual neural networks, where the residual connection channels are constructed by introducing auxiliary qubits to data-encoding and trainable blocks in quantum neural networks. We prove that when this particular network architecture is applied to a l-layer data-encoding, the number of frequency generation forms extends from one, namely the difference of the sum of generator eigenvalues, to $${{{{{{{\mathcal{O}}}}}}}}({l}^{2})$$
O

(

l

2

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, and the flexibility in adjusting Fourier coefficients can also be improved. It indicates that residual encoding can achieve better spectral richness and enhance the expressivity of various parameterized quantum circuits. Extensive numerical demonstrations in regression tasks and image classification are offered. Our work lays foundation for the complete quantum implementation of classical residual neural networks and offers a quantum feature map strategy for quantum machine learning.
Carrier transport mechanisms of titanium nitride and titanium oxynitride electron-selective contact in silicon heterojunction solar cells
Authors: Liu Chenxi , Wang Yang , Liu Jinpei , Ma Runze , Liu Hao , Wang Qi , Fu Yujun , Liu Qiming , He Deyan 
Published: July 2024

AbstractIt is widely accepted that an effective carrier-selective contact is indispensable for high performance crystalline silicon (c-Si) solar cells. However, the properties of these carrier-selective contact materials significantly differ from c-Si in terms of band gap, work function, lattice constant. Consequently, this gives rise to challenges such as band discontinuity and suspended bonds at the interface, which subsequently impact the specific carrier transport process and potentially lead to a reduction primarily in the fill factor at the device level. Titanium nitride (TiN) and titanium oxynitride (TiOxNy) have been employed as an electron-selective contact in both c-Si and perovskite solar cells, demonstrating their effectiveness in enhancing the performance of these devices. Based on the detailed characterizations of the band alignment, the carrier transport mechanisms are analyzed using multiple models, and the theoretical results are basically self-consistent through the verification of variable temperature experiments. These analytical methods can also provide solutions for analyzing the band structure and transport mechanism of diverse heterojunctions, ultimately contributing to the design and optimization of semiconductor heterojunction devices.
Particle-hole mixed Bogoliubov quasiparticles and Cooper instability in single-unit-cell FeSe/SrTiO3 films
Authors: Wei Zhiyuan , Li Shaozhi , Liu Bo , Sun Xiupeng , Hu Yinqi , Sun Shuai , Peng Shuting , Luo Yang , Huai Linwei , Shen Jianchang , Wang Bingqian , Miao Yu , Ou Zhipeng , Wang Yao , Jiang Kun , He Junfeng 
Published: July 2024

AbstractIn conventional superconductors, Bogoliubov quasiparticles and Cooper instability provide a paradigm to describe the superconducting state and the superconducting transition, respectively. However, whether these concepts can be adapted to describe Fe-based superconductors requires rigorous examinations from experiments. Here, we report angle-resolved photoemission studies on single-layer FeSe films grown on SrTiO3 substrate. Due to the improved clarity, our results reveal both particle and hole branches of the energy band with clear quasiparticles. The dispersion and coherence factors are extracted, which unveil the particle-hole mixed Bogoliubov quasiparticles in the superconducting state of the FeSe/STO films. Effective pairing susceptibility is also deduced as a function of temperature, which indicates the persistence of Cooper instability in Fe-based superconductors.