bluuebunny/crossref_metadata_2025_split huggingface dataset:
Utilizing ultra-early continuous physiologic data to develop automated measures of clinical severity in a traumatic brain injury population
Authors: Yang Shiming , Hu Peter , Kalpakis Konstantinos , Burdette Bradford , Chen Hegang , Parikh Gunjan , Felix Ryan , Podell Jamie , Badjatia Neeraj 
Published: April 2024

AbstractDetermination of prognosis in the triage process after traumatic brain injury (TBI) is difficult to achieve. Current severity measures like the Trauma and injury severity score (TRISS) and revised trauma score (RTS) rely on additional information from the Glasgow Coma Scale (GCS) and the Injury Severity Score (ISS) which may be inaccurate or delayed, limiting their usefulness in the rapid triage setting. We hypothesized that machine learning based estimations of GCS and ISS obtained through modeling of continuous vital sign features could be used to rapidly derive an automated RTS and TRISS. We derived variables from electrocardiograms (ECG), photoplethysmography (PPG), and blood pressure using continuous data obtained in the first 15 min of admission to build machine learning models of GCS and ISS (ML-GCS and ML-ISS). We compared the TRISS and RTS using ML-ISS and ML-GCS and its value using the actual ISS and GCS in predicting in-hospital mortality. Models were tested in TBI with systemic injury (head abbreviated injury scale (AIS) ≥ 1), and isolated TBI (head AIS ≥ 1 and other AIS ≤ 1). The area under the receiver operating characteristic curve (AUROC) was used to evaluate model performance. A total of 21,077 cases (2009–2015) were in the training set. 6057 cases from 2016 to 2017 were used for testing, with 472 (7.8%) severe TBI (GCS 3–8), 223 (3.7%) moderate TBI (GCS 9–12), and 5913 (88.5%) mild TBI (GCS 13–15). In the TBI with systemic injury group, ML-TRISS had similar AUROC (0.963) to TRISS (0.965) in predicting mortality. ML-RTS had AUROC (0.823) and RTS had AUROC 0.928. In the isolated TBI group, ML-TRISS had AUROC 0.977, and TRISS had AUROC 0.983. ML-RTS had AUROC 0.790 and RTS had AUROC 0.957. Estimation of ISS and GCS from machine learning based modeling of vital sign features can be utilized to provide accurate assessments of the RTS and TRISS in a population of TBI patients. Automation of these scores could be utilized to enhance triage and resource allocation during the ultra-early phase of resuscitation.
Adaptive neighborhood rough set model for hybrid data processing: a case study on Parkinson’s disease behavioral analysis
Authors: Raza Imran , Jamal Muhammad Hasan , Qureshi Rizwan , Shahid Abdul Karim , Vistorte Angel Olider Rojas , Samad Md Abdus , Ashraf Imran 
Published: April 2024

AbstractExtracting knowledge from hybrid data, comprising both categorical and numerical data, poses significant challenges due to the inherent difficulty in preserving information and practical meanings during the conversion process. To address this challenge, hybrid data processing methods, combining complementary rough sets, have emerged as a promising approach for handling uncertainty. However, selecting an appropriate model and effectively utilizing it in data mining requires a thorough qualitative and quantitative comparison of existing hybrid data processing models. This research aims to contribute to the analysis of hybrid data processing models based on neighborhood rough sets by investigating the inherent relationships among these models. We propose a generic neighborhood rough set-based hybrid model specifically designed for processing hybrid data, thereby enhancing the efficacy of the data mining process without resorting to discretization and avoiding information loss or practical meaning degradation in datasets. The proposed scheme dynamically adapts the threshold value for the neighborhood approximation space according to the characteristics of the given datasets, ensuring optimal performance without sacrificing accuracy. To evaluate the effectiveness of the proposed scheme, we develop a testbed tailored for Parkinson’s patients, a domain where hybrid data processing is particularly relevant. The experimental results demonstrate that the proposed scheme consistently outperforms existing schemes in adaptively handling both numerical and categorical data, achieving an impressive accuracy of 95% on the Parkinson’s dataset. Overall, this research contributes to advancing hybrid data processing techniques by providing a robust and adaptive solution that addresses the challenges associated with handling hybrid data, particularly in the context of Parkinson’s disease analysis.
Reliability and validity of a graphical computerized adaptive test Longshi scale for rapid assessment of activities of daily living in stroke survivors
Authors: Zhou Jing , Zha Fubing , Liu Fang , Wan Li , Zhou Mingchao , Long Jianjun , Chen Miaoling , Xue Kaiwen , Wang Yulong 
Published: April 2024

AbstractStroke survivors frequently experience difficulties in daily activities, such as bathing, feeding, and mobility. This study aimed to evaluate the reliability and validity of a computer-adaptive test-Longshi scale (CAT-LS) for assessing activities of daily living (ADL) in stroke survivors. This cross-sectional study collected data using an electronic application. The ADL function of stroke survivors in rehabilitation departments of hospitals was assessed using both the CAT-LS and BI. Correlations between the CAT-LS and Barthel index (BI) and concurrent validity were evaluated using Pearson’s correlation test and multiple linear regression. Interrater reliability was evaluated using the intraclass correlation coefficient based on a two-way random effect. The internal consistency of the CAT-LS was assessed using Cronbach’s coefficient (α) and corrected item-total correlations. Overall, 103 medical institutions in China were used in this study. In total, 7151 patients with stroke were included in this study. The CAT-LS classified patients into three ADL groups (bedridden, domestic, and community) with significantly different BI scores (P < 0.05). The CAT-LS results obtained using the decision-tree scoring model were consistent with the scores for each BI item. A strong correlation was observed between CAT-LS and BI (Pearson’s r: 0.6–0.894, P < 0.001). The CAT-LS demonstrated good internal consistency (Cronbach’s α, 0.803–0.894) and interrater reliability (ICC, 0.928–0.979). CAT-LS is time-efficient and requires < 1 min to administer. The CAT-LS is a reliable and valid tool for assessing ADL function in stroke survivors and can provide rapid and accurate assessments that reduce the burden on healthcare professionals. Further validation of this tool in other populations and settings is necessary.Study registration number: No.: ChiCTR2000034067;http://www.chictr.org.cn/showproj.aspx?proj=54770.
Mechanism of muscle atrophy in a normal-weight rat model of type 2 diabetes established by using a soft-pellet diet
Authors: Akieda-Asai Sayaka , Ma Hao , Han Wanxin , Nagata Junko , Yamaguchi Fumitake , Date Yukari 
Published: April 2024

AbstractDietary factors such as food texture affect feeding behavior and energy metabolism, potentially causing obesity and type 2 diabetes. We previously found that rats fed soft pellets (SPs) were neither hyperphagic nor overweight but demonstrated glucose intolerance, insulin resistance, and hyperplasia of pancreatic β-cells. In the present study, we investigated the mechanism of muscle atrophy in rats that had been fed SPs on a 3-h time-restricted feeding schedule for 24 weeks. As expected, the SP rats were normal weight; however, they developed insulin resistance, glucose intolerance, and fat accumulation. In addition, skeletal muscles of SP rats were histologically atrophic and demonstrated disrupted insulin signaling. Furthermore, we learned that the muscle atrophy of the SP rats developed via the IL-6–STAT3–SOCS3 and ubiquitin–proteasome pathways. Our data show that the dietary habit of consuming soft foods can lead to not only glucose intolerance or insulin resistance but also muscle atrophy.
Effect of different buried depth on the disintegration characteristic of red-bed soft rock and the evolution model of disintegration breakage under cyclic drying-wetting
Authors: Zhang Jun , Guo Yang , Huang Kai , Cui Wei , Ke Zhaibang , Chen Xiaochuang , Yue Tengsheng , Gao Kun 
Published: April 2024

AbstractThe disintegration of red-bed soft rock exhibits a strong correlation with various geological disasters. However, the investigation into the evolutionary mechanisms underlying disintegration breakage has not yet received extensive exploration. In order to comprehensively examine the disintegration characteristics of red-bed soft rock, the slake durability tests were conducted to red-bed soft rocks of varying burial depths. Subsequently, an investigation was carried out to examine the disintegration characteristics and the evolution of disintegration parameters, including the coefficient of uniformity (Cu), coefficient of curvature (Cc), disintegration rate (DRE), disintegration ratio (Dr), and fractal dimension (D), throughout the disintegration process. Finally, employing the energy dissipation theory, an energy dissipation model was developed to predicate the disintegration process of samples at various burial depths. The findings demonstrate a decrease in the abundance of large particles and a concurrent increase in the abundance of small particles as the number of drying-wetting cycles increases. Furthermore, as the number of drying-wetting cycles increases, a significant alteration is observed in the content of particles larger than 10 mm, whereas the content of particles smaller than 10 mm undergoes only minor changes. The disintegration parameters, including the curvature coefficient, non-uniformity coefficient, disintegration rate, and fractal dimension, exhibit a positive correlation with the number of drying-wetting cycles. Conversely, the disintegration index demonstrates a decreasing trend with the increasing number of cycles. Nevertheless, as the burial depth increases, a notable trend emerges in the disintegration process, characterized by a gradual increase in the content of large particles alongside a progressive decrease in the content of small particles. Concurrently, the curvature coefficient, non-uniformity coefficient, disintegration rate, and fractal dimension exhibit a gradual decline, while the durability index experiences a gradual increase. In addition, based on the principle of energy dissipation, it is revealed that the surface energy increment of red-bed soft rock increases with the increase of the number of drying-wetting cycles, but decreases with the increase of burial depth. Ultimately, by leveraging the outcomes of energy dissipation analyses, a theoretical model is constructed to elucidate the correlation between surface energy and both the number of drying-wetting cycles and burial depth. This model serves as a theoretical reference for predicting the disintegration behavior of samples, offering valuable insights for future research endeavors.
Permeation of photochemically-generated gaseous chlorine dioxide on Mars as a significant factor in destroying subsurface organic compounds
Authors: Newmark Jacob , Kounaves Samuel P. 
Published: April 2024

AbstractIt has been shown that ultraviolet (UV) irradiation is responsible for the destruction of organic compounds on the surface of Mars. When combined with the photochemically-driven production of oxychlorines (ClOx) it can generate highly reactive species that can alter or destroy organic compounds. However, it has been assumed that since UV only penetrates the top few millimeters of the martian regolith, reactive ClOx oxidants are only produced on the surface. Of all the oxychlorine intermediates produced, gaseous chlorine dioxide [ClO2(g)] is of particular interest, being a highly reactive gas with the ability to oxidize organic compounds. Here we report on a set of experiments under Mars ambient conditions showing the production and permeation of ClO2(g) and its reaction with alanine as a test compound. Contrary to the accepted paradigm that UV irradiation on Mars only interacts with a thin layer of surface regolith, our results show that photochemically-generated ClO2(g) can permeate below the surface, depositing ClOx species (mainly Cl− and $${\text{ClO}}_{3}^{ - }$$

ClO

3

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) and destroying organic compounds. With varying levels of humidity and abundant chloride and oxychlorines on Mars, our findings show that permeation of ClO2(g) must be considered as a significant contributing factor in altering, fragmenting, or potentially destroying buried organic compounds on Mars.
Surface defect detection of hot rolled steel based on multi-scale feature fusion and attention mechanism residual block
Authors: Zhang Hongkai , Li Suqiang , Miao Qiqi , Fang Ruidi , Xue Song , Hu Qianchuan , Hu Jie , Chan Sixian 
Published: April 2024

AbstractTo improve the precision of defect categorization and localization in images, this paper proposes an approach for detecting surface defects in hot-rolled steel strips. The approach uses an improved YOLOv5 network model to overcome the issues of inadequate feature extraction capacity and suboptimal feature integration when identifying surface defects on steel strips. The proposed method achieves higher detection accuracy and localization precision, making it more competitive and applicable in real production. Firstly, the multi-scale feature fusion (MSF) strategy is utilized to fuse shallow and deep features effectively and enrich detailed information relevant to target defects. Secondly, the CSPLayer Res2Attention block (CRA block) residual module is introduced to reduce the loss of defect information during hierarchical transmission, thereby enhancing the extraction of fine-grained features and improving the perception of details and global features. Finally, the experimental results indicate that the mAP on the NEU-DET and GC10-DET datasets approaches 78.5% and 67.3%, respectively, which is 4.9% and 2.1% higher than that of the baseline. Meanwhile, it has higher precision and more precise localization capabilities than other methods. Furthermore, it also achieves 59.2% mAP on the APDDD dataset, indicating its potential for growth in further domains.
CircGPC3 promotes hepatocellular carcinoma progression and metastasis by sponging miR-578 and regulating RAB7A/PSME3 expression
Authors: Ju Linling , Luo Yunfeng , Cui Xiaohui , Zhang Hao , Chen Lin , Yao Min 
Published: April 2024

AbstractCircRNAs are a class of highly stable noncoding RNAs that play an important role in the progression of many diseases, especially cancer. In this study, high-throughput sequencing was used to screen for abnormally expressed circRNAs, and we found that circGPC3 was overexpressed in HCC tissues. However, the underlying mechanism of circGPC3 in the development and metastasis of hepatocellular carcinoma (HCC) remains unknown. In our study, we found that circGPC3 was significantly upregulated in HCC tissues and cells and that its overexpression was positively correlated with overall survival, TNM stage and lymph node metastasis. In vivo and in vitro experiments showed that circGPC3 knockdown repressed HCC cell migration, invasion and proliferation and promoted apoptosis. Mechanistically, circGPC3 promoted HCC proliferation and metastasis through the miR-578/RAB7A/PSME3 axis. Our results demonstrate that circGPC3 contributes to the progression of HCC and provides an intervention target for HCC.
Spatial-temporal graph neural ODE networks for skeleton-based action recognition
Authors: Pan Longji , Lu Jianguang , Tang Xianghong 
Published: April 2024

AbstractIn the field of skeleton-based action recognition, accurately recognizing human actions is crucial for applications such as virtual reality and motion analysis. However, this task faces challenges such intraindividual action differences and long-term temporal dependencies. To address these challenges, we propose an innovative model called spatial-temporal graph neural ordinary differential equations (STG-NODE). First, in the data preprocessing stage, the dynamic time warping (DTW) algorithm is used to normalize and calculate 3D skeleton data to facilitate the derivation of customized adjacency matrices for improving the influence of intraindividual action differences. Secondly, a custom ordinary differential equation (ODE) integrator is applied based on the initial conditions of the temporal features, producing a solution function that simulates the dynamic evolution trend of the events of interest. Finally, the outstanding ODE solver is used to numerically solve the time features based on the solution function to increase the influence of long-term dependencies on the recognition accuracy of the model and provide it with a more powerful temporal modeling ability. Through extensive experiments conducted on the NTU RGB+D 60 and Kinetics Skeleton 400 benchmark datasets, we demonstrate the superior performance of STG-NODE in the action recognition domain. The success of the STG-NODE model also provides new ideas and methods for the future development of the action recognition field.
Clumped isotopes reveal relationship between mussel growth and river discharge
Authors: Brewer Melanie A. , Grossman Ethan L. , Randklev Charles R. 
Published: April 2024

AbstractFreshwater mussels preserve valuable information about hydrology, climate, and population dynamics, but developing seasonal chronologies can be problematic. Using clumped isotope thermometry, we produced high-resolution reconstructions of modern and historic (~ 1900) temperatures and δ18Owater from mussel shells collected from an impounded river, the Brazos in Texas, before and after damming. We also performed high-resolution growth band analyses to investigate relationships between mussel growth rate, rainfall, and seasonal temperature. Reconstructed δ18Owater and temperature vary little between the modern (3R5) and historic shell (H3R). However, a positive relationship between reconstructed δ18Owater and growth rate in H3R indicates that aside from diminished growth in winter, precipitation and flow rate are the strongest controls on mussel growth in both modern and pre-dam times. Overall, our results demonstrate (1) the impact, both positive and negative, of environmental factors such as flow alteration and temperature on mussel growth and (2) the potential for clumped isotopes in freshwater mussels as a paleohydrology and paleoclimate proxies in terrestrial environments.
Exercise effect on pain is associated with negative and positive affective components: A large-scale internet-based cross-sectional study in Japan
Authors: Wakaizumi Kenta , Shinohara Yuta , Kawate Morihiko , Matsudaira Ko , Oka Hiroyuki , Yamada Keiko , Jabakhanji Rami , Baliki Marwan N. 
Published: April 2024

AbstractPain is a global health problem that leads to sedentary behavior and tends to cause negative emotion. In contrast, exercise is widely recommended for a health promotion, while pain often worsens with physical activity. Although exercise therapy is often prescribed to people with pain, the mechanisms of exercise effect on pain remains unclear. In this study, we tried to identify a universal association factor between regular exercise and pain intensity utilizing a cross-sectional web-based survey involving 52,353 adult participants from a large national study conducted in Japan. Using principal component analysis, we uncovered a mediation model of exercise effect on pain through psychological components. Analyses were performed in half of the population with pain (n = 20,330) and validated in the other half (n = 20,330), and showed that high-frequency exercise had a significant association with reduction in pain intensity. We also found Negative Affect and Vigor, two psychological components, are fully associating the exercise effect on pain (indirect effect =  − 0.032, p < 0.001; association proportion = 0.99) with a dose-dependent response corresponding to the frequency of exercise. These findings were successfully validated (indirect effect of high-frequency exercise =  − 0.028, p < 0.001; association proportion = 0.85). Moreover, these findings were also identified in subpopulation analyses of people with low back, neck, knee pain, and the tendency of the exercise effect on pain was increased with older people. In conclusion, the effect of exercise on pain is associated with psychological components and these association effects increased in parallel with the frequency of exercise habit regardless pain location.
Diversity analysis of sea anemone peptide toxins in different tissues of Heteractis crispa based on transcriptomics
Authors: Guo Qiqi , Fu Jinxing , Yuan Lin , Liao Yanling , Li Ming , Li Xinzhong , Yi Bo , Zhang Junqing , Gao Bingmiao 
Published: April 2024

AbstractPeptide toxins found in sea anemones venom have diverse properties that make them important research subjects in the fields of pharmacology, neuroscience and biotechnology. This study used high-throughput sequencing technology to systematically analyze the venom components of the tentacles, column, and mesenterial filaments of sea anemone Heteractis crispa, revealing the diversity and complexity of sea anemone toxins in different tissues. A total of 1049 transcripts were identified and categorized into 60 families, of which 91.0% were proteins and 9.0% were peptides. Of those 1049 transcripts, 416, 291, and 307 putative proteins and peptide precursors were identified from tentacles, column, and mesenterial filaments respectively, while 428 were identified when the datasets were combined. Of these putative toxin sequences, 42 were detected in all three tissues, including 33 proteins and 9 peptides, with the majority of peptides being ShKT domain, β-defensin, and Kunitz-type. In addition, this study applied bioinformatics approaches to predict the family classification, 3D structures, and functional annotation of these representative peptides, as well as the evolutionary relationships between peptides, laying the foundation for the next step of peptide pharmacological activity research.