bluuebunny/crossref_metadata_2025_split huggingface dataset:
Artificial intelligence real-time automated recognition of the gastric antrum cross-sectional area and movement rhythm via bedside ultrasound: A pilot study
Authors: Zou Tongjuan , He Hao , Yang Jing , Wu You , Lv Cao , Zhao Lican , Yin Wanhong 
Published: November 2024

Abstract

Background
The cross-sectional area (CSA) of the gastric antrum and its motor rhythm reflect the gastrointestinal function of critically ill patients. Monitoring the CSA and motor rhythm is important but time-consuming and operator dependent. This study aimed to develop an artificial intelligence (AI) system for real-time automated recognition of the CSA and movement rhythm of the gastric antrum via bedside ultrasound.
Methods
Gastric antrum ultrasound videos were prospectively collected from West China Hospital, to serve as a training and validation dataset. The AI system’s predictions were compared with the judgments of senior doctors to determine its accuracy. Additionally, videos were collected to evaluate the performance of the AI system. The antrum motility rhythms of patients and volunteers were preliminarily classified to lay the foundation for the subsequent establishment of gastrointestinal motility rhythm phenotypes in critically ill patients.
Results
A total of 907 videos were collected, including 620 patients and 287 volunteers, to develop and validate the AI system from January 2022 to November 2023. 49240 images were used as training datasets to train the model's ability to locate and segment gastric antrum ultrasound images. The remaining 12309 images were used as the internal validation dataset, achieving a dice coefficient (mDice) of 87.36% and an intersection over union (mIOU) of 77.56%. For the external validation dataset, 2334 images were used, resulting in mDice and mIOU values of 86.82% and 76.26%, respectively. Moreover, the AI system performed well in video analysis, with an mDice of 90.23% and an mIOU of 85.16% across 105 videos. In terms of antrum motility rhythm phenotypes, we identified several distinct patterns, such as regular movement, minimal movement, and irregular movement, reflecting different statuses, such as fasting, postmeal, postexercise, and postduty.
Conclusions
We developed an AI system that is comparable to experienced clinicians in identifying the gastric antrum and measuring its CSA. Furthermore, the system can generate a curve representing the rhythm of antrum movement, reflecting the varying statuses of patients and volunteers. This system may guide the implementation of enteral nutrition, saving clinicians’ time and reducing operator dependence.
Trial registration
The study was approved by the Ethics Committee of West China Hospital Review Board for Human Research with the following reference number 2022 (1089) and was registered on Chinese Clinical Trials. Trial registration: ChiCTR2200062503. Retrospectively registered 9 August 2022. All methods were performed in accordance with the relevant guidelines and regulations.
A Comprehensive Survey of Specularity Detection: State-of-the-Art Techniques and Breakthroughs
Authors: Li Fengze , Ma Jieming , Liang Hai-Ning , Tian Zhongbei , Wu Zhijing , Wen Tianxi , Liu Dawei 
Published: November 2024

Abstract

Specularity poses significant challenges in computer vision (CV), often leading to performance degradation in various tasks. Despite its importance, the CV field lacks a comprehensive review of specularity detection techniques. This survey addresses this gap by synthesizing diverse definitions of specularity and providing a unified framework to enhance consistency. It also presents a systematic review of traditional and deep learning-based methods for detecting specularity. Comparative experiments on a standardized dataset enable in-depth evaluation of each method, highlighting their strengths and limitations. The survey further provides structured insights and guidance for selecting appropriate methods across diverse scenarios. Through this, it identifies key areas for future research, aiming to support the development of more advanced detection models. By integrating diverse methodologies and quantitative analyzes, this survey contributes to a deeper understanding of current advancements and potential innovations in specularity detection.
Roles of metformin and pioglitazone in regulating neurotoxic astrocyte activation in EAE mice
Authors: Qin Suping , Yuan Bohui , Guo Jing jing , Zhang Xuejiao , Ding Yi , Zhang Tianxin , Wang Xiaotian , Li Xiangyang , Hua Hui , Zhou Feng , Liu Xiaomei 
Published: November 2024

Abstract

Neuro-inflammation is a vital mediator involved in the pathology of multiple sclerosis (MS) and experimental autoimmune encephalomyelitis (EAE), an animal model for multiple sclerosis. Neurotoxic A1 reactive astrocytes associate with neuro-inflammatory reactivity in MS/EAE. Although Metformin and Pioglitazone, two antidiabetic drugs, inhibit inflammation, the regulation mechanisms underlying A1 astrocytes in MS remain unclear. Herein, we show that Metformin and/or Pioglitazone downregulate inflammatory response, the activation of A1 astrocytes and the AKT/mTOR/STAT3 signaling pathway in primary mouse astrocytes treated with IL-17. In EAE mice, Metformin and/or Pioglitazone decreased significantly inflammation and demyelination and ameliorated the pathological process of disease. Overall, these findings uncover that Metformin and/or Pioglitazone suppress the inflammatory response in A1 astrocytes and alleviate the pathogenesis of EAE mice in vitro and in vivo. Targeting the cytotoxicity of A1 astrocytes may be a promising strategy for treating demyelinating diseases.
Study on the Aggregation Patterns of Fleas Parasitizing the Great Gerbil (Rhombomys opimus) in the Junggar Basin Plague Natural Focus
Authors: Li Fang , Zhao Guoyu , Wang Yu , Zhan Shang , Tang Xine , Luo Tao , Moming Abulimiti , Wang Huiqian , Chen Jianhui , Wang Qiguo , Wu Haiyan , Zhang Yujiang 
Published: November 2024

Abstract

Background
The great gerbil (Rhombomys opimus), its ectoparasitic fleas significantly influence the transmission and prevalence of plague, was the dominant rodent specie in the Junggar Basin in northwestern China. However, the distribution pattern of fleas parasitizing the great gerbils and whether that pattern affected the intensity of plague prevalence in different regions remain unclear.
Methods
A total of 17,780 fleas were collected from 2,258 great gerbils through 90 investigations. This study focused on analyzing the rate of flea infestation and the flea indices of species that parasitize the great gerbils. The aggregation patterns of fleas parasitizing the great gerbils were measured by the parameter b of Taylor's power law, and the differences in the aggregation index of plague epidemic areas were compared.
Results
We observed an aggregated distribution of ectoparasitic fleas in the great gerbils. The aggregation degree of combined fleas was higher (P > 0.05) in the eastern area of the Junggar Basin than in the western area. The primary species of ectoparasitic fleas of the great gerbils were X.skrjabini, X.minax, X.hirtipes, and N.laeviceps. The X.skrjabini exhibited the highest (P < 0.01) degree of aggregation in the eastern zone (Ⅲ), with an aggregation index of 1.61. Additionally, in the middle zone (Ⅱ), the aggregation index of X.minax and X.hirtipes reached their peak, with values of 1.53 and 1.56, respectively. Conversely, the degree of aggregation of N.laeviceps was more pronounced in the eastern zone than in the western zone of the Junggar Basin. Notably, the aggregation degree of the combined fleas of the great gerbils during the low-intensity plague epidemic period, with an index of 1.93, was significantly higher(P < 0.001) than that during the high-intensity epidemic period, with an index of 1.50.
Conclusions
Fleas exhibited an aggregated distribution of the great gerbil population. The levels of flea aggregation varied across zones characterized by differing intensities of plague epidemics. Additionally, the degree of flea aggregation was significantly correlated with the intensity of plague prevalence.
A robust fixed point method to solve system of nonlinear equations
Authors: Amattouch First M.R. 
Published: November 2024

Abstract

Solving nonlinear partial differential equations requires addressing systems of nonlinear algebraic equations. In this article, we propose a new fixed-point method for solving these systems. We assume that the method is both fast and globally convergent. Additionally, this method can be accelerated using Aitken or Anderson acceleration techniques. Several numerical test cases are presented to illustrate the efficiency of the proposed method.
Experimental Determination of Giant Polarization in Wurtzite III-Nitride Semiconductors
Authors: Wang Xinqiang , Ye Haotian , Wang Ping , Wang Rui , Wang Jinlin , Xu Xifan , Feng Ran , Wang Tao , Tong Wenyi , Liu Fang , Sheng Bowen , Chen Zhaoying , Duan Chun-Gang , Ge Weikun , Shen Bo 
Published: November 2024

Abstract

Polarization engineering has revolutionized the photonic and electronic landscape of III-nitride semiconductors over the past decades. However, recent revelations of giant ferroelectric polarization in wurtzite III-nitrides challenge the long-standing paradigms. Here, we experimentally elucidate the polarization, including its magnitude and orientation, and its relationship to lattice polarity in III-nitrides. Those experimentally determined polarizations exceeding 1 C/m2 with an upward orientation in metal-polar wurtzite nitride compounds align with recent theoretical predictions. This unified framework redefines the polarization landscape in contemporary GaN heterostructures, quantum structures, and ferroelectric heterostructures. Furthermore, we predict significant tunability and a dramatic increase in sheet electron concentration in ferroelectric ScAlN/GaN heterostructures, heralding advancements in high-power, high-frequency, and reconfigurable transistors, and non-volatile memories. This work bridges the critical gap in the understanding of polarization in both conventional and ferroelectric wurtzite nitrides, offering fundamental insights and paving the way for next-generation photonic, electronic, and acoustic devices.
COVID-19 Induces Prolonged Immunological Exhaustion Leading To Relapse Of Hematological Malignancies Except In Hematopoietic Cell Transplant Recipients
Authors: Chakrabarti Suparno , Banerjee Snigdha , Agarwal Mahak , Bhagawati Gitali , Saha Nilanjan , Jaiswal Sarita Rani 
Published: November 2024

Abstract

We studied the impact of COVID-19 on relapse in patients with hematological malignancies who had achieved complete remission (CR) and were either treatment-free or maintained on uninterrupted therapy over a 24-month period. Among 144 patients fulfilling the inclusion criteria, the overall relapse rate was 30.9%, with a significantly higher incidence in COVID-19-positive patients (60.9%) compared to COVID-19-negative individuals (25.2%, HR- p = 0.0001). Stratification by disease risk index (DRI) revealed a pronounced effect of COVID-19 on relapse in the DRI-high cohort (64.3% vs. 20.1%, p = 0.0001). Hematopoietic cell transplantation (HCT) appeared protective, with relapse rates of 22% in HCT recipients and 36.9% in the non-HCT cohort (p = 0.06), with no impact of COVID-19 on relapse in patients undergoing HCT. However, the effect of COVID-19 on relapse was predominantly observed in the non-HCT group (92.3% vs. 27.8%, HR-8.9, p < 0.001). Immune exhaustion markers, including PD-1 on T cells and NKG2A on NK cells, were significantly upregulated in COVID-19-exposed patients, particularly in those who relapsed, compared to recipients of allogeneic HCT with and without exposure to SARS-CoV2. These findings suggest COVID-19-induced immune dysregulation may facilitate relapse, particularly in non-HCT patients, warranting further exploration of the immunobiological mechanisms responsible for this phenomenon and possible immune-targeted interventions in this context.
Estimation of Atmospheric Haze Variability and Seasonal Variation of Convective Boundary Layer (CBL) Height Over Kasei Valles of Mars
Authors: Kalita Jyotirmoy , Pathak Binita , jain Sonal , Mishra Manoj Kumar , Guha Anirban 
Published: November 2024

Abstract

Images from Mars Color Camera (MCC) onboard India’s first Mars Orbiter Mission (MOM) during Martian years 33 and 34 provides the evidence of dense haze, water ice cloud, and all scale dust storms over Kasei Valles. The clouds and haze contained both fine mode and coarse mode particles with effective radius of 0.3 to 2.8 microns, further contributing to the variation of Atmospheric Optical Depth (AOD). This variation temporally perturbs the atmospheric circulation process over the Valles. The Atmospheric Optical Depth (AOD) varies from ~ 1.2 to ~ 2.3, with a varying scale height optical depth of ~ 6 to ~ 10 km. Estimated temperature varies from 180K ± 10K up to 240 K ± 15K, creates a favorable condition for deep convection activity. A very high wind speed of ~ 70 to ~ 100 m/s is conducive for redistributing the aerosols over the Kasei Valles. This is further evidenced by the variation of Convective Boundary Layer (CBL) height. CBL height varies from ~ 3km to ~ 9km in the temporal range of Ls = 50° to Ls = 280°. During the non-dust storm season (Ls = 50° to Ls = 100°.), adiabatic perturbation and downward enhanced precipitation contribute to the appearance of water ice haze over the valley. We also reported the presence of carbon like elements in the fog/morning haze based on the analysis of green spectral channel with varying AOD from 1.8 to 2.3. Acidalia Storm Track (AST) puts significant input in the dust variability process over Kasei Valles at (24.6°N/ 65.0°W) during the observed period.
Food Insecurity Modifies the Association Between the Gut Microbiome and the Risk of Cognitive Impairment in Adults
Authors: Eggers Shoshannah , Hoggarth Zachary E. , Nagdeo Kiran , Banas Maria J. , Lane Jamil M. , Rechtman Elza , Gennings Chris , O’Neal Elizabeth , Peppard Paul E. , Sethi Ajay K , Safdar Nasia , Malecki Kristen MC , Schultz Amy A. , Midya Vishal 
Published: November 2024

Abstract

Background
Recent studies have shown associations between relative abundances of specific gut microbes and cognitive function; however, few studies have explored the potential interplay between the gut microbiome and food insecurity in association with the risk of cognitive impairment (RCI). This study investigated the role of food insecurity as an effect modifier between the gut microbiome, including groups of gut microbes (microbial cliques), and RCI.
Methods
Data came from the Survey of the Health of Wisconsin and its ancillary Wisconsin Microbiome Study. The analytical sample (n = 360) included adult participants with complete data on food insecurity, RCI, and 16S rRNA sequencing data from stool samples. A “mini-cog” memory test was implemented to assess RCI. Food insecurity was assessed using a set of survey-based questions. Alpha diversity and individual taxa associations with RCI were estimated using linear regression. Microbial cliques associated with RCI were identified using an interpretable machine-learning-based algorithm. All analyses were stratified by food insecurity level, and regression models were adjusted for relevant confounders.
Results
Food insecurity status was weakly associated with RCI (b = 0.06, 95%CI=[0.00, 0.12]). Gut microbiome a-diversity had an inverse association with RCI in both the food secure (b=-0.08, 95%CI=[-0.15, -0.02]) and insecure groups (b=-0.09, 95%CI=[-0.26, 0.07]). Bacteroides sp. was associated with RCI in the food secure group only (b = 0.09, 95%CI= [0.05, 1.36]. We identified two microbial cliques whose associations with RCI were modified by food insecurity status. The presence of the microbial clique with either Eisenbergiella or Eubacterium was more strongly associated with RCI for the food-insecure group (β = 0.29, p < 0.0001) than the food-secure group (β = 0.05, p < 0.001). Alternatively, a microbial clique representing the presence of Ruminococcus torques, Bacteroides, CAG-352F, and/or Eubacterium had a stronger association with RCI for the food-secure group (β = 0.1, p < 0.0001) than the food-insecure group (β = 0.07, p = 0.01).
Conclusions
Food insecurity may modify the relationship between the gut microbiome and RCI. These findings suggest environmental and lifestyle factors in potential prevention strategies against RCI.
Case Report: Smudge Cells as a Valuable Prognostic Marker in Infectious Mononucleosis
Authors: Kapshikar Rajvardhan 
Published: November 2024

Abstract

Infectious mononucleosis is a very common infection. It is caused mostly by Epstein‒Barr virus (EBV), which belongs to herpesviridae or sometimes by the other member, Cytomegalovirus (CMV). It spreads through saliva. It has typical symptoms such as fever, extreme fatigue, sore throat and tonsillitis, enlarged lymph nodes in the neck, armpit, skin rashes, bodyache, and obstruction in the upper respiratory tract, and enlarged spleen and liver. It can lead to other complications such as anemia, encephalitis, meningitis, inflammation of the kidney, heart muscles, and rupture of the spleen. Laboratory diagnosis is costly. The physician may order heterophile antibody tests (monospot-test), EBV- or CMV- specific antibody tests, and viral DNA load. Patients with lower financial means do not want to go for such tests. This research indicates that a close examination of a blood sample for unusual lymphocytes and smudge cells (Downey cells) could also be the least expensive way to address this problem, although it is not a confirmatory test.
Gender-specific differences in depressive symptomatology associated with autistic traits in a non-clinical population
Authors: Siedler Agnieszka 
Published: November 2024

Abstract

Autism Spectrum Disorder (ASD) often presents comorbidity with depression, sharing similar characteristics between the two disorders with respect to social interaction, regulation of emotions, and flexibility in cognition. The current study investigates the relationship between autistic traits related to BAP and depressive symptoms in a general population sample, considering possible differences according to gender. In a sample of 239 adults, the results indicated that autistic traits, especially in the domains of communication and social skills, showed a significant association with depressive symptoms, with even more robust associations specifically in women. These results emphasize the presence of important sex differences in the associations found between autistic traits and specific depressive symptoms. In women, significant positive correlations were observed for autistic traits related to communication, social skills, and difficulties with attention-shifting, with depressive symptoms regarding thoughts of death, feelings of pessimism, experiences of alienation, cognitive impairments, and psychosomatic presentations. In contrast, males showed fewer significant associations, with only attention to detail significantly related to depressive symptoms such as cognitive deficits and decreased energy levels. It thus appears that there might be sex differences in the way the different dimensions of the autism spectrum relate to the various dimensions of depressive symptomatology. Furthermore, moderation analysis showed that gender influences the strength of these relationships, which highlights the need for gender-sensitive approaches both in research and clinical practice when assessing and targeting depressive symptoms in subclinical ASD populations. The implications for clinical practice as well as the limitations of the study are discussed.
A Novel Quantum-Backed Decision Vector Framework for Ransomware Detection Using Nonlinear Signal Entropy Mapping
Authors: Knaapen Pascal , Carter Henry , Davies Charlotte , Robinson George , Martin Thomas 
Published: November 2024

Abstract

The increasing complexity and sophistication of modern cyber threats need innovative detection mechanisms capable of adapting to rapidly evolving attack vectors. A quantum-inspired framework was introduced to address the challenges of identifying ransomware through advanced decision-making algorithms and nonlinear entropy analysis. The integration of entropy mapping allowed the detection system to capture subtle deviations in system behavior, facilitating early-stage identification of malicious activities. Quantum decision vectors provided a robust mechanism for evaluating and classifying ransomware patterns across diverse datasets without relying on static signatures. Experimental evaluations demonstrated superior performance in detection accuracy, latency, and resource efficiency compared to traditional heuristic and machine learning-based methods. Polymorphic ransomware variants, often evading conventional approaches, were effectively detected through the framework's generalized analytical capabilities. The system exhibited adaptability to imbalanced datasets, maintaining high reliability and precision across varying distributions of benign and malicious activities. Results highlighted its computational efficiency, with significantly reduced resource demands, enabling deployment in resource-constrained and high-throughput environments. The framework's modular design supports scalability and integration with existing cybersecurity infrastructures. Comprehensive analysis revealed substantial reductions in false positive rates, enhancing the reliability of automated detection processes. The study underscores the practical viability and theoretical contributions of advanced quantum-inspired methodologies in improving cybersecurity defenses.
Effect of Pretreatments and Temperatures on the Drying Characteristics, Thermodynamic and Functional Properties of Oven-Dried Green Mango Pulp (Mangifera indica L.)
Authors: Rayhan S. M. Johir , Boni SK Fahim Tahmid , Hossain Md. Sajjad , Rahman Mohammad Nurur , Rifat Tahmid Al , Mahmud Md. Zahir , Rahman Md. Bayjidur 
Published: November 2024

Abstract

This research investigates different pretreatments and drying temperatures influence on the dehydration kinetics, thermodynamic parameters, and functional properties of green mango pulp slices dried in an oven as sustainable preservation technique. Green mangoes were pretreated with hot water blanching and a 1% citric acid solution, then dried at 60°C, 65°C, and 70°C. Among these, HWB samples demonstrated the shortest drying times, with drying rates increasing with temperature. The Midilli model provided the best fit for describing thin-layer drying behavior. Effective moisture diffusivity ranged from 1.50 × 10⁻⁶ to 2.92 × 10⁻⁶ m²/s, following an Arrhenius-type temperature dependence, with activation energies of 28.67, 26.22, and 39.74 kJ/mol for control, citric acid, and HWB samples, respectively. Functional property analysis revealed significant impacts of temperature and pretreatment on bulk density (BD), water absorption capacity (WAC), and oil absorption capacity (OAC). HWB pretreatment, in particular, resulted in higher BD and WAC, while OAC was lower compared to control samples. These findings highlight the importance of optimizing pretreatment and drying conditions to enhance the quality and functional properties of dried green mango powder, contributing to its potential as a sustainable food ingredient.