bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impact of loneliness on blood glucose: a cross-sectional survey among Japanese
Authors: Quyen An Tran , Nakamura Sho , Watanabe Kaname , Chei Choy-Lye , Narimatsu Hiroto 
Published: October 2023

Abstract

Although the correlation between loneliness and both the development and management of diabetes has been gradually uncovered, the finding was inclusive. Furthermore, this aspect has yet to be researched in the Japanese populace. Therefore, this cross-sectional study analyzed data from the Kanagawa prospective “ME-BYO” Cohort Study (ME-BYO cohort) to investigate the correlation between loneliness and HbA1c. A total of 666 participants were included in the analysis, with a mean age of 54.1 years and a mean BMI of 23 kg/m2. Half of the participants had obtained an education level beyond high school. The mean household income and physical activity level were reported as 6.83 million Japanese yen and 12.3 METs-h/day, respectively. Linear regression analysis determined no significant association between the loneliness scale and HbA1c (p = 0.512). The lack of association persisted even after adjusting for age, sex, and BMI. Thus, the study found no link between loneliness and HbA1c in the high SES population. This finding contradicts prior results and may be attributed to the impact of population characteristics, SES status, or genetic backgrounds.
Trial registration: not applicable
Identification of wood specimens utilizing fs-LIBS and machine learning techniques
Authors: Sarafis A. , Gerodimos T. , Kechaoglou E. , Anagnostopoulos D. F. , Kosmidis C. 
Published: October 2023

Abstract
We report on the ability to identify wood specimens by utilizing 30 fs Laser Induced Breakdown Spectroscopy (LIBS) in conjunction with machine learning techniques. Ten different wood specimens have been studied. The spectral features were assigned to atomic / ionic and diatomic molecular transitions. The origin of the latter has been explored by investigating the dynamics of the created plume in ambient and argon atmosphere. Principal Component Analysis (PCA) was employed for dimensionality reduction based on the primary LIBS analysis. The principal components formation is grounded on the CN, Ca+, Ca, and Na, LIBS data. Furthermore, applying the weighted k nearest neighbor (kNN) algorithm led to an accurate identification of the investigated specimens, since the evaluation metrics of specificity value were found to be in the range of 0.96–1.00, while that of accuracy was within 0.93–1.00.
Perceived Organizational Support and Employee Loyalty as Mediators in the Relationship Between Leadership Effectiveness and Task Performance: A Study of Nurses in Erbil City
Authors: Sherwani Karwan , Bala Hussaini , Khatoon Ghousia 
Published: October 2023

Abstract

In today's complex workplaces, good leadership is crucial for making employees do their jobs better. The aim of the study is to evaluate the role of Perceived Organizational Support and Employee Loyalty in the relationship between Leadership Effectiveness and Task Performance. While previous research has shed light on how leadership affects task performance, this study goes a step further by investigating how feeling supported and being loyal to the organization come into play. For this purpose, researchers have used a self-administered questionnaire and sampled 193 nurses in eight hospitals in Erbil, Kurdistan, Iraq about their experiences. The researchers used SMARTPLS using structure equation modeling to investigate the objective of the research. The study’s findings show that loyalty and organizational support serve as pivotal assets in mediating the relationship between leadership effectiveness and task performance. Loyal employees are highly motivated and engaged, leading to enhanced productivity, while organizational support creates a positive work environment, fostering job satisfaction and performance being loyal and feeling supported by the organization really matter when it comes to how good leadership affects job performance loyal employees work harder, and when the organization supports them, they feel happier and work better. This study provides valuable insights for organizations aiming to optimize their leadership strategies to enhance employee performance and satisfaction in complex and dynamic work environments.
Physical activity and hippocampal volume in young adults
Authors: Cherednichenko Anastasia , Miró-Padilla Anna , Adrián-Ventura Jesús , Monzonís-Carda Irene , Beltran-Valls Maria Reyes , Moliner-Urdiales Diego , Ávila César 
Published: October 2023

Abstract
Evidence from previous studies suggests that physical activity may contribute to a functional and structural hippocampal change throughout the lifespan. However, there is little evidence available in the young adult population. In addition, the personality traits that may influence this association remain unclear. With a sample of 84 young adults (43 girls; age 22.7 ± 2.8y), the main aim of the current study was to analyze the association between objective and self-reported measures of daily physical activity and hippocampal gray matter volume, and to examine the role of the personality trait of punishment sensitivity in this association. Our results showed that only vigorous levels of objectively measured physical activity (but not moderate or light) were positively associated with hippocampal volume and negatively with the personality trait of punishment sensitivity. Moreover, self-reported measures of total and vigorous physical activity were only associated with the personality measures, but not with the hippocampus volume. Thus, our data suggest that vigorous physical activity may contribute to enhancing the hippocampal volume in young adults.
Long-term outcomes and predictive factors of achieving low disease activity in childhood systemic lupus erythematosus: a Chinese bicentric retrospective registered study
Authors: Yu Xi Xi , Deng Jia , Chen Qiu XIa , Qiu Shi Yuan , Jiang Chao Hui , Wu Yi Qian , Qin Yang , Zhang Gao Fu , Yang Hai Ping , Zhao Fei , Li Qiu , Zhang Aihua , Wang Mo 
Published: October 2023

Abstract
Backgroud
Explore the clinical value of LDAS in the T2T strategy of pediatric systemic lupus erythematosus (pSLE) and find the risk factors for never reaching low disease activity state (LDAS).
Methods
Total of 272 children with SLE who were diagnosed and followed up in the Children's Hospital of Chongqing Medical University or Children's Hospital of Nanjing Medical University during the period from January 2012 to December 2019 were involved in this study, and the clinical presentation, pathology, treatment were retrospectively studied.
Results
Of the 272 patients enrolled in this study. During follow-up, 230 children achieved LDAS, and 42 were never been in. Male (P = 0.018), mucosal ulcer (P = 0.048), liver function damage (P = 0.026), cardiac effusion (P = 0.034), hematuria (P = 0.048), urine red blood cells (P = 0.017), urinary leukocytes (P = 0.032), endothelial cell proliferation (P = 0.004), these indexes have statistical differences between the two groups. Male (P = 0.045) and endothelial cell proliferation (P = 0.010) are independent risk factors for never achieving LDAS by multivariate logistic analysis. Children with biologics achieved LDAS at a higher rate than children without biologics (P = 0.038). The proportion of pSDI ≥ 1 in patients never been in LDAS was significantly higher than that in patients who achieved LDAS (P < 0.001).
Conclusions
Male and endothelial cell proliferation were independent risk factors for never achieving LDAS. At the end of the follow-up, the organ damage in the remission group was similar to that in the LDAS group, indicating that LDAS can be used as a target for pSLE treatment.
Trial registration
This study was registered at the Chinese Clinical Trial Registry (ChiCTR2100046357, date: 2021.06.01-2023.06.01) and the National Center for Biotechnology Information (#NCT04942314, date: 2021.07.01-2023.06.01).
Seasonal extreme temperatures and short-term fine particulate matter increases child respiratory hospitalizations in a sparsely populated region of the intermountain western United States
Authors: Landguth Erin L. , Knudson Jonathon , Graham Jon , Orr Ava , Coyle Emily A. , Smith Paul , Semmens Erin O. , Noonan Curtis 
Published: October 2023

Abstract
Background
Western Montana, USA, experiences complex air pollution patterns with predominant exposure sources from summer wildfire smoke and winter wood smoke. In addition, climate change related temperatures events are becoming more extreme and expected to contribute to increases in hospital admissions for a range of health outcomes. Few studies have evaluated these exposures (air pollution and temperature) that often occur simultaneously and may act synergistically on health.
Methods
We explored short-term exposure to air pollution on childhood respiratory health outcomes and how extreme temperature or seasonal period modify the risk of air pollution-associated hospitalizations. The main outcome measure included all respiratory-related hospital admissions for three categories: asthma, lower respiratory tract infections (LRTI), and upper respiratory tract infections (URTI) across western Montana for all individuals aged 0–17 from 2017–2020. We used a time-stratified, case-crossover analysis and distributed lag models to identify sensitive exposure windows of fine particulate matter (PM2.5) lagged from 0 (same-day) to 15 prior-days modified by temperature or season.
Results
Short-term exposure increases of 1 µg/m3 in PM2.5 were associated with elevated odds of all three respiratory hospital admission categories. PM2.5 was associated with the largest increased odds of hospitalizations for asthma at lag 7–13 days [1.87(1.17–2.97)], for LRTI at lag 6–12 days [2.18(1.20–3.97)], and for URTI at a cumulative lag of 13 days [1.29(1.07–1.57)]. The impact of PM2.5 varied by temperature and season for each respiratory outcome scenario. For asthma, PM2.5 was associated most strongly during colder temperatures [3.11(1.40–6.89)] and the winter season [3.26(1.07–9.95)]. Also in colder temperatures, PM2.5 was associated with increased odds of LRTI hospitalization [2.61(1.15–5.94)], but no seasonal effect was observed. Finally, 13 days of cumulative PM2.5 prior to admissions date was associated with the greatest increased odds of URTI hospitalization during summer days [3.35(1.85–6.04)] and hotter temperatures [1.71(1.31–2.22)].
Conclusions
Children’s respiratory-related hospital admissions were associated with short-term exposure to PM2.5. PM2.5 associations with asthma and LRTI hospitalizations were strongest during cold periods, whereas associations with URTI were largest during hot periods.
Classification
: environmental public health, fine particulate matter air pollution, respiratory infections
Pseudo-Labeling and Clustering based Active Learning for Imbalanced Classification of Wafer Bin Map Defects
Authors: Manivannan Siyamalan 
Published: October 2023

Abstract
Wafer Bin Map (WBM) defect patterns play a crucial role in identifying the root cause of manufacturing defects in the semiconductor industry. Although various deep learning-based approaches have been proposed for automated defect pattern classification, they often demand a large amount of labeled data for effective training. However, manual labeling is a costly and time-consuming process that requires specialized expertise.To address this challenge, this work introduces a novel active learning framework aimed at reducing the labeling cost by strategically selecting which WBMs should be labeled. An approach is proposed to mitigate issues related to class imbalance; the WBM patterns from identified classes are clustered, and a set of samples from each cluster is carefully selected, with a particular emphasis on classes with limited labeled data. This intelligent selection process effectively reduces human labeling efforts and mitigates problems associated with class-imbalanced training.The effectiveness of the proposed approach is demonstrated through significant improvements compared to other active learning methods. Remarkably, the state-of-the-art F1 score of 91.6% on the large-scale public WBM dataset, WM-811K, is achieved using only 4.3K labeled WBM images, while existing approaches require over 100K labeled images to achieve similar results. This outcome showcases the efficiency and practicality of the proposed approach. The code is available at \url{https://github.com}
The Effect of Tomato C2H2 Greening Factor H on Gland Hair and Flower Color
Authors: 冉 家俊 , Zong Chen , Xie Zhao , Yang Zhongzhou , Guo Ziqing , Dai Zuyun 
Published: October 2023

Abstract
The Tomato H gene is responsible for encoding a C2H2 zinc finger protein, which is a critical transcription factor of the C2HC subfamily for the development of glandular trichomes. Through the analysis of zinc finger protein structure and subfamily, it is hypothesized that other genes similar to H may also have a regulatory role in the formation of epidermal hairs in Solanaceae plants. Functional validation of these genes has shown that when the H gene is overexpressed, there is a significant increase in the density of leaf trichomes, as well as a noticeable dwarf phenotype in the plants. The most notable effects are observed in floral organs, particularly in terms of flower morphology, corolla color, and differences in the morphology of epidermal cells.
Multi-classification of Grading Stages for Osteoporosis Using Abdominal Computed Tomography with Clinical Variables: Application of Deep Learning with a Convolutional Neural Network
Authors: Ha Tae Jun , Kim Hee sang , Hwang Dong Hwan , Kang Seong Uk , Yeo Na Young , Kim Woo Jin , Choi Hyun-Soo , Kim Jeong Hyun , Kim Yoon , Moon Ki Won , Park Sang Won , Bak So Hyeon 
Published: October 2023

Abstract
Background: Osteoporosis is a significant global health concern and is often undetected until a fracture occurs. To improve early detection, a deep learning (DL) model was developed to classify osteoporosis stages using abdominal computed tomography (CT) scans.
Materials and Methods: This study retrospectively collected data from scanned contrast-enhanced abdominal CT. A total of 3,012 acquired CT scan data DL models were constructed for using image data, demographic information, and multi-modality data, respectively. The three groups were defined according to T-score [normal (T-Score ≥ –1.0), osteopenia (–2.5 < T-Score < –1.0), and osteoporosis (T-Score ≤ –2.5)] derived from dual-energy X-ray absorptiometry and assessed by a qualified radiologist. In the DL process, we used the Gradient-Weighted Class Activation Mapping (Grad-CAM) technique to identify features and accurately interpret clinical areas.
Results: Of the 3,012 data sets, the results of the multimodal dataset models showed the highest area under the receiver operating characteristic curve (AUC) (0.94) and accuracy (ACC) (0.80), while the image data model showed an AUC of 0.93 and an ACC of 0.79. The model using demographic information independently showed the worst performance with an AUC of 0.85 and an ACC of 0.68. The sensitivity and specificity for the multimodal model are 0.80 and 0.90, respectively, while the demographic data model scored 0.69 and 0.84. In addition, the Grad-CAM identified informative extracted features through the convolutional neural network, indicating femoral neck was the most common cause of femoral fractures across all three grading stages.
Conclusions: We developed a DL model for the multi-classification of osteoporosis using real-world clinical data, combining CT-scanned images with variables. This implies that DL can be fully applied to medical data for the classification stage of osteoporosis. Our results suggest that abdominal CT could be important in osteoporosis screening and could lead to appropriate treatment for the reduction of osteoporotic fractures.
A comparison of pain between laparoscopic and open procedures for inguinal hernia in young children
Authors: Sato Hideaki , Adachi Sei , Tominaga Miri , Kimura Shouta , Fukunaga Natsu , Osawa Syunsuke , Tayama Ai , Nakayama Noriyoshi , Watarai Yu 
Published: October 2023

Abstract
Purpose
The laparoscopic approach (LA) for inguinal hernia (IH) repair has become widely spread as a minimally invasive surgical technique, but few studies have compared its degree of pain with that of the open approach (OA). However, invasiveness is difficult to evaluate in children under five years old, as they cannot adequately express their pain. Therefore, the present study compared the degree of pain with the LA with that of the OA in children under five years old using a pain scoring system at our institute.
Material and methods
The records of 74 IH patients under 5 years old who underwent surgery between January 2022 and July 2023 were reviewed and compared in terms of the age, operative method, operative diagnosis, operation time, postoperative symptoms, and complications. The revised Face, Legs, Activity, Cry, and Consolability (FLACC) scores were used to evaluate the degree of pain quantitatively.
Results
Forty-seven patients with a mean age of 2.85 years old underwent the OA, while 27 patients with a mean age of 2.37 years old underwent the LA. During the LA, the contralateral patent processus vaginalis was identified in eight patients. The average operation time was 46.2 min for the OA and 37.8 min for LA. Both groups experienced similar rates of a postoperative fever, pain, and vomiting. The mean FLACC scores were 0.21 and 0.44 in the OA and LA groups, respectively. In a subanalysis by age groups, the average operation durations in patients < 2 years old were 48.9 and 33.9 min in the OA and LA groups, respectively, whereas those in patient 3–5 years old were 44.0 and 39.6 min in the OA and LA groups, respectively. The FLACC scores in patients < 2 years old were 0.09 and 0.5 in the OA and LA groups, respectively, and those in patients 3–5 years old were 0.3 and 0.18 in the OA and LA groups, respectively.
Conclusion
The LA can be performed to avoid contralateral recurrences, and the operation time is reduced in patients under two years old. However, the reduced invasiveness of the LA compared with the OA did not minimize postoperative pain, especially in patients under two years old.
Low oxygen environment effect on the tomato cell wall composition during the fruit ripening process
Authors: Leszczuk Agata , Kutyrieva-Nowak Nataliia , Nowak Artur , Nosalewicz Artur , Zdunek Artur 
Published: October 2023

Abstract

Background
Oxygen concentration is a key characteristic of the fruit storage environment determining shelf life and fruit quality. The aim of the work was to identify cell wall components that are related to the response to low oxygen conditions in fruit and to determine the effects of such conditions on the ripening process. Tomato (Solanum lycopersicum) fruits at different stages of the ripening process were stored in an anoxic and hypoxic environment, at 0% and 5% oxygen concentrations, respectively. We used comprehensive and comparative methods: from microscopic immunolabelling and estimation of enzymatic activities to detailed molecular approaches. Changes in the composition of extensin, arabinogalactan proteins, rhamnogalacturonan-I, low methyl-esterified homogalacturonan, and high methyl-esterified homogalacturonan were analysed.
Results
The results indicate alterations in fruits stored in the low oxygen environment, i.e. changes in fruit morphology, protein content, distribution of low methyl-esterified homogalacturonan, secretion of callose, modified activities of β-1,3-glucanase, endo-β-1,4-glucanase, and guaiacol peroxidase (GPX), and numerous disruptions in the cell wall structure at the molecular level.
Conclusions
In-depth molecular analyses showed that low oxygen stress immediately affected the cell wall composition. This may be considered a way of cell wall remodelling as a response to unfavourable conditions. Taken together, our results confirm that the 5% oxygen concentration slows down the ripening process and 0% oxygen accelerates the changes taking place during ripening. Thus, the observed molecular reset occurring in tomato cell walls in hypoxic and anoxic conditions emerges to be a result of regulatory and protective mechanisms modulating ripening processes.
Clothing-Change Person Re-identification Based on Fusion of RGB Modality and Gait Features
Authors: Tu Hongbin , Liu Chao , Peng Yuanyuan , Xiong Haibo , Wang Haotian 
Published: October 2023

Abstract
Clothing-change person re-identification is an emerging research topic aimed at reidentifying individuals who have changed their clothing. This topic is highly challenging and has not received sufficient research attention to date. Most existing person re-identification methods primarily focus on clothing features, but in real-world scenarios where individuals change their attire, the identification accuracy of conventional person re-identification networks significantly decreases. The key challenge is how to effectively extract clothing-agnostic human features. This paper presents a method for clothing-change person re-identification that combines gait recognition and RGB modality. Initially, we use a ResNet50-based network for feature extraction from pedestrian images to obtain initial person features. We train the network using a clothing-aware loss function, which emphasizes features such as the face, body posture, or shoes that are less likely to change with different clothing. Additionally, we employ semantic segmentation to transform the original pedestrian images into gait energy maps. To address the limited gait information within a single image, we introduce a gait prediction module and use the GaitSet network to extract multi-frame pedestrian gait features. Finally, we fuse the extracted gait features with clothing-agnostic features to enhance the network's robustness. Experimental results demonstrate the effectiveness of this approach on a new dataset, showcasing improved robustness and higher accuracy compared to existing methods, thus confirming its superiority.
Prognostic modeling of glycosylation in TNBC and screening of related key genes through a comprehensive analysis of multi-omics studies
Authors: Zhou Han , Wang Zhiwei , Guo Jun , Zhu Zihui , Sun Gang 
Published: October 2023

Abstract
Background
The most common malignancy in women is breast cancer, and the prognosis varies greatly according to its typing, among which the worst prognosis is TNBC. The glycosylation is one of the most priorities among reasons influencing the prognosis with TNBC of patients. We aim to develop a tumor prognosis model by analyzing genes related to glycosylation in order to predict patient prognosis.
Methods
The dataset was downloaded from the TCGA databank and the predictive genes were identified through Cox one-way regression analysis. The model genes with the highest risk scores among the 18 samples were obtained by lasso regression analysis, and the model was established. The related pathways affecting the progression of TNBC were analyzed, and the key genes of the disease were discovered for subsequent research.
Results
The model was constructed using TCGA database data, and The model underwent verification through K-M curve analysis and ROC curve. Then, we analyzed that the high expression of tumor-related chemokines in high-risk group may be associated with poor tumor prognosis. Finally, We conducted a random survival forest analysis and identified two significant genes, namely DPM2 and PINK1, which have been selected for further investigation.
Conclusion
The prognostic analysis model constructed by the TNBC glycosylation gene has excellent validation efficacy. It can be used for prognostic analysis of relevant TNBC patients.