bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Data-Driven Framework for Designing Microstructure of Multifunctional Composites with Deep-Learned Diffusion-Based Generative Models
Authors: Lee Kang-Hyun , Lim Hyoung Jun , Yun Gun Jin 
Published: July 2023

Abstract
This paper puts forward a novel integrated microstructure design methodology that replaces the common existing design approaches for multifunctional composites: 1) reconstruction of microstructures, 2) analyzing and quantifying material properties, and 3) inverse design of materials using the diffusion-based generative model (DGM). The problem of microstructure reconstruction is addressed using DGM, which is a new state-of-the-art generative model formulated with a forward Markovian diffusion process and the reverse process. Then, the conditional formulation of DGM is introduced for guidance to the embedded desired material properties with a transformer-based attention mechanism, which enables the inverse design of multifunctional composites. A convolutional neural network (CNN)-based surrogate model is utilized to facilitate the prediction of nonlinear material properties for building microstructure-property linkages. Combined, the proposed artificial intelligence-based design framework enables large data processing and database construction that is often not affordable with resource-intensive finite element method (FEM)-based direct numerical simulation (DNS) and iterative reconstruction methods. What is important is that the proposed DGM-based methodology is not susceptible to unstable training or mode collapse, which are common issues in neural network models that are often difficult to address even with extensive hyperparameter tuning. An example case is presented to demonstrate the effectiveness of the proposed approach, which is designing mechanoluminescence (ML) particulate composites made of europium and dysprosium ions. The results show that the inversely-designed multiple ML microstructure candidates with the proposed generative and surrogate models meet the multiple design requirements (e.g., volume fraction, elastic constant, and light sensitivity). The evaluation of the generated samples' quality and the surrogate models' performance using appropriate metrics are also included. This assessment demonstrates that the proposed integrated methodology offers an end-to-end solution for practical material design applications.
Effects of introducing the WHO Labour Care Guide on Caesarean section: a pragmatic, stepped-wedge, cluster randomized trial in India
Authors: Vogel Joshua , Pujar Yeshita , Vernekar Sunil , Armari Elizabeth , Pingray Veronica , Althabe Fernando , Gibbons Luz , Berrueta Mabel , Somannavar Manjunath , Ciganda Alvaro , Rodriguez Rocio , Bendigeri Savitri , Kumar Jayashree Ashok , Patil Shruti Bhavi , Karinagannanavar Aravind , Anteen Raveendra , R. Pavithra M. , Shetty Shukla , B Latha , M. Megha H. , Gaddi Suman , Chikkagowdra Shaila , Raghavendra Bellara , Homer Caroline , Lavender Tina , Kushtagi Pralhad , Hofmeyr Justus , Derman Richard , Goudar Shivaprasad 
Published: July 2023

Abstract
The World Health Organization’s Labour Care Guide (LCG) is the “next generation” partograph, designed to improve the quality of intrapartum care and enhance women’s experiences. However, the effects of the LCG on maternal and newborn outcomes have not been evaluated. We developed a novel strategy to introduce the LCG into routine intrapartum care, comprising a co-designed training program for labour ward clinicians, alongside monthly audit and feedback. We implemented the strategy and measured its effects using a stepped-wedge, randomised trial in four hospitals in India. We captured data from 26,331 women who gave birth at >=20 weeks’ gestation, over a 54-week period. Following implementation, a 5.5% crude absolute reduction in the Caesarean section rate amongst women in Robson Group 1 was observed (45.2% vs 39.7%; relative risk 0.85, 95% confidence interval 0.54-1.33). Maternal process-of-care measures were not significantly different, though labour augmentation with oxytocin was 18.0% lower with the LCG strategy. No differences were observed for maternal, fetal or newborn health outcomes, or women’s birth experiences. This “proof of concept” study provides important evidence on the effects of introducing LCG into routine practice, suggesting a 15% relative risk reduction in Caesarean section use amongst women in Robson Group 1. Larger trials are warranted, particularly in settings where urgent reversal of the Caesarean section epidemic is needed.
Automated Lung and Colon Cancer Classification using Histopathological Images
Authors: Ji Jie , Zhang Weifeng , Geng Yiqun , Wang Heli , Wang Chuan , Dong Yuejiao , Lin Ruilin , Chen Zhuofeng , Huang Jiexiong , Hong Liangli , Gu Jiang 
Published: July 2023

Abstract
Background
Cancer is the leading cause of mortality in the world. And among all cancers lung and colon cancers are two of the most common causes of death and morbidity. The aim of this study was to develop an automated lung and colon cancer classification system using histopathological images.
Methods
An automated lung and colon classification system was developed using histopathological images from the LC25000 dataset. The algorithm development included data splitting, deep neural network model selection, on the fly image augmentation, training and validation. The core of the algorithm was a Swin Transform V2 model. The model performance was evaluated using Accuracy, Kappa, confusion matrix, precision, recall, and F1. Extensive experiments were conducted to compare the performances of different neural networks including both mainstream convolutional neural networks and vision transformers.
Results
The Swin Transform V2 model achieved perfect results on all metrics, and it outperformed other models of this study and all models of previous studies.
Conclusions
The Swin Transformer V2 model has the potential to be used to assist pathologists in classifying lung and colon cancers using histopathology images. Moreover, the LC25000 dataset is too easy and should no longer be used independently.
Multivalent binding of the tardigrade Dsup protein to chromatin promotes yeast survival and longevity upon exposure to oxidative damage
Authors: Tyler Jessica , Aguilar Rhiannon , Arslanovic Nina , Birmingham Kaylah , Kaliwal kritika , chakraborty Ujani , Postnikoff spike , Hickman Allison , Kahn laiba , Watson Rachel , Ezell Ryan , Willis Hannah , Cowles Martis , Marunde Matthew , Keogh Michael-C. , gutierrez Ignacio , Shim abraham , Garner Richard 
Published: July 2023

Abstract
Tardigrades are remarkable in their ability to survive extreme environments. The damage suppressor (Dsup) protein is thought responsible for their extreme resistance to reactive oxygen species (ROS) generated by irradiation. Here we show that expression of Ramazzottius varieornatus Dsup in Saccharomyces cerevisiae reduces oxidative DNA damage and extends the lifespan of budding yeast exposed to chronic oxidative genotoxicity. This protection from ROS requires either the Dsup HMGN-like domain or sequences C-terminal to same. Dsup associates with no apparent bias across the yeast genome, using multiple modes of nucleosome binding; the HMGN-like region interacts with both the H2A/H2B acidic patch and H3/H4 histone tails, while the C-terminal region binds DNA. These findings give precedent for engineering an organism by physically shielding its genome to promote survival and longevity in the face of oxidative damage.
Enhancing the Thermal Properties of Ultra-High Molecular Weight Polyethylene through the Addition of Carbon Nanotubes: A Promising Approach for Total Joint Replacement Applications
Authors: K Praveen Kumar , V Chittaranjan Das , A Surya Prakash , Unnam Sai Pranay 
Published: July 2023

Abstract
Ultra-high molecular weight polyethylene(UMWPE) is commonly utilized in orthopaedic applications, particularly joint arthroplasty, due to its exceptional qualities. However, the limitations of pure UHMWPE, such as inadequate wear resistance and potential joint issues, necessitate further advancements to enhance its longevity and reduce the need for revision procedures. This study investigates the integration of multi-walled carbon nanotubes (MWCNTs) as reinforcements in the UHMWPE matrix. The mechanical properties of MWCNTs at varying concentrations were assessed following ASTM standards, while thermal characteristics were evaluated to gauge the impact of MWCNT incorporation. The results demonstrate that the addition of MWCNTs significantly enhances the thermal properties of UHMWPE. Differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) reveal a reduction in weight loss from 4.3–1.3% and an increase in crystallinity from 52–64%. These findings underscore the potential of MWCNT-reinforced UHMWPE as a viable option for total joint replacement, as it improves thermal characteristics and material durability. This research contributes to the advancement of superior orthopaedic materials with enhanced thermal properties, consequently extending the lifespan of joint arthroplasty devices by addressing the challenges associated with pure UHMWPE.
Opsoclonus-myoclonus-ataxia syndrome due to Covid-19
Authors: Adamaszek Michael , Langner Soenke , Heinrich Alexander , Mehrholz Jan 
Published: July 2023

Abstract

Opsoclonus myoclonus syndrome (OMS) is a rare neurological encephalopathic entity associated with non-specific infections or cancer processes that has been occasionally described in the setting of SARS-CoV-2 infection. We report a case of a 53-year-old man with SARS-CoV-2 infection, who developed clinical features of opsoclonus-myoclonus syndrome. Of particular note, cerebrospinal fluid analysis (CSF) analysis revealed the production of myelin oligodendrocyte glycoprotein (MOG) antibodies, suggesting an underlying neuroimmunological mechanism associated with infection with the novel SARS-CoV-2 virus.
Correlation of Mental Health, Physical Activity, and Body Composition in American College Students After the COVID-19 Pandemic Lockdown
Authors: Caciula Manuela , Torres Luis , Tomoioaga Simion , Gugu-Gramadopol Carmen 
Published: July 2023

Abstract
Background: The restrictions associated with the COVID-19 pandemic forced American college students to significantly reduce their daily energy expenditure and increase sedentary behavior. This presumably led to increased mental health symptoms, decreased physical activity levels, and the promotion of unhealthy eating habits.
Methods: American college students completed pre-existing, well-validated surveys for both mental health (Hospital Anxiety and Depression Scale) and physical activity (International Physical Activity Questionnaire Long Form). Body composition was assessed in-person with the use of an Inbody 570 device.
Results: Of the 90 American college students (M age = 22.52 ± 4.54, 50 females) who participated in this study, depressive and anxious symptom scores consistent with 58% (N = 52) heightened symptomatology, 17% (N = 15) moderate borderline symptomatology, and 25% (N = 23) asymptomatology were reported. In regard to physical activity, 79% (N = 71) of the students were highly physically active, 18% (N = 16) were moderately active, and 3% (N = 3) reported low levels of physical activity. Additionally, 46% (N = 41) of the students maintained an unhealthy body fat percentage based on World Health Organization recommendations. Strong, significant relationships were found between anxiety and depression symptomatology and body fat percentage (P = .003) and skeletal muscle mass (P = .015), with said symptomatology increasing with added body fat and decreasing with added skeletal muscle mass.
Conclusions: Future health interventions for American college students should be focused on strategies to reduce stress, anxiety, and depressive characteristics, as well as nutritional information on healthy eating, regardless of self-reported physical activity levels.
Using E-learning environments in relation to cognitive styles for developing college students' English vocabulary learning and motivation
Authors: Mekheimer Mohamed , Mahdi Eman 
Published: July 2023

Abstract
This study aimed to identify the effects of e-learning environment of Google Classroom in relation to cognitive styles (independence/dependence) on developing college students' English vocabulary learning and instrumental motivation as well as deciding on the correlations between students' vocabulary learning, instrumental motivation, and cognitive styles. Participants in this study comprised 117 randomly selected EFL juniors, assigned to two experimental groups classified according to their cognitive styles (independence vs dependence). Instruments of the study included the English Vocabulary Retention and Retrieval Test (EVRRT), a cognitive styles inventory (GEFT) for identifying participants’ cognitive styles, and the Instrumental Motivation for Achievement Scale (IMAS). The study revealed that there were statistically significant differences between the mean scores of the experimental group with context-independent cognitive styles and the experimental group with context-dependent cognitive styles to the good of the former group on vocabulary retention on post-testing. In addition, statistically significant differences between the mean scores of the experimental group with context-independent cognitive styles and the experimental group with context-dependent cognitive styles to the good of the former group on post-testing of instrumental motivation. Overall, there was no correlation between vocabulary retention as a function of using the Google Classroom e-learning environment, and motivation mean responses on post-testing.
Analysis of the Arab Singer Shadia’s Lyrics
Authors: Khamis Mohamed A. , Gomaa Walid 
Published: July 2023

Abstract

In this work, we analyze the lyrics of one of the most famous and influential Arab artists in the twentieth century, namely, \<شادية> (Shadia). Lyrics analysis provides a deep insight into the artist's career evolution, her interactions with the surrounding environment including the social, political, and economic conditions. In order to perform such analysis we had to collect and compile the lyrics of Shadia accompanied with the necessary metadata into an organized and structured form. The data are preprocessed by removing stop words and doing some normalization operations over the songs prose. We did not perform any lemmatization or stemming as the original form of the tokens convey much more information than the source words.We performed a lexical analysis in order to study both the lexical density and diversity over the course of Shadia career life. We have as well studied the most significant words, idioms, and terms played in the songs using tools such as word clouds and more quantitative measures such as term frequency-inverse document frequency. We have divided the career life of Shadia into sub-decades of length \(5\) years and all analyses are done both in a yearly fashion and more coarsely over such sub-decades. We have found a strong correlation between our statistical analysis and the socio-political status in Egypt and the Arab world during that time.This is especially relevant knowing that Shadia is very much truly considered the daughter of the generation of the 1952 revolution in Egypt. The significance of Shadia and her lyrics stem essentially from being contemporaneous to radical changes in Egypt across all sectors including political (support of liberation movements across the world, and the conflict with Israel), and socio-economic (especially changing the social class structure in Egypt). We also investigated the potential effectiveness of PoS (Part of Speech) tagging in genre analysis and classification.
Discretely Observed Brownian Motion Goverened byTelegrapher's Process: Estimation and Applications to Finance
Authors: Eada Surya Teja , Pozdnyakov Vladimir , Yan Jun 
Published: July 2023

Abstract

Regime-switching models are commonly used in financial econometrics to capturechanges in market dynamics over time. However, classic Markov regime-switching models cannot handle irregularly spaced time series. To address this limitation,we propose a continuous-time regime-switching model with two states that can handle irregularly spaced time series. The model uses a Brownian motion with state-specific drift and volatility in each state, and a telegrapher's process with exponential holding times to characterize unobserved states. We develop inferences for model parameters using the hidden Markov model with discretely spaced time series, and facilitate exact likelihood evaluation with dynamicprogramming and occupation time results. Our simulation study validates the performance of the method, and in application to a collection of stock prices,we find that our model is competitive with the popular GARCH model.
Homogeneous multigrid method for hybridizable interior penalty method
Authors: Lu Peipei , Wang Juan 
Published: July 2023

Abstract

In this paper, we present a rigorous convergence analysis of a homogeneous multigrid method for the hybridizable interior penalty (IP-H) method. In particular,we use the injection operator developed in [1]. It is shown that when the penalization parameter is sufficiently large, our proposed multigrid method is optimal under the assumption of elliptic regularity. Numerical experiments are provided to confirm the theoretical results.
Trend constrained optimal predictor discretisation for insurance and banking using a network flow approach
Authors: Bodenstein Gerhardus J , Kruger Machiel F , Brydon Humphrey C , Blignaut Renette J 
Published: July 2023

Abstract
Discretising (or binning) continuous predictors forms part of the data preparation and exploration phase when building predictive models. Discretisation involves partitioning a predictor variable's support into a set of disjunct intervals separated by cut-points. Discretisation is a valuable tool in predictive modelling and preliminary variable selection methods. It eases the interpretability and assists researchers in gauging the relationship between a predictor and the response of interest while providing automatic protection against outliers and missing values. Some machine learning and data mining techniques work better with categorical or discretized continuous variables instead of raw quantitative values. In some instances, the discretisation process must consider business, operational, or best practice constraints concerning selected intervals and parameter trends across consecutive intervals of a predictor variable. Choosing cut-points while maintaining a specified relationship trend between the discretised predictor and the response variable while adhering to additional side constraints, is difficult. The aim is to provide a concise summary of a predictor variable with as little loss of predictive power as possible while satisfying side constraints. This optimal trade-off between predictive power and arity for a constrained solution space allows the discretisation problem to be formulated as a mathematical optimisation problem. This paper provides a flexible mixed-integer programming formulation for the constrained optimal supervised discretisation problem with automatic trend detection using a novel network flow approach. The flexibility of the proposed framework allows us to implement and compare different supervised discretisation predictive performance measures on a real-world dataset.
Multi-omics Analysis of the Prognostic and Biological role of Cuproptosis-Related Gene in Gastric Cancer
Authors: Zhang Ruopeng , Zhang Feiyang , Liu Zekun , Huang Yuqian , Liu Lei , Li Shatong , Li Yinghe , Zhao Baiwei , Chen Wanqi 
Published: July 2023

Abstract

Background
Gastric cancer (GC)remains the third leading cause of cancer-related death. Cuproptosis has a high correlation with cancer development and progression, while Cuproptosis-related genes (CRGs) are rarely reported in GC. The aim of this multi-omics study was to investigate the prognostic value and biological functions of CRGs in GC, which may help guide precision medicine-based decision-making in GC patients.
Methods
RNA sequencing (RNA-seq) data, Copy number variations (CNV) data, and Single nucleotide variant (SNV) data were obtained from The cancer genome atlas (TCGA) database. Chi-squared test was adopted to screen differentially expressed CRGs (DE-CRGs) between samples from 14 kinds of carcinoma and adjacent tissue samples. Then, gastric cancer (GC) samples were divided into high- and low-expressed groups based on DE-CRGs for further overall survival (OS), progression-free survival (PFS), disease-free survival (DFS), and disease-special survival (DSS) analysis. After single-gene Receiver operating characteristic (ROC) analysis, biomarkers of GC was obtained eventually. Besides, methylation sites related with biomarkers were acquired and survival analysis was performed based on those sites. Next, the correlation between immune cells and biomarkers was verified. Finally, we established miRNA-mRNA, TFs-mRNA, and co-expression networks to detect factors that have a regulating effect on biomarkers.
Results
Four DE-CRGs including CDKN2A, DLD, GLS, LIAS, and PDHB in most of the 14 cancers were screened out. Seven CRGs including GLS, LIAS, CDKN2A, DLD, LDAT, MTF1 and PDHA1 have a significant difference in the survival of GC patients. Next, single-gene ROC proved that PDHB, CDKN2A, LIAS, and GLS significantly correlate with GC prognosis. Three CRGs including LIAS, GLS, and CDKN2A were remain as biomarkers based on the results we got previously, and were used to generate a nomogram. After, 3 methylation sites with a significant survival relationship which include cg13601799, 07562918, and 07253264 were found. Then, we found that B cells native is significantly correlated with CDKN2A, 4 immune cells such as T cells regulatory (Tregs) are significantly correlated with GLS, and 2 immune cells such as T cells CD4 memory activated are significantly correlated with LIAS. Moreover, we found 10 miRNA in the miRNA-mRNA network and 3 TFs in the TFs-mRNA network have a significant correlation with OS. Finally, 20 enrichment functions were obtained such as cardiac septum development, collagen fibril organization, and sensory organ morphogenesis on the basis of the co-expression network.
Conclusions
3 biomarkers with a prognosis prediction value of GC were found, and multi-factor regulatory networks was constructed to screen out 13 factors with regulating influences of biomarkers.