bluuebunny/crossref_metadata_2025_split huggingface dataset:
A systems biology-based mathematical model demonstrates the potential anti-stress effectiveness of a multi-nutrient botanical formulation
Authors: Saxena Abha , Prabhudesai Kaushiki S. , Damle Aparna , Ramakrishnan Shyam , Durairaj Palaniyamma , Kalankariyan Sumathi , Vijayalakshmi A. B. , Venkatesh K. V. 
Published: April 2024

AbstractStress is an adaptive response to the stressors that adversely affects physiological and psychological health. Stress elicits HPA axis activation, resulting in cortisol release, ultimately contributing to oxidative, inflammatory, physiological and mental stress. Nutritional supplementations with antioxidant, anti-inflammatory, and stress-relieving properties are among widely preferred complementary approaches for the stress management. However, there is limited research on the potential combined impact of vitamins, minerals and natural ingredients on stress. In the present study, we have investigated the effect of a multi-nutrient botanical formulation, Nutrilite® Daily Plus, on clinical stress parameters. The stress-modulatory effects were quantified at population level using a customized sub-clinical inflammation mathematical model. The model suggested that combined intervention of botanical and micronutrients lead to significant decline in physical stress (75% decline), mental stress (70% decline), oxidative stress (55% decline) and inflammatory stress (75% decline) as evident from reduction in key stress parameters such as ROS, TNF-α, blood pressure, cortisol levels and PSS scores at both individual and population levels. Further, at the population level, the intervention relieved stress in 85% of individuals who moved towards a healthy state. The in silico studies strongly predicts the use of Gotukola based Nutrilite® Daily Plus as promising anti-stress formulation.
Evolutionarily conserved ovarian fluid proteins are responsible for extending egg viability in salmonid fish
Authors: Gueho Aurélie , Żarski Daniel , Rime Hélène , Guével Blandine , Com Emmanuelle , Lavigne Régis , Nguyen Thaovi , Montfort Jérôme , Pineau Charles , Bobe Julien 
Published: April 2024

AbstractIn contrast to most fishes, salmonids exhibit the unique ability to hold their eggs for several days after ovulation without significant loss of viability. During this period, eggs are held in the body cavity in a biological fluid, the coelomic fluid (CF) that is responsible for preserving egg viability. To identify CF proteins responsible for preserving egg viability, a proteomic comparison was performed using 3 salmonid species and 3 non-salmonid species to identify salmonid-specific highly abundant proteins. In parallel, rainbow trout CF fractions were purified and used in a biological test to estimate their egg viability preservation potential. The most biologically active CF fractions were then subjected to mass spectrometry analysis. We identified 50 proteins overabundant in salmonids and present in analytical fractions with high egg viability preservation potential. The identity of these proteins illuminates the biological processes participating in egg viability preservation. Among identified proteins of interest, the ovarian-specific expression and abundance in CF at ovulation of N-acetylneuraminic acid synthase a (Nansa) suggest a previously unsuspected role. We show that salmonid CF is a complex biological fluid containing a diversity of proteins related to immunity, calcium binding, lipid metabolism, proteolysis, extracellular matrix and sialic acid metabolic pathway that are collectively responsible for preserving egg viability.
Quantitative measurement and comparison of breakthroughs inside the gas diffusion layer using lattice Boltzmann method and computed tomography scan
Authors: Pourrahmani Hossein , Hosseini Milad , Moussaoui Hamza , Oveisi Emad , Siavashi Majid , Van Herle Jan 
Published: April 2024

AbstractIn Proton Exchange Membrane Fuel Cells (PEMFCs), the presence of residual water within the Gas Diffusion Layer (GDL) poses challenges during cold starts and accelerates degradation. A computational model based on the Lattice Boltzmann Method (LBM) was developed to consider the capillary pressure inside the PEMFC and to analyze the exact geometries of the GDLs, which were obtained using the Computed Tomography scan. The novelty of this study is to suggest a methodology to compare the quantitative water removal performance of the GDLs without long-term experimental testing. Two different samples of GDLs were considered, pristine and aged. The results of quantitative measurements revealed the amount of water columns (breakthroughs) inside each sample. Considering the volume of 12,250,000 µm3 for each sample, the pristine and the aged samples are prone to have 774,200 µm3 (6.32%) and 1,239,700 µm3 (10.12%) as water columns in their porous domain. Micro-structural properties such as connectivity, mean diameter, effective diffusivity, etc. were also compared to observe the impacts of aging on the properties of the GDL.
Palaeogenomic insights into the origins of early settlers on the island of Cyprus
Authors: Heraclides Alexandros , Aristodemou Aris , Georgiou Andrea N. , Antoniou Marios , Ilgner Elisabeth , Davranoglou Leonidas-Romanos 
Published: April 2024

AbstractArchaeological evidence supports sporadic seafaring visits to the Eastern Mediterranean island of Cyprus by Epipaleolithic hunter-gatherers over 12,000 years ago, followed by permanent settlements during the early Neolithic. The geographical origins of these early seafarers have so far remained elusive. By systematically analysing all available genomes from the late Pleistocene to early Holocene Near East (c. 14,000–7000 cal BCE), we provide a comprehensive overview of the genetic landscape of the early Neolithic Fertile Crescent and Anatolia and infer the likely origins of three recently published genomes from Kissonerga-Mylouthkia (Cypriot Late Pre-Pottery Neolithic B, c. 7600–6800 cal BCE). These appear to derive roughly 80% of their ancestry from Aceramic Neolithic Central Anatolians residing in or near the Konya plain, and the remainder from a genetically basal Levantine population. Based on genome-wide weighted ancestry covariance analysis, we infer that this admixture event took place roughly between 14,000 and 10,000 BCE, coinciding with the transition from the Cypriot late Epipaleolithic to the Pre-Pottery Neolithic A (PPNA). Additionally, we identify strong genetic affinities between the examined Cypro-LPPNB individuals and later northwestern Anatolians and the earliest European Neolithic farmers. Our results inform archaeological evidence on prehistoric demographic processes in the Eastern Mediterranean, providing important insights into early seafaring, maritime connections, and insular settlement.
A GAN-BO-XGBoost model for high-quality patents identification
Authors: Wu Zengyuan , Zhao Jiali , Li Ying , Wang Zelin , He Bin , Chen Liang 
Published: April 2024

AbstractThe number of patents increases quickly, while more and more low-quality patents are emerging. It’s important to identify high-quality patents from massive data quickly and accurately for organizational R&D decision-making and patent layout. However, due to low percentage of high-quality patents, it is challenging to identify them efficiently. In order to solve above problem, we reconstruct the existing index system for identifying high-quality patents by adding 4 features from technological strength of patentees. Furthermore, we propose an improved model by integrating resampling technique and ensemble learning algorithm. First, generative adversarial networks (GAN) are used to expand minority samples. Second, Extreme Gradient Boosting algorithm (XGBoost) with Bayesian optimization (BO) is used to identify high-quality patents. For clarity, this model is called a GAN-BO-XGBoost model. To test the effectiveness of above model, we use patent data in field of lithography technology. Tenfold cross-validation is carried out to evaluate the performance between our proposed model and other models. The results show that GAN-BO-XGBoost model performs better and it’s more stable than other models.
Passing path predicts shooting outcome in football
Authors: Cao Shun 
Published: April 2024

AbstractWhat determines the outcome of a shot (scored or unscored) in football (soccer)? Numerous studies have investigated various aspects of this question, including the skills and physical/mental state of the shooter or goalkeeper, the positional information of shots, as well as the attacking styles and defensive formations of the opposing team. However, a critical question has received limited attention: How does the passing path affect the outcome of a shot? In other words, does the path of the ball before shooting significantly influence the result when the same player takes two shots from the same location? This study aims to fill the gap in the literature by conducting qualitative studies using a dataset comprising 34,938 shots, along with corresponding passing paths from top-tier football leagues and international competitions such as the World Cup. Eighteen path features were extracted and applied to three different machine-learning models. The results indicate that the passing path, whether with or without the positional information of shots, can indeed predict shooting outcomes and reveal influential path features. Moreover, it suggests that taking quick actions to move the ball across areas with a high probability of scoring a goal can significantly increases the chance of a successful shot. Interestingly, certain path features that are commonly considered important for team performance, such as the distribution of passes among players and the overall path length, were found to be less significant for shooting outcomes. These findings enhance our understanding of the effective ball-passing and provide valuable insights into the critical factors for achieving successful shots in football games.
A SARS-CoV-2 outbreak investigation at a storage and distribution centre in England: an assessment of worker- and workplace-related risk factors
Authors: Raja Amber I. , Nicholls Gillian , Coldwell Matthew , van Veldhoven Karin , Sandys Vince , Atkinson Barry , Nicholls Ian , Spencer Antony , Graham Alice , Higgins Hannah , Atchison Christina , Keen Chris , Fletcher Tony , Pearce Neil , Brickley Elizabeth B. , Chen Yiqun 
Published: April 2024

AbstractAn outbreak of SARS-CoV-2 (1 March to 10 May 2021) with an attack rate of 26.5% among approximately 1150 workers at a storage and distribution centre in England prompted a multidisciplinary outbreak investigation (5 May to 6 August 2021), with the aim of better understanding worker- and workplace-related risk factors for viral transmission in the warehousing sector. Overall, environmental factors (e.g., ventilation, humidity and temperature) were assessed to be appropriate at the facility. Nevertheless, 39 (51.3%) surface samples from across the site tested positive for low/ very low levels of SARS-CoV-2 RNA (Ct value ≥ 32.0 for all). Among the study participants, of whom 35.6% were confirmed or suspected cases, 95.5% reported having received COVID-19 prevention training, 100.0% reported handwashing, and 80.0% reported use of face coverings at work. Notably, 43.9% and 19.0% reported working with a symptomatic and a positive contact respectively. Furthermore, 80.5% and 46.3% had concerns regarding reduction in their income and future unemployment, respectively, due to self-isolation. The findings of this study suggest that, in addition to targeted workplace infection control measures and tailored work area specific risk assessments, an enhanced and equitable sick leave policy may help limit presenteeism and viral transmission in large workplaces.
Phyco-synthesis of silver nanoparticles by environmentally safe approach and their applications
Authors: Choudhary Sunita , Kumawat Geetanjali , Khandelwal Manisha , Khangarot Rama Kanwar , Saharan Vinod , Nigam Subhasha , Harish  
Published: April 2024

AbstractIn recent years, there has been an increasing interest in the green synthesis of metallic nanoparticles, mostly because of the evident limitations associated with chemical and physical methods. Green synthesis, commonly referred to as "biogenic synthesis," is seen as an alternative approach to produce AgNPs (silver nanoparticles). The current work focuses on the use of Asterarcys sp. (microalga) for biological reduction of AgNO3 to produce AgNPs. The optimal parameters for the reduction of AgNPs were determined as molarity of 3 mM for AgNO3 and an incubation duration of 24 h at pH 9, using a 20:80 ratio of algal extract to AgNO3. The biosynthesized Ast-AgNPs were characterised using ultraviolet–visible spectroscopy (UV–Vis), zeta potential, scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), energy-dispersive X-ray spectroscopy (EDX), and high-resolution transmission electron microscopy (HR-TEM) with selected area electron diffraction (SAED) patterns. The nanoparticles exhibited their highest absorption in the UV–visible spectra at 425 nm. The X-ray diffraction (XRD) investigation indicated the presence of characteristic peaks at certain angles: 38.30° (1 1 1), 44.40° (2 0 0), 64.64° (2 2 0), and 77.59° (3 1 1) according to the JCPDS file No. 04-0783. Based on SEM and TEM, the Ast-AgNPs had an average size of 35 nm and 52 nm, respectively. The zeta potential was determined to be − 20.8 mV, indicating their stability. The highest antibacterial effectiveness is shown against Staphylococcus aureus, with a zone of inhibition of 25.66 ± 1.52 mm at 250 μL/mL conc. of Ast-AgNPs. Likewise, Ast-AgNPs significantly suppressed the growth of Fusarium sp. and Curvularia sp. by 78.22% and 85.05%, respectively, at 150 μL/mL conc. of Ast-AgNPs. In addition, the Ast-AgNPs exhibited significant photocatalytic activity in degrading methylene blue (MB), achieving an 88.59% degradation in 120 min, revealing multiple downstream applications of Ast-AgNPs.
A new prediction nomogram of non-sentinel lymph node metastasis in cT1-2 breast cancer patients with positive sentinel lymph nodes
Authors: Yang Liu , Zhao Xueyi , Yang Lixian , Chang Yan , Cao Congbo , Li Xiaolong , Wang Quanle , Song Zhenchuan 
Published: April 2024

AbstractWe aimed to analyze the risk factors and construct a new nomogram to predict non-sentinel lymph node (NSLN) metastasis for cT1-2 breast cancer patients with positivity after sentinel lymph node biopsy (SLNB). A total of 830 breast cancer patients who underwent surgery between 2016 and 2021 at multi-center were included in the retrospective analysis. Patients were divided into training (n = 410), internal validation (n = 298), and external validation cohorts (n = 122) based on periods and centers. A nomogram-based prediction model for the risk of NSLN metastasis was constructed by incorporating independent predictors of NSLN metastasis identified through univariate and multivariate logistic regression analyses in the training cohort and then validated by validation cohorts. The multivariate logistic regression analysis revealed that the number of positive sentinel lymph nodes (SLNs) (P < 0.001), the proportion of positive SLNs (P = 0.029), lymph-vascular invasion (P = 0.029), perineural invasion (P = 0.023), and estrogen receptor (ER) status (P = 0.034) were independent risk factors for NSLN metastasis. The area under the receiver operating characteristics curve (AUC) value of this model was 0.730 (95% CI 0.676–0.785) for the training, 0.701 (95% CI 0.630–0.773) for internal validation, and 0.813 (95% CI 0.734–0.891) for external validation cohorts. Decision curve analysis also showed that the model could be effectively applied in clinical practice. The proposed nomogram estimated the likelihood of positive NSLNs and assisted the surgeon in deciding whether to perform further axillary lymph node dissection (ALND) and avoid non-essential ALND as well as postoperative complications.
The southernmost Errina antarctica hydrocoral savannah in Patagonian waters
Authors: De la Torriente Ana , Espinoza-León Ingrid M. , Valenzuela-Lobos Lorena A. , Antolinez Ana , Serrano Alberto 
Published: April 2024

AbstractMarine animal forest (MAF) are animal-dominated megabenthic communities that support high biodiversity levels and play key roles in ecosystem functioning. However, there is limited data available in Patagonian waters related to the presence of these vulnerable benthic communities. We report a monospecific MAF of Errina antartica in Angostura Tomms, which represents the southernmost known living MAF of this species. With coverages reaching up to 28.5% of the substrate from 1.23 m to, at least, 33 m depth is the shallowest stylasterid assemblage described worldwide to date. The size of the colonies ranged from 0.14 to 15.8 cm, with small colonies (< 10 cm) being the most abundant (99%). We hypothesize that this MAF might correspond to a recent colonization of a space, extending its distribution range towards shallower areas or it could be an assemblage formed at the limit of the species’ distribution in which the environmental conditions are not optimal for the major development of the colonies. Additionally, results showed that habitats structured by three-dimensional sessile invertebrate such as E. antarctica showed higher values of species richness and alpha diversity than non-biogenic habitats. Analyses were based on 297 photos taken at 22 different sites in the western Strait of Magellan, along vertical transects from 5 to 25 m depth. Our study highlights the importance of the benthic communities existing in Patagonian waters, evidencing the need to act actively to ensure their maintenance.
A multimodal approach to cross-lingual sentiment analysis with ensemble of transformer and LLM
Authors: Miah Md Saef Ullah , Kabir Md Mohsin , Sarwar Talha Bin , Safran Mejdl , Alfarhood Sultan , Mridha M. F. 
Published: April 2024

AbstractSentiment analysis is an essential task in natural language processing that involves identifying a text’s polarity, whether it expresses positive, negative, or neutral sentiments. With the growth of social media and the Internet, sentiment analysis has become increasingly important in various fields, such as marketing, politics, and customer service. However, sentiment analysis becomes challenging when dealing with foreign languages, particularly without labelled data for training models. In this study, we propose an ensemble model of transformers and a large language model (LLM) that leverages sentiment analysis of foreign languages by translating them into a base language, English. We used four languages, Arabic, Chinese, French, and Italian, and translated them using two neural machine translation models: LibreTranslate and Google Translate. Sentences were then analyzed for sentiment using an ensemble of pre-trained sentiment analysis models: Twitter-Roberta-Base-Sentiment-Latest, bert-base-multilingual-uncased-sentiment, and GPT-3, which is an LLM from OpenAI. Our experimental results showed that the accuracy of sentiment analysis on translated sentences was over 86% using the proposed model, indicating that foreign language sentiment analysis is possible through translation to English, and the proposed ensemble model works better than the independent pre-trained models and LLM.
Variable characteristics overlooked in human K-562 leukemia cell lines with a common signature
Authors: Kasai Fumio , Mizukoshi Kumiko , Nakamura Yukio 
Published: April 2024

AbstractK-562 is a well-known in vitro cellular model that represents human leukemia cell lines. Although the K-562 cells have been extensively characterized, there are inconsistencies in the data across publications, showing the presence of multiple K-562 cell lines. This suggests that analyzing a single K-562 cell line is insufficient to provide reliable reference data. In this study, we compared three K-562 cell lines with different IDs (RCB0027, RCB1635, and RCB1897) to investigate the fundamental characteristics of K-562 cells. Amplifications of the BCR-ABL1 fusion gene and at 13q31 were detected in all three cell lines, whereas each genome exhibited distinctive features of sequence variants and loss of heterozygosity. This implies that each K-562 cell line can be characterized by common and unique features through a comparison of multiple K-562 cell lines. Variations in transcriptome profiles and hemoglobin synthesis were also observed among the three cell lines, indicating that they should be considered sublines that have diverged from the common ancestral K-562 despite no changes from the original cell name. This leads to unintentional differences in genotypes and/or phenotypes among cell lines that share the same name. These data show that characterizing a single K-562 cell line does not necessarily provide data that are applicable to other K-562 cells. In this context, it is essential to modify cell names in accordance with changes in characteristics during cell culture. Furthermore, our data could serve as a reference for evaluating other K-562 sublines, facilitating the discovery of new K-562 sublines with distinct characteristics. This approach results in the accumulation of K-562 sublines with diverged characteristics and expands the options available, which may help in selecting the most suitable K-562 subline for each experiment.
Increasing the efficiency of cone-beam CT based delta-radiomics using automated contours to predict radiotherapy-related toxicities in prostate cancer
Authors: Delgadillo Rodrigo , Deana Anthony M. , Ford John C. , Studenski Matthew T. , Padgett Kyle R. , Abramowitz Matthew C. , Pra Alan Dal , Spieler Benjamin O. , Dogan Nesrin 
Published: April 2024

AbstractExtracting longitudinal image quantitative data, known as delta-radiomics, has the potential to capture changes in a patient’s anatomy throughout the course of radiation treatment for prostate cancer. Some of the major challenges of delta-radiomics studies are contouring the structures for individual fractions and accruing patients’ data in an efficient manner. The manual contouring process is often time consuming and would limit the efficiency of accruing larger sample sizes for future studies. The problem is amplified because the contours are often made by highly trained radiation oncologists with limited time to dedicate to research studies of this nature. This work compares the use of automated prostate contours generated using a deformable image-based algorithm to make predictive models of genitourinary and changes in total international prostate symptom score in comparison to manually contours for a cohort of fifty patients. Area under the curve of manual and automated models were compared using the Delong test. This study demonstrated that the delta-radiomics models were similar for both automated and manual delta-radiomics models.