bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effects of Breed, Diet and Sex on Expression of Growth-related Genes in Nigerian Goats
Authors: Mallam Iliya , Yakubu Abdulmojeed , Ari Maikano Mohammed , Musa Ibrahim Suleiman , Achi Neyu Patrick 
Published: December 2024

Abstract

This experiment was conducted to study the effects of breed, diet and sex on expression of growth-related genes in Nigerian goats. A total of 36 weaner goats of two breeds (West African dwarf, n=18; 9 males + 9 females) and Red Sokoto goats, n=18; 9 males + 9 females) of about 3-4 months of age were used for the experiment. A 2×3×2 factorial experiment comprising 2 breeds, 3 diets groups and 2 sexes in a Completely Randomized Design was used. The data for gene expression and their interaction effects were all analysed using Statistix Analytical software, file version 8.0. Results showed that breed, diet and breed × diet interaction had significant (P<0.05) effect on IGF-1 tissue expression. Sex had significant (P<0.05) effect on myostatin tissue expression. Breed × diet interaction had no significant (P>0.05) effect on MSTN expression in the tissue of goats. The results for breed × sex interaction had no significant (P>0.05) effect on the level of IGF-1 expression in the tissue of goats but had significant (P<0.05) effect on myostatin gene. Red Sokoto breed is recommended over West African dwarf breed while RS×50%DS+50%GH is recommended for IGF-1 expression.There is need for further research to fully understand the IGF-1 and MSTN gene expression levels in various tissues and organs of goats at different stages of growth.
Who freezes her eggs and why? Psychological predictors, reasons, and outcomes of social egg freezing
Authors: Schmid Julia Jeannine , Weber Seraina , Ehlert Ulrike 
Published: December 2024

Abstract

Background
Despite the growing use of social egg freezing (SEF), research focusing on its psychological aspects is lacking. This study aimed to investigate possible psychological predictors, reasons, and outcomes of SEF in German-speaking countries.
Methods
The cross-sectional study included 1,131 women (average age 31 years) who had never used medical egg freezing. The participants were at different stages of SEF decision-making: women who cannot imagine using SEF (SEF-non-use), women who can imagine using SEF (SEF-possible-use), women who plan to use SEF (SEF-planned-use), women who have used SEF (SEF-use), and women who have used their oocytes frozen during SEF for assisted reproduction (SEF + ART-use). Data on sociodemographic and psychological characteristics, attitudes towards motherhood, well-being, and reasons for SEF were assessed. We used multinomial logistic regression to identify predictors of SEF decision-making stages, principal components analysis to examine motives for SEF, and multiple linear regression to analyze associations between motives and psychological variables.
Results
The probability of belonging to the SEF-use group rather than SEF-non-use was higher among childless single women with tertiary education, high levels of employment, and high importance placed on the genetic relationship to the child, and rose with increasing age and importance of motherhood. The probability of belonging to the SEF-use group rather than SEF-planned-use was higher among childless women with a high importance placed on the genetic relationship to the child, and increased with age. The probability of belonging to the SEF + ART-use group rather than SEF-use depended mainly on the presence of infertility. The women froze eggs mainly to gain time to fulfill their desire for conventional parenthood (59%), including finding the right partner and enabling a genetic relationship to the child. Using SEF to actively shape one’s life and family planning was associated with positive psychological outcomes, whereas relying on SEF in the hope of personal and societal changes (e.g. improving fertility) was associated with negative outcomes.
Conclusion
SEF users can be characterized as mainly single, career-oriented, and greatly valuing genetic motherhood. As the motives for SEF, rather than its use per se, are linked to psychological variables, these should be considered when counseling and supporting women.
Long-Term Outcomes of Corneal Transplantation: A Review of 8,378 Patients
Authors: Hashemian Mohammad Nasser , Zia Mohammad Javad , Khorrami-Nejad Masoud , Abed Qaysser Sattar , Hashemian Hesam 
Published: December 2024

Abstract

Purpose
To analyze the outcomes of corneal transplantation procedures over a 13-year period at Farabi Eye Hospital, Tehran, Iran, to identify trends and determinants influencing the frequency and success of primary and re-transplantations.
Methods
Utilizing a comprehensive dataset from the hospital's Hospital Information System, the study reviewed the records of 8,378 patients who underwent corneal transplants between 2009 and 2022. This analysis included demographic information, surgical details, and follow-up data. Statistical methods were applied to assess the impact of variables such as age, gender, surgeon experience, and surgical techniques on the likelihood of re-transplantation.
Results
Of the 8,378 transplants, 7,660 (91.4%) were primary procedures while 718 (8.6%) involved re-transplantation. The most common primary transplant was penetrating keratoplasty (PKP, 50.3%), followed by Descemet's stripping endothelial keratoplasty (DSAEK, 29.3%), and deep anterior lamellar keratoplasty (DALK, 18.5%). Analysis revealed no significant association between re-transplantation rates and patient gender or nationality. However, older recipient age and procedures performed by more experienced attending surgeons correlated with higher re-transplantation incidence. Specific pre-transplant comorbidities (e.g., keratoconus, ulcers), concurrent surgeries (e.g., vitrectomy), and prior/interim procedures like glaucoma surgeries or intraocular lens implantation also increased re-transplantation risk significantly.
Conclusion
The study highlights the importance of patient age, surgeon experience, and the choice of surgical technique in the success rates of corneal transplants. These factors are crucial for optimizing patient outcomes and minimizing the necessity for re-transplantations.
Effects of herbage distribution, botanical composition, and rumen inoculum source on in vitro gas fermentation kinetics
Authors: Nampanzira Dorothy K , Kiggundu Muhamad , Esaete Josephine , Muwanika Vincent , Namatovu Azida , Mutebi Emmanuel , Tabuti John R S 
Published: December 2024

Abstract

The random sourcing of rumen liquor from abattoir-slaughtered cattle may influence in vitro gas production results due to variations in diet composition. This study evaluated the effects of herbage distribution, botanical composition, and rumen liquor source on rumen fermentation kinetics using the in vitro gas production technique. Three districts in Uganda's cattle corridor with different grazing systems and vegetation types were purposively selected: Mbarara (improved mixed pastures), Nakasongola (woody shrublands), and Napak (grasslands). Karamoja had the highest herbage diversity (n = 40), followed by Mbarara (n = 32) and Nakasongola (n = 19), with grasses comprising over 75% of samples from Nakasongola and Mbarara, and 53% from Karamoja. Karamoja herbage had significantly higher crude protein (132.6 g/Kg DM). Gas production varied significantly (P < 0.001) across herbage sources during incubation. Fermentation kinetics were influenced by both the rumen liquor and herbage source (P < 0.001), while gas production rate from the insoluble fraction (b) was only affected by herbage source (P < 0.001). In vitro organic matter digestibility (IVOMD) estimated metabolizable energy (ME), and short-chain fatty acids (SCFA) were also significantly influenced by herbage source (P < 0.001). Herbage composition had a greater impact on fermentation kinetics and IVOMD than the source of rumen liquor. These results suggest that rumen liquor from cattle raised under similar grazing systems yields consistent in vitro gas production results.
Applying Grid Search, Random Search, Bayesian Optimization, Genetic Algorithm, and Particle Swarm Optimization to fine- tune the hyperparameters of the ensemble of ML models enhances its predictive accuracy for mud loss
Authors: Malakouti Seyed Matin , Menhaj Mohammad Bagher , Suratgar Amir Abolfazl 
Published: December 2024

Abstract

Oil and gas wells frequently encounter the issue of drilling fluid loss circulation as drilling progresses, leading to significant complications and expenses. Effective prediction of mud loss during drilling is crucial for optimizing the selection of loss circulation materials (LCMs), improving drilling efficiency, and reducing costs and risks. This study evaluates an ensemble method comprising five machine learning algorithms: Decision Tree (DT), Linear Regressor (LR), K-Nearest Neighbors (KNN), Gradient Boosting Regressor (GBR), and Random Forest (RF). Hyperparameters for these algorithms were fine-tuned using genetic algorithm (GA), particle swarm optimization (PSO), Grid Search, Random Search, and Bayesian Optimization to achieve optimal performance (R² close to 1).The ensemble method (DT, LR, KNN, GBR) achieved the highest prediction accuracy with R² = 0.969 and RMSE = 15.89. The K-Nearest Neighbors algorithm showed the least performance with an R² of 0.519. Effective hyperparameter tuning using various optimization techniques significantly improved model performance. The purpose of this article is to apply Grid Search, Random Search, Bayesian Optimization, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) to fine-tune the hyperparameters of an ensemble of machine learning (ML) models, thereby enhancing their predictive accuracy for mud loss. These methods offer a lower computational volume compared to deep learning techniques and simultaneously provide high execution speed.
The Impact of Foreign Direct Investment on CO2 Emissions in Middle East and North African Countries in the Period 1990-2020: Using the Panel ARDL Model
Authors: Louail Bilal , Mebrek Nacer 
Published: December 2024

Abstract

To determine whether foreign direct investment (FDI) promotes environmental degradation or aids in the adoption of cleaner technology between 1990 and 2020, the article will examine the relationship between FDI and CO2 emissions in Middle Eastern and North African (MENA) nations. utilizing the Panel ARDL Model to investigate both immediate and long-term impacts. The analysis discovered a long-term positive correlation between GDP, CO2 emissions, population increase, power consumption, and FDI. Open trade lowers emissions. The GDP and power consumption were found to considerably increase emissions in the short-term coefficients; however, the Error Correction Term indicated a slow adjustment to equilibrium. The study is constrained by the availability of data, possible measurement mistakes, regional variations, and the consequences of short-term fluctuation. For sustainability, policymakers should support clean technology, control foreign direct investment in industries that pollute, and support trade liberalization. In MENA and East Asia, the study examines FDI's environmental effects in a novel way.
JEL Classification : F21 ; Q53 ; Q56 ; O13 ; C23.
MS-UNet: a lightweight Multi-Scale UNet for skin lesion segmentation
Authors: Yu Jiahao , Sun Gaofei , Han Ying , Cao Qiongfang , Huang Tianze 
Published: December 2024

Abstract

In recent years, transformer and convolutional neural networks (CNNs) have been widely employed in medical image segmentation due to their excellent feature extraction capabilities. However, the computational requirements imposed by the large number of model parameters have posed significant limitations on the practical applications of these models.In this paper, we propose a lightweight Multi-Scale UNet(MS-UNet), which significantly reduces the number of model parameters. Simultaneously, the model maintains high accuracy through the utilization of depth-wise separable convolutions and multi-scale fusion modules.Moreover, an improved pyramid convolution(PConv) is employed in encoders, while utilizing different strip convolutions in decoders to extract features of different scales.Experimental results demonstrate that MS-UNet achieves the performance of state-of-the-art (SOTA) methods with very low parameters and computation cost.Specifically, our MS-UNet has only 23.7K parameters and 78.3M GFLOPs on the ISIC2017 and ISIC2018 datasets, compared with EGE-UNet which is the first model with a parameter size within 50KB.
Correlation measures in metagenomic data: the blessing of dimensionality
Authors: Fuschi Alessandro , Merlotti Alessandra , Tran Thi Dong Binh , Nguyen Hoan , Weinstock George M. , Remondini Daniel 
Published: December 2024

Abstract

Microbiome analysis has revolutionised our understanding of various biological processes, spanning human health, epidemiology (including antimicrobial resistance and horizontal gene transfer), as well as environmental and agricultural studies. At the heart of microbiome analysis lies the characterization of microbial communities through the quantification of microbial taxa and their dynamics. In the study of bacterial abundances, it is becoming more relevant to consider their relationship, to embed these data in the framework of network theory, allowing characterization of features like node relevance, pathway and community structure. In this study, we address the primary biases encountered in reconstructing networks through correlation measures, particularly considering the compositional nature of the data, within-sample diversity, and the presence of a high number of unobserved species. These factors can lead to inaccurate correlation estimates. To tackle these challenges, we employ simulated data to demonstrate how many of these issues can be mitigated by applying typical transformations designed for compositional data. These transformations enable the use of straightforward measures like Pearson's correlation to correctly identify positive and negative relationships among relative abundances, especially in high-dimensional data, without having any need for further corrections. However, some challenges persist, such as addressing data sparsity, as neglecting this aspect can result in an underestimation of negative correlations.
Extraction efficacy, phytochemical profiling and radical scavenging potential of different parts of Ixora coccinea L.
Authors: Desai Nivas 
Published: December 2024

Abstract

The present work was aimed to evaluate extraction efficiency and quantification of phytochemicals, and antioxidant potential of different parts of Ixora coccinea L. Different extraction techniques like Continuous shaking extraction, steam bath assisted extraction, ultrasonic extraction and microwave assisted extraction were used for the extraction of phenolic compounds and antioxidants. Presence of wide range of plant metabolites were observed from the preliminary study. Ultra sound assisted extraction for 20 min was found most suitable for extraction phenolics (44.14 ± 2.25 mg of gallic acid equivalent per gram of dry weight) and flavonoids (23.95 ± 1.15 mg of quercetin equivalent per gram of dry weight). High performance liquid chromatography confirmed the presence of six pharmaceutically important phenolic acids. 1, 1- diphenyl-1-picryl hydrazyl (DPPH) assay, 2, 2′-azino-bis (3-ethylbenzothiazoline-6-sulphonic acid) scavenging (ABTS) assays were used to determine antioxidant potential. Leaves showed higher yields of illustrated biochemical parameters, however fresh fruit pulp was found a chief source of polyphenols. The current investigation highlighted the variety of phytoconstituents from different parts of I. coccinea and its medicinal and nutritional implications were confirmed.
Enhancing perinatal brain maturity estimation using surface deep learning and cross-modal relationship inference technology
Authors: Yang Ziyi , He Rongzhao , Sheng Yucen , Zhu Dalin , Wang Ying , Zhang Yu , Hu Bin , Zheng Weihao 
Published: December 2024

Abstract

Neonates with marked brain developmental delays are at increased risk of neurodevelopmental disorders. Brain chronological age is a valuable biomarker for assessing abnormal maturation in developing brains; however, accurately estimating brain age at birth remains challenging. In this study, we introduce a cross-modal relationship inference network (CMRINet) that integrates structural and diffusion magnetic resonance imaging data to improve the accuracy of neonatal brain age estimation. The CMRINet employs a Transformer encoder and relational inference module to capture both the long- and short-range dependencies of multimodal features among cortical parcels. Our model outperformed others in predicting neonatal brain age, achieving a mean squared error of 0.51 and a mean absolute error of 0.55 on the test set. By applying the model trained on full-term neonates to preterm infants at term-equivalent age, we found that the predicted age was significantly lower than the chronological age, suggesting delayed development in preterm brains. Furthermore, the deviation of predicted age was significantly associated with long-term motor development of preterm infants. These findings highlight the effectiveness of the CMRINet for neonatal brain age estimation, with potential clinical utility in early detection of neurodevelopmental risks during the perinatal period.
Prevalence, awareness, treatment-seeking behaviours and its implications in the control of malaria in Dumbu community, Donga-Mantung Division, North West Region, Cameroon
Authors: Eyong Ebanga Echi Joan , Calvin Ebai Bissong , Purity Awah , Antoine Kada Sanda 
Published: December 2024

Abstract

Malaria remains a great public health problem in Cameroon where it accounts for high rates of morbidity and mortality. The management of the disease has been made worst in the North West region of Cameroon and in Dumbu in particular by the on-going socio-political crisis since 2016, which has limited the transporation of drugs to this community and has also forced the inhabitants of this community to rely on traditional concoctions for treatment with the notion that it is cheap. The aim of this study was to determine the prevalence of malaria, assess the malaria awareness level, the treatment seeking behaviours, and its implications on the prevalence of malaria in the Dumbu community. Questionnaires were administered to consented individuals. Blood samples were collected by finger prick using sterile lancets and blood films prepared on well labeled glass slides. The dry blood films were stained using 3% Giemsa staining solution for 30 mins. Data was collected were later analyzed using SPSS. Out of the 385 persons screened, malaria was recorded in 107 persons (27.8%). Those in the age group 11–25 years old were the most infected with malaria prevalence of 32.3% (41/127) while those in the age group ≥ 50 years recorded the least prevalence [19.3% (11/57)] and the difference was not significant (χ2 = 3.716, p = 0.294). Yaounde quarter recorded the highest prevalence 32.7% (35/107) and males were more infected than females. On awareness level, they have heard of malaria and knew its causal agent to be an infected mosquito, 48.3% considered fever as the sign of the infection. Sixty-nine-point 8 percent (69.8%) of the population rushes to the health centre for treatment while 5.6% prefers herbal treatment. Malaria is still a health challenge in this area and people infected should be advised to seek treatment, whenever they have malaria from a health facility to ensure that the treatment given is appropriate.
Business Optimization of Quantitative Analytics for Trade-off between Cost and System Availability in Cloud Logistics
Authors: Chiang Yi-Ju , Lee Dung-Lun , Shih Cheng-Min , Lai Chun-Ming , Jiang Fuu-Cheng 
Published: December 2024

Abstract

Cost item is one of the main issues that will drive the design of logistics on cloud computing (CC), especially of those devoted to provide services in a long-term and steady manner. The high availability of cloud platform has emerged to an essential requirement for CC business. To keep the service level agreement committed to cloud customers, the CC platform should adopt necessary cloud logistic approach to backup breakdown servers. Aiming at searching for a feasible architecture with optimal running cost, two control parameters of standbys and repairmen in the cloud logistics has been considered to maintain regulated service quality for the cloud enterprise. Based on the queuing theory, quantitative exploration with MRP (Machine Repair Problem) model has been adopted and conducted for optimal methodology. The proposed cloud logistic framework has been designed in systematic profile. Comprehensive mathematical materials have been elaborated in details between cost pattern and system availability. Relevant numerical experiments have been conducted to verify the feasibility for trade-off between cost and availability as well. On engineering application, the proposed approach indeed provides a feasibly cost-effective decision-making support to adapt cloud-based logistics.
WITHDRAWN: Integrative transcriptomic and proteomic analysis reveals that SERPING1 inhibits neuronal proliferation via the CaMKII-CREB-BDNF pathway in schizophrenia
Authors: Li Xing , Ti Wang , Li Wang , Wei Biao 
Published: December 2024

Abstract

BACKGROUND
Schizophrenia (SZ), a chronic and widespread brain disorder, presents with complex etiology and pathogenesis that remain inadequately understood. Despite the absence of a universally recognized endophenotype, peripheral blood mononuclear cells (PBMCs) serve as a robust model for investigating intracellular alterations linked to SZ.
AIM
To preliminarily investigate potential pathogenic mechanisms and identify novel biomarkers for SZ.
METHODS
PBMCs from SZ patients were subjected to integrative transcriptomic and proteomic analyses to uncover differentially expressed genes (DEGs) and differentially expressed proteins while mapping putative disease-associated signaling pathways. Key findings were validated using western blot (WB) and real-time fluorescence quantitative PCR (RT-qPCR). RNAi-lentivirus was employed to transfect rat hippocampal CA1 neurons in vitro, with subsequent verification of target gene expression via RT-qPCR. The levels of neuronal conduction proteins, including caMKII, CREB, and BDNF, were assessed through WB. Apoptosis was quantified by flow cytometry, while cell proliferation and viability were evaluated using the Cell Counting Kit-8 assay.
RESULTS
The integration of transcriptomic and proteomic analyses identified 6079 co-expressed genes, among which 25 DEGs were significantly altered between the SZ group and healthy controls. Notably, HP, LTF, and SERPING1 exhibited marked upregulation. KEGG pathway enrichment analysis implicated neuroactive ligand-receptor interaction pathways in disease pathogenesis. Clinical sample validation demonstrated elevated protein and mRNA levels of HP, LTF, and SERPING1 in the SZ group compared to controls. WB analysis of all clinical samples further corroborated the significant upregulation of SERPING1. In hippocampal CA1 neurons transfected with lentivirus, reduced SERPING1 expression was accompanied by increased levels of CaMKII, CREB, and BDNF, enhanced cell viability, and reduced apoptosis.
CONCLUSION
SERPING1 may suppress neural cell proliferation in SZ patients via modulation of the CaMKII-CREB-BDNF signaling pathway.