bluuebunny/crossref_metadata_2025_split huggingface dataset:
Exploring Prospects of Cotton Cultivation under Rainfed Conditions of Pothwar
Authors: Khan Haider Hayat , Mujtaba Ghulam , Khan Nasir Mehmood , Khan Nanak , Kashif Muhammad , Ashraf Hafiz Muhammad Aftab , Khan Muhammad Amaid 
Published: November 2024

Abstract

Cotton production has been drastically affected by the climate change through its effect on agronomic practices such as sowing time and adoption of genotypes to various agro-ecological conditions. Interaction of genotypes and sowing date is an important strategy to analyze cotton yield and quality in a rainfed environment. Various morphological, and yield parameters including plant height, boll weight, crop growth rate, lint yield, seed cotton yield, ginning out turn (GOT), fiber fineness, fiber length and fiber strength was recorded. Results of the study revealed that N-878 performed best on an average during all the growing seasons followed by IUB-2013, SS-32 and BS-15 respectively. The best yield and quality were achieved when the cotton cultivars were sown on 22nd April followed by 8th April and 6th June respectively. This study will be helpful to look into performance of genotypes and possibilities of cotton cultivation under rainfed conditions of Pothwar. Also, sowing window of studied cotton genotypes will be explored for cotton cultivation under rainfed conditions of Pothwar. In addition, the present piece of work will provide future direction for research on cotton in the Pothwar region.
Gut microbiota dysbiosis promotes coronary heart disease comorbid with depression through lipopolysaccharides and TLR4 receptors
Authors: Pan Yangyu , Luo Yanna , Wu Ge , Lu Yaxi , Yang Ping , Kong Pengyun , Zheng Chuncheng , Wang Chengxiang , Yang Liqiang , Li Xiaohong 
Published: November 2024

Abstract

Background: Coronary heart disease (CHD) and depression often coexist, complicating patient care due to their intertwined pathophysiology. Gut microbiota plays a crucial role in overall health and is involved in both conditions. Dysbiosis, especially increased levels of lipopolysaccharides (LPS), can activate the TLR4 receptor, triggering inflammatory pathways linked to CHD and depression. Although some associations have been observed, the direct mechanistic link between gut dysbiosis, LPS, TLR4 activation, and the comorbidity of CHD with depression remains unclear. This study aims to explore this relationship and the potential of gut microbiota modulation as a therapeutic strategy.
Methods: A rat model of CHD and depression was established using a high-fat diet and chronic unpredictable mild stress (CUMS), verified by electrocardiogram (ECG), behavioral assessments, and cardiac markers. Fecal microbiota transplantation (FMT) was performed, transferring microbiota from diseased rats to healthy rats (FMT-Disease group), compared to an FMT-Normal group. To explore inflammatory pathways, the TLR4 inhibitor TAK-242 was administered, creating Disease+TAK-242 and FMT-Disease-TAK-242 groups. Blood LPS levels, TLR4/MYD88/NF-κB marker expression in cardiac and hippocampal tissues, and health indicators were evaluated using 16S rRNA sequencing, ELISA, RT-qPCR, and Western blot.
Results: Diseased rats showed significantly reduced gut microbiota α- and β-diversity, indicating dysbiosis. FMT from diseased rats increased LPS levels, activated the TLR4/MYD88/NF-κB pathway, and heightened susceptibility to CHD and depression in healthy rats. TAK-242 lowered LPS levels and reduced inflammatory markers, confirming the role of the TLR4 pathway in disease progression. The Disease+Normal FMT group demonstrated improved gut microbiota composition, reduced myocardial damage, and alleviated depressive behaviors, highlighting the therapeutic potential of healthy microbiota transplantation.
Conclusion: This study establishes a link between gut dysbiosis and the comorbidity of CHD with depression. Dysbiosis, marked by elevated LPS, activates the TLR4/MYD88/NF-κB pathway, increasing susceptibility to both conditions. FMT from healthy rats alleviated symptoms, suggesting that gut microbiota modulation could be a promising therapeutic strategy for these comorbidities.
Regulation of Autologous Stem Cell Therapies: Report on Canadian Unsanctioned Businesses
Authors: Ni Anjie , Joly Yann 
Published: November 2024

Abstract

Advances in the field of regenerative medicine have allowed for the rapid development of stem cell-based therapies in the hope of treating numerous debilitating diseases. Most of these therapies are not fully ready to be administered to patients, but hundreds of businesses around the world exploit the excitement around stem cells’ therapeutic potential to sell unproven stem cell products to the general public. In 2019, Health Canada issued a Policy position paper on autologous cell therapy products, classifying stem cell-based therapies as drugs under the Food and Drug Regulations. However, five years later, there still remain several predatory autologous stem cell businesses offering services in Canada. The health of Canadians is put at risk with unproven therapies that can create extremely serious adverse effects for patients. A thorough review of the autologous stem cell businesses currently in activity in Canada can provide patient advocates, health professionals, scientists, and policymakers at the federal and provincial level the evidence needed for coordinated action. This communication will present the results of a comprehensive study where we assessed the effects of Health Canada’s 2019 legal revision by examining potential changes for 30 Canadian businesses identified by Turner (2018) and by identifying new businesses operating similarly. Our findings indicate that legal enforcement has been insufficient to curb businesses’ marketing of unproven stem cell therapies and reveal an alarmingly vast marketplace for platelet-rich plasma, another type of unproven regenerative medicine-based therapy, in Canada. The findings of this study are crucial to identifying proactive measures to ensure that the law and its enforcement respond more effectively to the existing threat level represented by faulty regenerative medicine-based therapies.
Multi-Modal Deep Learning Architecture for Improved Colposcopy Image Classification
Authors: Chatterjee Priyadarshini , Siddiqui Shadab 
Published: November 2024

Abstract

Colposcopy image classification is vital for early cervical cancer detection, yet it remains challenging due to the significant variation in lesion appearances. Although deep learning models have advanced medical image classification, few studies have explored combining different model architectures to enhance diagnostic accuracy in colposcopy. This study addresses this gap by proposing a lesion-specific, multi-branch architecture that integrates attention mechanisms, deep feature extraction, and ensemble learning. Multi-task learning is employed to manage multiple lesion-specific classification tasks, while an ensemble of classifiers—Logistic Regression, XGBoost, and CatBoost—enhances decision-making accuracy. The architecture includes deep learning branches using EfficientNetB0 and MobileNetV2 for rich feature extraction from colposcopy images, with their outputs combined through a soft voting ensemble. Hyperparameter tuning, k-fold cross-validation, PCA visualization, and AUC plots for multiclass performance were used to optimize and assess model effectiveness. Training and validation accuracy were tracked in two phases: after the training phase, training accuracy reached 97.85% and validation accuracy was 97.33%; after the final ensemble classification, training accuracy improved to 99.95% and validation accuracy to 99.85%, surpassing individual model performance and demonstrating enhanced generalization. This model shows substantial promise for improving colposcopy classification accuracy, providing a valuable tool for clinical decision support in cervical cancer diagnosis.
Enhancing COVID-19 Forecasts Through Multivariate Deep Learning Models
Authors: Oh Jooha , Liu Zhe , Han Kyulhee , Goo Taewan , Song Hanbyul , Park Jiwon , Park Taesung 
Published: November 2024

Abstract

Background
It is well known that deep learning (DL) models often struggle with low prediction performance due to data scarcity. This scarcity hampers the effectiveness of DL methods that typically require large datasets to generate reliable forecasts. Recently, several DL models have been proposed for predicting the spread of COVID-19. These models are country specific models and thus use the COVID-19 data only from the target country. To improve COVID-19 forecasting using DL models, we propose multivariate DL models using the additional data available from other countries.
Methods
Based on the rankings determined by Dynamic Time Warping (DTW) distance, which calculates the similarity of infection trends across countries, univariate DL models using only the target country data were extended to multivariate models which integrated data from the top-ranked countries to optimize performance. We considered seven DL models including the Transformer model, TCN, CNN-LSTM, BiLSTM, GRU, RNN, and LSTM.
Results
Our results showed that the multivariate transformer model achieved the most significant improvements in forecasting accuracy, with an average reduction of 60.15% in mean root mean square error (RMSE) across the five target countries and five time periods when integrating data from additional countries, compared to univariate models using only the target country data. Additionally, multivariate transformer models consistently demonstrated significant improvements over univariate models in terms of mean RMSE, as evidenced by the Diebold-Mariano test. Other multivariate DL models also showed performance gains, with the TCN model achieving an average reduction in RMSE of 36.28%, followed by CNN-LSTM at 29.47%, BiLSTM at 21.07%, GRU at 21.43%, RNN at 17.46%, and LSTM at 16.38%.
Conclusions
These findings indicate that leveraging similar infection patterns from data of other countries can provide valuable information for predicting the COVID-19 spread in the target country, especially when data is scarce, thereby enhancing forecasting performance.
Assessing acrimony and coparenting in dissolved relationships: validation of the BACS-8 and the MCS-DR in a Spanish sample
Authors: Ordóñez-Carabaño Ángela , Martínez-Díaz Pilar , Caperos José Manuel , Charro-Baena Belén 
Published: November 2024

Abstract

Background: The aim of this study was to validate two instruments, the Brief Acrimony Scale-8 (BACS-8) and the Multidimensional Co-Parenting Scale for Dissolved Relationships (MCS-DR), in a Spanish sample of divorced parents. These instruments were designed to assess acrimony and coparenting dynamics, including overt and covert conflict, in postdivorce relationships.
Methods: The sample consisted of 237 divorced parents (142 women, 95 men), with an average age of 49.1 years. The participants completed the BACS-8, the MCS-DR, the CARE questionnaire and other validation criteria. To assess the internal structure of the scales, confirmatory factor analyses (CFAs) were performed. To evaluate convergent and discriminant validity, Pearson correlations were employed.
Results: The CFA results indicated that the model fit was adequate to good for both the BACS-8 and the MCS-DR. A strong correlation was identified between acrimony and coparenting conflict, particularly overt conflict. Furthermore, a significant relationship was observed between covert conflict and acrimony, although to a lesser extent. The provision of coparenting support was found to be negatively associated with the occurrence of acrimony and conflict and positively related to the quality of postdivorce relationships.
Conclusions: The BACS-8 and MCS-DR are reliable and valid instruments for the assessment of acrimony and coparenting dynamics in Spanish divorced families.
This study emphasises the pivotal role of overt and covert conflict in the development of postdivorce coparenting relationships. Furthermore, this highlights the necessity for targeted interventions aimed at reducing conflict and enhancing coparenting support.
Impact of tree density in silvopastoral systems on thermal environment and productivity of beef cattle grazing native grasslands during summer
Authors: Llanos Pablo N. , Gonzalez C. M. , Fedrigo Jean K. , Benitez Valentina , Viñoles Carolina 
Published: November 2024

Abstract

Silvopastoral systems (SPS) can create a more comfortable thermal environment and improve animal productivity, an effect that is probably related to tree density. To study the impact of tree density on the productivity of beef cows grazing native grasslands during the summer, 29 cows (Brangus and their crossbreeds with British breeds) and their calves were randomly assigned to 3 treatments: 1) Full sun (0 trees/ha), 2) Low density (160 trees/ha), 3) High density (240 trees/ha), replicated in 3 blocks (9 plots). Grazing intensity was managed through sward height at 7–10 cm in all treatments. During the hottest hours of the day, BGTHI was lower under the trees in both SPS (86 ± 0.4) compared to the alley (88 ± 0.4) and full sun (88 ± 0.4), but lowest in the alley of high (75 ± 1.4) than low (84 ± 1.4) tree density SPS at 10:00 (P < 0.05). Photosynthetically active radiation (PAR) transmission was lower under the trees (86%) than in the alley of the high tree density (96%) and full sun (100%; P < 0.05). However, forage allowance (4–5 kgDM/kg live weight), its chemical composition (crude protein: 7–8%; neutral detergent fibre: 65–66%; acid detergent fibre: 34–35%) and cow productivity (35–127 kg calf weaned/cow mated) were similar between treatments (P > 0.05). We conclude that cows grazing native grasslands with 0, 160 and 240 trees/ha, two years after planting, had similar productivity, associated to mild changes in thermal comfort and PAR transmission, that had no effect of the quality and quality of forage offered to the animals.
Bronze Age frontiers and pottery circulation. Political and economic relations at the northern fringes of El Argar, Southeast Iberia, ca. 2200-1550 BCE
Authors: Gil Adrià Moreno , García Carla Garrido , Soriano Bárbara Bonora , Gómez-Gras David , Risch Roberto 
Published: November 2024

Abstract

This paper explores the nature and dynamics of economic and political borders emerging in Later Prehistory between highly centralised and exploitative societies and their much more dispersed and small scale neighbours. While increasing evidence indicates that Early Bronze Age entities such as El Argar, Únětice, or Minoan Crete reached highly complex economic and political forms around 1850–1750 BCE, the processes by which their relations and borders with adjacent, less hierarchical groups were established and maintained still remain poorly understood. To identify such economic and political borders and asymmetric interactions in archaeology a specific methodological approach was developed which combined extensive field survey, pottery petrography, and spatial modelling of pottery production and circulation areas. We focused on the middle and upper Segura river valley, where, according to previous research, the border of one of the most complex entities of Bronze Age Europe, El Argar, was expected to have evolved between c. 2000 − 1550 BCE. Still today, this region is an archaeologically largely unexplored borderland region between markedly different geographic units of the Iberian peninsula. While El Argar expanded over semi arid Southeast, archaeological research in La Mancha and the Spanish Levant, adjacent to the middle and upper Segura river valley, has identified much smaller scale socio-economic entities, known, respectively, as La Mancha or Las Motillas and the Valencian Bronze Age cultures. At the junction between these three entities, over 4800 km² including 61 settlements were surveyed and their pottery sampled. This allowed to carry out the largest petrographic analysis of Bronze Pottery in the Iberian Bronze Age, including 1643 pottery sherds. Spatial modelling of the petrographic results was developed to trace the production and circulation of pottery and raw materials, offering insights into economic exchanges, social boundaries, and the long-term articulation of borderland spaces. By identifying distinct pottery-making practices and mapping their distribution, we reveal the nature of interactions between El Argar’s core regions and its neighbouring La Mancha and Valencian Bronze Age communities. This study serves to highlight the potential of ceramic production and circulation as indicators of border dynamics. Similar studies in other regions are expected to lead to a better understanding of how borders shaped Bronze Age societies and contributed to broader patterns of regional organisation and inter-group relations.
Understanding Host-Pathogen Interaction: Malassezia spp. infecting keratinocytes in a lipid-rich media
Authors: Rios-Navarro Andrea , Mora-Restrepo Felipe , Gonzalez John M. , Santos-Barbosa Juan Carlos , Cristancho Natalia Inés Bolaños , Ramirez Adriana Marcela Celis 
Published: November 2024

Abstract

The epidermis is the first protective barrier against pathogenic agents. It hosts a complex of beneficial microorganisms for the skin, such as lipid-dependent yeasts of the Malassezia genus. In the epidermis, this yeast interacts with keratinocytes while follicles that contain the sebaceous glands with a variety of lipids such as squalene, triglycerides, free fatty acids, and others, turning the skin into a suitable place for these yeasts to establish their ecological niche. Little is known about the mechanisms by which Malassezia overcomes the skin natural barrier. This study assessed the addition of representative cutaneous fatty acids (stearic acid, oleic acid, and squalene) during the infection of Malassezia globosa, M. restricta, M. furfur, and M. sympodialis with the Human Epidermal Keratinocytes, adult (HEKa) cell line. Keratinocytes were infected with Malassezia spp., and parameters associated with the infection, such as cell viability, cytotoxicity, invasiveness, and immune response, were evaluated. Gene expression related to virulence was analyzed by real-time PCR. Adding fatty acids changes the infection process, decreases cell viability, and causes a cytotoxic effect on keratinocytes. M. furfur and M. sympodialis, showed a negative impact against keratinocytes and is increased by the presence of fatty acids. Malassezia spp. express genes related to pathogenesis-like lipases, allergens, and peptidases to interact with skin cells. Moreover, Malassezia spp. promote the secretion of inflammatory cytokines by keratinocytes, such as IL-8, IL-18, and IL-33, when infecting in a lipid-supplemented media.
Revolutionizing MRI-Based Brain Tumor Classification with BrainMRI-NetX for Superior Accuracy and Reliability
Authors: Arora Sonia , Mishra Gouri Sankar 
Published: November 2024

Abstract

This study aims to enhance and ensure reliable MRI-based brain tumor classification through the development of an innovative BrainMRI-NetX model, incorporating advanced techniques such as Depthwise Separable Convolutions, Residual Blocks, Squeeze-and-Excite Blocks, and Self-Attention Layers. For feature extraction, we utilized a hybrid VGG19 and LSTM model. Our primary goal is to develop and evaluate a CNN model that outperforms state-of-the-art models in terms of F-score, recall, accuracy, and precision.The proposed BrainMRI-NetX model was trained using cutting-edge optimization techniques on a large dataset of FigShare MRI brain images, significantly enhancing its performance. We thoroughly evaluated the model's critical performance indicators: F-score, recall, accuracy, and precision. When benchmarked against popular models such as ResNet-152, DenseNet121, and VGG16, our proposed model demonstrated superior performance, achieving an F-score of 0.96, and recall, accuracy, and precision all at 0.99. In comparison, DenseNet121 showed an accuracy of 0.85, precision of 0.89, recall of 0.90, and F-score of 0.88. ResNet-152 and VGG16 exhibited lower performance metrics, with accuracy at 0.86, precision at 0.85, recall at 0.84, and F-score at 0.87. The exceptional performance of our proposed BrainMRI-NetX model highlights its potential for advancing medical diagnostics, particularly in MRI-based brain tumor classification.
Bridging the Energy Divide: The Impact of Digital Inclusive Finance on Urban-Rural Electricity Consumption Inequality
Authors: Zhang Lei , Ji Hongkang 
Published: November 2024

Abstract

In this paper, we investigate the impact of digital inclusive finance on the inequality in electricity consumption between urban and rural residents in China. Using provincial panel data from 2011 to 2021, we employ a two-way fixed effects model to assess the effects of digital inclusive finance on urban-rural electricity consumption inequality. The findings unveil that digital inclusive finance significantly reduces electricity consumption inequality, particularly in less developed central and western regions. Our mediation analysis demonstrates that digital inclusive finance mitigates electricity consumption inequality through two main channels: reducing income inequality between urban and rural residents and enhancing the supply of renewable energy. As revealed in the threshold effect analysis, financial regulation intensity plays the moderating role. To be specific, although positive impact of digital inclusive finance is propelled by moderate regulation, its benefits could be decreased due to excessive regulation. In short, this study has not only validated the function of digital inclusive finance in tackling with energy inequality, but also provided policy insights to balance financial innovation and regulation.
Affective Space and Motor Response: The Impact of Proprioceptive/Tactile Cues on Movement Organization
Authors: LACHAUD L. , VERNAZZA-MARTIN S. 
Published: November 2024

Abstract

The present study investigates whether action organization is enhanced when the affectively marked space corresponding to the affective outcome of a response is primarily perceived through vision (Rv-E hypothesis) or proprioception (Rp-E hypothesis). Sixty-four right-handed participants were seated in front of a vertical computer screen and were required to perform pointing movements on a graphic tablet. This tablet was positioned horizontally or vertically, and participants used a stylus to trigger the appearance of a positive or negative visual outcome. The tablet was concealed under a device that completely hid the tablet, stylus, and participants' forearms. The results reveal three critical characteristics of motor response coding. First, coding highly depends on the affectively marked space where the movement occurs. Second, this affective coding is primarily based on predictions from proprioceptive and tactile information, which guide the movement's direction. Third, the coding of affective space must align with the affective coding of the response to ensure effective movement organization. This study provides new insights into the affective coding of motor responses by analyzing movement's temporal and spatial aspects through kinematic analysis.