Search Results (316)

Background: The regulation of oil and protein contents in cottonseed is governed by a complex genetic network. Gaining insight into the mechanisms controlling these traits is necessary for dissecting the formation patterns of cottonseed quality. Method: In this study, Xinluzhong 37 (P₁) and Xinluzhong 51 (P₂) were selected as parental lines for two reciprocal crosses: P₁ × P₂ (F₁) and its reciprocal P₂ × P₁ (F₁′). Each F1 was selfed and backcrossed to both parents to generate the F₂ (F₂′), B₁ (B₁′), and B₂ (B₂′) generations. To assess nutritional traits in hairy (non-delinted) and lint-free (delinted) seeds, two indicators, oil content and protein content, were measured in both seed types. Joint segregation analysis was employed to analyze the inheritance of these traits, based on a major gene plus polygene model. Results: In the orthogonal crosses, the CVs for the four nutritional traits ranged at 2.710–7.879%, 4.086–11.070%, 2.724–6.727%, and 3.717–9.602%. In the reciprocal crosses, CVs ranged at 2.710–8.053%, 4.086–9.572%, 2.724–6.376%, and 3.717–8.845%. All traits exhibited normal or skewed-normal distributions. For oil content in undelinted/delinted seeds, polygenic heritabilities in the orthogonal cross were 0.64/0.52, and 0.40/0.36 in the reciprocal cross. For protein content, major-gene heritabilities in the orthogonal cross were 0.79 (undelinted) and 0.78 (delinted), while those in the reciprocal cross were both 0.62. Conclusions: Oil and protein contents in cottonseeds are quantitative traits. In both orthogonal and reciprocal crosses, oil content is controlled by multiple genes and is shaped by additive, dominance, and epistatic effects. Protein content, in contrast, is largely controlled by two major genes along with minor genes. In the P₁ × P₂ combination, major genes act through additive, dominance, and epistatic effects, while in the P₂ × P₁ combination, their effects are additive only. In both combinations, minor genes contribute through additive and dominance effects. In summary, the oil content in cottonseed is mainly regulated by polygenes, whereas the protein content is primarily determined by major genes. These genetic features in both linted, and lint-free seeds may offer a theoretical foundation for molecular breeding aimed at improving cottonseed oil and protein quality. Full article

Children’s safety, well-being and inclusion in the school environment can be severely impacted by social isolation and bullying. This study examined these threats in a sample of 14 group-classes (291 students) from four different primary education schools. A total of 44 special educational needs (SEN) students and 44 students without SEN were selected. The social network structure of each group-class was analysed, as well as the number of friendship ties, the degree of emotional well-being and social participation (both inside and outside of the school), and the possible cases of bullying. The results show a significantly greater rate of social rejection, emotional distress and risk of exclusion in SEN students with respect to their peers, as well as a considerably higher perception of bullying (38.6% vs. 4.8%). The SEN students who reported bullying were mostly schooled in social networks with a more segregated and fragmented structure. Moreover, the mean value obtained in these groups was lower for all the analysed indicators, although the differences were not statistically significant. These data support the idea that the social capital of the group class could influence the prevention of violence and bullying towards SEN students. However, further studies with larger samples are needed to confirm this. Lastly, strategies to promote the social inclusion of SEN students in primary education schools are discussed. Full article

Functional segregation in brain networks refers to the division of specialized cognitive functions across distinct regions, enabling efficient and dedicated information processing. This paper explores the significance of functional segregation in shaping brain network architecture, highlighting methodologies such as modularity and local efficiency that quantify the degree of specialization and intra-regional communication. We examine how these metrics reveal the presence of specialized modules underpinning various cognitive processes and behaviors and discuss the implications of disruptions in functional segregation in neurological and psychiatric disorders. Our findings underscore the fact that understanding functional segregation is crucial for elucidating normal brain function, identifying biomarkers, and developing therapeutic interventions. Overall, functional segregation is a fundamental principle governing brain organization, and ongoing research into its mechanisms promises to advance our comprehension of the brain’s complex architecture and its impact on human health. Full article

Tuberculosis (TB) is the most serious worldwide infectious disease and the leading cause of death among people with HIV. Early diagnosis and prompt treatment can cut off the rising number of TB deaths, and analysis of chest X-rays is a cost-effective method. We describe a deep learning-based cascade algorithm for detecting TB in chest X-rays. Firstly, the lung regions were segregated from other anatomical structures by an encoder–decoder with an atrous separable convolution network—DeepLabv3+ with an XceptionNet backbone, DLabv3+X, and then cropped by a bounding box. Using the cropped lung images, we trained several pre-trained Deep Convolutional Neural Networks (DCNNs) on the images with hyperparameters optimized by a Bayesian algorithm. Different combinations of trained DCNNs were compared, and the combination with the maximum accuracy was retained as the winning combination. The ensemble classifier was designed to predict the presence of TB by fusing DCNNs from the winning combination via weighted averaging. Our lung segmentation was evaluated on three publicly available datasets: it provided better Intercept over Union (IoU) values: 95.1% for Montgomery County (MC), 92.8% for Shenzhen (SZ), and 96.1% for JSRT datasets. For TB prediction, our ensemble classifier produced a better accuracy of 92.7% for the MC dataset and obtained a comparable accuracy of 95.5% for the SZ dataset. Finally, occlusion sensitivity and gradient-weighted class activation maps (Grad-CAM) were generated to indicate the most influential regions for the prediction of TB and to localize TB manifestations. Full article

Triatoma dimidiata is a widely distributed vector of Trypanosoma cruzi in Mexico and Central America, found across a range of habitats from sylvatic to domestic. Vector control has relied heavily on indoor residual spraying with pyrethroids; however, reinfestation and emerging resistance have limited its long-term effectiveness. In this study, we analyzed the genetic diversity and population structure of T. dimidiata from Veracruz, Oaxaca, and Yucatan using mitochondrial markers (cyt b and ND4) and screened for knockdown resistance (kdr)-type mutations in the voltage-gated sodium channel (VGSC) gene. High haplotype diversity and regional differentiation were observed, with most genetic variation occurring between populations. The ND4 marker provided greater resolution than cyt b, revealing ten haplotypes and supporting evidence of recent population expansion. Haplotype networks showed clear geographic segregation, particularly between populations east and west of the Isthmus of Tehuantepec. The L925I mutation, highly associated with pyrethroid resistance, was detected for the first time in Mexican populations of T. dimidiata, albeit at low frequencies. These findings highlight the importance of integrating population genetic data and resistance surveillance into regionally adapted vector control strategies for Chagas disease. Full article

The Minority Game (MG) is a paradigmatic model in econophysics, widely used to study inductive reasoning and self-organization in multi-agent systems. Traditionally, coordinated phases in the MG are associated with spontaneous symmetry breaking, where agents differentiate into polarized roles. Recent work shows that policy-based reinforcement-learning can give rise to a new form of symmetric coordination—one achieved without role segregation or strategy specialization. In this study, we thoroughly analyze this novel coordination using tools from complex networks. By constructing the correlation networks among agents, we carry out a structural, functional, and temporal analysis of the emergent symmetric coordination. Our results confirm the preservation of symmetry at the collective level, and reveal a consistent and robust form of distributed coordination, demonstrating the power of network-based approaches in understanding the emergent order in adaptive multi-agent systems. Full article

Cotton is one of the most valuable non-food agricultural products in the world. However, cotton production is often hampered by the invasion of disease. In most cases, these plant diseases are a result of insect or pest infestations, which can have a significant impact on production if not addressed promptly. It is, therefore, crucial to accurately identify leaf diseases in cotton plants to prevent any negative effects on yield. This paper presents a hybrid deep learning approach based on Bidirectional Encoder Representations from Transformers with Residual network and particle swarm optimization (BERT-ResNet-PSO) for detecting cotton plant diseases. This approach starts with image pre-processing, which they pass to a BERT-like encoder after linearly embedding the image patches. It results in segregating disease regions. Then, the output of the encoded feature is passed to ResNet-based architecture for feature extraction and further optimized by PSO to increase the classification accuracy. The approach is tested on a cotton dataset from the Plant Village dataset, where the experimental results show the effectiveness of this hybrid deep learning approach, achieving an accuracy of 98.5%, precision of 98.2% and recall of 98.7% compared to the existing deep learning approaches such as ResNet50, VGG19, InceptionV3, and ResNet152V2. This study shows that the hybrid deep learning approach is capable of dealing with the cotton plant disease detection problem effectively. This study suggests that the proposed approach is beneficial to help avoid crop losses on a large scale and support effective farming management practices. Full article

By developing and making use of the multi-scale theoretical approach, we identify the main factors that affect the conductivity of a composite composed of a diblock copolymer (DBC) system and conductive particles. This approach relies on the consistent phase-field model of DBC, Monte-Carlo simulations of the filler localization in DBC, and the resistor network model that mimics the conductive filler network formed in DBC. Based on the described approach, we thoroughly explore the relation among the morphological state of the microphase-separated DBC, localization of fillers in DBC, and the electrical response of the composite. Good agreement with experimental results confirms the accuracy of our theoretical predictions regarding the localization of fillers in the DBC microphases. The main factors affecting the composite conductivity are found to be: (i) affinities of fillers for copolymer blocks; (ii) degree of the segregation of a host DBC system, driven by external stimuli; (iii) geometry of the microphases formed in the microphase-separated DBC; and (iv) interactions between fillers. The conductor-insulator transition in the filler network is found to be caused by the order-disorder transition in the symmetric DBC. The order-order transition between the ordered lamellae and cylindrical microphases of asymmetric DBC causes a spike in the composite conductivity. Full article

Despite substantial research on how environmental factors affect fungal diversity, the mechanisms shaping regional-scale diversity patterns remain poorly understood. This study employed ITS high-throughput sequencing to evaluate soil fungal diversity, community composition, and co-occurrence networks across alpine meadows, desert steppes, and alpine shrublands in the southwestern Tibetan Plateau. We found significantly higher fungal α-diversity in alpine meadows and desert steppes than in alpine shrublands. Random forest and CAP analyses identified the mean annual temperature (MAT) and normalized difference vegetation index (NDVI) as major ecological drivers. Mantel tests revealed that soil physicochemical properties explained more variation than climate, indicating an indirect climatic influence via soil characteristics. Distance–decay relationships suggested that environmental heterogeneity and species interactions drive community isolation. Structural equation modeling confirmed that the MAT and NDVI regulate soil pH and carbon/nitrogen availability, thereby influencing fungal richness. The highly modular fungal co-occurrence network depended on key nodes for connectivity. Vegetation coverage correlated positively with network structure, while soil pH strongly affected network stability. Spatial heterogeneity constrained stability and diversity through resource distribution and niche segregation, whereas stable networks concentrated resources among dominant species. These findings enhance our understanding of fungal assemblage processes at a regional scale, providing a scientific basis for the management of soil fungal resources in plateau ecosystems. Full article

Genomic instability has been increasingly recognized over the past decade as a fundamental driver of cancer initiation and progression, largely owing to its association with specific genes and cellular mechanisms that offer therapeutic potential. However, a comprehensive molecular framework that captures the interconnected processes underlying this phenomenon remains elusive. In this study, we focused on polo-like kinase 1 (PLK1), a key cell cycle regulator frequently overexpressed in diverse human tumors, to reconstruct a regulatory network that consolidates pre-existing biological knowledge exclusively related to pathways involved in genome stability maintenance and cancer. The resulting model integrates nine biological processes, 1030 reactions, and 716 molecular species to form a literature-supported network in which PLK1 serves as a central regulatory node. However, rather than depicting an isolated PLK1-centric system, this network reflects a broader and more complex architecture of interrelated genomic instability mechanisms. As expected, the simulations reproduced known behaviors associated with PLK1 dysregulation, reinforcing the well-established role of the kinase in genome destabilization. Importantly, this model also enables the exploration of additional, less-characterized dynamics, including the potential involvement of genes such as kif2c, incenp, and other regulators of chromosomal segregation and DNA repair, which appear to contribute to instability events downstream of PLK1. While these findings are grounded in mechanistic simulations and require further experimental validation, gene expression and survival analyses across tumor types support their clinical relevance by linking them to poor prognosis in specific cancers. Overall, the model provides a systemic and adaptable foundation for studying PLK1-related genomic instability, enabling both the reinforcement of known mechanisms and discovery of candidate genes and circuits that may drive tumorigenesis through compromised genome integrity across distinct cancer contexts. Full article

Allergic rhinitis and asthma are significant public health concerns worldwide. While previous studies have explored how nasal and buccal bacteriotas influence these conditions, few have directly compared their bacteriomes within the same cohort. To bridge this gap, I analyzed 16S rRNA next-generation sequencing data from 347 individuals, including participants with allergic rhinitis, asthma and healthy controls. The nasal and buccal bacteriomes shared all dominant bacterial taxa but differed significantly in their phylum- and genus-level relative abundances. Alpha-diversity was significantly higher in the buccal cavity, while beta-diversity varied significantly across all indices and clinical groups. Over 80% of the predicted metabolic pathways were differentially regulated between the two cavities, yet these functional differences remained fairly consistent across clinical groups. Naso-buccal bacterial networks exhibited striking differences in structure, complexity and hub nodes. Notably, the network of healthy controls showed a clear segregation between nasal and buccal bacteria, with 93.5% of the interactions occurring within each respective cavity, and contained few pathogenic keystone taxa. In contrast, bacterial networks from diseased individuals exhibited reduced ecological specialization and more pathogenic keystone taxa linked to airway disease. These findings, thus, demonstrate that the naso-buccal bacteriome plays distinct yet interconnected roles in allergic rhinitis and asthma. Full article

Zr-4 alloy tubes, as the primary cladding material in nuclear reactor cores, face the critical challenge of oxidative attack in 1200 °C steam environments. To address this issue, high-temperature oxidation-resistant coatings fabricated via extreme high-speed laser cladding (EHLA) present a promising mitigation strategy. In this study, Y₂O₃-modified (0.0–5.0 wt.%) Cr/FeCrAl composite coatings were designed and fabricated on Zr-4 substrates using the EHLA process, followed by systematic investigation of Y doping effects on coating microstructures and steam oxidation resistance (1200 °C, H₂O atmosphere). Experimental results demonstrate that Y₂O₃ doping remarkably enhanced the oxidation resistance, with optimal performance achieved at 2.0 wt.% Y₂O₃ (31% oxidation mass gain compared to the substrate after 120-min exposure). Microstructural analysis reveals that the dense grain boundary network facilitates rapid surface diffusion of Al, promoting continuous Al₂O₃ protective film formation. Additionally, Y segregation at grain boundaries suppressed outward diffusion of Cr³⁺ cations, effectively inhibiting void formation at the oxide-coating interface and improving interfacial stability. The developed rare-earth-oxide-doped composite coating via extreme high-speed laser cladding process shows promising applications in surface-strengthening engineering for nuclear reactor Zr-4 alloy cladding tubes, providing both theoretical insights and technical references for the design of high-temperature oxidation-resistant coatings in nuclear industry. Full article

This scoping review investigates the labor market challenges and coping strategies of highly skilled second-generation immigrants in Europe who, despite their educational and professional accomplishments, face persistent barriers related to ethnic, cultural, and religious identities. Synthesizing existing literature, the review examines obstacles to the economic integration of highly educated children of immigrants, highlighting both their perceptions of these barriers and the adaptive strategies they employ. A systematic search was conducted across Scopus, Web of Science, and EBSCOhost to identify studies published between 2010 and 2024. The selection process followed a structured five-stage framework, including defining research questions, identifying and selecting relevant studies, charting the data, and synthesizing findings. A total of 1192 records were initially identified, with 1022 retained after duplicate removal. After applying inclusion and exclusion criteria, 68 studies were included in the review. Findings indicate that hiring discrimination, occupational segregation, and exclusion from elite professional networks remain key barriers, particularly for those of Muslim background. Despite achieving professional success, many continue to encounter symbolic boundaries that limit career advancement. In response, second-generation professionals adopt various coping strategies, including ethnic niche formation, entrepreneurship, and transnational mobility, to navigate labor market disadvantages. Challenging traditional assimilation narratives, findings reveal that professional success does not guarantee societal acceptance, as ethnic and cultural identities continue to pose significant barriers. The review concludes by identifying key research gaps, advocating for further exploration of organizational practices that perpetuate ethnic inequalities within high-skill professions, and examining transnational mobility as a coping strategy for second-generation elites. Future research should explore how gender and ethnicity intersect to shape career trajectories for second-generation women. Additionally, expanding research beyond the predominant focus on Muslim professionals to include other religious and ethnic groups would provide a more comprehensive understanding of how identity markers influence labor market outcomes. Finally, as demographic shifts reshape European labor markets, comparative studies should assess how different institutional and cultural frameworks influence patterns of inclusion and exclusion for highly skilled second-generation professionals. Full article

Background: MicroRNAs (miRNAs) play crucial roles in regulating tissue-specific gene expression and plant development. This study explores the identification and functional characterization of miRNAs in Plukenetia volubilis (sacha inchi), an economically and nutritionally significant crop native to the Amazon basin, across three organs: root, stem, and leaf. Methods: Small RNA libraries were sequenced on the Illumina Novaseq 6000 platform, yielding high-quality reads that facilitated the discovery of known and novel miRNAs using miRDeep-P. Results: A total of 277 miRNAs were identified, comprising 71 conserved and 206 novel miRNAs, across root, stem, and leaf tissues. In addition, differential expression analysis using DESeq2 identified distinct miRNAs exhibiting tissue-specific regulation. Notably, novel miRNAs like novel_1, novel_88, and novel_189 showed significant roles in processes such as auxin signaling, lignin biosynthesis, and stress response. Functional enrichment analysis of miRNA target genes revealed pathways related to hormonal regulation, structural reinforcement, and environmental adaptation, highlighting tissue-specific functions. The Principal Component Analysis and PERMANOVA confirmed clear segregation of miRNA expression profiles among tissues, underlining organ-specific regulation. Differential expression patterns emphasized unique regulatory roles in each organ: roots prioritized stress response and nutrient uptake, leaves focused on photosynthesis and UV protection, and stems contributed to structural integrity and nutrient transport, suggesting evolutionary adaptations in P. volubilis. Conclusions: This study identified novel miRNA-mediated networks that regulate developmental and adaptive processes in P. volubilis, underscoring its molecular adaptations for resilience and productivity. By characterizing both conserved and novel miRNAs, the findings lay a foundation for genetic improvement and molecular breeding strategies aimed at enhancing agronomic traits, stress tolerance, and the production of bioactive compounds. Full article

Rail travel is one of the safest means of transportation, with increasing usage in recent years. One of the major safety concerns in the railway relates to intrusions. Therefore, the timely detection of obstacles is crucial for ensuring operational safety. This is a complex problem with multiple contributing factors, from environmental to psychological. While machine learning (ML) has proven effective in related applications, such as autonomous road-based driving, the railway sector faces unique challenges due to limited image data availability and difficult data acquisition, hindering the applicability of conventional ML methods. To mitigate this, the present study proposes a novel framework leveraging LiDAR technology (Light Detection and Ranging) and previous knowledge to address these data scarcity limitations and enhance obstacle detection capabilities on railways. The proposed framework combines the strengths of long-range LiDAR (capable of detecting obstacles up to 500 m away) and GNSS data, which results in precise coordinates that accurately describe the train’s position relative to any obstacles. Using a data fusion approach, pre-existing knowledge about the track topography is incorporated into the LiDAR data processing pipeline in conjunction with the DBSCAN clustering algorithm to identify and classify potential obstacles based on point cloud density patterns. This step effectively segregates potential obstacles from background noise and track structures. The proposed framework was tested within the operational environment of a CP 2600-2620 series locomotive in a short section of the Contumil-Leixões line. This real-world testing scenario allowed the evaluation of the framework’s effectiveness under realistic operating conditions. The unique advantages of this approach relate to its effectiveness in tackling data scarcity, which is often an issue for other methods, in a way that enhances obstacle detection in railway operations and may lead to significant improvements in safety and operational efficiency within railway networks. Full article