Search Results (12,366)

A narrow genetic basis limits further the improvement of modern Gossypium hirsutum cultivar. The abundant genetic diversity of wild species provides available resources to solve this dilemma. In the present study, a chromosome segment substitution line (CSSL) population including 553 individuals was established using G. darwinii accession 5-7 as the donor parent and G. hirsutum cultivar CCRI35 as the recipient parent. After constructing a high-density genetic map with the BC₁ population, the genotype and phenotype of the CSSL population were investigated. A total of 235 QTLs, including 104 QTLs for fiber-related traits and 132 QTLs for seed-related traits, were identified from four environments. Among these QTLs, twenty-seven QTLs were identified in two or more environments, and twenty-five QTL clusters consisted of 114 QTLs. Moreover, we identified three candidate genes for three stable QTLs, including GH_A01G1096 (ARF5) and GH_A10G0141 (PDF2) for lint percentage, and GH_D01G0047 (KCS4) for seed index or oil content. These results pave way for understanding the molecular regulatory mechanism of fiber and seed development and would provide valuable information for marker-assisted genetic improvement in cotton. Full article

The H9 subtype of avian influenza virus (AIV) has been characterized by its rapid spread, wide range of prevalence, and continuous evolution in recent years, leading to an increasing ability for cross-species transmission. This not only severely impacts the economic benefits of the aquaculture industry, but also poses a significant threat to human health. Therefore, developing a rapid and sensitive detection method is crucial for the timely diagnosis and prevention of H9 AIVs. In this study, a real-time fluorescent reverse transcription recombinase-aided isothermal amplification (RT–RAA) technique targeting the hemagglutinin (HA) of H9 AIVs was established. This technique can be used for detection in just 30 min at a constant temperature of 42 °C, and it exhibits good specificity without cross-reactivity with other viruses. Sensitivity tests revealed that the detection limit of RT–RAA was 163 copies per reaction, and the visual detection limit was 1759 copies per reaction at a 95% confidence interval, both of which are capable of detecting low concentrations of standards. Furthermore, RT–RAA was applied to detect 155 clinical samples, and compared to real-time fluorescent quantitative PCR (RT–qPCR), RT–RAA demonstrated high accuracy, with a specificity of 100% and a kappa value of 0.96, indicating good correlation. Additionally, with the assistance of a portable blue imaging device, we can visually observe the amplification products, greatly facilitating rapid detection in resource-limited environments. The RT–RAA detection method developed in this study does not require expensive equipment or highly skilled staff, making it beneficial for the accurate and low-cost detection of H9 AIVs. Full article

Accessing plant resources to extract compounds of interest can sometimes be challenging. To facilitate access and limit the environmental impact, innovative cultivation strategies can be developed. Forskolin is a molecule of high interest, mainly found in the roots of Coleus forskohlii. The aim of this study was to develop aeroponic cultivation methods to provide a local source of Coleus forskohlii and to study the impact of abiotic stress on forskolin and bioactive metabolite production. Three cultivation itineraries (LED lighting, biostimulant, and hydric stress) along with a control itinerary were established. The forskolin content in the plant roots was quantified using HPLC-ELSD, and the results showed that LED treatment proved to be the most promising, increasing root biomass and the total forskolin content recovered at the end of the cultivation period threefold (710.1 ± 21.3 mg vs. 229.9 ± 17.7 mg). Statistical analysis comparing the LED itinerary to the control itinerary identified stress-affected metabolites, showing that LEDs positively influence mainly the concentration of phenolic compounds in the roots and diterpenes in the aerial parts of Coleus forskohlii. Moreover, to better define the phytochemical composition of Coleus forskohlii cultivated in France using aeroponic cultivation, an untargeted metabolomic analysis was conducted using UHPLC-HRMS/MS analysis and molecular networks on both the root and aerial parts. This study demonstrates that aeroponic cultivation, especially with the application of an LED treatment, could be a very promising alternative for a local source of Coleus forskohlii leading to easy access to the roots and aerial parts rich in forskolin and other bioactive compounds. Full article

ChatGPT is a large language model trained on increasingly large datasets to perform diverse language-based tasks. It is capable of answering multiple-choice questions, such as those posed by diverse medical examinations. ChatGPT has been generating considerable attention in both academic and non-academic domains in recent months. In this study, we aimed to assess GPT’s performance on anatomical multiple-choice questions retrieved from medical licensing examinations in Germany. Two different versions were compared. GPT-3.5 demonstrated moderate accuracy, correctly answering 60–64% of questions from the autumn 2022 and spring 2021 exams. In contrast, GPT-4.o showed significant improvement, achieving 93% accuracy on the autumn 2022 exam and 100% on the spring 2021 exam. When tested on 30 unique questions not available online, GPT-4.o maintained a 96% accuracy rate. Furthermore, GPT-4.o consistently outperformed medical students across six state exams, with a statistically significant mean score of 95.54% compared with the students’ 72.15%. The study demonstrates that GPT-4.o outperforms both its predecessor, GPT-3.5, and a cohort of medical students, indicating its potential as a powerful tool in medical education and assessment. This improvement highlights the rapid evolution of LLMs and suggests that AI could play an increasingly important role in supporting and enhancing medical training, potentially offering supplementary resources for students and professionals. However, further research is needed to assess the limitations and practical applications of such AI systems in real-world medical practice. Full article

Distal radioulnar joint (DRUJ) instability is a complex condition that can severely affect forearm function, causing pain, limited range of motion, and reduced strength. This review aims to consolidate current knowledge on the diagnosis and management of DRUJ instability, emphasizing a new classification system that we propose. The review synthesizes anatomical and biomechanical factors essential for DRUJ stability, focusing on the interrelationship between the bones and surrounding soft tissues. Our methodology involved a thorough examination of recent studies, incorporating clinical assessments and advanced imaging techniques such as MRI, ultrasound, and dynamic CT. This approach allowed us to develop a classification system that categorizes DRUJ injuries into three distinct grades. This system is intended to be practical for both clinical and radiological evaluations, offering clear guidance for treatment based on injury severity. The review discusses a range of treatment options, from conservative measures like splinting and physiotherapy to surgical procedures, including arthroscopy and DRUJ arthroplasty. The proposed classification system enhances the accuracy of diagnosis and supports more effective decision making in clinical practice. In summary, our findings suggest that the integration of advanced imaging techniques with minimally invasive surgical interventions can lead to better outcomes for patients. This review serves as a valuable resource for clinicians, providing a structured approach to managing DRUJ instability and improving patient care through the implementation of our new classification system. Full article

This paper proposes a scheme that adds XOR bit operations into the encoding and decoding process of the conventional non-orthogonal multiple access (NOMA) system to alleviate performance degradation caused by the power distribution of the original signal. Because the conventional NOMA combines and sends multiple data within limited resources, it has a higher data rate than orthogonal multiple access (OMA), at the expense of error performance. However, by using the proposed scheme, both error performance and sum rate can be improved. In the proposed scheme, the transmitter sends the original data and the redundancy data in which the exclusive OR (XOR) values of the data are compressed using the superposition coding (SC) technique. After this process, the data rate of users decreases due to redundancy data, but since the original data are sent without power allocation, the data rate of users with poor channel conditions increases compared to the conventional NOMA. As a result, the error performance and sum rate of the proposed scheme are better than those of the conventional NOMA. Additionally, we derive an exact closed-form bit error rate (BER) expression for the proposed downlink NOMA design over Rayleigh fading channels. Full article

In endotherms, body size tends to increase with elevation and latitude (i.e., with decreasing temperatures) (Bergmann’s rule). These patterns are explained in terms of heat balance since larger animals need to produce less heat relative to their size to maintain stable body temperatures. In ectotherms like most insects, where this mechanism cannot operate, a reverse pattern is frequently observed, as a higher surface area-to-volume ratio in colder climates may allow for more rapid heating and cooling. However, patterns of increasing body size with decreasing temperatures can also be observed in ectotherms if selection for more stable internal temperatures leads to smaller surface area-to-volume ratios. Data on tenebrionids from Latium (Central Italy) were used to model elevational variations in average values of body size (total length, mass and volume) and surface area-to-volume ratio. Analyses were performed by considering the whole fauna and two ecological groups separately: ground-dwelling species (geophilous) and arboreal (xylophilous) species. The surface area-to-volume ratios declined with increasing elevation in all cases, indicating that the need for heat conservation is more important than rapid heating and cooling. However, in xylophilous species (which typically live under bark), body size increased with increasing elevation, and in geophilous species, an opposite pattern was observed up to about 1000 m, followed by an increasing pattern. This suggests that a reduction in resource availability with elevation limits body size in geophilous species up to a certain elevation but not in xylophilopus species, which benefit from more climatically stable conditions and constant resources and need energy for overwintering. Full article

Channel estimation is a critical component in orthogonal frequency division multiplexing (OFDM) systems for ensuring reliable wireless communication. In this study, we propose a fast super-resolution convolutional neural network (FSRCNN) model for channel estimation, designed to reduce computational complexity while maintaining high estimation accuracy. The proposed FSRCNN model incorporates modifications such as replacing linear interpolation with zero padding and leveraging a new fast CNN architecture to estimate channel coefficients. Our numerical experiments and simulations demonstrate that the FSRCNN model significantly outperforms traditional methods, such as least square (LS) and linear minimum mean square error (LMMSE), in terms of mean square error (MSE) across various signal-to-noise ratios (SNRs). Specifically, the FSRCNN model achieves MSE values comparable to MMSE estimation, particularly at higher SNRs, while maintaining lower computational complexity. At an SNR of 20 dB, the FSRCNN model shows a notable improvement in MSE performance compared to the ChannelNet and LS methods. The proposed model also demonstrates robust performance across different SNR levels, with optimal results observed when the training SNR is close to the operating SNR. These findings validate the effectiveness of the FSRCNN model in providing a low-complexity, high-accuracy alternative for channel estimation, making it suitable for real-time applications and devices with limited computational resources. This advancement holds significant promise for enhancing the reliability and efficiency of current and future wireless communication networks. Full article

While urban populations are rapidly increasing around the world, floods have been frequently and seriously occurring due to the climate crisis. As existing disaster prevention facilities have specific limitations in completely protecting against flood damages, the concept of resilience, which emphasizes the ability to recover after becoming injured and harmed by a flood, is necessary to mitigate such damages. However, there is still a scarcity of studies that quantitatively show the relationship between the resilience and the socioeconomic costs, even though a variety of evaluation methods exist in the literature. This study aims to quantitively analyze the socioeconomic impact of flooding on the urban environment based on the concept of resilience. A method of evaluating four properties of resilience (redundancy, rapidity, resourcefulness, and robustness) through damage function and network analysis was used to measure changes in resilience against flood damages. In addition, to determine the socioeconomic impact of flooding, the costs incurred due to transportation delays and the lack of labor participation were evaluated. Differences in structural and social systems have led to variations in resilience and socioeconomic costs. As a future study, if the circumstances after flood events based on risk-based recovery can be evaluated, more effective urban flooding defense decisions would be expected. Full article

Food insecurity (FI) is a critical issue in developing countries, particularly in low-resource settings, where it can worsen women’s mental health. Psychosocial factors such as low household income, limited education, multiparity, and vulnerability are linked to depressive symptoms during pregnancy. Additionally, the family environment influences parental practices, which may impact mental health. This study evaluates the association of socioeconomic factors, parental practices, FI risk, and home visit frequency with depressive symptoms in pregnant women enrolled in the Happy Child Program (Programa Criança Feliz—PCF) in the Federal District, Brazil. In this cross-sectional study, 132 pregnant women monitored by PCF from May to July 2023 were assessed using a self-administered questionnaire for socioeconomic data, the two-item Triage for Food Insecurity (TRIA) instrument for FI risk, the Scale of Parental Beliefs and Early Childhood Care Practices, and the Beck Depression Inventory-II for depressive symptoms. Most participants were multiparous (87.9%), had low income (under 200 USD/month; 80.8%), presented depressive symptoms (67.4%) and were at risk of FI (81.8%). About half demonstrated adequate parental practices (50.8%) and received four home visits per month during pregnancy (54.5%). Women who received four PCF home visits had a lower prevalence of depressive symptoms compared to those with fewer visits (PR 0.76, 95% CI 0.59–0.98). No significant association was found between FI or parental practices and depressive symptoms. These findings suggest that the PCF home-visiting program may strengthen vulnerable families, support social networks, and improve mental health during pregnancy. Additionally, the results of this study highlight the need for targeted interventions aimed at reducing food insecurity and promoting mental health during pregnancy, particularly among socially vulnerable populations. Furthermore, they reinforce the importance of expanding access to home-visiting programs as an effective strategy to improve maternal mental health and well-being, while fostering healthier prenatal environments for both mothers and their children. Full article

Parkinson’s disease (PD), the second most prevalent neurodegenerative disorder worldwide, frequently presents with early-stage speech impairments. Recent advancements in Artificial Intelligence (AI), particularly deep learning (DL), have significantly enhanced PD diagnosis through the analysis of speech data. Nevertheless, the progress of research is restricted by the limited availability of publicly accessible speech-based PD datasets, primarily due to privacy concerns. The goal of this systematic review is to explore the current landscape of speech-based DL approaches for PD classification, based on 33 scientific works published between January 2020 and March 2024. We discuss their available resources, capabilities, and potential limitations, and issues related to bias, explainability, and privacy. Furthermore, this review provides an overview of publicly accessible speech-based datasets and open-source material for PD. The DL approaches identified are categorized into end-to-end (E2E) learning, transfer learning (TL), and deep acoustic feature extraction (DAFE). Among E2E approaches, Convolutional Neural Networks (CNNs) are prevalent, though Transformers are increasingly popular. E2E approaches face challenges such as limited data and computational resources, especially with Transformers. TL addresses these issues by providing more robust PD diagnosis and better generalizability across languages. DAFE aims to improve the explainability and interpretability of results by examining the specific effects of deep features on both other DL approaches and more traditional machine learning (ML) methods. However, it often underperforms compared to E2E and TL approaches. Full article

This study presents a comprehensive bibliometric analysis of research on bank erosion and control measures, utilizing the Scopus database and VOSviewer software. Key terms such as “bank”, “erosion”, “control”, and “protection” frequently appear in the literature, underscoring their importance in studies on riverbank erosion. Since 2000, scientific production has steadily increased, particularly in disciplines such as Environmental Sciences and Earth and Planetary Sciences, driven by growing concerns about climate change and sustainable water resource management. Countries with substantial research resources, such as the United States and China, lead in the production of studies, reflecting their commitment to addressing this global issue. In parallel, the evaluation of erosion mitigation practices in Colombia revealed that, although effective techniques such as gabion walls and riparian vegetation exist, 40% of respondents do not implement specific measures. This lack of implementation is attributed to insufficient knowledge, limited resources, and misconceptions about the effectiveness of these techniques. The findings highlight the need to promote proven practices and enhance professional training. Future research should focus on developing more accurate predictive models, integrating interdisciplinary approaches, and assessing the impacts of climate change on bank erosion. Addressing barriers to applying effective techniques at the local level and improving access to resources and knowledge are critical steps to reducing bank erosion and ensuring sustainable water management. Full article

Obstacle avoidance in UAV swarms is crucial for ensuring the stability and safety of cluster flights. However, traditional methods of swarm obstacle avoidance often fail to meet the requirements of frequent spatiotemporal dynamic changes in UAV swarms, especially in complex environments such as forest firefighting, mine monitoring, and earthquake disaster relief. Consequently, the trained obstacle avoidance strategy differs from the expected or optimal obstacle avoidance scheme, leading to decision bias. To solve this problem, this paper proposes a method of UAV swarm obstacle avoidance decision making based on the end-edge-cloud collaboration model. In this method, the UAV swarm generates training data through environmental interaction. Sparse rewards are converted into dense rewards, considering the complex environmental state information and limited resources, and the actions of the UAVs are evaluated according to the reward values, to accurately assess the advantages and disadvantages of each agent’s actions. Finally, the training data and evaluation signals are utilized to optimize the parameters of the neural network through strategy-updating operations, aiming to improve the decision-making strategy. The experimental results demonstrate that the UAV swarm obstacle avoidance method proposed in this paper exhibits high obstacle avoidance efficiency, swarm stability, and completeness compared to other obstacle avoidance methods. Full article

Mobile Edge Computing (MEC) has been widely applied in various Internet of Things (IoT) scenarios due to its advantages of low latency and low energy consumption. However, the offloading of tasks generated by terminal devices to edge servers inevitably raises privacy leakage concerns. Given the limited resources in MEC networks, this paper proposes a task scheduling strategy, named DQN-DP, to minimize location privacy leakage under the constraint of offloading costs. The strategy is based on a differential privacy location obfuscation probability density function. Theoretical analysis demonstrates that the probability density function employed in this study is valid and satisfies $ϵ$-differential privacy in terms of security. Numerical results indicate that, compared to existing baseline approaches, the proposed DQN-DP algorithm effectively balances privacy leakage and offloading cost. Specifically, DQN-DP reduces privacy leakage by approximately 20% relative to baseline approaches. Full article

Investigating the complex interactions between microbiota and immunity is crucial for a fruitful understanding progress of human health and disease. This review assesses animal models, next-generation in vitro models, and in silico approaches that are used to decipher the microbiome-immunity axis, evaluating their strengths and limitations. While animal models provide a comprehensive biological context, they also raise ethical and practical concerns. Conversely, modern in vitro models reduce animal involvement but require specific costs and materials. When considering the environmental impact of these models, in silico approaches emerge as promising for resource reduction, but they require robust experimental validation and ongoing refinement. Their potential is significant, paving the way for a more sustainable and ethical future in microbiome-immunity research. Full article