Search Results (60)

Relatively little attention has been given to mixed anxiety and depression in autistic youth, particularly how this differs between males and females. This study investigated sex-based differences in the prevalence and correlates of mixed anxiety and depression in a sample of 51 autistic males (M age = 10.16 yr, SD = 2.81 yr, and range = 6 yr to 17 yr) and 51 autistic females (M age = − 10.07 yr, SD = 2.76 yr, and range = 6 yr to 17 yr), matched for age, IQ, and autism severity. Self-reports on generalised anxiety disorder and major depressive disorder, morning salivary cortisol, ADOS-2 scores, and WASI-II full-scale scores were collected from these autistic youth, and data on the ASD-related symptoms of these youth were collected from their parents. The data were analysed for total anxiety–depression score levels, for the underlying components of this scale, and for the individual items used in the scale. The results indicate no significant sex differences for the prevalence of mixed anxiety and depression total scores or the underlying components of anxiety and depression or for the individual items of the mixed anxiety–depression scale. There were sex differences in the significant correlates of mixed anxiety and depression: morning cortisol and ASD-related difficulties in social interaction for females, and ASD-related behaviour for males. Males’ feelings of being restless or edgy were correlated with their social interaction and repetitive and restricted behaviour. Females’ difficulties in social interaction were correlated with their concerns about their abilities and their sleeping problems. Females’ sleeping problems, their tendency to talk about dying, and feeling worthless, were correlated with their morning cortisol. These findings suggest that, while mixed anxiety and depression is experienced similarly by autistic males and females at the global, component, and individual item levels, specific aspects of the symptomatology of mixed anxiety and depression are differently associated with aspects of their ASD-related symptomatology and their levels of chronic physiological stress for males and females. Full article

The 22q11.2 deletion syndrome (DS) can have a significant impact on functionality. The purpose was to describe 22q11.2DS children with functioning from a biopsychosocial perspective, focusing on the impact of children’s health condition from domains of the International Classification of Functioning, Disability, and Health (ICF). Methods: A descriptive, cross-sectional case series study with seven 22q11.2DS children. A questionnaire with an ICF checklist for 22q11.2DS was completed using a structured interview. The Wechsler Abbreviated Scale of Intelligence (WASI) was used to determine the Intelligence Quotient (IQ). Results: Seven participants from 7 to 12 years old, presented some level of IQ impairment. It was observed that 22q11.2DS children experience significant intellectual, cognitive, and speech impairments across ICF Body Function domains. Impairments related to nose and pharynx were found in only one patient. The most relevant categories considered limitations in the Activity and Participation components pertained to producing nonverbal messages, communication, handling stress, and social interaction. Family, health professionals, and acquaintances were perceived as facilitators in the component Environmental Factors. Conclusion: The sample has its functioning affected by aspects that go beyond impairments in body structure and function. The organization of information from the perspective of the ICF is a different approach that helps clinical reasoning. Full article

The objective of this study is to examine the impact of climate and technology on forest efficiency (FE) in China’s provinces from 2002 to 2020. First, the study used SBM-data envelopment analysis (SBM-DEA) to estimate Chinese provinces’ FE using multidimensional forest inputs and outputs. The climate influence is assessed using temperature, precipitation, sunlight hours, and carbon dioxide levels in the second phase. A climate index was created using principal component analysis (PCA) for a complete estimation. In addition to prior research, we analyze the technology impact through two technological indicators: (i) research and development, and (ii) investment in forests. Furthermore, we explore the non-linear influence of economic development on both FE and climate quality. The regression study by CupFM and CupBC found that temperature and precipitation increase FE, whereas sunlight hours and carbon emissions decrease it. The positive association observed between Climate Index1, and the negative relationship noted for Climate Index2, suggests that forests positively influence climate conditions, signifying that an improvement in FE leads to an improvement in climate quality. Technology boosts forest productivity and climatic quality. The environmental Kuznets curve shows an inverted U-shape relationship between economic development and FE. Similarly, climate and economic development have an inverted U-shaped EKC relationship. Urbanization reduces FE due to human growth and activity. Our findings are important for forest management, climate change, and sustainable development policymakers and scholars. Full article

Brassica napus (B. napus) is susceptible to multiple abiotic stresses that can affect plant growth and development, ultimately reducing crop yields. In the past, many genes that provide tolerance to abiotic stresses have been identified and characterized. Peroxidase (POD) proteins, members of the oxidoreductase enzyme family, play a critical role in protecting plants against abiotic stresses. This study demonstrated a comprehensive investigation of the POD gene family in B. napus. As a result, a total of 109 POD genes were identified across the 19 chromosomes and classified into five distinct subgroups. Further, 44 duplicate events were identified; of these, two gene pairs were tandem and 42 were segmental. Synteny analysis revealed that segmental duplication was more prominent than tandem duplication among POD genes. Expression pattern analysis based on the RNA-seq data of B. napus indicated that BnPOD genes were expressed differently in various tissues; most of them were expressed in roots rather than in other tissues. To validate these findings, we performed RT-qPCR analysis on ten genes; these genes showed various expression levels under abiotic stresses. Our findings not only furnish valuable insights into the evolutionary dynamics of the BnPOD gene family but also serve as a foundation for subsequent investigations into the functional roles of POD genes in B. napus. Full article

The efficient and sustainable management of forestry resources is crucial in ensuring economic and societal sustainability. The Chinese government has invested significantly in regulations, afforestation, and technology to enhance the forest resource efficiency, reduce technological disparities, and boost productivity growth. However, the success level of this undertaking is unclear and worth exploring. To this end, this study applied DEA-SBM, meta-frontier analysis, and the Malmquist productivity index to gauge the forest resource efficiency (FRE), regional technology heterogeneity (TGR), and total factor productivity growth (MI) in 31 Chinese provinces for a study period of 2001–2020. Results revealed that the average FRE was 0.5430, with potential growth of 45.70%, to enhance the efficiency level in forestry resource utilization. Anhui, Tibet, Fujian, Shanghai, and Hainan were found to be the top performers in forestry utilization during the study period. The southern forest region was ranked highest, with the highest TGR of 0.915, indicating advanced production technologies. The average MI score was 0.9644, signifying a 3.56% decline in forestry resource productivity. This deterioration is primarily attributed to technological change (TC), which decreased by 5.2%, while efficiency change (EC) witnessed 1.74% growth over the study period. The Southern Chinese forest region, indicating an average 3.06% increase in total factor productivity, ranked highest in all four regions. Guangxi, Tianjin, Shandong, Chongqing, and Jiangxi were the top performers, with prominent growth in MI. Finally, the Kruskal–Wallis test found a significant statistical difference among all four regions for FRE and TGR. Full article

The objective of this study is to examine the impact of climate change on forestry efficiency (FRE) and total factor productivity change (TFPC) in 31 provinces of China for a study period of 2001–2020. Additionally, the study aims to evaluate the success level of governmental initiatives used to mitigate climate change. Using the DEA-SBM, this study estimates the forestry efficiency for 31 Chinese provinces and seven regions. Results indicate that the average forestry efficiency score obtained is 0.7155. After considering climatic factors, the efficiency level is 0.5412. East China demonstrates the highest average efficiency with a value of 0.9247, while the lowest score of 0.2473 is observed in Northwest China. Heilongjiang, Anhui, Yunnan, and Tibet exhibit the highest efficiency scores. Mongolia, Heilongjiang, Sichuan, Hebei, and Hunan are the five provinces most affected by climate change. This study’s findings indicate that the average total factor forestry productivity (TFPC) is 1.0480, representing an increase of 4.80%. The primary determinant for change is technology change (TC), which surpasses efficiency change (EC). Including climate variables reduces total factor productivity change (TFPC) to 1.0205, mainly driven by a decrease in TC. The region of South China exhibits the highest total factor productivity change (TFPC) with a value of 1.087, whereas both Northeast China and Central China observe falls below 1 in TFPC. The Mann–Whitney U test provides evidence of statistically significant disparities in forestry efficiency and TFPC scores when estimated with and without incorporating climate factors. Kruskal–Wallis found a statistically significant difference in FRE and TFPC among seven regions. Full article

This study explores the multi-dimensional relationships between technology, fiscal decentralization, and forest resource efficiency, and the pivotal role played by the digital economy as a mediator in 2002–2020. First, this study evaluates the Chinese provinces’ forest resource efficiency using multi-dimensional inputs and outputs of forest sectors. Further, we use two sorts of technology: high-technology expenditure and forest technology education. Fiscal decentralization in terms of local government expenditure on forest resources makes the study innovative and richer in analysis. A SBM-DEA analysis showed that the Anhui, Beijing, Jiangsu, Shanghai, and Zhejiang provinces have the highest efficiency scores, implying very efficient forest resource management. Subsequently, the robust econometric estimator Driscoll and Kraay is applied. The study’s findings disclose that both dimensions of technology increase the Chinese provinces’ forest resource efficiency through technological expenditure and forest technology education. Fiscal decentralization towards forest resource management expenditure increases the efficiency of forests. Urbanization and economic development reduce the efficiency of forests. The digital economy can effectively help to improve the efficiency of forest resources. The presence of moderating effects reveals that the influence of the digital economy on forest resource efficiency is positive when it is coupled with economic development, fiscal decentralization, technology, and urbanization. Full article

Autism Spectrum Disorder (ASD) is characterized by both atypical functional brain connectivity and cognitive challenges across multiple cognitive domains. The relationship between task-dependent brain connectivity and cognitive abilities, however, remains poorly understood. In this study, children with ASD and their typically developing (TD) peers engaged in semantic and pragmatic language tasks while their task-dependent brain connectivity was mapped and compared. A multivariate statistical approach revealed associations between connectivity and psychometric assessments of relevant cognitive abilities. While both groups exhibited brain–behavior correlations, the nature of these associations diverged, particularly in the directionality of overall correlations across various psychometric categories. Specifically, greater disparities in functional connectivity between the groups were linked to larger differences in Autism Questionnaire, BRIEF, MSCS, and SRS-2 scores but smaller differences in WASI, pragmatic language, and Theory of Mind scores. Our findings suggest that children with ASD utilize distinct neural communication patterns for language processing. Although networks recruited by children with ASD may appear less efficient than those typically engaged, they could serve as compensatory mechanisms for potential disruptions in conventional brain networks. Full article

China is currently facing the significant task of effectively managing its water resources to satisfy the rising needs while grappling with the growing worries of water shortage. In this context, it becomes crucial to comprehend the importance of resource agglomeration and technological adoption. Thus, this research examines the relationship between water resource agglomeration and the adoption of innovative conservation technologies in enhancing water usage efficiency at provincial and regional levels in China (2006–2020). In the first stage, the study utilizes a super SBM-Data Envelopment Analysis (DEA) methodology to evaluate the water usage efficiency of China’s provinces and regions. In the second stage, we find the dynamic nexuses between water resources, water technologies (recycling, sprinkler irrigation) and water usage efficiency by applying a systematic econometric approach. SBM-DEA analysis revealed that Beijing (1.08), Shaanxi (1.01), Shanghai (1.23) and Tianjin (1.01) remained the higher efficient over the years. Six provinces (Guangdong, Shandong, Jiangsu, Inner Mongolia, Hebei, and Zhejiang) are in the middle ranges (0.55–0.83). In contrast, nineteen provinces have the lowest water usage efficiency (0.21–049). Qinghai and Ningxia are on the lowest rank (0.21) and (0.22), respectively. The findings recommended that the water resources impact is negative. In comparison, the impact of water-saving mechanisms on the efficiency of water usage seems to be positive, as recycling technology significantly enhances the water usage efficiency in China’s province. The study found that GDP growth has a negative impact on water usage efficiency in the early stages of economic development. Still, as economies mature, this negative impact diminishes, indicating a tendency to allocate more resources to water conservation and efficiency. Water recycling technology, the modernization of irrigation methods, and water resource management can enhance water efficiency. Full article

A critical unmet medical need in prostate cancer (PCa) clinical management centers around distinguishing indolent from aggressive tumors. Traditionally, Gleason grading has been utilized for this purpose. However, tumor classification using Gleason Grade 7 is often ambiguous, as the clinical behavior of these tumors follows a variable clinical course. This study aimed to investigate the application of machine learning techniques (ML) to classify patients into indolent and aggressive PCas. We used gene expression data from The Cancer Genome Atlas and compared gene expression levels between indolent and aggressive tumors to identify features for developing and validating a range of ML and stacking algorithms. ML algorithms accurately distinguished indolent from aggressive PCas. With the accuracy of 96%, the stacking model was superior to individual ML algorithms when all samples with primary Gleason Grades 6 to 10 were used. Excluding samples with Gleason Grade 7 improved accuracy to 97%. This study shows that ML algorithms and stacking models are powerful approaches for the accurate classification of indolent versus aggressive PCas. Future implementation of this methodology may significantly impact clinical decision making and patient outcomes in the clinical management of prostate cancer. Full article

Society and the environment are severely impacted by catastrophic events, specifically floods. Inadequate emergency preparedness and response are frequently the result of the absence of a comprehensive plan for flood management. This article proposes a novel flood disaster management (FDM) system using the full lifecycle disaster event model (FLCNDEM), an abstract model based on the function super object. The proposed FDM system integrates data from existing flood protocols, languages, and patterns and analyzes viewing requests at various phases of an event to enhance preparedness and response. The construction of a task library and knowledge base to initialize FLCNDEM results in FLCDEM flooding response. The proposed FDM system improves the emergency response by offering a comprehensive framework for flood management, including pre-disaster planning, real-time monitoring, and post-disaster evaluation. The proposed system can be modified to accommodate various flood scenarios and enhance global flood management. Full article

Disaster management is a critical area that requires efficient methods and techniques to address various challenges. This comprehensive assessment offers an in-depth overview of disaster management systems, methods, obstacles, and potential future paths. Specifically, it focuses on flood control, a significant and recurrent category of natural disasters. The analysis begins by exploring various types of natural catastrophes, including earthquakes, wildfires, and floods. It then delves into the different domains that collectively contribute to effective flood management. These domains encompass cutting-edge technologies such as big data analysis and cloud computing, providing scalable and reliable infrastructure for data storage, processing, and analysis. The study investigates the potential of the Internet of Things and sensor networks to gather real-time data from flood-prone areas, enhancing situational awareness and enabling prompt actions. Model-driven engineering is examined for its utility in developing and modeling flood scenarios, aiding in preparation and response planning. This study includes the Google Earth engine (GEE) and examines previous studies involving GEE. Moreover, we discuss remote sensing; remote sensing is undoubtedly a valuable tool for disaster management, and offers geographical data in various situations. We explore the application of Geographical Information System (GIS) and Spatial Data Management for visualizing and analyzing spatial data and facilitating informed decision-making and resource allocation during floods. In the final section, the focus shifts to the utilization of machine learning and data analytics in flood management. These methodologies offer predictive models and data-driven insights, enhancing early warning systems, risk assessment, and mitigation strategies. Through this in-depth analysis, the significance of incorporating these spheres into flood control procedures is highlighted, with the aim of improving disaster management techniques and enhancing resilience in flood-prone regions. The paper addresses existing challenges and provides future research directions, ultimately striving for a clearer and more coherent representation of disaster management techniques. Full article

Withania coagulans (Stocks) Dunal is used in traditional medicine to treat diseases and has numerous pharmacological properties due to its biological compounds. The plant is a subshrub native to Asia, especially the tropical and temperate regions of western Asia. Its medicinal effects derive from its biological components, which are linked to human health. Conventional medicine uses these compounds to treat a variety of diseases, such as neurological issues, diabetes, and asthma. The long-term benefits of W. coagulans necessitate conservation strategies and plant biotechnological techniques such as micropropagation, synthetic seed, cell suspension, and hairy root elicitation technology, and genetic transformation can all play significant roles in conservation and sustainable utilization of the biological compounds for clinical uses. The objective of this review is to provide a comprehensive overview of the W. cogaulans medicinal properties, potential applications, and innovative approaches for sustainable utilization, making it a unique contribution to the existing body of knowledge. Multi-omics methods for the production of withanolides were also examined in order to gain a better understanding of the genome structure, prospective genes, and candidate proteins involved in the production. Full article

Protein molecules show varying degrees of flexibility throughout their three-dimensional structures. The flexibility is determined by the fluctuations in torsion angles, specifically phi (φ) and psi (ψ), which define the protein backbone. These angle fluctuations are derived from variations in backbone torsion angles observed in different models. By analyzing the fluctuations in Cartesian coordinate space, we can understand the structural flexibility of proteins. Predicting torsion angle fluctuations is valuable for determining protein function and structure when these angles act as constraints. In this study, a machine learning method called TAFPred is developed to predict torsion angle fluctuations using protein sequences directly. The method incorporates various features, such as disorder probability, position-specific scoring matrix profiles, secondary structure probabilities, and more. TAFPred, employing an optimized Light Gradient Boosting Machine Regressor (LightGBM), achieved high accuracy with correlation coefficients of 0.746 and 0.737 and mean absolute errors of 0.114 and 0.123 for the φ and ψ angles, respectively. Compared to the state-of-the-art method, TAFPred demonstrated significant improvements of 10.08% in MAE and 24.83% in PCC for the phi angle and 9.93% in MAE, and 22.37% in PCC for the psi angle. Full article