Search Results (18,567)

The alarming pace of species extinction severely threatens terrestrial and aquatic ecosystems, undermining the crucial ecological services vital for environmental sustainability and human well-being. Anthropogenic activities, such as urbanization, agriculture, industrialization, and those inducing climate change, intensify these risks, further imperilling biodiversity. Of particular importance are aquatic organisms, pivotal in biodiscovery and biotechnology. They contribute significantly to natural product chemistry, drug development, and various biotechnological applications. To safeguard these invaluable resources, establishing and maintaining aquatic biomaterial repositories (ABRs) is imperative. This review explores the complex landscape of ABRs, emphasizing the need for standardized procedures from collection to distribution. It identifies key legislative and regulatory frameworks, such as the Nagoya Protocol and EU directives, essential for ensuring responsible and equitable biorepository operations. Drawing on extensive literature and database searches, this study compiles existing recommendations and practices into a cohesive framework with which to guide the establishment and sustainable management of ABRs. Through collaborative efforts and adherence to best practices, ABRs can play a transformative role in the future of marine biotechnology and environmental conservation. Full article

This study carries out a comprehensive bibliometric analysis of scientific production on wildfires, soil erosion and land degradation, with the aim of understanding trends, critical gaps in scientific knowledge and research patterns. A total of 1400 articles published between 2001 and 2023 were analyzed with bibliometric tools (Bibliometrix and VOSviewer), revealing a steady growth in the number of publications over time. International collaboration between countries such as the United States, Spain, China and Portugal is evident, highlighting the global approach to tackling these issues, as well as the mobility and collaboration between scientists. Analyzing the conceptual structure through the co-occurrence of keywords reveals central themes such as “soil erosion” and “wildfire”, indicating areas of primary focus in research. This study highlights the continuing importance of these themes and the need for global collaboration to tackle the environmental challenges affecting forest ecosystems, and particularly the soil layer, caused by wildfires, which affect wildlands all over the world. Full article

Background: The development of motor competence (MC) during childhood is crucial for future physical activity and health outcomes, and it is affected by both biological and psychosocial factors. Most MC research has focused on children’s age, with fewer studies examining separate associations between MC and biological maturation. Methods: This cross-sectional study used network analysis to assess the nonlinear associations between biological maturation (the child’s percentage of predicted mature stature to indicate somatic maturation), chronological age, sex, BMI, and MC (Test of Gross Motor Development, third edition) in 218 children (100 boys, 118 girls) aged 7–9 years. Results: Biological maturation was not significantly associated with MC in boys and weakly associated with MC in girls for the dribble, under-hand throw, and gallop. Age was positively associated with MC in girls and boys. Centrality measures indicated that the gallop and slide in girls and the dribble, catch, and run in boys were the most important network variables. Positive associations were observed between maturation and BMI for girls (r = 0.579) and, to a lesser degree, for boys (r = 0.267). Conclusions: The findings suggest that age, rather than biological maturation, is positively associated with MC in 7- to 9-year-olds. Centrality measures showed that some skills may influence other skills. Full article

Event type discovery in open domains aims to automate the induction of event types from completely unlabeled text data. Conventionally, small models utilize clustering techniques to address this task. Nonetheless, the fully unsupervised nature of these methods results in suboptimal performance by small models in this context. Recently, large language models (LLMs) have demonstrated excellent capabilities in contextual understanding, providing additional relevant information for specific task scenarios, albeit with challenges in precision and cost effectiveness. In this paper, we use LLM to guide the clustering of event texts and distill this process into a fine-tuning task for training smaller pre-trained language models. This approach enables effective event type discovery even in scenarios lacking annotated data. The study unfolds in three stages: in action acquisition, leveraging LLMs to extract type-relevant information from each event text, ensuring that the event representations are particular to task-specific details; in clustering refinement and dual-fine-tune, LLMs refine results from both task-agnostic and task-specific perspectives, with the refinement process designed as fine-tuning tasks under different viewpoints to adjust encoders; and in type generation, post-clustering, LLMs generate meaningful event type labels for each cluster. Experiments show that our method outperforms current state-of-the-art approaches and excels in event type discovery tasks even in completely open-domain with no labeled data. Full article

Background/Objectives: During pregnancy, physiological changes in maternal circulating glucose levels and its metabolism are essential to meet maternal and fetal energy demands. Major changes in glucose metabolism occur throughout pregnancy and consist of higher insulin resistance and a compensatory increase in insulin secretion to maintain glucose homeostasis. For some women, this change is insufficient to maintain normoglycemia, leading to gestational diabetes mellitus (GDM), a condition characterized by maternal glucose intolerance and hyperglycaemia first diagnosed during the second or third trimester of pregnancy. GDM is diagnosed in approximately 14.0% of pregnancies globally, and it is often associated with short- and long-term adverse health outcomes in both mothers and offspring. Although recent studies have highlighted the role of genetic determinants in the development of GDM, research in this area is still lacking, hindering the development of prevention and treatment strategies. Methods: In this paper, we review recent advances in the understanding of genetic determinants of GDM and glycaemic traits during pregnancy. Results/Conclusions: Our review highlights the need for further collaborative efforts as well as larger and more diverse genotyped pregnancy cohorts to deepen our understanding of the genetic aetiology of GDM, address research gaps, and further improve diagnostic and treatment strategies. Full article

Background/Objectives: To ensure that non-melanoma skin cancer (NMSC) is completely removed in healthy tissue, micrographically controlled surgery (3D histology) is often performed, which can prolong the inpatient stay. This study examined ex vivo reflectance confocal microscopy (evRCM) for perioperative assessment of surgical margins, specifically in cases where re-excision was necessary due to incomplete removal of cutaneous tumor tissue. Methods: NMSC re-excisions were evaluated using evRCM by a cutaneous surgeon, with retrospective review by an independent pathologist when results differed from histology. Results: evRCM demonstrated high specificity (0.96; 95% CI, 0.90–0.99) but low sensitivity (0.20; 95% CI, 0.06–0.51). Unlike pathology, which discards outer surgical margins, evRCM examined the true surgical margins. Retrospective pathology analysis of the misdiagnosed cases confirmed that 25% (n = 2/8) were false negative and 75% (n = 6/8) were potentially false positive, resulting in a sensitivity of 0.2–0.8. Notably, evRCM led to a 113-day reduction in in-hospital stays, probably resulting in increased patient satisfaction and cost-effectiveness. Conclusions: evRCM was valuable for speeding up the assessment of surgical margins in patients with re-excised NMSC. Proper tissue preparation and assessment require interdisciplinary collaboration between cutaneous surgeons, pathologists, and physician assistants, emphasizing the need for standardized operating procedures. Full article

Architecture can be very influential in enabling health and wellbeing in the residential built environment. In arid regions, health-supportive design would consider major environmental hazards, such as heat and dust, as well as social and cultural factors that influence household size and composition. Under current and projected conditions, the effects of climate change will increase, presenting opportunities and challenges to architects to demonstrate and deliver best practice in climate-appropriate and culturally appropriate housing design. This paper brings together a multidisciplinary team of researchers to identify the current and future needs for tropical semi-arid zone design with a focus on Indigenous communities in northern Central Australia. In a narrative review of the literature and exploratory fieldwork, current and projected impacts of climate change on housing, health, energy systems and behavioural practices are explored in a holistic way. Our findings demonstrate the growing awareness of climate-related issues for remote Indigenous housing but also the lack of place-based studies that might inform adaptation strategies. Overwhelmingly, there is limited understanding of local lived experiences that may be highly significant for developing resilience in a changing climate. Consideration of the beliefs, values and practices of Indigenous people will be integral to design solutions. Full article

Multi-objective optimization problems (MOPs) constitute a vital component in the field of mathematical optimization and operations research. The multi-objective evolutionary algorithm based on decomposition (MOEA/D) decomposes a MOP into a set of single-objective subproblems and approximates the true Pareto front (PF) by optimizing these subproblems in a collaborative manner. However, most existing MOEA/Ds maintain population diversity by limiting the replacement region or scale, which come at the cost of decreasing convergence. To better balance convergence and diversity, we introduce auction theory into algorithm design and propose an auction-based matching (ABM) mechanism to coordinate the replacement procedure in MOEA/D. In the ABM mechanism, each subproblem can be associated with its preferred individual in a competitive manner by simulating the auction process in economic activities. The integration of ABM into MOEA/D forms the proposed MOEA/D-ABM. Furthermore, to make the appropriate distribution of weight vectors, a modified adjustment strategy is utilized to adaptively adjust the weight vectors during the evolution process, where the trigger timing is determined by the convergence activity of the population. Finally, MOEA/D-ABM is compared with six state-of-the-art multi-objective evolutionary algorithms (MOEAs) on some benchmark problems with two to ten objectives. The experimental results show the competitiveness of MOEA/D-ABM in the performance of diversity and convergence. They also demonstrate that the use of the ABM mechanism can greatly improve the convergence rate of the algorithm. Full article

Steroidogenic factor 1 (SF-1) is a nuclear receptor that regulates steroidogenesis and reproductive development. NR5A1/SF-1 variants are associated with a broad spectrum of phenotypes across individuals with disorders of sex development (DSDs). Oligogenic inheritance has been suggested as an explanation. SF-1 interacts with numerous partners. Here, we investigated a constellation of gene variants identified in a 46,XY severely undervirilized individual carrying an ACMG-categorized ‘pathogenic’ NR5A1/SF-1 variant in comparison to the healthy carrier father. Candidate genes were revealed by whole exome sequencing, and pathogenicity was predicted by different in silico tools. We found variants in NR1H2 and INHA associated with steroidogenesis, sex development, and reproduction. The identified variants were tested in cell models. Novel SF-1 and NR1H2 binding sites in the AR and INHA gene promoters were found. Transactivation studies showed that wild-type NR5A1/SF-1 regulates INHA and AR gene expression, while the NR5A1/SF-1 variant had decreased transcriptional activity. NR1H2 was found to regulate AR gene transcription; however, the NR1H2 variant showed normal activity. This study expands the NR5A1/SF-1 network of interacting partners, while not solving the exact interplay of different variants that might be involved in revealing the observed DSD phenotype. It also illustrates that understanding complex genetics in DSDs is challenging. Full article

Private Islamic schools in Southern Thailand face significant challenges, particularly in adhering to national educational standards, which impacts students’ academic outcomes. Most Islamic Studies teachers lack formal pedagogical training, resulting in a teaching approach that often fails to engage students effectively. This study employed participatory action research (PAR) involving 32 Islamic Studies teachers across 10 schools. The research was structured around the four phases of PAR: establishing relationships, collaborative design, implementation through LS and Open Approach, and reflective assessment. The implementation of Lesson Study and Open Approach led to significant improvements in teaching practices, shifting from traditional teacher-centered methods to learner-centered approaches that emphasized critical thinking, self-directed learning, and the integration of ICT. This study demonstrates that adapting LS to the context of Islamic education can enhance professional development for teachers and improve educational outcomes. This approach could serve as a model for broader educational reforms in the region. Full article

Angiogenesis is essential for remodeling and repairing existing vessels, and this process requires signaling pathways including those controlled by transforming growth factor beta (TGF-β). We have previously reported crosstalk between TGF-β and the protein kinase With No lysine (K) 1 (WNK1). Homozygous disruption of the gene encoding WNK1 results in lethality in mice near embryonic day E12 due to impaired angiogenesis, and this defect can be rescued by the endothelial-specific expression of an activated form of the WNK1 substrate kinase Oxidative Stress-Responsive 1 (OSR1). However, molecular processes regulated via a collaboration between TGF-β and WNK1/OSR1 are not well understood. Here, we show that WNK1 interacts with the E3 ubiquitin ligases SMURF1/2. In addition, we discovered that WNK1 regulates SMURF1/2 protein stability and vice versa. We also demonstrate that WNK1 activity regulates TGF-β receptor levels, in turn, controlling TGF-β signaling. Full article

In business process management, business process change analysis is the key link to ensure the flexibility and adaptability of the system. The existing methods mostly focus on the change analysis of a single business process from the perspective of control flow, ignoring the influence of data changes on collaborative processes with information interaction. In order to compensate for this deficiency, this paper proposes a rule-driven consistency analysis method for data changes in collaborative processes. Firstly, it analyzes the influence of data changes on other elements (such as activities, data, roles, and guards) in collaborative processes, and gives the definition of data influence. Secondly, the optimal alignment technology is used to explore how data changes interfere with the expected behavior of deviation activities, and decision rules are integrated into the Petri net model to accurately evaluate and screen out the effective expected behavior that conforms to business logic and established rules. Finally, the initial optimal alignment is repaired according to the screened effective expected behavior, and the consistency of business processes is recalculated. The experimental results show that the introduced rule constraint mechanism can effectively avoid the misjudgment of abnormal behavior. Compared with the traditional method, the average accuracy, recall rate, and F1-score of effective expected behavior are improved by 4%, 4.7%, and 4.3%, respectively. In addition, the repaired optimal alignment significantly enhances the system’s ability to respond quickly and self-adjust to data changes, providing a strong support for the intelligent and automated transformation of business process management. Full article

This study aims to provide a comprehensive bibliometric analysis covering the common areas between artificial intelligence (AI) applications and research focused on local or regional contexts. The analysis covers the period between the year 2002 and the year 2023, utilizing data sourced from the Web of Science database. Employing the Bibliometrix package within RStudio and VOSviewer software, the study identifies a significant increase in AI-related publications, with an annual growth rate of 22.67%. Notably, key journals such as Remote Sensing, PLOS ONE, and Sustainability rank among the top contributing sources. From the perspective of prominent contributing affiliations, institutions like Duy Tan University, Ton Duc Thang University, and the Chinese Academy of Sciences emerge as leading contributors, with Vietnam, Portugal, and China being the countries with the highest citation counts. Furthermore, a word cloud analysis is able to highlight the recurring keywords, including “model”, “classification”, “prediction”, “logistic regression”, “innovation”, “performance”, “random forest”, “impact”, “machine learning”, “artificial intelligence”, and “deep learning”. The co-occurrence network analysis reveals five clusters, amongst them being “artificial neural network”, “regional development”, “climate change”, “regional economy”, “management”, “technology”, “risk”, and “fuzzy inference system”. Our findings support the fact that AI is increasingly employed to address complex regional challenges, such as resource management and urban planning. AI applications, including machine learning algorithms and neural networks, have become essential for optimizing processes and decision-making at the local level. The study concludes with the fact that while AI holds vast potential for transforming local and regional research, ongoing international collaboration and the development of adaptable AI models are essential for maximizing the benefits of these technologies. Such efforts will ensure the effective implementation of AI in diverse contexts, thereby supporting sustainable regional development. Full article

As digital crime continues to rise, the preservation of digital evidence has become a critical phase in digital forensic investigations. This phase focuses on securing and maintaining the integrity of evidence for legal proceedings. Existing solutions for evidence preservation, such as centralized storage systems and cloud frameworks, present challenges related to security and collaboration. In this paper, we propose a novel framework that addresses these challenges in the preservation phase of forensics. Our framework employs a combination of advanced technologies, including the following: (1) Segmenting evidence into smaller components for improved security and manageability, (2) Utilizing steganography for covert evidence preservation, and (3) Implementing blockchain to ensure the integrity and immutability of evidence. Additionally, we incorporate Long Short-Term Memory (LSTM) networks to enhance steganography in the evidence preservation process. This approach aims to provide a secure, scalable, and reliable solution for preserving digital evidence, contributing to the effectiveness of digital forensic investigations. An experiment using linguistic steganography showed that the LSTM autoencoder effectively generates coherent text from bit streams, with low perplexity and high accuracy. Our solution outperforms existing methods across multiple datasets, providing a secure and scalable approach for digital evidence preservation. Full article

Some wrong decisions and faulty practices applied during the repair and restoration of traditional buildings cause more damage to the structures due to the materials used in the repair. The aim of this study is to establish a scientific basis for material selection in the repair of traditional buildings in the Konuralp region through chemical and petrographic analyses. In this study, brick, mortar, plaster, and wood samples were taken from one registered building in the Konuralp neighborhood of Düzce Province that has survived to the present day by preserving its original structural features and reflecting the characteristics of traditional housing. Chemical and petrographic analyses were carried out on the samples. In line with these analyses, a scientific basis was created for selecting material properties in the repair and reuse processes of traditional buildings and suggestions are made for the analysis of materials specific to traditional buildings in Konuralp. Full article