Search Results (619)

Background and Objectives: The management of type 2 diabetes (T2D) extends beyond glycemic control, requiring a more global strategy that includes optimization of body composition, even more so in the context of sarcopenia and visceral adiposity, as they contribute to poor outcomes. Past reviews have typically been focused on weight reduction or glycemic effectiveness, with limited inclusion of new therapies’ effects on muscle and fat distribution. In addition, the emergence of incretin-based therapies and dual agonists such as tirzepatide requires an updated synthesis of their impacts on body composition. This review attempts to bridge the gap by taking a systematic approach to how current blood-glucose lowering therapies affect lean body mass, fat mass, and the risk of sarcopenia in T2D patients. Materials and Methods: Between January 2015 and March 2025, we conducted a narrative review by searching the PubMed, Scopus, and Web of Science databases for English-language articles. The keywords were combinations of the following: “type 2 diabetes,” “lean body mass,” “fat mass,” “body composition,” “sarcopenia,” “GLP-1 receptor agonists,” “SGLT2 inhibitors,” “tirzepatide,” and “antidiabetic pharmacotherapy.” Reference lists were searched manually as well. The highest precedence was assigned to studies that aimed at adult type 2 diabetic subjects and reported body composition results. Inclusion criteria for studies were: (1) type 2 diabetic mellitus adult patients and (2) reporting measures of body composition (e.g., lean body mass, fat mass, or muscle function). We prioritized randomized controlled trials and large observational studies and excluded mixed diabetic populations, non-pharmacological interventions only, and poor reporting of body composition. Results: Metformin was widely found to be weight-neutral with minimal effects on muscle mass. Insulin therapy, being an anabolic hormone, often leads to fat mass accumulation and increases the risk of sarcopenic obesity. Incretin-based therapies induced substantial weight loss, mostly from fat mass. Notable results were observed in studies with tirzepatide, demonstrating superior reduction not only in fat mass, but also in visceral fat. Sodium-glucose cotransporter 2 inhibitors (SGLT2 inhibitors) promote fat loss but are associated with a small yet significant decrease in lean muscle mass. Conclusions: Blood-glucose lowering therapies demonstrated clinically relevant effects on body composition. Treatment should be personalized, balancing glycemic control, cardiovascular, and renal benefits, together with optimal impact on muscle mass along with glycemic, cardiovascular, and renal benefits. Full article

Despite decades of intense interest and investment in cognitive science, there remains a not only incomplete but also highly inconsistent body of evidence regarding how adult brains recover from even the most focal injuries associated with stroke. In this paper, I provide a broad narrative review of the studies of post-stroke aphasia recovery that have sought to identify the mechanisms of language recovery through longitudinal functional imaging. I start with studies that used functional imaging in groups of neurotypical individuals that have revealed areas of the brain that are reliably activated by language tasks and are functionally connected, referred to here as the “language network.” I then review group studies in which functional imaging data were averaged across groups of people with post-stroke aphasia to characterize the neurobiology of recovery. These group studies of post-stroke aphasia have yielded very different results and have led to conflicting conclusions. Subsequently, I examine results of studies of single subjects that have employed longitudinal functional imaging to identify mechanisms of language recovery. Together, these single subject studies make it clear that mechanisms of neural recovery are far from uniform, even in people with very similar lesions and time since stroke. On this basis, I argue that it is not justifiable to average functional imaging data across individuals with post-stroke aphasia to draw meaningful insights into how brain networks change to support language recovery. Each individual’s brain networks change over time, but in divergent ways that depend on the extent of disruption to the normal language network, interventions to facilitate recovery, the health of the intact brain, and other variables yet to be identified. While averaging results across participants with post-stroke aphasia might be able to identify certain changes in the networks that are correlated with specific language gains, uncovering the range of mechanisms and dynamics of language recovery after stroke requires longitudinal imaging of individuals. Full article

Many recent discoveries highlight the existence of a robust bidirectional link between nutrition and psychological processes. Despite these developments, the systematic and formalized study of this connection is only beginning to be undertaken, and nutritional psychology is not yet considered a formal area of study within the psychological sciences. This paper defines the scope of nutritional psychology through 6 core areas of conceptualization, each informed by an interdisciplinary and growing body of evidence spanning the psychological and nutritional sciences. These include the diet-conative/affective, diet-cognitive, diet-sensory/perception, diet-interoceptive, diet-psychosocial, and diet-environmental relationships. Introducing these conceptualizations contributes to the development of innovative interdisciplinary language, method, and conceptualization of the diet-mental health relationship within nutritional psychology. Full article

Vocabulary acquisition is a fundamental challenge in second language (L2) learning. Recent research highlights the benefits of using gestures to enhance vocabulary retention. This comprehensive review explores the theoretical, empirical, and neuroscientific foundations of gesture-enhanced learning. Findings show that the human body, specifically sensorimotor engagement, can be harnessed as an effective cognitive tool to support long-term word learning. This paper examines the limitations of traditional vocabulary learning methods, introduces embodied cognition as a theoretical framework, presents behavioral and neuroscientific evidence supporting gesture-based learning, and offers practical applications for educational settings. This integration of multidisciplinary research provides a robust foundation for reconceptualizing the role of physical engagement in second language acquisition. Full article

Microplastic (MP) pollution is an escalating global environmental concern, with a growing body of research addressing diverse dimensions of this issue. Despite this progress, the field remains hindered by generating large, heterogeneous datasets that follow inconsistent reporting standards, resulting in fragmented and often incompatible databases. While various databases on MPs have been developed, they primarily operate in isolation, limiting the accessibility and cross-comparison of data. This study presents a foundational approach to aggregating and accessing existing MP pollution datasets. A comprehensive review of the currently available databases was conducted to evaluate their integration potential. It revealed key challenges such as non-standardized data formats, limited accessibility, and difficulty performing comparative analyses across sources. To address these barriers, a prototype web-based platform was developed that enables unified access to MP datasets. The architecture includes a smart standardization layer that harmonizes inputs from disparate sources. The integration of Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) techniques was proposed to facilitate natural language querying. This enables researchers to interact with the platform intuitively and extract meaningful insights more efficiently. The proposed system aims to enhance data discoverability, promote interoperability, and support robust, data-driven environmental research, paving the way toward more informed policy-making and scientific collaboration in the fight against MP pollution. With this platform, there is a potential for new discoveries and a future in which the tools to effectively combat this global issue are available, making the audience realize the potential for new discoveries. Full article

This article examines the theological trajectories of Watchman Nee (1903–1972) and Witness Lee (1905–1997) on sanctification, union with Christ, and deification, situating their contributions within recent reappraisals of the doctrine of theosis in the academy. Though deification was universally affirmed by the early church and retained in various forms in medieval and early Protestant theology, post-Reformation Western Christianity marginalized this theme in favor of juridical and forensic soteriological categories. Against this backdrop, Nee and Lee offer a theologically rich, biblically grounded, and experientially oriented articulation of deification that warrants greater scholarly attention. Drawing from the Keswick Holiness tradition, patristic sources, and Christian mysticism, Nee developed a soteriology that integrates justification, sanctification, and glorification within an organic model of progressive union with God. Though he does not explicitly use the term “deification”, the language he employs regarding union and participation closely mirrors classical expressions of Christian theosis. For Nee, sanctification is not merely moral improvement but the transformative increase of the divine life, culminating in conformity to Christ’s image. Lee builds upon and expands Nee’s participatory soteriology into a comprehensive theology of deification, explicitly referring to it as “the high peak of the divine revelation” in the Holy Scriptures. For Lee, humans become God “in life and nature but not in the Godhead”. By employing the phrase “not in the Godhead”, Lee upholds the Creator–creature distinction—i.e., humans never participate in the ontological Trinity or God’s incommunicable attributes. Yet, in the first portion of his description, he affirms that human beings undergo an organic, transformative process by which they become God in deeply significant ways. His framework structures sanctification as a seven-stage process, culminating in the believer’s transformation and incorporation into the Body of Christ to become a constituent of a corporate God-man. This corporate dimension—often overlooked in Western accounts—lies at the heart of Lee’s ecclesiology, which he sees as being consummated in the eschatological New Jerusalem. Ultimately, this study argues that Nee and Lee provide a coherent, non-speculative model of deification that integrates biblical exegesis, theological tradition, and practical spirituality, and thus, present a compelling alternative to individualistic and forensic soteriologies while also highlighting the need for deeper engagement across global theological discourse on sanctification, union with Christ, and the Triune God. Full article

“Coexpressivity” is the property of utterances that marshal multiple linguistic elements and modalities simultaneously to perform the distinct linguistic functions of Jakobson’s classic analysis (1960). This study draws on a longitudinal corpus of natural conversation recorded over six decades with an accomplished “master speaker” of Tsotsil (Mayan), adept at using his language to manage different aspects of social life. The research aims to elaborate the notion of coexpressivity through detailed examples drawn from a range of circumstances. It begins with codified emic speech genres linked to prayer and formal declamation and then ranges through conversational narratives to gossip-laden multiparty interaction, to emphasize coexpressive connections between speech as text and concurrent gesture, gaze, and posture among interlocutors; audible modalities such as sound symbolism, pitch, and speech rate; and finally, specific morphological characteristics and the multifunctional effects of lexical choices themselves. The study thus explores how multiple functions may, in principle, be coexpressed simultaneously or contemporaneously in individual utterances if one takes this range of modalities and expressive resources into account. The notion of “master speaker” relates to coexpressive virtuosity by linking the resources available in speech, body, and interactive environments to accomplishing a wide range of social ends, perhaps with a special flourish although not excluded from humbler, plainer talk. Full article

Sign language is a complex and dynamic visual language that requires the coordinated movement of various body parts, such as the hands, arms, and limbs—making it an ideal application domain for sensor networks to capture and interpret human gestures accurately. To address the intricate task of precise and expedient SLR from raw videos, this study introduces a novel deep learning approach by devising a multimodal framework for SLR. Specifically, feature extraction models are built based on two modalities: skeleton and RGB images. In this paper, we firstly propose a Multi-Stream Spatio-Temporal Graph Convolutional Network (MSGCN) that relies on three modules: a decoupling graph convolutional network, a self-emphasizing temporal convolutional network, and a spatio-temporal joint attention module. These modules are combined to capture the spatio-temporal information in multi-stream skeleton features. Secondly, we propose a 3D ResNet model based on deformable convolution (D-ResNet) to model complex spatial and temporal sequences in the original raw images. Finally, a gating mechanism-based Multi-Stream Fusion Module (MFM) is employed to merge the results of the two modalities. Extensive experiments are conducted on the public datasets AUTSL and WLASL, achieving competitive results compared to state-of-the-art systems. Full article

The summarized content of one or more extensive text documents helps users extract only the most important key information, instead of reviewing and reading hundreds of pages of text. This study uses extractive and abstractive mechanisms to automatically extract and summarize information retrieved from various web documents on the same topic. The research aims to develop a methodology for designing and developing an information system for pre- and post-processing natural language obtained through web content search and web scraping, and for the automatic generation of a summary of the retrieved text. The research outlines two subtasks. As a first step, the system is designed to collect and process up-to-date information based on specific criteria from diverse web resources related to project funding, initiated by various organizations such as startups, sustainable companies, municipalities, government bodies, schools, the NGO sector, and others. As a second step, the collected extensive textual information about current projects and programs, which is typically intended for financial professionals, is to be summarized into a shorter version and transformed into a suitable format for a wide range of non-specialist users. The automated AI software tool, which will be developed using the proposed methodology, will be able to crawl and read project funding information from various web documents, select, process, and prepare a shortened version containing only the most important key information for its clients. Full article

Software-defined networking (SDN) represents a transformative shift in network architecture by decoupling the control plane from the data plane, enabling centralized and flexible management of network resources. However, this architectural shift introduces significant security challenges, as SDN’s centralized control becomes an attractive target for various types of attacks. While the body of current research on attack detection in SDN has yielded important results, several critical gaps remain that require further exploration. Addressing challenges in feature selection, broadening the scope beyond Distributed Denial of Service (DDoS) attacks, strengthening attack decisions based on multi-flow analysis, and building models capable of detecting unseen attacks that they have not been explicitly trained on are essential steps toward advancing security measures in SDN environments. In this paper, we introduce a novel approach that leverages Natural Language Processing (NLP) and the pre-trained Bidirectional Encoder Representations from Transformers (BERT)-base-uncased model to enhance the detection of attacks in SDN environments. Our approach transforms network flow data into a format interpretable by language models, allowing BERT-base-uncased to capture intricate patterns and relationships within network traffic. By utilizing Random Forest for feature selection, we optimize model performance and reduce computational overhead, ensuring efficient and accurate detection. Attack decisions are made based on several flows, providing stronger and more reliable detection of malicious traffic. Furthermore, our proposed method is specifically designed to detect previously unseen attacks, offering a solution for identifying threats that the model was not explicitly trained on. To rigorously evaluate our approach, we conducted experiments in two scenarios: one focused on detecting known attacks, achieving an accuracy, precision, recall, and F1-score of 99.96%, and another on detecting previously unseen attacks, where our model achieved 99.96% in all metrics, demonstrating the robustness and precision of our framework in detecting evolving threats, and reinforcing its potential to improve the security and resilience of SDN networks. Full article

There is a growing body of literature that focuses on the applicability of artificial intelligence (AI) in English as a Foreign Language (EFL) and English Language (EL) classrooms; however, educational application of AI in the EFL and EL classroom for gifted students presents a new paradigm. This paper explores the existing research to highlight current practices and future possibilities of AI for teaching EFL and EL to address gifted students’ special needs. In general, the uses of AI are being established for class instruction and intervention; nevertheless, there is still uncertainty about practitioner use of AI with gifted students in EFL and EL classrooms. This review identifies 42 examples of GenAI Models that can be used in gifted EFL and EL classrooms. In addition, the research conducted thus far has highlighted the positive contribution of the use of AI in EFL and EL environments, albeit some disadvantages and challenges have also been identified. The results also endorse the use of AI with gifted students as an asset and highlight the need for AI literacy for both teachers and gifted students in order to adapt to this new educational paradigm. In conclusion, more studies are needed, as many aspects regarding both teachers’ and gifted students’ use of AI remain to be elucidated to improve future applications of AI to teach EFL and EL to gifted students. Full article

Earth observation data serve as a fundamental resource in Earth system science. The rapid advancement of remote sensing and in situ measurement technologies has led to the generation of massive volumes of data, accompanied by a growing body of geographic textual information. Efficient and accurate classification and management of these geographic texts has become a critical challenge in the field. However, the effectiveness of traditional classification approaches is hindered by several issues, including data sparsity, class imbalance, semantic ambiguity, and the prevalence of domain-specific terminology. To address these limitations and enable the intelligent management of geographic information, this study proposes an efficient geographic text classification framework based on large language models (LLMs), tailored to the unique semantic and structural characteristics of geographic data. Specifically, LLM-based data augmentation strategies are employed to mitigate the scarcity of labeled data and class imbalance. A semantic vector database is utilized to filter the label space prior to inference, enhancing the model’s adaptability to diverse geographic terms. Furthermore, few-shot prompt learning guides LLMs in understanding domain-specific language, while an output alignment mechanism improves classification stability for complex descriptions. This approach offers a scalable solution for the automated semantic classification of geographic text for unlocking the potential of ever-expanding geospatial big data, thereby advancing intelligent information processing and knowledge discovery in the geospatial domain. Full article

Generative artificial intelligence (AI) is rapidly transforming healthcare systems since the advent of OpenAI in 2022. It encompasses a class of machine learning techniques designed to create new content and is classified into large language models (LLMs) for text generation and image-generating models for creating or enhancing visual data. These generative AI models have shown widespread applications in clinical practice and research. Such applications range from medical documentation and diagnostics to patient communication and drug discovery. These models are capable of generating text messages, answering clinical questions, interpreting CT scan and MRI images, assisting in rare diagnoses, discovering new molecules, and providing medical education and training. Early studies have indicated that generative AI models can improve efficiency, reduce administrative burdens, and enhance patient engagement, although most findings are preliminary and require rigorous validation. However, the technology also raises serious concerns around accuracy, bias, privacy, ethical use, and clinical safety. Regulatory bodies, including the FDA and EMA, are beginning to define governance frameworks, while academic institutions and healthcare organizations emphasize the need for transparency, supervision, and evidence-based implementation. Generative AI is not a replacement for medical professionals but a potential partner—augmenting decision-making, streamlining communication, and supporting personalized care. Its responsible integration into healthcare could mark a paradigm shift toward more proactive, precise, and patient-centered systems. Full article

Vajrayāna teaching places the guru outside space and time, while simultaneously manifest in the teacher’s physical body. Those who regard Danzanravjaa primarily as a Buddhist teacher even today have his poems as a potent source of his teaching and consequently as a catalyst for their own spiritual development. But what can we hear across two centuries, and how can we actively listen to his religious teaching through his singular, aphoristic, and complex poetics? And to what extent can we understand today his nomadic perspective on Buddhist teaching in order better to understand the particular nature of Mongolian Buddhism? This paper will examine Danzanravjaa’s poetry in both Mongolian and Tibetan through the intertwining outer, inner, and secret levels of Tibeto-Mongolian Vajrayāna Buddhism, listening to how his poetic language and down-to-earth themes might have spoken to his contemporaries, as well as how they might speak to us today. In doing so, it presents Danzanravjaa’s poetry in a different light—not in terms of nineteenth century literature but as actionable spiritual wisdom from a teacher who, like any other, presents his own direct apprehension of Buddha nature in a challenging, personal style. Full article

Background/Objectives: Cognitive impairment often occurs in various parkinsonian syndromes. The course of deficits in cognitive functions ranges from mild cognitive decline to severe deterioration. Affected cognitive domains are also variable. The genetic background of patients exhibiting parkinsonism with concomitant cognitive decline is still elusive. A significant part of current research in Parkinson’s disease and other parkinsonian syndromes is targeted towards the genetic aspects of these disorders. The aim of the present review was to summarize existing studies focusing on the investigation of the interplay between genetic data in parkinsonism and associated cognitive symptoms. Methods: A review of English-language articles published between 2000 and 2024 was conducted, focusing on genetic studies of Parkinson’s disease and atypical parkinsonian syndromes with cognitive decline, using the databases PUBMED, SCOPUS, and EMBASE. Results: We have selected a clinical phenotype-wise assessment of parkinsonian conditions with cognitive deficits, including typical or early-onset Parkinson’s disease, dementia with Lewy bodies, Corticobasal Syndrome, Progressive Supranuclear Palsy, and frontotemporal dementia with parkinsonism. Both typical and atypical parkinsonian syndromes with concomitant cognitive decline were explored. Conclusions: Genetic background likely contributes to the heterogeneity of cognitive impairment in parkinsonian syndromes, with specific mutations linked to distinct cognitive symptoms. The integration of genetic data and a more thorough neuropsychological assessment with clinical, imaging, and biomarkers may enhance diagnosis and enable personalized therapies. Full article