Search Results (1,926)

Background/Objectives: Liver fat represents an early metabolic lesion in the development of diabetes and its cardiometabolic complications. Diets high in free sugars, particularly from sugar-sweetened beverages (SSBs), are associated with abdominal obesity and increased cardiometabolic risk, prompting global guidelines to limit SSBs as a major public health strategy. Low-fat cow’s milk is promoted as the preferred caloric replacement strategy for SSBs due to its high nutritional value and cardiometabolic advantages. Fortified soymilk is a plant-based alternative with approved health claims for cholesterol and coronary heart disease risk reduction that offers an equivalent nutritional value to cow’s milk. However, given concerns about its classification as an ultra-processed food (UPF), it is unclear whether soymilk offers comparable metabolic health benefits to milk as part of clinical and public health strategies to reduce SSB intake. The Soy Treatment Evaluation for Metabolic (STEM) health trial seeks to evaluate the impact of replacing SSBs with either 2% soymilk or 2% cow’s milk on liver fat and other cardiometabolic risk factors in habitual adult consumers of SSBs with obesity. Methods: The STEM trial is a 24-week, pragmatic, 3-arm, parallel, randomized trial. We recruited adults with obesity (high BMI plus high waist circumference based on ethnic specific cut-offs) consuming ≥1 SSB/day. Participants were randomized to one of three groups based on their usual SSB intake at baseline (servings/day): continued SSB (355 mL can) intake; replacement with fortified, sweetened 2% soymilk (250 mL); or replacement with 2% cow’s milk (250 mL). The primary outcome is the change in intrahepatocellular lipid (IHCL) measured by ¹H-MRS at 24 weeks. Hierarchical testing will be done to reduce the familywise error rate. The superiority of cow’s milk to SSBs will be assessed first to establish assay sensitivity. If superiority is established, then the non-inferiority of soymilk to cow’s milk will be assessed using a pre-specified non-inferiority margin of 1.5% IHCL units (assessed by difference of means using a 90% confidence interval [CI]). Analyses will be conducted according to the intention-to-treat (ITT) principle using inverse probability weighting (IPW) for superiority testing and per-protocol analyses for non-inferiority testing, using ANCOVA adjusted for age, sex, metabolic dysfunction-associated steatotic liver disease (MASLD) status, medication use, intervention dose, and baseline levels. We hypothesize that soymilk will be non-inferior to cow’s milk (Clinicaltrials.gov NCT05191160). Results: Recruitment began in November 2021. A total of 3050 individuals were screened. We randomized 186 participants (62 per group) between 19 April 2022 and 16 April 2024. Participants are 57% male; with a mean [SD] age of 39.9 [11.8] years; BMI of 34.6 [6.1] kg/m², waist circumference of 112.6 [13.8] cm; IHCL of 10.0 [8.2] % with 64.1% meeting the criteria for MASLD; and SSBs intake of 2.3 [1.3] servings/day. Conclusions: Baseline characteristics were balanced across the study arms, with participants representing adults with a high-risk metabolic phenotype, and 64.1% meeting the criteria for MASLD. Findings will contribute to evidence on the cardiometabolic benefits of soymilk, informing clinical practice guidelines and public health policy. Full article

This study aimed to evaluate the efficacy of various control alternatives against Varroa destructor in Apis mellifera colonies in a semi-arid region of Mexico. One hundred and ten homogeneous colonies, with a uniform population and infestation level of V. destructor, were randomly distributed into the following 11 experimental groups (10 colonies/group): amitraz, oxalic acid in glycerin (OA-G), oxalic acid in sugar syrup (OA-SS), ethanolic extracts of Bursera penicillata, Larrea tridentata, and Lippia graveolens, powdered sugar dusting, three vehicle controls (vegetable oil, ethanol, glycerin), and one untreated control. Efficacy was determined by recording mite fall during the treatment period relative to a subsequent reference treatment. Significant differences were observed among treatments (p < 0.0001). Amitraz was the most effective (94.4%), followed by OA-G (85.1%). The OA-SS and plant extracts showed intermediate efficacy (62.1% to 73.7%), while sugar dusting showed lower values (55.8%) but still higher than the control (31.2%). These findings support the restricting of amitraz use to minimize resistance risk and suggest implementing OA-G as a high-efficacy alternative. Furthermore, ethanolic plant extracts and powdered sugar dusting combined with sticky bottom boards may serve as accessible, complementary tools within integrated pest management programs to reduce reliance on synthetic acaricides and mitigate the development of resistance. Full article

Cognitive impairments are a core feature of psychotic disorders and are strongly associated with long-term functional disability. Although Cognitive Remediation Therapy (CRT) is an evidence-based intervention for improving cognition in psychosis, its feasibility and preliminary effects in acute inpatient settings—particularly using web-based platforms—remain underexplored. This single-arm, pre–post pilot study evaluated the feasibility of delivering a web-based CRT program and examined preliminary cognitive outcomes in a secure psychiatric inpatient facility. Thirteen inpatients with psychotic and non-psychotic diagnoses completed a 15-week intervention comprising twice-weekly sessions that included adaptive computerized CRT exercises (Happy Neuron Pro) and therapist-led bridging discussions focused on metacognitive reflection and functional application. Cognitive performance was assessed pre- and post-intervention using the MATRICS Consensus Cognitive Battery. All participants completed the study with no withdrawals or adverse events, attending a mean of 27.77 of 30 sessions (93.0%). Pre–post improvements were observed in processing speed, verbal learning, and overall composite cognition, with large within-sample effect sizes that remained robust in sensitivity analyses. Exploratory analyses suggested potential associations between sex, history of self-harm, and cognitive change, though these findings require cautious interpretation. Findings support the feasibility of inpatient web-based CRT and provide preliminary cognitive effect-size estimates. Given the single-arm design and absence of systematic medication monitoring, results should be interpreted as exploratory signals warranting controlled validation. Overall, findings support the feasibility of inpatient web-based CRT and provide preliminary signals of cognitive benefit, warranting evaluation in larger controlled studies. Full article

Background: An emerging tool to better simulate the complexity of tumour biology in vitro is 3D culture models. Several approaches have been introduced, yet many face challenges such as technical complexity or limited ability to reproduce critical tumour traits. Modular tissue engineering is a well-known method in tissue transplantation, where it has been used to develop various healthy tissue constructs. In this study, we set out to adapt this established approach to fabricate cancer microtissues and to assess their effectiveness as a tumour model that can capture essential features of cancer biology and drug-treatment response. Methods: Two triple-negative breast cancer (TNBC) cell lines, HCC1806 and MDA-MB-231, were cultured in microtissues and assessed for viability, cell death, generation of hypoxia and response to chemotherapy. To benchmark our model, we utilized flow cytometry to analyze the CD44⁺/CD24⁻ cancer stem cell (CSC) phenotype across microtissues, 2D monolayers, and established 3D models, including spheroids, collagen domes, and laminin-rich domes. Results: The cells showed sustained cell viability with minimal cell death, along with natural development of tumour properties, such as hypoxia. Crucially, flow cytometry revealed a cell-line-dependent regulation of the CD44⁺/CD24⁻ phenotype, underscoring the complex influence of the 3D microenvironment on stem cell regulation. Furthermore, by screening the model with standard anti-breast cancer chemotherapeutics, we observed drug resistance at concentrations comparable to those used in the clinic. Conclusions: Our model offers the unique ability to spontaneously reproduce fundamental features of tumours in vitro, capturing the cellular heterogeneity and reprogramming that drive clinical drug resistance. Full article

This study focuses on the spatial variation in seed germination characteristics of Picea glauca and P. mariana, prominent and widespread species within the Canadian boreal forest. The main objective was to determine seed germination requirements of geographically distinct seed collections of P. glauca and P. mariana. A total of 73 collections of P. glauca and 62 collections of P. mariana were selected across Canada and tested for germination under various temperatures. Base temperature (T_b) and thermal time required to reach 50% germination (TH₅₀) were derived from thermal model parameters for all seed collections. Correlation analyses between seed germination traits, geographic, and climatic variables were conducted. Base temperatures for germination of P. glauca ranged from 5.2 to 11.9 °C while P. mariana had base temperatures ranging from 6.2 to 12.8 °C, indicating a broader temperature range for the former to initiate germination. Optimal germination temperatures ranged from 15 to 20 °C for P. glauca and from 17.5 to 30 °C for P. mariana. Thermal time requirements for 50% germination were higher for P. glauca than for P. mariana, indicating that the former takes longer to germinate under the same temperature conditions. Latitudinal-related variables such as temperature of sites had a stronger influence on germination relative to precipitation or potential evaporation and affected seed viability, final germination and germination capacity of all seed sources. Seed viability was lower in northern seed collections and germination capacity was diminished at lower temperatures for both species. The results from this study can be built into models predicting shifts in boreal forest species under climate change. Full article

Biophilic design has gained increasing importance in contemporary architecture due to its potential to enhance human well-being, environmental quality, and the integration of nature within built environments. While a growing body of literature exists, there remains a limited understanding of how biophilic architecture is visually expressed in diverse regional contexts. As the exterior of a building constitutes its most immediate expression, examining these characteristics is essential. This research gap raises the question of how exterior biophilic design characteristics are articulated in architectural practice within different geographic settings. To address this gap, this study examines the exterior architectural characteristics of biophilic design through selected case studies from Asia, Europe, and Australia, focusing on how these characteristics are manifested in each regional context. The research adopts a three-step methodological approach. First, on-site photographic documentation was conducted. Second, the collected photographs were systematically coded. Third, descriptive analysis was employed to examine the distribution of biophilic design characteristics in diverse regional contexts. Across all regions, biophilic attributes are most prominently manifested with natural colors, natural materials, and biomorphic or natural forms. In contrast, spatial attributes appear less consistently documented in photographs. This study is limited by its reliance on photographic analysis and a relatively small sample size. Future research should integrate multimethod approaches and expanded case studies to capture experiential and environmental dimensions of biophilic design beyond visual attributes. Full article

Most falls occur while walking, making gait quality a logical therapeutic target. Many temporo-spatial variables have been implicated in increased fall risk, but these are dependent upon kinematic parameters of the joints involved in the gait cycle. The widespread availability of wearable sensors has made the acquisition of kinematic data feasible, and those related to the ankle are most relevant, as they relate most closely to causes of falls, trips, slips, and mis-steps. The purpose of this study is to estimate the extent to which measures of ankle angular velocity (AV) during walking are associated with falls. This is a comparative study of ankle AV metrics between people who have or have not experienced a fall in the past year. Data came from experimental use of the Heel2Toe™ sensor in a variety of settings, including demonstrations and clinical research studies. The sample comprised 387 participants, of whom 68 (17.6%) self-reported falling in the past year. Logistic regression with a natural cubic spline with 3 degrees of freedom identified AV of the angle at heel strike to discriminate between fallers and non-fallers, and the regression parameters were used to propose an algorithm to estimate fall risk. Applying the algorithm to the existing data yielded a range of probabilities from 0.0480 to 0.7245 depending on age of the person assessed. Further testing of this algorithm in different samples is warranted. Full article

CD8⁺ memory T (T_M) cells are essential for vaccine-induced protective immunity. While transforming growth factor beta (TGF-β) triggers CD8⁺ T_M cell differentiation, the underlying molecular mechanism(s) has yet to be uncovered. We therefore used a well-established cell culture protocol to prepare TGF-β-triggered CD8⁺ T_M cells derived from chicken ovalbumin (OVA)-specific T cell receptor (TCR) transgenic OTI mice, and systematically characterized them using Western blotting, confocal microscopy, flow cytometry and Seahorse assay analyses. We found that TGF-β/T cells exhibit a T_M cell phenotype (CD62L⁺KLRG1⁻) and display long-term survival upon adoptive transfer into mice. To elucidate the signaling circuitry underpinning the observed transcriptional and metabolic changes required to promote CD8⁺ T_M cell differentiation, we measured the expression of several critical factors and found that TGF-β triggered weak mTORC1 (mTORC1^Weak) signaling. mTORC1^Weak signaling in turn led to an increase in the abundance of key transcriptional (TCF1, FOXO1 and Eomes) and metabolic (AMPK-α1, ATG7, ULK1, SIRT1, OPA1 and LAL) factors and an elevation in mitochondrial mass and reliance on fatty acid oxidation (FAO). Our data thus reveal for the first time that TGF-β regulates CD8⁺ T cell memory by triggering mTORC1^Weak-mediated activation of the transcriptional FOXO1-TCF1-Eomes and metabolic AMPK-ULK1-ATG7 pathways. Given that induction of more qualified CD8⁺ T_M cells is one of the ultimate goals of vaccination, our findings identify additional targets critical to TGF-β-induced T cell memory, which may greatly impact future vaccine development for the treatment of cancer and infectious diseases. Full article

Forest fires are a major concern due to their significant impact on the environment, economy, and wildlife habitats. Efficient early detection systems can significantly mitigate their devastating effects. This paper provides a comprehensive review of forest fire detection (FFD) techniques and traces their evolution from basic lookout-based methods to sophisticated remote sensing technologies, including recent Internet of Things (IoT)- and Unmanned Aerial Vehicle (UAV)-based sensor network systems. Historical methods, characterized primarily by human surveillance and basic electronic sensors, laid the foundation for modern techniques. Recently, there has been a noticeable shift toward ground-based sensors, automated camera systems, aerial surveillance using drones and aircraft, and satellite imaging. Moreover, the rise of Artificial Intelligence (AI), Machine Learning (ML), and the IoT introduces a new era of advanced detection capabilities. These detection systems are being actively deployed in wildfire-prone regions, where early alerts have proven critical in minimizing damage and aiding rapid response. All FFD techniques follow a common path of data collection, pre-processing, data compression, transmission, and post-processing. Providing sufficient power to complete these tasks is also an important area of research. Recent research focuses on image compression techniques, data transmission, the application of ML and AI at edge nodes and servers, and the minimization of energy consumption, among other emerging directions. However, to build a sustainable FFD model, proper sensor deployment is essential. Sensors can be either fixed at specific geographic locations or attached to UAVs. In some cases, a combination of fixed and UAV-mounted sensors may be used. Careful planning of sensor deployment is essential for the success of the model. Moreover, ensuring adequate energy supply for both ground-based and UAV-based sensors is important. Replacing sensor batteries or recharging UAVs in remote areas is highly challenging, particularly in the absence of an operator. Hence, future FFD systems must prioritize not only detection accuracy but also long-term energy autonomy and strategic sensor placement. Integrating renewable energy sources, optimizing data processing, and ensuring minimal human intervention will be key to developing truly sustainable and scalable solutions. This review aims to guide researchers and developers in designing next-generation FFD systems aligned with practical field demands and environmental resilience. Full article

Flowering time is an important developmental stage in plants, influenced by multiple genes and environmental factors. Understanding its genetic basis and interaction with the environment facilitates the development of improved varieties adapted to different environments. Conventional Genome-Wide Association Studies (GWAS) have been widely used to associate genetic markers with heritable traits, but they do not inherently capture interactions among single nucleotide polymorphisms (SNPs) or between SNPs and the environment. Machine Learning (ML) approaches can model these interactions and improve trait prediction even in the presence of noise and missing data. In this study, multi-environment lentil (Lens culinaris Medik.) data were analysed using GWAS and two widely used ML models, Random Forest and XGBoost, to identify genetic markers associated with flowering time. Model interpretability was enhanced using Explainable AI (XAI) techniques, including SHapley Additive exPlanations. GWAS identified eight significant loci across chromosomes one, two, five and seven, with the most significant SNP located at Chr2_530433205, while ML approaches identified nine markers on chromosomes one, two, three, five and seven, with the most significant SNP at Chr7_523220088. The majority of the identified markers were linked to candidate genes for flowering, while ML also identified potential epistasis. These findings highlight ML as a powerful complementary tool to GWAS for trait association. Full article

Urban sprawl is reshaping metropolitan landscapes and placing increasing pressure on wetland ecosystems. Using Colombo, Sri Lanka as a case study, multi-temporal Landsat-based land use/land cover classifications for 1997, 2007, and 2017 were integrated with Cellular Automata–Markov land use simulation and Shannon entropy analysis to quantify historical urban growth and project future wetland exposure to 2060 under a business-as-usual scenario. Results indicate that built-up land has expanded sharply over the study period, while wetlands have declined by roughly one-quarter, indicating intensifying development pressure on ecologically sensitive areas. Model projections under a business-as-usual scenario showed continued urban expansion and a potential reduction in wetlands to less than one-fifth of their 1997 extent by 2060. Shannon entropy analysis reveals increasingly dispersed suburban growth alongside saturation in several core urban zones, confirming a spatial shift toward decentralized development. The combined results indicate rising wetland vulnerability, particularly in transitional peri-urban areas where future losses are likely to concentrate. These trends pose direct risks to flood regulation, stormwater retention, and microclimate moderation, underscoring the need for strengthened wetland safeguards, green infrastructure integration, and more controlled development strategies. The study demonstrates the utility of CA–Markov modelling for anticipating wetland vulnerability under urban expansion and provides evidence to support resilience-focused planning in rapidly urbanizing regions. Full article

Glass fiber reinforced polymers (GFRPs) have drawn significant attention given their lightweight, mechanical resistance and tunable properties through constituent selection. Due to environmental concerns, research efforts have focused on incorporating sustainable materials, such as bio-epoxy resins, to reduce the ecological impact of GFRPs. This study characterizes a GFRP containing a bio-epoxy resin matrix, various loadings of titanium dioxide (TiO₂) nanoparticles, and a stabilized arrangement of glass fiber. The unreinforced composite exhibited a tensile strength and modulus of 214 MPa, and 13 GPa, respectively, and a flexural strength and modulus of 375 MPa and 14.5 GPa, respectively. The addition of TiO₂ produced an improvement in mechanical response for all the composites. The formulation with 1 wt.% TiO₂ showed the best tensile response with an improvement of 13% and 14% for its tensile strength, and modulus, respectively; meanwhile, the composites with 2 wt.% TiO₂ attained an improvement of 19% and 40% for the flexural strength and modulus, respectively. Scanning electron microscopy (SEM) revealed significant changes in the fracture mechanism of the composites, while energy-dispersive spectroscopy (EDS) confirmed an even nanoparticle distribution. Additionally, machine learning (ML) models were developed to predict the mechanical response as a function of the TiO₂ content. Full article

The increasing severity of climate change poses profound challenges to global food security, particularly affecting vulnerable populations such as migrants and refugees. This scoping review examines the nexus between climate change, food security, and migration, focusing on the impacts and responses within affected communities. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR), this review synthesized literature across multiple databases, including Ovid MEDLINE, Embase, Global Health, Public Health, Web of Science, and PsycINFO. The search yielded 908 records, with nine articles meeting the inclusion criteria. Across studies, climate-related stressors such as rainfall variability, flooding, and drought were consistently linked to livelihood disruption and food insecurity, often shaping migration and displacement decisions. However, food security outcomes were defined and measured inconsistently, ranging from crop yields and food availability to coping strategies and self-reported hunger, limiting comparability across studies. Evidence on food security initiatives was largely descriptive, with few studies assessing intervention effectiveness or post-displacement food security outcomes. Overall, the mapped literature emphasizes food insecurity as a key mediating pathway between climate change and mobility, but reveals important gaps related to standardized outcome measures, evaluation of food security initiatives, and the food security experiences of displaced populations at destination. Full article