Search Results (4,806)

Forest decline in genus Abies has been increasingly associated with drought, warming, pollution, and biotic disturbances. However, the importance of these drivers and the degree to which drought–bark beetles’ interactions have been studied in Abies remain insufficiently documented. This review aimed to identify the main reported causes of decline in Abies species worldwide and to assess the current state of knowledge on the drought–bark beetles’ interaction, with special attention to sacred fir (Abies religiosa) because of its ecological importance in Mexico and its role as a winter habitat for the monarch butterfly (Danaus plexippus). We reviewed 237 articles indexed in Scopus and complemented this analysis with bibliometric indicators and keyword co-occurrence maps generated using the Bibliometrix R package (version 5.4.1) and VOSviewer (version 1.6.12). The main causes of decline were drought, pollution, and rising temperatures, affecting 41 taxa in 28 countries. Although drought has been reported throughout the study period (1977–2026), publications linking drought and warming to Abies decline have increased markedly during the last 15 years. Bibliometric results indicate that drought–bark beetles’ interactions have been extensively studied in Pinus and Picea, whereas comparable evidence for Abies remains limited. Future management should integrate monitoring, stand-density regulation, pest surveillance, and climate-adaptation strategies implemented by forest managers, conservation agencies, and local communities. Full article

To boost the local microalgae sector, Switzerland needs to better understand the current state of the industry, which is not fully represented in the existing literature. Only by identifying the strengths and weaknesses of the Swiss microalgae industry, will the country be able to develop strategies toward a strong and sustainable sector in the future. This work provides the first structured assessment of Switzerland’s fragmented and poorly documented microalgae sector through desktop research and an online survey of the country’s microalgae stakeholders. First, research articles with Swiss authors and patents with Swiss applicants were mapped. Then, a survey consisting of 8 questions was designed to gather information about the location, purpose, employees, production capacity, activities, and installations of 42 organizations with a research and/or commercial focus. The growing number of organizations working with microalgae in Switzerland is dominated by small companies (<50 employees) that provide services rather than biomass or bioproducts. Microalgae biomass production is about 2 tons DW per year and is also dominated by small-scale producers (<100 kg DW per year). One third of Swiss companies that sell microalgae-based products produce their own biomass abroad or purchase from abroad. Our findings highlight the growth potential of the Swiss microalgae sector. This systematic summary of research interests, technological innovations, and current market parameters is the first step toward future improvements in the sector. Full article

Rapid and reliable flood segmentation utilizing optical remote-sensing imagery is critical for effective flood disaster response and risk assessment. Nevertheless, current models frequently struggle with imprecise boundary delineation and fragmented predictions in complex environments, especially where floodwater displays high spectral variability and closely resembles shadows, dark pavements, or wet soil. To overcome these challenges, we introduce FloodSeg, an innovative Mamba-CNN encoder–decoder network incorporating two lightweight yet highly effective components: a Shift module and a sequence-shuffle module. The spatial Shift module leverages spatially shifted feature aggregation to fortify boundary-aware representations, thereby ensuring the continuity of inundation contours even under varying illumination and cluttered backgrounds. Meanwhile, the sequence-shuffle module reorganizes multi-scale features via sequence-wise mixing and cross-regional interaction, significantly enhancing long-range dependency modeling. This facilitates the generation of globally consistent flood masks while mitigating local overfitting to dataset-specific textures. Evaluated on the Kaggle and FloodNet benchmark datasets, FloodSeg achieves outstanding mIoU scores of 81.85% and 91.21%, respectively. By outperforming various state-of-the-art CNN-, Transformer-, and Mamba-based baselines, our model demonstrates a superior accuracy-efficiency trade-off. These results substantiate that FloodSeg significantly advances boundary recognition and overall segmentation completeness, establishing it as a robust and practical solution for real-world remote-sensing flood mapping applications. Full article

Obesity and dental caries are highly prevalent chronic conditions with significant global health impact. Although an association between these diseases has been suggested, the nature of this relationship remains unclear. This state-of-the-art review aims to synthesize current evidence on the interplay between obesity and dental caries, focusing on shared risk factors, salivary alterations and underlying biological mechanisms. Evidence indicates that obesity and dental caries share common behavioral and socioeconomic determinants, namely unhealthy dietary patterns with high intake of free sugars, poor oral hygiene habits and social disadvantage. Salivary alterations observed in obesity may also create a more cariogenic oral environment. Additionally, inflammatory mediators, oxidative stress markers and changes in the oral microbiome suggest biologically plausible links between both conditions. However, current data does not support a direct causal relationship, but rather a complex multifactorial interaction between obesity and dental caries driven by shared risk factors and modifiable behaviors. Preventive strategies should adopt an integrated approach targeting shared determinants, particularly diet, oral hygiene habits and socioeconomic status. Nevertheless, the predominance of cross-sectional evidence limits causal inference, highlighting the need for longitudinal studies that simultaneously assess obesity and dental caries, and that address salivary biomarkers using standardized methodologies across different age groups to clarify underlying mechanisms and assess their clinical relevance. Full article

Bacillus anthracis is endemic in the United States causing periodic outbreaks in wildlife and domestic animals. Currently, human anthrax cases in the U.S. are rare but were common in the 1950s–1960s due to industrial work with imported B. anthracis-contaminated animal products. Multiple-locus variable-number tandem repeat analysis (MLVA) initially differentiated B. anthracis into 89 genotypes and two major clades. Recently, whole-genome sequencing (WGS) was implemented to differentiate B. anthracis which allows for higher resolution and can eliminate risk of homoplasy. To assess the molecular diversity of U.S.-established isolates, we performed MLVA and WGS on 81 B. anthracis isolates from domestic animals or soil. By MLVA, most isolates (n = 58, 72%) were in the Western North America (WNA)/A1.a cluster. Isolates were also observed in the Ames (A3.b), Vollum (A4), and Group B clusters. Using WGS, two major clades (A and B) and four clusters (WNA, Ames, Vollum, Group B) were identified. The four WGS clusters correlated with previously established MLVA clusters (A1a, A3b, A4, and B1, respectively). Further differentiation of the WNA cluster showed that isolates collected from the same state generally clustered together and more broadly by region (west, central, Texas). In the current study, we provide an update on the genetic diversity of domestically established B. anthracis strains using MLVA and WGS. WGS was able to provide additional differentiation, particularly within the WNA cluster, which can lend assistance in epidemiological investigations. Full article

Traditional evaluations of artificial intelligence (AI) systems in the dynamic, operator-dependent, and time-sensitive field of interventional radiology (IR), focusing solely on algorithmic performance, often fail to capture their real-world clinical impact. This narrative review aims to provide an overview of the current state of the art of AI integration in IR through human–AI interaction (HAI), while offering a critical perspective on their clinical integration, limitations, and future directions. A comprehensive survey of recent literature was performed, focusing on AI applications across procedural phases. The review emphasizes systems providing decision support, real-time procedural verification, and immersive interfaces (augmented and virtual reality), while critically evaluating determinants of effective clinical adoption. AI has shown preliminary potential to support operator performance in selected interventional radiology tasks, although most applications remain experimental, retrospective, or evaluated in phantom or preclinical settings. Potential benefits include structuring uncertainty in patient selection and procedural planning, supporting assessment of device positioning and treatment outcomes, and integrating AI-derived outputs into the operator’s spatial field through immersive technologies. The clinical utility of these systems appears to be influenced by human–AI interaction, with interpretability, workflow integration, and trust calibration representing key determinants of effective use beyond algorithmic accuracy alone. The potential value of AI in interventional radiology appears to derive from its integration into human decision-making rather than from standalone predictive performance alone. A human-centered, interaction-based model supports understanding current applications, address challenges, and guide the development of adaptive, real-time systems for dynamic procedural environments. Full article

Background: Electroencephalography (EEG) signals are widely used for monitoring anesthesia depth during surgery. Current commercial indicators are largely closed-source and may reflect dynamic changes with some delay. Methods: This study proposes a multitask deep learning model for continuous Bispectral Index (BIS) estimation, binary anesthesia-state classification, and prediction of transitions toward light anesthesia at different time intervals. Dual-channel EEG signals from 5471 surgical cases in the VitalDB dataset were divided into 60 s windows. Short-Time Fourier Transform (STFT) captured instantaneous frequency changes to transform the signal into a two-dimensional map. A ResNet-SE architecture incorporating Squeeze-and-Excitation blocks was used to identify EEG features associated with anesthesia depth. Results: A Mean Absolute Error of 3.27 and a Root Mean Square Error of 5.48 were obtained in anesthesia depth estimation. Light anesthesia classification achieved an AUC of 0.99 on the internal test set. Conclusions: The proposed multitask model enables the assessment of anesthesia depth and transitions toward light anesthesia using EEG signals. Full article

Salmonella remains one of the most critical zoonotic pathogens in the poultry sector, linked to animal disease, foodborne illness, and the global crisis of antimicrobial resistance (AMR). Poultry acts as a major reservoir, enabling Salmonella transmission from hatchery to retail products through horizontal, vertical, and environmental routes. Despite the use of biosecurity, vaccination, antibiotics, and chemical decontamination, effective and sustainable control across the poultry value chain remains difficult, particularly in the face of rising multidrug-resistant strains and growing consumer concerns over chemical residues. Bacteriophages (phages), viruses that selectively infect and lyse bacteria, have emerged as a promising biological alternative for Salmonella control. Although many studies have reported the effectiveness of phages against bacterial species, including Salmonella, in the poultry industry, reports on their full potential to combat antimicrobial-resistant Salmonella across the entire poultry value chain remain limited. Therefore, this review synthesizes current evidence on the application of phages throughout the poultry value chain, including on-farm interventions, processing plant decontamination, and food packaging and storage. Findings from the reviewed articles indicate over a 90% reduction in Salmonella spp. in poultry farms and post-harvest meat, along with lower mortality in phage-treated groups compared to untreated groups; however, these outcomes depend on several factors (e.g., phage strains, concentrations, application methods, and environmental conditions). Laboratory, pilot, and field studies consistently demonstrate that phage preparations, especially when formulated as cocktails or combined with complementary interventions, can achieve substantial reductions in Salmonella, including antibiotic-resistant serovars, in live birds, eggs, poultry environments, and meat products. Unlike antibiotics and chemical sanitizers, phages act with high specificity, preserving beneficial microbiota and maintaining the sensory and nutritional quality of poultry products. Their safety has been supported by toxicological and genomic assessments, and several phage-based products have obtained regulatory approval, including Generally Recognized as Safe (GRAS) status for food applications in the United States. By integrating efficacy, safety, regulatory, and practical deployment data, this review highlights bacteriophages as a scientifically validated and One Health–aligned tool capable of reducing Salmonella transmission from farm to fork across the poultry value chain, thereby laying the foundation for their future adoption in the poultry industry. Phage-based interventions offer a sustainable pathway to enhance food safety, limit antimicrobial resistance (AMR) dissemination, and strengthen consumer confidence in poultry products. However, the major limitation is the emergence of phage-resistant bacterial strains, as well as the potential involvement of some phages in the transfer of resistance and virulence genes, which could raise public concern. Nevertheless, the use of phage cocktails and whole-genome sequencing, involving tools such as ResFinder and virulence finder, can facilitate the selection of safe phages for application. Full article

Nystagmus, the involuntary rhythmic oscillation of the eyes, is a critical diagnostic marker in vestibular medicine, distinguishing life-threatening central disorders such as stroke from benign peripheral conditions including Benign Paroxysmal Positional Vertigo (BPPV). Despite its clinical importance, accurate nystagmus assessment has long been constrained by expensive infrared video-oculography equipment such as videonystagmography, specialist dependency, and the episodic nature of vestibular symptoms that are often resolved before a clinical encounter. This review synthesizes approximately 50 papers published between 1952 and 2026 across four thematic domains: AI-driven nystagmus analysis, clinical medicine, smartphone and portable hardware innovations, and telemedicine and remote monitoring. On the AI front, classical machine learning models achieve up to 98.77% nystagmus recognition accuracy using ensemble methods, while deep learning frameworks spanning CNNs, U-Nets, LSTMs, and optical flow networks demonstrate clinical-grade slow-phase velocity measurement equivalent to gold standard video-oculography on standard smartphone RGB video. Large language and vision models including GPT-4V and Gemini 2.0 show early-stage promise as zero-shot triage tools but currently fall well below specialist-level diagnostic accuracy. Concurrently, portable hardware innovations ranging from 3D-printed goggle systems to ARKit-based smartphone applications are narrowing the accessibility gap, while telemedicine frameworks enable ictal recording and cloud-based specialist review outside the clinic. Across all domains, the common barriers to clinical translation are dataset scarcity for rare BPPV subtypes, sensitivity to ambient conditions, and the absence of explainable AI mechanisms. This review maps the current state of the field and identifies multimodal data fusion, prospective clinical validation, and interpretable AI as the critical next steps toward equitable, specialist independent vestibular diagnostics. Full article

We report the oldest female identified to date with a pathogenic NBEA variant who has been followed longitudinally. She presented with a complex, diagnostically inconclusive psychiatric phenotype extending in adulthood and a suspected mild neurodevelopmental impairment. The 64-year-old patient experienced recurrent episodes of mental state decompensation characterized predominantly by persecutory and health-related delusional ideation and anxiety. Her most recent psychiatric diagnosis was mixed conversion disorder. Although she never underwent formal cognitive testing, mild intellectual disability was suspected based on her educational attainment, occupational history, and social functioning. Additionally, the patient presented with a likely coincidental tremor. A history of childhood epilepsy could not be confirmed, as detailed epilepsy records were unavailable. Furthermore, the patient declined neuroimaging, precluding assessment of a possible relationship with the identified EXT2 deletion. This case expands the currently recognized neuropsychiatric spectrum possibly associated with pathogenic NBEA variants, highlights the importance of extending phenotypic characterization in later adulthood, and underscores the value of longitudinal follow-up. Full article

The influence of standardized driving-cycle characteristics on the dynamic and energy performance of a parallel hybrid electric vehicle controlled by a fixed-gain PID speed controller was investigated. A control-oriented MATLAB/Simulink model was developed, including an electric traction subsystem, an electric battery pack, a simplified internal combustion engine subsystem, a supervisory torque-split controller and longitudinal vehicle dynamics. The same controller configuration was evaluated under the FTP75, HWFET and US06 cycles, with the shorter cycles repeated to obtain comparable durations. Control quality was assessed using RMSE, MAE, IAE and ITAE, whereas energy performance was quantified using battery state-of-charge variation, fuel consumption, engine utilization and traction motor current loading. FTP75 yielded favorable performance, with RMSE = 0.265 m/s, fuel consumption of 4.824 L/100 km and an SoC decrease of 19.698%, whereas US06 proved severe, with RMSE = 4.567 m/s, fuel consumption of 10.328 L/100 km, an SoC decrease of 41.630% and a peak motor current of 580.9 A. Sensitivity analysis showed that ±20% PID-gain variations do not materially alter the principal conclusion, while supervisory energy-management parameters exert a stronger influence on the trade-off between tracking quality, fuel expenditure and charge maintenance. The results confirm that fixed-gain PID control is cycle-dependent and becomes inadequate under aggressive driving conditions. Full article

Saudi Arabia currently lacks a nationally representative, multi-day National Household Travel Survey comparable to the US, UK, or New Zealand programmes; existing official data products focus on aggregate road-transport indicators or general household statistics rather than detailed day-to-day travel diaries. This study develops a benchmark-driven framework for NHTS–KSA by comparing Saudi demographic, geographic, infrastructure, climate, and mobility indicators with those of the United States, United Kingdom, and New Zealand, and by systematically assessing 15 survey-design indicators across their national household travel surveys. Context benchmarking identifies the United States as the closest for highway-oriented interurban structure and motorisation level, New Zealand for geography and demographic structure (in particular, near-identical physiological density on limited arable land), and the United Kingdom as the most aspirationally aligned benchmark for the multimodal mobility patterns Saudi Arabia aims to develop under Vision 2030. Design benchmarking shows that the three surveys are closely matched in aggregate similarity but lead on distinct elements: New Zealand on diary length and integrated passive tracking, the US on digital tools and emerging-behaviour modules, and the UK on interviewer-led recruitment and multimodal analysis, a pattern that proves robust to plausible variation in individual scores. The resulting NHTS–KSA blueprint specifies a statistically justified, stratified multistage annual household sample, a two-day diary with rolling 12-month fieldwork, interviewer-assisted recruitment, a digital-first diary with optional GPS tracking, and modules on long-distance travel, telework, e-commerce, gendered mobility, accessibility, safety, and environmental attitudes. While preserving international comparability, the framework provides the data foundation required to steer public-transport investment, demand-management measures, and land-use policies in line with Saudi Arabia’s Vision 2030 objectives for sustainable, inclusive, and smart mobility. Full article

Declines of skeletal muscle mass and functions are implicated in the progression of various clinical conditions such as cancers, obesity, insulin resistance, diabetes, and osteoporosis. While no effective and safe drugs against muscle wasting, such as sarcopenia and disease-associated cachexia, have been discovered, it is well documented that dietary essential amino acids (EAAs) or high-quality protein work synergistically to enhance the anabolic effect of resistance exercise training (RT), leading to gains in muscle mass, strength, and muscle quality. Dietary EAAs serve as precursors and signaling molecules for the synthesis of new muscle proteins (both contractile and mitochondrial) and stimulate neuromuscular junction remodeling. Furthermore, EAAs consumed in the post-absorptive state improve endurance capacity via stimulation of mitochondrial biogenesis (independent of PGC1-α) and mitochondrial dynamics (mitochondrial protein synthesis and fission). Here, we discuss (1) traditional molecular mechanisms regulating the muscle proteome through constant turnover (synthesis and breakdown), (2) novel mechanisms by which dietary supplementation of EAAs during RT simultaneously improves muscle strength and endurance, (3) stable isotope tracer methodologies that enable understanding of the dynamic muscle proteome and accurate assessment of functional muscle mass, and finally, (4) clinical implications of combined EAA and RT interventions in the context of muscle and metabolic dysfunction, including sarcopenia, cachexia, obesity, and chronic disease. Collectively, current evidence underscores the potential of balanced EAAs, particularly when combined with resistance training, as a safe, effective, and translationally relevant nutritional strategy to preserve and enhance muscle and metabolic health across healthy and clinical populations. Full article

Microplastics (MPs) have emerged as persistent environmental contaminants due to their persistence, widespread distribution, and potential risks to the environment and human health. This review focuses on the sources of MPs, their potential environmental risks, and human impacts, as documented in the recent literature from 2020 to 2026. Recent studies focusing on pathways, environmental weathering, and toxicity were evaluated and synthesized into the analysis. Previous studies have demonstrated that microplastics are transported across and between environmental compartments. Environmental degradation, driven by ultraviolet radiation, mechanical fragmentation, and oxidation, can alter microplastics’ surface characteristics, which may affect microplastic mobility, reactivity, and the solid-state adsorption of contaminants. Human exposure occurs primarily through ingestion and inhalation, with dermal and occupational exposure also contributing under certain conditions. Emerging evidence from in vitro, animal, and human tissue studies suggests that smaller particles, particularly nanoplastics, may contribute to oxidative stress, inflammation, and cellular injury; however, important uncertainties remain regarding environmentally realistic exposure levels, long-term health outcomes, and the extrapolation of experimental findings to real-world human health risk. Overall, the current literature highlights the need for standardized methodologies, improved integration of environmental monitoring and exposure assessment, and stronger evidence to support risk assessment and policy development. Full article

Wireless power transfer (WPT) systems for electric vehicles (EVs) are increasingly being studied in the MHz range to increase power density and reduce the size of passive components. However, operation at higher frequencies significantly changes electromagnetic interference (EMI) behaavior. Fast switching in SiC- and GaN-based inverters, high-Q resonant operation, and frequency-dependent parasitic capacitances create conductive, capacitive, and magnetic interference mechanisms that are less significant in conventional kHz-range systems. Although many existing studies focus on power-transfer efficiency and converter optimization, EMI mechanisms in MHz-range EV WPT systems remain insufficiently systematized from a system-level electromagnetic perspective. This paper presents a state-of-the-art review of EMI generation mechanisms and system-level effects in high-frequency WPT systems for electric vehicles. The review considers the main interference sources and coupling paths, including switching-induced common-mode currents, resonant amplification of current and voltage stress, capacitive coupling between the coupler and nearby conductive structures, and magnetic-field redistribution caused by coil misalignment. Special attention is given to the transition from lumped-element assumptions to more distributed electromagnetic behavior at higher frequencies. The review also discusses the possible impact of these mechanisms on vehicle electronic subsystems and highlights the need for frequency-aware electromagnetic design, integrated modeling, and more rigorous EMC assessment for reliable MHz-range wireless EV charging systems. Full article