Search Results (645)

The accuracy of classification and localization of plants on images obtained from the board of an unmanned aerial vehicle (UAV) is of great importance when implementing precision farming technologies. It allows for the effective application of variable rate technologies, which not only saves chemicals but also reduces the environmental load on cultivated fields. Machine learning algorithms are widely used for plant classification. Research on the application of the YOLO algorithm is conducted for simultaneous identification, localization, and classification of plants. However, the quality of the algorithm significantly depends on the training set. The aim of this study is not only the detection of a cultivated plant (soybean) but also weeds growing in the field. The dataset developed in the course of the research allows for solving this issue by detecting not only soybean but also seven weed species common in the fields of Kazakhstan. The article describes an approach to the preparation of a training set of images for soybean fields using preliminary thresholding and bound box (Bbox) segmentation of marked images, which allows for improving the quality of plant classification and localization. The conducted research and computational experiments determined that Bbox segmentation shows the best results. The quality of classification and localization with the application of Bbox segmentation significantly increased (f1 score increased from 0.64 to 0.959, mAP50 from 0.72 to 0.979); for a cultivated plant (soybean), the best classification results known to date were achieved with the application of YOLOv8x on images obtained from the UAV, with an f1 score = 0.984. At the same time, the plant detection rate increased by 13 times compared to the model proposed earlier in the literature. Full article

Background: Human metapneumovirus (hMPV) is a respiratory pathogen that causes illness ranging from mild upper respiratory tract infections to severe pneumonia, particularly in individuals with comorbidities. Fatal cases of hMPV-induced hemorrhagic pneumonia are rare and likely under-reported. Diagnosis is often delayed due to overlapping symptoms with other respiratory viruses and the rapid progression of the disease. Case presentation: We report the case of a 55-year-old man with a complex medical history, including liver cirrhosis and diabetes mellitus, who developed acute viral pneumonia. Initial symptoms appeared three days before a sudden clinical deterioration marked by shortness of breath, hemoptysis, and respiratory failure. A nasopharyngeal swab taken on the third day of illness tested positive for hMPV by qRT-PCR. The patient died the following day. Postmortem molecular testing confirmed hMPV in lung tissue and alveolar contents. Autopsy revealed bilateral hemorrhagic pneumonia with regional lymphadenopathy. Histopathological examination showed alveolar hemorrhage, multinucleated cells, neutrophilic infiltration, activated autophagy in macrophages, and numerous cytoplasmic eosinophilic viral inclusions. Conclusions: This is the first documented case of fatal hMPV pneumonia in Armenia. It highlights the potential severity of hMPV in adults with chronic health conditions and emphasizes the need for timely molecular diagnostics. Postmortem identification of characteristic viral inclusions may serve as a cost-effective histopathological marker of hMPV-associated lung pathology. Full article

Background/Objectives: Neurodegenerative proteinopathies, such as Alzheimer’s disease (AD), Parkinson’s disease (PD), and dementia with Lewy bodies (DLB), are increasingly prevalent worldwide mainly due to population aging. These conditions are marked by complex etiologies, overlapping pathologies, and progressive clinical decline, with significant consequences for patients, caregivers, and healthcare systems. This review aims to synthesize evidence on the healthcare complexities of major neurodegenerative proteinopathies to highlight current knowledge gaps, and to inform future care models, policies, and research directions. Methods: We conducted a comprehensive literature search in PubMed/MEDLINE using combinations of MeSH terms and keywords related to neurodegenerative diseases, proteinopathies, diagnosis, sex, management, treatment, caregiver burden, and healthcare delivery. Studies were included if they addressed the clinical, pathophysiological, economic, or care-related complexities of aging-related neurodegenerative proteinopathies. Results: Key themes identified include the following: (1) multifactorial and unclear etiologies with frequent co-pathologies; (2) long prodromal phases with emerging biomarkers; (3) lack of effective disease-modifying therapies; (4) progressive nature requiring ongoing and individualized care; (5) high caregiver burden; (6) escalating healthcare and societal costs; and (7) the critical role of multidisciplinary and multi-domain care models involving specialists, primary care, and allied health professionals. Conclusions: The complexity and cost of neurodegenerative proteinopathies highlight the urgent need for prevention-focused strategies, innovative care models, early interventions, and integrated policies that support patients and caregivers. Prevention through the early identification of risk factors and prodromal signs is critical. Investing in research to develop effective disease-modifying therapies and improve early detection will be essential to reducing the long-term burden of these disorders. Full article

The mapping and monitoring of coastal regions are critical to ensure their sustainable use and viability in the long term. Delineation of coastlines is becoming increasingly important in the light of climate change and rising sea levels. However, many coastlines are highly dynamic; therefore, mono-temporal assessments of coastal ecosystems and coastlines are mere snapshots of limited practical value for space-based planning. Understanding of the spatio-temporal dynamics of coastal ecosystem boundaries is important to inform ecosystem management but also for a meaningful delineation of the high-water mark, which is used as a benchmark for coastal spatial planning in South Africa. This research aimed to use hyper-temporal Sentinel-2 imagery to extract ecological zones on the coast of KwaZulu-Natal, South Africa. A total of 613 images, collected between 2019 and 2023, were classified into four distinct coastal ecological zones—vegetation, bare, surf, and water—using a Random Forest model. Across all classifications, the percentage of each of the four classes’ occurrence per pixel over time was determined. This enabled the identification of ecosystem locations, spatially static ecosystem boundaries, and the occurrence of ecosystem boundaries with a more dynamic location over time, such as the non-permanent vegetation zone of the foredune area as well as the intertidal zone. The overall accuracy of the model was 98.13%, while the Kappa coefficient was 0.975, with user’s and producer’s accuracies ranging between 93.02% and 100%. These results indicate that cloud-based analysis of Sentinel-2 time series holds potential not just for delineating coastal ecosystem boundaries, but also for enhancing the understanding of spatio-temporal dynamics between them, to inform meaningful environmental management, spatial planning, and climate adaptation strategies. Full article

In the process of the work of a coal power station is formed ash and slag, which, along with process water, are deposited in the dumps. Coal ash waste dumps significantly degrade the surrounding environment due to their unprotected surfaces, which are highly susceptible to wind and water erosion. This results in the dispersion of contaminants into adjacent ecosystems. Pollutants migrate into terrestrial and aquatic systems, compromising soil quality and water resources, and posing documented risks to the environment and human health. Primary succession on the coal ash dumps of the Apatity thermal power plant (Murmansk Region, NW Russia) was initiated by cyanobacterial colonization. We studied cyanobacterial communities inhabiting three spoil sites that varied in time since decommissioning. These sites are characterized by exceptionally high concentrations of calcium and magnesium oxides—levels approximately double those found in the region’s natural soils. A total of 18 cyanobacterial taxa were identified in disposal sites. Morphological analysis of visible surface crusts revealed 16 distinct species. Furthermore, 24 cyanobacterial strains representing 11 species were successfully isolated into unialgal culture and tested with a molecular genetic approach to confirm their identification from 16S rRNA. Three species were determined with molecular evidence. Cyanobacterial colonization of coal fly ash disposal sites begins immediately after deposition. Primary communities initially exhibit low species diversity (four taxa) and do not form a continuous ground cover in the early years. However, as succession progresses—illustrated by observations from a 30-year-old deposit—spontaneous surface revegetation occurs, accompanied by a marked increase in cyanobacterial diversity, reaching 12 species. Full article

Primary and secondary immunodeficiencies comprise a wide array of illnesses marked by immune system abnormalities, resulting in heightened vulnerability to infections, autoimmunity, and cancers. Notwithstanding progress in diagnostic instruments and an enhanced comprehension of the underlying pathophysiology, delayed diagnosis and underreporting persist as considerable obstacles. The implementation of artificial intelligence into clinical practice has surfaced as a viable method to enhance early detection, risk assessment, and management of immunodeficiencies. Recent advancements illustrate how artificial intelligence-driven models, such as predictive algorithms, electronic phenotyping, and automated flow cytometry analysis, might enable early diagnosis, minimize diagnostic delays, and enhance personalized treatment methods. Furthermore, artificial intelligence-driven immunopeptidomics and phenotypic categorization are enhancing vaccine development and biomarker identification. Successful implementation necessitates overcoming problems associated with data standardization, model validation, and ethical issues. Future advancements will necessitate a multidisciplinary partnership among physicians, data scientists, and governments to effectively use the revolutionary capabilities of artificial intelligence, therefore ushering in an age of precision medicine in immunodeficiencies. Full article

This study aims to determine the model parameters of proton exchange membrane fuel cells (PEMFC) by employing the Sea Horse Optimization (SHO) algorithm, a novel metaheuristic approach inspired by natural behaviors. Although conventional algorithms in the literature have achieved considerable success in parametric modeling accuracy, many of them suffer from inherent drawbacks, such as premature convergence and entrapment in local minima. The SHO algorithm, with its adaptive and dynamic nature, is designed to overcome these limitations. To further evaluate its performance, a detailed parametric sensitivity analysis is conducted on SHO-specific control parameters. In this work, experimental polarization data from a Ballard Mark V PEMFC is used as a reference to estimate the equivalent circuit model parameters ${ϵ}_1$, ${ϵ}_2$, ${ϵ}_3$, ${ϵ}_4$, $\beta$, $\lambda$, $R_c$. The SHO algorithm achieved a mean absolute error (MAE) of 0.001079 and a coefficient of determination (R²) of 0.999791, with a model-to-experiment fit ratio of 99.92%. Compared to similar studies reported in the literature, the results indicate that the SHO algorithm offers competitive performance. Moreover, the average convergence time is recorded as 1.74 s for 5000 iteration, highlighting the algorithm’s rapid convergence and low computational cost. Overall, the SHO algorithm is demonstrated to be an efficient, robust, and promising alternative to conventional methods for parameter identification in PEMFC modeling. Full article

The advance of 5G technology marks a significant evolution in wireless communications, characterized by ultra-high data rates, low latency, and massive connectivity across varied areas. A fundamental enabler of these capabilities is represented by beamforming, an advanced signal processing technique that focuses radio energy to a specific user equipment (UE), thereby enhancing signal quality—crucial for maximizing spectral efficiency. The work presents a classification of beamforming techniques, categorized according to the implementation within 5G New Radio (NR) architectures. Furthermore, the paper investigates beam management (BM) procedures, which are essential Layer 1 and Layer 2 mechanisms responsible for the dynamic configuration, monitoring, and maintenance of optimal beam pair links between gNodeBs and UEs. The article emphasizes the spectral spectrogram of Synchronization Signal Blocks (SSBs) generated under various deployment scenarios, illustrating how parameters such as subcarrier spacing (SCS), frequency band, and the number of SSBs influence the spectral occupancy and synchronization performance. These insights provide a technical foundation for optimizing initial access and beam tracking in high-frequency 5G deployments, particularly within Frequency Range (FR2). Additionally, the versatility of 5G’s time-frequency structure is demonstrated by the spectrogram analysis of SSBs in a variety of deployment scenarios. These results provide insight into how different configurations affect the synchronization signals’ temporal and spectral occupancy, which directly affects initial access, cell identification, and energy efficiency. Full article

River embankments are critical flood defense structures, stretching for thousands of kilometers across alluvial plains. They often originated as natural levees resulting from overbank flows and were later enlarged using locally available soils yet rarely designed according to modern engineering standards. Substantially under-characterized, their performance to extreme events provides an invaluable opportunity to highlight their vulnerability and then to improve monitoring, management, and reinforcement strategies. In May 2023, two extreme meteorological events hit the Emilia-Romagna region in rapid succession, causing numerous breaches along river embankments and therefore widespread flooding of cities and territories. These were followed by two additional intense events in September and October 2024, marking an unprecedented frequency of extreme precipitation episodes in the history of the region. This study presents the methodology adopted to create a regional database of 66 major breaches and damages that occurred during May 2023 extensive floods. The database integrates multi-source information, including field surveys; remote sensing data; and eyewitness documentation collected before, during, and after the events. Preliminary interpretation enabled the identification of the most likely failure mechanisms—primarily external erosion, internal erosion, and slope instability—often acting in combination. The database, unprecedented in Italy and with few parallels worldwide, also supported a statistical analysis of breach widths in relation to failure mechanisms, crucial for improving flood hazard models, which often rely on generalized assumptions about breach development. By offering insights into the real-scale behavior of a regional river defense system, the dataset provides an important tool to support river embankments risk assessment and future resilience strategies. Full article

Background/Objectives: The overuse of short-acting β₂-agonists (SABAs) has been associated with increased asthma morbidity and mortality, prompting changes in treatment guidelines. However, the role of frequent short-acting muscarinic antagonists (SAMAs) use remains poorly defined and unaddressed in current recommendations. This study offers the first real-world analysis of SAMA overuse in asthma, quantifying its association with exacerbation risk and healthcare utilization and comparing its predictive value to that of SABAs. Methods: A retrospective multicenter cohort study analyzed electronic health records (EHRs) from 132 adults with asthma in the Spanish National Health System (SNS). Associations between annual SAMA use and clinical outcomes were assessed using negative binomial regression and 5000-sample bootstrap simulations. Interaction and threshold models were applied to explore how SAMA use affected outcomes and identify clinically actionable cutoffs. Results: SAMA use was independently associated with a 19.2% increase in exacerbation frequency per canister and a nearly sixfold increase in the odds of experiencing ≥1 exacerbation (OR = 5.97; 95% CI: 2.43–14.66). An inflection point at 2.5 canisters/year marked the threshold beyond which annual exacerbations exceeded one. Increased SAMA use was also associated with a higher number of respiratory consultations and with more frequent prescriptions of systemic corticosteroids and antibiotics. The risk increased more sharply with SAMAs than with SABAs, and the lack of correlation between them suggests distinct clinical patterns underlying their use. Conclusions: SAMA use emerges as a digitally traceable and clinically meaningful indicator of asthma instability. While the associations observed are robust and consistent across multiple outcomes, they should be considered provisional due to the study’s retrospective design and limited sample size. Replication in larger and more diverse cohorts is needed to confirm external validity. These findings support the integration of SAMA tracking into asthma management tools—alongside SABAs—to enable the earlier identification of uncontrolled disease and guide therapeutic adjustment. Full article

Intravascular lymphoma (IVL) is a rare, aggressive subtype of non-Hodgkin lymphoma (NHL) characterized by the selective proliferation of neoplastic lymphoid cells within small and medium-sized blood vessels, most frequently of B-cell origin (IVLBCL). Its protean clinical presentation, lack of pathognomonic findings, and absence of tumor masses or lymphadenopathies often lead to diagnostic delays and poor outcomes. IVLBCL can manifest in classic, hemophagocytic syndrome-associated (HPS), or cutaneous variants, with extremely variable organ involvement including the central nervous system (CNS), skin, lungs, and endocrine system. Diagnosis requires histopathologic identification of neoplastic intravascular lymphoid cells via targeted or random tissue biopsies. Tumor cells are highly atypical and display a non-GCB B-cell phenotype, often expressing CD20, MUM1, BCL2, and MYC; molecularly, they frequently harbor mutations in MYD88 and CD79B, defining a molecular profile shared with ABC-type DLBCL of immune-privileged sites. Therapeutic approaches are based on rituximab-containing chemotherapy regimens (R-CHOP), often supplemented with CNS-directed therapy due to the disease’s marked neurotropism. Emerging strategies include autologous stem cell transplantation (ASCT) and novel immunotherapeutic approaches, potentially exploiting the frequent expression of PD-L1 by tumor cells. A distinct but related entity, intravascular NK/T-cell lymphoma (IVNKTCL), is an exceedingly rare EBV-associated lymphoma, showing unique own histologic, immunophenotypic, and molecular features and an even poorer outcome. This review provides a comprehensive overview of the current understandings about clinicopathological, molecular, and therapeutic landscape of IVL, emphasizing the need for increased clinical awareness, standardized diagnostic protocols, and individualized treatment strategies for this aggressive yet intriguing malignancy. Full article

Pediatric delirium (PD) is an acute neuropsychiatric syndrome marked by fluctuating disturbances in attention and cognition, frequently observed in pediatric intensive care units (PICUs) and associated with increased morbidity, mortality, and long-term cognitive impairment. Despite its clinical significance, PD remains underdiagnosed due to challenges inherent in assessing consciousness and cognition in children at varying developmental stages. Several bedside tools have been developed and validated in recent years, including the Cornell Assessment of Pediatric Delirium (CAPD), PreSchool Confusion Assessment Method for the Intensive Care Unit (psCAM-ICU); Pediatric Confusion Assessment Method for the Intensive Care Unit (pCAM-ICU), and Sophia Observation Withdrawal Symptoms—Pediatric Delirium Scale (SOS-PD), enhancing early recognition and management of PD in critically ill children. This narrative review explores the historical background, epidemiology, risk factors, pathophysiology, clinical subtypes, diagnostic tools, and current prevention and treatment strategies for PD from newborns to 21 years old. The screening tools available and the integration of non-pharmacological interventions, such as environmental modifications and family-centered care, as well as cautious and selective pharmacological management, are emphasized in this review. Early identification and targeted interventions are essential to mitigate the adverse outcomes associated with PD. Full article

Paeoniflorin (PF), a monomeric compound extracted from the dry roots of Paeonia lactiflora, has been widely used in the treatment of nervous system diseases, marking it as a critical formula in Parkinson’s disease (PD). However, the action of PF against PD and its molecular mechanism are still unclear. In this study, tandem mass tags quantitative proteomics was performed to systematically clarify the underlying mechanism of action of PF against PD and to confirm it using in vivo and in vitro studies. The results showed that PF notably enhanced the viability of PC12 cells and mitigated MPP⁺-induced mitochondrial dysfunction, oxidative stress, and apoptosis. Tandem mass tag-based quantitative proteome analysis revealed the identification of 6405 proteins, of which 92 were downregulated and 190 were upregulated. Among them, the levels of PI3K, AKT, CREB, and BDNF in the MPP⁺-induced PC12 cell and MPTP mice were considerably lower than in the control group, indicating the role of the BDNF/CREB pathway in the pathogenesis of neuroprotection. The related DEP (PI3K, AKT, CREB, and BDNF) expression levels were verified by Western blot. PF effectively restored the altered expression of the four DEPs induced by MPP⁺ and MPTP. Summarily, PF exerted remarkable neuroprotective effects in MPP⁺-induced PC12 cell and MPTP-induced mice. Further, our research may provide proteomics insights that contribute to the further exploration of PF as a potential treatment for PD. Full article

The rise of hybrid electric vehicles (HEVs) marks a shift away from traditional engines driven by environmental and economic concerns. With the rapid growth of HEVs worldwide, their reliability becomes of utmost concern; thus, guaranteeing the proper operation of HEVs is a crucial quest. Condition-based monitoring (CBM), which intends to observe different kinds of parameters in the system to detect defects and reduce any unwanted breakdowns and equipment failure, plays an efficient role in enhancing HEVs’ reliability and ensuring their healthy operation. The permanent magnet machine (PMM) is the most used electric machine in the electric propulsion system of HEVs, as well as the most expensive. Hence, the condition monitoring of this machine is of great importance. The magnet crack is one of the most severe faults that may arise in this machine. Artificial intelligence (AI) is showing high capability in the field of CBM, fault detection, and fault identification and prevention. Hence, the aim of this paper is to present two data-based fault detection approaches, which are the support vector machine (SVM) and the Hidden Markov Model (HMM). Their capability to detect primitive faults like tiny cracks in the machine’s magnet will be shown. Applying and evaluating various CBM methods is essential to identifying the most effective approach to maximizing reliability, minimizing downtime, and optimizing maintenance strategies. A strategy to specify the remaining useful life (RUL) of the defected element is proposed. Full article

Vibrio species are among the primary pathogenic bacteria affecting abalone aquaculture, posing significant threats to farming practices. Current clinical control predominantly relies on antibiotics, which can result in antibiotic residues in both abalone and the surrounding marine environments. Lactobacillus plantarum (LP) has been shown to release bioactive antagonistic substances and exhibits potent inhibitory effects against marine pathogenic bacteria. This study aimed to screen and characterize the probiotic properties of LP strains isolated from rice wine lees to develop a novel biocontrol strategy against Vibriosis in abalone. The methods employed included selective media cultivation, streak plate isolation, and single-colony purification for strain screening, followed by Gram staining, 16S rDNA sequencing, and phylogenetic tree construction using MEGA11 for identification. The resilience, antimicrobial activity, and in vivo antagonistic efficacy of the strains were evaluated through stress tolerance assays, agar diffusion tests, and animal experiments. The results demonstrated the successful isolation and purification of four LP strains (NDMJ-1 to NDMJ-4). Phylogenetic analysis revealed closer genetic relationships between NDMJ-3 and NDMJ-4, while NDMJ-1 and NDMJ-2 were found to be more distantly related. All strains exhibited γ-hemolytic activity, bile salt tolerance (0.3–3.0%), and resistance to both acid (pH 2.5) and alkali (pH 8.5), although they were temperature sensitive (inactivated above 45 °C). The strains showed susceptibility to most of the 20 tested antibiotics, with marked variations in hydrophobicity (1.91–93.15%) and auto-aggregation (13.29–60.63%). In vitro antibacterial assays revealed that cell-free supernatants of the strains significantly inhibited Vibrio parahaemolyticus, V. alginolyticus, and V. natriegens, with NDMJ-4 displaying the strongest inhibitory activity. In vivo experiments confirmed that NDMJ-4 significantly reduced mortality in abalone infected with V. parahaemolyticus. In conclusion, the LP strains isolated from rice wine lees (NDMJ-1 to NDMJ-4) possess robust stress resistance, adhesion capabilities, and broad antibiotic susceptibility. Their metabolites exhibit significant inhibition against abalone-pathogenic Vibrios, particularly NDMJ-4, which demonstrates exceptional potential as a candidate strain for developing eco-friendly biocontrol agents against Vibriosis in abalone aquaculture. Full article