Search Results (346)

Background: Caffeine, dairy products and other food items may influence retinal microcirculation. Retinal microvascular indices provide quantitative biomarkers of systemic microvascular health and are increasingly used in clinical and research settings. The aim of this study was to elucidate the possible effects of these food products on structural and functional indices of the retinal microcirculation. Methods: Based on a registered protocol, we identified eligible interventional/observational studies examining the association of these factors with retinal biomarkers, including central retinal artery equivalent (CRAE), central retinal vein equivalent (CRVE), arteriolar-to-venular diameter ratio (AVR), retinal vascular tortuosity, vessel diameter index (VDI) and retinal vessel flicker light-induced dilation (FID). Results: Fourteen studies were included addressing caffeine (4), dairy products (2) and other food (9) consumption. Acutely, caffeine intake was dose-dependently associated with narrowed CRAE, CRVE, decreased VDI and increased AVR and FID. Long-term, caffeine consumption was associated with larger CRVE and lower AVR, while decaffeinated coffee with larger CRAE and AVR and narrower CRVE. Low-fat dairy products, fish and fiber were associated with larger CRAE, smaller CRVE, and increased AVR, while red meat consumption was associated with narrower CRAE and lower AVR. Increased salt intake was associated with increased venular tortuosity, while almond consumption was associated with larger CRVE. Owing to substantial study heterogeneity, a meta-analysis was not feasible. Conclusions: Potentially clinically meaningful associations between food groups and retinal indices were identified. These associations should be considered when evaluating retinal microcirculation and assessing CVD risk since modification of these factors may be beneficial for the cardiovascular system. Full article

AIG1 (avrRpt2-induced gene 1)-like proteins are a class of GTPases that play crucial roles in plants, functioning both in chloroplast protein import and disease resistance. However, their evolutionary history and the mechanisms driving this functional diversification remain poorly understood. Here, we performed a comprehensive genomic and evolutionary analysis of this gene family across the plant kingdom. We identified 90 AIG1-like genes from 11 sequenced plant species, representing major lineages from green algae to angiosperms. Phylogenetic analysis revealed that plant AIG1-like proteins form three monophyletic lineages corresponding to the Toc34, Toc159, and IAN subfamilies, which originated via two ancient duplications predating the divergence of green algae and land plants. These lineages exhibit dramatically divergent evolutionary patterns. The Toc34 subfamily is evolutionarily conserved, maintaining stable copy numbers and gene structure, indicative of strong functional constraints in its core role in plastid import. In contrast, the Toc159 and IAN subfamilies have undergone dynamic expansion via lineage-specific duplication mechanisms, including segmental duplication and prolific tandem duplication, respectively. Notably, we uncovered a novel mechanism for generating head-to-head tandem duplicates in the IAN subfamily, mediated by recombination between inverted repeats. Our analysis of ancestral gene numbers and gene gain/loss dynamics further highlights that functional diversification was driven by both the acquisition of distinct C-terminal targeting domains (M and TM domains) and profound differences in evolutionary rates and duplication modes among subfamilies. This study provides the first full-scale evolutionary framework for plant AIG1-like genes, establishing that functional specialization is rooted in distinct modes of sequence and genomic evolution. Full article

In this study, a Superb Fairy-wren Optimization Algorithm (SFOA)-based proportional–integral–derivative (PID) controller is proposed for the first time in the literature to improve transient voltage stability performance of automatic voltage regulator (AVR) systems. The proposed approach aims to optimally tune the PID controller gain parameters $K_P$, $K_I$ and $K_D$, which are used for voltage regulation in AVR systems. As a result of the optimization performed using SFOA, PID gain parameters are obtained as $K_P$ = 0.5914, $K_I$ = 0.4078 and $K_D$ = 0.1954. According to the transient voltage response analysis results, the SFOA-based PID controller showed superior performance, with a maximum overshoot of 0.02307, a rise time of 0.33 s, peak time of 0.636 s, fastest stabilization with settling time of 0.514 s within the ±2% tolerance band and steady-state error of 0.0012. Its performance was superior to several state-of-the-art, optimization-based methods reported in the literature. According to commonly used objective functions in AVR systems, integral of time absolute error (ITAE) and Zwe-Lee Gaing’s objective functions, the best results were obtained with values of 0.0489 and 0.0826, respectively. The results show that a SFOA-based PID controller can be an alternative and effective control approach for AVR systems with strong potential for optimization-based control applications in electric power systems. Full article

The pepper Bs2 resistance gene confers resistance to susceptible Solanaceae plants against pathogenic strains of Xanthomonas campestris pv. vesicatoria carrying the avrBs2 avirulence gene. Previously, we generated Bs2-transgenic Citrus sinensis plants that exhibited enhanced resistance to citrus canker caused by Xanthomonas citri subsp. citri (Xcc), although the underlying mechanisms remained unknown. To elucidate the molecular basis of the early defense response, we performed a comparative transcriptomic analysis of Bs2-expressing and non-transgenic plants 48 h after Xcc inoculation. A total of 2022 differentially expressed genes (DEGs) were identified, including 1356 up-regulated and 666 down-regulated genes. In Bs2-plants, 36.8% of the up-regulated DEGs were associated with defense responses and biotic stress. Functional annotation revealed major changes in genes encoding receptor-like kinases, transcription factors, hormone biosynthesis enzymes, pathogenesis-related proteins, secondary metabolism, and cell wall modification. Among hormone-related pathways, genes linked to ethylene biosynthesis and signaling were the most strongly regulated. Consistently, endogenous ethylene levels increased in Bs2-plants following Xcc infection, and treatment with an ethylene-releasing compound enhanced resistance in non-transgenic plants. Overall, our results indicate the Bs2 expression activates a complex defense network in citrus and may represent a valuable strategy for controlling canker and other Xanthomonas-induced diseases. Full article

Background: New-onset postoperative atrial fibrillation (NOAF) is the most prevalent arrythmia after cardiac surgery with a significant clinical and economic impact. Therefore, simple and practical biomarkers for NOAF prediction remain a clinical priority. Increasing evidence indicates that malnutrition is linked to postoperative complications, including the onset of atrial fibrillation. The Prognostic Nutritional Index (PNI), which reflects the immunonutritional and inflammatory status through serum albumin concentration and lymphocyte count, has emerged as a reliable prognostic indicator in cardiovascular disease. The present study aimed to investigate the association between PNI and the development of NOAF in patients undergoing surgical aortic valve replacement (SAVR). Methods: A total of 241 consecutive patients who underwent AVR for severe aortic stenosis or regurgitation were enrolled in this study. The population was stratified into two groups according to the development of NOAF (NOAF group) or the lack thereof (no NOAF group). Results: In both univariate and multivariate logistic regression analyses adjusted for several established NOAF determinants, age and PNI, both as continuous variables, were independently associated with NOAF in both univariate (OR = 1.03; CI 95% = 1.01–1.06, p = 0.009, and OR = 0.9; CI 95% = 0.8–0.9, p = 0.01, respectively) and multivariate models (OR = 1.02; CI 95% = 1.01–1.06, p = 0.05, and OR = 0.9; CI 95% = 0.8–0.9, p = 0.03, respectively). When PNI was analyzed by tertiles, patients in the lowest tertile (PNI < 41.5) showed a significantly higher risk of developing NOAF at both univariate (OR = 1.9; CI 95% = 1.2–2.8, p = 0.004) and multivariate analysis (OR = 1.6; CI 95% = 1–2.6, p = 0.03), whereas age lost statistical significance (OR = 1.0; 95% CI = 0.9–1.05; p = 0.06). Furthermore, when the study population was divided into two groups based on the median age (70 years), PNI values differed significantly between NOAF and no NOAF patients only in patients under 70 years (p = 0.01). In this younger subgroup, PNI remained an independent predictor of NOAF, both when considered as a continuous variable (OR = 0.86; CI 95% = 0.74–0.98, p = 0.02), and nominal variable (PNI < 41.5, OR = 0.88; CI 95% = 0.80–0.97, p = 0.01). Conclusions: Overall, these findings identify PNI as an independent predictor of NOAF following SAVR, particularly in patients younger than 70 years. This study underlines the potential clinical value of preoperative nutritional assessment for risk stratification. Incorporating nutritional parameters such as PNI into current predictive models may enhance the accuracy of prognostic evaluation and support targeted perioperative management strategies. Full article

Salmonella is a globally prevalent and diverse group of pathogenic bacteria that reside in food animals, such as swine. They possess transmissible antimicrobial resistance (AMR) and virulence factors, causing outbreaks with varying disease outcomes. This study identified and characterized 110 Salmonella enterica isolates from swine meat in abattoirs and wet markets of Metro Manila, Philippines. Thirteen different S. enterica serovars were identified using the Check & Trace microarray platform. The most prevalent were Rissen, Typhimurium 1, 4, [5], 12:i:-, Anatum, and Derby. This study is also the first to report serovar Soerenga in the Philippines and Asia. A high prevalence of virulence genes was observed, namely, hilA (75.45%), avrA (73.64%), mgtC (72.73%), pipB (66.36%), sseC (58.18%), and spi4R (53.64%), with no plasmid-borne spvC and spvR. A high prevalence of bla_TEM (44.55%) was also observed, consistent with the phenotypic AMR profiles. Additionally, 14.81% of the isolates exhibited multidrug resistance. Statistical associations and predictions were also found among virulence genes, serovars, and location types, which highlight implications of Salmonella contamination and serovar variations. These findings suggest the need for continuous surveillance of Salmonella, especially for emerging or rare serovars, the deeper investigation of virulence and AMR mechanisms, and improved regulation and sanitation throughout food animal industries. Full article

Resource-constrained sensor nodes in Internet-of-Things (IoT) and embedded sensing applications frequently rely on low-cost microcontrollers, where even basic algorithmic choices directly impact latency, energy consumption, and memory footprint. This study evaluates six sorting algorithms—Bubble Sort, Insertion Sort, Selection Sort, Merge Sort, Quick Sort, and Heap Sort—in the restricted environment that microcontrollers provide. Three Arduino boards were used: Arduino Uno, Arduino Leonardo, and Arduino Mega 2560. Each algorithm was implemented in its unoptimized form and tested on datasets of increasing size, emulating buffered time-series sensor readings in random, ascending, and descending order. Execution time, number of write operations, and memory usage were measured. The tests show clear distinctions between the slower $O\left(n^2\right)$ algorithms and the more efficient $O(nlogn)$ algorithms. For random inputs of $n=1000$ elements, Bubble Sort required 1,958,193.75 $\mathsf{\mu }$son average, whereas Quick Sort completed it in 54,260.50 $\mathsf{\mu }$s and Heap Sort in 92,429.00 $\mathsf{\mu }$s, i.e., speedups of more than one order of magnitude compared to the quadratic baseline. These gains, however, come with very different memory footprints. Merge Sort kept the runtime below 100,000 $\mathsf{\mu }$s at $n=1000$ but required approximately 2023 bytes of additional static random-access memory (SRAM), effectively exhausting the 2 kB SRAM of the Arduino Uno. QuickSort used approximately 311 bytes of extra SRAM and failed to process larger ascending and descending datasets on the more constrained boards due to its recursive pattern and stack usage. Heap Sort offered the best overall trade-off: it successfully executed all tested sizes up to the SRAM limit of each board while using only about 12–13 bytes of additional SRAM and keeping the runtime below 100,000 $\mathsf{\mu }$s for $n=1000$. The results provide practical guidelines for selecting sorting algorithms on 8-bit AVR Arduino-class microcontrollers, which are widely used as simple sensing and prototyping nodes operating under strict RAM, program-memory, and energy constraints. Full article

This paper proposes a Gudermannian function-based proportional–integral–derivative (G-PID) controller to enhance the transient performance of automatic voltage regulator (AVR) systems operating under highly dynamic conditions. By embedding the smooth and bounded nonlinear mapping of the Gudermannian function into the classical PID structure, the proposed controller improves adaptability to large signal variations while effectively suppressing overshoot. The controller parameters are optimally tuned using the starfish optimization algorithm (SFOA), which provides a robust balance between exploration and exploitation in nonlinear search spaces. Simulation results demonstrate that the SFOA-optimized G-PID controller achieves superior transient performance, with a rise time of 0.0551 s, zero overshoot, and a settling time of 0.0830 s. Comparative evaluations confirm that the proposed approach outperforms widely used optimization algorithms (particle swarm optimization, grey wolf optimizer, success history-based adaptive differential evolution with linear population size, and Kirchhoff’s law algorithm) and advanced AVR control schemes, including fractional-order and higher-order PID-based designs. These results indicate that the proposed SFOA optimized G-PID controller offers a computationally efficient and structurally simple solution for high-performance voltage regulation in modern power systems. Full article

Rice blast, caused by Magnaporthe oryzae, is a devastating global disease. Its control through the deployment of host resistance genes relies on a detailed knowledge of the pathogen’s race structure and the corresponding avirulence (Avr) genes. To guide effective rice breeding for blast resistance, this study investigated the population dynamics of M. oryzae in Jilin Province from 2022 to 2024. The distribution frequencies of seven Avr genes were detected using PCR and Avr gene-specific primers, and the physiological race structure of 193 isolates was characterized using a set of Chinese differential cultivars, which contains seven cultivars. The results revealed a high prevalence and stability of specific Avr genes, with Avr-Pi9, Avr-Pias, Avr-Piz-t, and Avr-Pib all exhibiting detection frequencies exceeding 80%. In particular, Avr-Pib showed a high frequency (80.83%) and a very low disease incidence (0.64%) on the differential variety Sifeng 43 (which carries Pib), confirming its low mutation rate and the ongoing effectiveness of the corresponding resistance gene. Conversely, the significant decline in Avr-co39 suggests that its corresponding resistance gene should be avoided. Race diversity increased over the three-year period, characterized by a shift toward a more complex structure dominated by ZG1, ZA17, ZA43, and ZB31. Based on the gene-for-gene interactions and pathogen population structure, we recommend a breeding strategy that prioritizes the incorporation of the highly effective Pib, Pi54, and Pik genes, utilizing resistant donors like Sifeng 43. These results can help inform the design of sustainable management strategies adapted to the changing pathogen population. Full article

Background: Automatic vehicle recognition has recently become an area of great interest, providing substantial support for multiple use cases, including law enforcement and surveillance applications. In real traffic conditions, where for various reasons license plate recognition is impossible or license plates are forged, alternative solutions are required to support human personnel in identifying vehicles used for illegal activities. In such cases, appearance-based approaches relying on vehicle make and model recognition (VMMR) and vehicle color recognition (VCR) can successfully complement license plate recognition. Methods: This research addresses appearance-based vehicle identification, in which VMMR and VCR rely on inherent visual cues such as body contours, stylistic details, and exterior color. In the first stage, vehicles passing through an intersection are detected, and essential visual characteristics are extracted for the two recognition tasks. The proposed system employs deep learning with semantic segmentation and data augmentation for color recognition, while histogram of oriented gradients (HOG) feature extraction combined with a support vector machine (SVM) classifier is used for make-model recognition. For the VCR task, five different neural network architectures are evaluated to identify the most effective solution. Results: The proposed system achieves an overall accuracy of 94.89% for vehicle make and model recognition. For vehicle color recognition, the best-performing models obtain a Top-1 accuracy of 94.17% and a Top-2 accuracy of 98.41%, demonstrating strong robustness under real-world traffic conditions. Conclusions: The experimental results show that the proposed automatic vehicle recognition system provides an efficient and reliable solution for appearance-based vehicle identification. By combining region-tailored data, segmentation-guided processing, and complementary recognition strategies, the system effectively supports real-world surveillance and law-enforcement scenarios where license plate recognition alone is insufficient. Full article

Objective: Prosthesis–patient mismatch (PPM) occurs after aortic valve replacement (AVR) when the effective orifice area of the implanted prosthetic valve is small relative to the patient’s body surface area. Beyond simply elevating transvalvular gradient, PPM profoundly affects cardiac remodeling, coronary physiology, and ultimately patient survival. This comprehensive review synthesizes current evidence regarding PPM pathophysiology, clinical consequences, and therapeutic strategies. Methods: We conducted a narrative review of PPM in surgical (SAVR) and transcatheter (TAVR) aortic valve replacement. PubMed and Embase were systematically searched using terms related to AVR and PPM and reference lists of key studies and reviews were screened. Studies addressing PPM prevalence, hemodynamic impact, clinical outcomes, and mitigation strategies were included. Results: PPM, defined as an iEOA ≤ 0.85 cm²/m² (moderate) or ≤0.65 cm²/m² (severe), demonstrates variable prevalence across studies, typically ranging from 5 to 30% after SAVR and 2–35% after TAVR. It is associated with increased transvalvular gradients, reduced left ventricular mass regression, persistent coronary flow abnormalities, higher rates of heart failure, and both early and late mortality. Supra-annular self-expanding transcatheter aortic valve replacement (TAVR) devices and newer generation stentless or bovine pericardial surgical valves exhibit lower PPM rates than older stented or porcine valves. Valve-in-valve (ViV) TAVR and bioprosthetic valve fracture (BVF) can improve outcomes in failed surgical valves but are less effective in small annuli. TAVR-in-TAVR procedures are limited by anatomic and technical constraints, especially in maintaining coronary access and minimizing residual gradients. Conclusions: PPM remains a common and clinically consequential complication of AVR that compromises long-term outcomes. It is largely preventable through accurate preoperative imaging, valve sizing, and consideration of annular enlargement. Optimal outcomes require matching valve characteristics to individual patient anatomy and physiology. In an era of expanding TAVR use, preventing PPM during the index procedure is critical to optimizing survival and preserving future reintervention options. Full article

The regulation of voltage in transmission networks is becoming increasingly complex due to the dynamic behavior of modern power systems and the growing penetration of renewable generation. This study presents a comparative analysis of three artificial intelligence approaches—Deep Q-Learning (DQL), Genetic Algorithms (GAs), and Particle Swarm Optimization (PSO)—for training agents capable of performing autonomous voltage control. A unified neural architecture was implemented and tested on the IEEE 30-bus system, where the agent was tasked with adjusting reactive power set points and transformer tap positions to maintain voltages within secure operating limits under a range of load conditions and contingencies. The experiments were carried out using the GridCal simulation environment, and performance was assessed through multiple indicators, including convergence rate, action efficiency, and cumulative reward. Quantitative results demonstrate that PSO achieved 3% higher cumulative rewards compared to GA and 5% higher than DQL, while requiring 8% fewer actions to stabilize the system. GA showed intermediate performance with 6% faster initial convergence than DQL but 4% more variable results than PSO. DQL demonstrated consistent learning progression throughout training, though it required approximately 12% more episodes to achieve similar performance levels. The quasi-dynamic validation confirmed PSO’s advantages over conventional AVR-based strategies, achieving voltage stabilization approximately 15% faster. These findings underscore the potential of neuroevolutionary algorithms as competitive alternatives for advanced voltage regulation in smart grids and point to promising research avenues such as topology optimization, hybrid metaheuristics, and federated learning for scalable deployment in distributed power systems. Full article

Background: Compared with isolated aortic valve replacement (AVR), echocardiographic hemodynamics after Wheat and Bentall procedures, both involving replacement of the proximal ascending aorta with a smaller-diameter graft, have been less thoroughly investigated. Methods: We analyzed 213 patients who received 21 mm or 23 mm aortic bioprostheses (AVR, n = 138; Wheat, n = 43; Bentall, n = 32). Transthoracic echocardiography was performed before and after surgery, and the proximal ascending aortic area (Aa) was assessed using contrast-enhanced computed tomography. Results: The maximal pressure gradient (PG max), derived from the simplified Bernoulli equation, was significantly lower in the Bentall group, whereas pressure recovery (PR), calculated using Voelker’s equation, was lower in the AVR group. A smaller Aa was associated with a higher PG max in the AVR group. The Bentall group exhibited significantly lower energy loss (EL). In propensity score-matched analyses to minimize potential confounding factors, the AVR group showed a significantly lower PR and higher EL than the Wheat group; a significantly higher PG max, lower PR, and higher EL than the Bentall group; and a significantly similar PR but lower EL in the Bentall group compared with the Wheat group. Conclusions: Although limited to bioprosthetic valves, caution is warranted when interpreting echocardiographic PG max after AVR in patients with a small ascending aorta. However, overestimation of PG max was not observed in either the Wheat or Bentall groups, even though both demonstrated higher PR and lower EL compared with the AVR group. Full article

Systematic Severe aortic stenosis (AS) is a progressive disease and one of the most common valvular heart diseases in developed countries. The management of asymptomatic severe AS remains challenging and requires a tailored, patient-specific approach. Optimal timing of intervention in asymptomatic patients continues to be a matter of ongoing debate. In individuals with severe AS and reduced left ventricular ejection fraction (LVEF), both the ESC/EACTS and ACC/AHA guidelines recommend aortic valve replacement (AVR), regardless of symptom status. In contrast, for patients with preserved LVEF (≥50%), the decision to proceed with AVR must be individualized and based on a comprehensive risk assessment. Risk stratification plays a central role in guiding early intervention strategies and should incorporate clinical findings, echocardiographic parameters, biomarkers, and advanced imaging techniques such as cardiac magnetic resonance and computed tomography. Recent randomized controlled trials have yielded mixed results regarding the mortality benefit of early AVR but have consistently demonstrated a reduction in heart failure-related hospitalizations. Timely intervention in carefully selected high-risk patients may improve long-term outcomes, while avoiding unnecessary procedures in lower-risk individuals remains equally important. Full article

Commercial buildings and shopping malls face rising electricity costs and increasing pressure to adopt sustainable practices. This paper presents the first long-term, multi-site empirical validation of Automatic Voltage Regulator (AVR) deployment in Thai retail facilities, providing robust evidence for tropical, motor-heavy load contexts. The study evaluates the engineering, economic, and environmental performance of an AVR with an autotransformer core under real operating conditions. High-resolution measurements were collected before and after AVR installation, using Class 0.2s analyzers and a Building Energy Management System (BEMS) across multiple branches during a four-month monitoring campaign (February–May). Results indicate that a modest voltage reduction of 8.06% yielded a 12.02% decrease in active power demand, a 6.22% current reduction, and a 2.26% improvement in power factor. The greatest savings occurred in HVAC (8.19%) and refrigeration loads (8.20%), while lighting loads remained nearly unchanged. Economically, the system delivered ~177 kWh/day savings, equivalent to 262,212 THB/year, with a simple payback of 2.67 years and an ROI of 37.5%. Environmentally, the AVR reduced 36.6 tCO₂/year (±5%), aligning with Thailand’s Energy Efficiency Plan (EEP) 2018–2037 and Carbon Neutrality Roadmap and offering additional potential for T-VER monetization. These findings confirm AVR technology as a scalable, standards-compliant, and high-return retrofit solution for commercial facilities in tropical climates. Full article