Search Results (3,090)

In this study, we apply machine learning to model the spatiotemporal dynamics of gene expression during early Drosophila embryogenesis. By optimizing model architecture, feature selection, and spatial grid resolution, we developed a predictive pipeline capable of accurately classifying active nuclei and forecasting their future distribution in time. We evaluated the model on two reporter constructs for the short gastrulation (sog) gene, sogD and sogD_∆Su(H), allowing us to assess its performance across distinct genetic contexts. The model achieved high accuracy on the wild-type sogD dataset, particularly along the dorsal–ventral (DV) axis during nuclear cycle 14 (NC14), and accurately predicted expression in the central regions of both wild-type and Suppressor of Hairless (Su(H)) mutant enhancers, sogD_∆Su(H). Bootstrap analysis confirmed that the model performed better in the central region than at the edges, where prediction accuracy dropped. Our previous work showed that Su(H) can act both as a repressor at the borders and as a stabilizer of transcriptional bursts in the center of the sog expression domain. This dual function is not unique to Su(H); other broadly expressed transcription factors also exhibit context-dependent regulatory roles, functioning as activators in some regions and repressors in others. These results highlight the importance of spatial context in transcriptional regulation and demonstrate the ability of machine learning to capture such nuanced behavior. Looking ahead, incorporating mechanistic features such as transcriptional bursting parameters into predictive models could enable simulations that forecast not just where genes are expressed but also how their dynamics unfold over time. This form of in silico enhancer mutagenesis would make it possible to predict the effects of specific binding site changes on both spatial expression patterns and underlying transcriptional activity, offering a powerful framework for studying cis-regulatory logic and modeling early developmental processes across diverse genetic backgrounds. Full article

The reliability and lifetime of insulated gate bipolar transistors (IGBTs) are critical to ensuring the stability and safety of power electronic systems. IGBTs are widely used in electric vehicles, renewable energy systems, and industrial automation. However, their degradation over time poses a significant risk to system performance. Therefore, this paper proposes a data-driven approach based on a Long Short-Term Memory (LSTM) network optimized by a Genetic Algorithm (GA) to predict IGBT degradation. The study examines the health monitoring of insulated gate bipolar transistors from a device physics perspective. Degradation mechanisms that alter parasitics and electro-thermal stress produce characteristic changes in the turn-off overvoltage and the on-state voltage. Using power-cycling data from packaged half-bridge modules, an LSTM-based sequence model configured by a genetic algorithm search reduces error against an identically trained baseline (RMSE = 0.0073, MAE = 0.057, MAPE = 0.726%) under the shared protocol, with the clearest advantages in the early stage of degradation. These results support predictive maintenance and health management in power-electronic systems. Full article

Virtual reality (VR) has emerged as a promising tool for transforming science, technology, engineering, and mathematics (STEM) education, yet its adoption in K–12 classrooms remains uneven and often limited to short-term pilots. While prior studies highlight VR’s potential to increase engagement and support conceptual understanding, questions persist about scalability, sustainability, and equity in implementation. This paper addresses these gaps by synthesizing recent scholarship and proposing a structured framework of best practices for integrating VR into K–12 STEM education. Drawing on academic literature, U.S. policy reports, and case studies, we identify persistent challenges that include high costs, lack of teacher preparation, infrastructure disparities, and overlooked accessibility concerns. We use these findings to inform a phased implementation roadmap. Our framework emphasizes assessment and planning, technical integration, teacher preparation, student implementation, and iterative evaluation, providing actionable strategies for schools and districts. Results of this synthesis indicate that successful VR adoption requires coordinated attention to pedagogy, funding, professional development, and equity. We conclude that moving VR from isolated novelty projects to sustainable and equitable tools in STEM classrooms depends on aligning technology with curricular goals, investing in teacher pipelines, and embedding VR within long-term evaluation and improvement cycles. Full article

Flexible and wearable thermoelectric devices can convert body waste heat into electricity, showing a new direction to solve the long-lasting issue of energy supply on portable devices. However, thermoelectric fibers are prone to short circuits and failure due to sweat stains and washing practices. Therefore, it is quite necessary to solve this problem to realize the practical thermoelectric device. PDMS, with its excellent insulation and flexibility, can effectively address short-circuit issues by encapsulating the surface of thermoelectric fibers. In this work, hydrophilic nano-silica (H-SiO₂)-modified PDMS that insulates materials was prepared and coated on the surfaces of polyethyleneimine (PEI)- and hydrochloric acid (HCl)-treated dual-surface-modified thermoelectric fibers. The encapsulated fibers were then woven into spacer fabric to prepare thermoelectric textiles (TETs). After 50 water washing cycles, the fibers retained 97% of their conductivity, and the textiles continued to function normally underwater, indicating that the thermoelectric fibers are effectively protected under PDMS encapsulation. Full article

Retinal laser therapy has been a mainstay for treating proliferative diabetic retinopathy, retinal vascular disease, and retinal breaks since 1961. However, conventional millisecond photocoagulation can cause permanent scarring and procedure discomfort, motivating the development of damage-sparing approaches that preserve the neurosensory retina. Clinically, panretinal photocoagulation remains effective for proliferative disease but trades off peripheral visual field and night vision. This review synthesizes development, mechanisms, and clinical evidence for laser modalities, including short-pulse selective retinal therapy (SRT), subthreshold diode micropulse (SDM), and pattern-scanning photocoagulation. We conducted a targeted narrative search of PubMed/MEDLINE, Embase, Web of Science, and trial registries (1960–September 2025), supplemented by reference list screening. We prioritized randomized/prospective studies, large cohorts, systematic reviews, mechanistic modeling, and relevant preclinical work. Pulse duration is the primary determinant of laser–tissue interaction. In the microsecond regime, SRT yields retinal pigment epithelium (RPE)-selective photodisruption via microcavitation and uses real-time optoacoustic or OCT feedback. SDM 100–300 µs delivers nondamaging thermal stress with low duty cycles and titration-based dosing. Pattern-scanning platforms improve throughput and tolerance yet remain destructive photocoagulation. Feedback-controlled SRT shows anatomic/functional benefit in chronic central serous chorioretinopathy and feasibility in diabetic macular edema. SDM can match threshold macular laser for selected DME and may reduce anti-VEGF injection burden. Sub-nanosecond “rejuvenation” lasers show no overall benefit in intermediate AMD and may be harmful in specific phenotypes. Advances in delivery, dosimetry, and closed-loop feedback aim to minimize collateral damage while retaining therapeutic effect. Key gaps include head-to-head trials (SRT vs. PDT/SDM), standardized feedback thresholds across pigmentation and devices, and long-term macular safety to guide broader clinical adoption. Full article

Exercise-unacclimatized dromedary camels regularly perform strenuous work in desert heat; however, their short-term hematologic and biochemical recovery is not well defined. In this prospective repeated-measures experiment, seven healthy bulls underwent a standardized 90 min outdoor exercise bout, with blood sampled before exercise and at 0, 3, 6, 24, and 48 h of recovery. The analytical panel included hematology, primary hemostasis, electrolytes, osmolality, protein fractions, metabolites, and serum enzymes. Red-cell indices remained stable, indicating minimal erythrocyte mobilization, while bleeding time shortened sharply at 0 h and normalized by 3 h. Sodium and osmolality followed a biphasic pattern with an early rise at 3 h, a nadir at 6 h, and partial rebound by 24 h, whereas potassium and phosphate stayed depressed from 6 to 48 h. Proteins and glucose showed transient changes, and muscle-associated enzymes, especially lactate dehydrogenase, peaked early before declining. These findings demonstrate that camels tolerate combined exercise and heat stress but require up to 48 h to re-establish biochemical balance. The recovery timeline provides a clinically relevant framework for sampling, welfare monitoring, and management of work–rest cycles in desert environments. Full article

Objectives: Sports-related concussion (SRC) is mostly associated with contact and combat sports. However, emerging evidence suggest that cyclists are also at risk of repeated concussion injury. Moreover, long-term neurophysiological outcomes in cycling cohorts remain underexplored. This novel study investigated the long-term effect of repetitive concussions in cyclists. Road, mountain biking (MTB), and BMX riders with a history of concussions and self-reported persistent symptoms were assess for neurophysiology and cognitive–motor performance compared to previously concussed cyclists with no ongoing symptoms. Both groups were compared to age-matched with controls. Methods: Using a cross-sectional between-group design, 25 cyclists with a history of concussions (15 symptomatic, 10 asymptomatic) and 20 controls completed symptom reporting, cognitive and balance assessments (SCAT5), sensorimotor testing using vibrotactile stimulation, and neurophysiological assessments via transcranial magnetic stimulation (TMS). Results: Symptomatic cyclists reported a higher number of concussions compared to asymptomatic cyclists (p = 0.041). Cognitive testing revealed large effects (d > 1.0), with impaired concentration in symptomatic cyclists compared to controls (p = 0.005). Motor assessments demonstrated large effects (d > 1.0), with slower tandem gait times (p < 0.001) and greater errors (p = 0.02) in the symptomatic group. Sensorimotor testing indicated slowed simple reaction times (p = 0.001) and poorer temporal order judgement (p = 0.038). TMS showed large effects (d > 1.0) in increased cortical inhibition in the symptomatic group, with prolong cortical silent periods (p < 0.05) and large effects (d > 1.0), and reduced short interval intracortical inhibition (p = 0.001) compared to asymptomatic cyclists and controls. Conclusions: Cyclists reporting persistent symptoms showed greater cortical inhibition and impaired cognitive–motor performance, consistent with findings in contact sport athletes. These results suggest that repeated concussions in cycling carry risk of chronic neurophysiological alterations. Cycling disciplines should consider more rigorous concussion identification protocols and stricter management strategies to mitigate persistent and long-term consequences. Full article

The quality of input data is critical to the performance of time-series classification models, particularly in the domain for industrial sensor data where noise and anomalies are frequent. This study investigates how various filtering-based preprocessing techniques impact the accuracy and robustness of a Transformer model that predicts power efficiency states (Normal, Caution, Warning) from minute-level IIoT sensor data. We evaluated five techniques: a baseline, Simple Moving Average, Median filter, Hampel filter, and Kalman filter. For each technique, we conducted systematic experiments across time windows (360 and 720 min) that reflect real-world industrial inspection cycles, along with five prediction offsets (up to 2880 min). To ensure statistical robustness, we repeated each experiment 20 times with different random seeds. The results show that the Simple Moving Average filter, combined with a 360 min window and a short-term prediction offset, yielded the best overall performance and stability. While other techniques such as the Kalman and Median filters showed situational strengths, methods focused on outlier removal, like the Hampel filter, adversely affected performance. This study provides empirical evidence that a simple and efficient filtering strategy such as Simple Moving Average, can significantly and reliably enhance model performance for power efficiency prediction tasks. Full article

Metallic glass, as an emerging catalytic material, possesses an atomic structure characterized by long-range disorder and short-range order, which creates abundant and accessible active sites that enhance the adsorption and reactivity toward pollutant molecules, particularly dye compounds. In treating highly colored and recalcitrant Reactive Black 5 (RB5) dye wastewater, Mo₅₁Fe₃₄B₁₅ metallic glass wire demonstrate outstanding catalytic degradation performance within a conventional Fenton-like system. Under acidic conditions (pH = 2), the material exhibits a degradation rate constant of 0.698 min⁻¹ for a 20 ppm RB5 dye solution, achieving a degradation efficiency of 98.8% within 10 min. After 10 consecutive cycles, the efficiency remains at 95%, and throughout 15 cycles, it consistently maintains a performance level above 90%. As the reaction proceeds, the degradation rate gradually decreases, primarily due to the accumulation of corrosion products on the catalyst surface, which are predominantly composed of MoO₃ and Fe₂O₃. During the degradation process, metallic Mo⁰ and Fe⁰ serve as electron donors that facilitate the decomposition of H₂O₂, generating highly reactive hydroxyl radicals (•OH). These radicals attack the chromophoric structure of the dye, leading to its structural disruption and enabling rapid decolorization. Full article

Mesenchymal stem cells (MSCs), particularly their secreted exosomes, small microvesicles, represent a major focus in regenerative medicine due to their therapeutic potential. Exosomes exhibit growth factors and cytokines and are loaded with microRNAs (miRNA) and short interfering RNA (siRNA) that can be transferred to other cells, potentially affecting their function. Exosomes are crucial mediators of intercellular communication, are immunomodulatory, and are promoters of tissue regeneration. Despite their promise, the standardized methods for exosome isolation and characterization remain weak. This exploratory study addresses this gap by detailing an effective method for isolating exosomes from adipose tissue mesenchymal stem cells (AT-MSCs), emphasizing precipitation as a technique yielding a high efficiency and purity compared to other methods. Functionally, we aimed to confirm the AT-MSC exosomes’ ability to exert an effective protective activity on the skin and its main components, such as fibroblasts, collagen, and elastin. To achieve this goal, we had to demonstrate that AT-MSC exosomes are safe and free of toxic substances. They can express specific proteins such as CD9, CD63, and CD81, which are well-known exosome markers. These exosomes also contain key miRNAs, including miRNA-203 A, miRNA-203 B, and miRNA-3196, important for skin regeneration, as well as enhancers of cell integrity and proliferation. We eventually confirmed the ability of exosomes to exert protective and recovery effects on fibroblasts after H₂O₂-induced damage in vitro, as well as on mouse skin after UVB-induced damage in vivo. These effects were verified by measuring levels of reactive oxidative species (ROS), assessing SA-β-Galactosidase (SA-β-Gal) activity, analyzing the cell cycle, evaluating the telomere length of fibroblasts by RT-PCR, and conducting histological assessments of collagen and elastin structure in murine skin after UVB exposure. This exploratory work provides valuable insights into the isolation, characterization, and bioactive and reparative properties of exosomes from AT-MSCs, supporting their development for future studies and therapeutic applications. Full article

Cucumis sativus L. is a globally important vegetable crop that occupies a significant position in protected agriculture due to its high nutritional value, short cultivation cycle, and considerable economic benefits. As a cold-sensitive plant, however, cucumber is highly susceptible to low-temperature stress. which can severely inhibit growth and development, hinder seed germination, and reduce photosynthetic efficiency. Under low-temperature stress, cucumber plants typically incur damage to cellular membrane structures, experience an accumulation of reactive oxygen species (ROS), exhibit a disruption in hormonal homeostasis, and suffer from the inhibition of pivotal metabolic pathways. In response, cucumber plants activate an array of resistance mechanisms, encompassing osmotic adjustment, reinforcement of the antioxidant system, and modulation of cold-responsive gene expression. This review summarizes the physiological and molecular mechanisms underlying cucumber’s response to low-temperature stress, aiming to provide effective strategies for improving abiotic stress resistance. The main findings are as follows: (1) Low-temperature stress damages cucumber cell membranes, suppresses photosynthesis and respiration, suppresses water and nutrient uptake/transport, and suppresses growth retardation. (2) Cucumber counters these adverse effects by orchestrating the accumulation of osmoregulators (e.g., soluble sugars, proline), activating activation defenses (e.g., SOD, CAT), and rebalancing its phytohormone network (e.g., ABA, GA, SA, ethylene). (3) At the molecular level, cucumber activates low-temperature-responsive genes (e.g., COR, GoIS) through transcription factors such as CBF, MYB, and WRKY, thereby enhancing cold tolerance. (4) Application of exogenous protectants (e.g., hydrogen sulfide, melatonin, oligosaccharides) significantly improves cucumber’s low-temperature tolerance by modulating the antioxidant system, promoting osmoregulatory substances accumulation, and regulating hormone signaling pathways. Future research should focus on elucidating the molecular regulatory network in cucumber under low-temperature stress and developing gene editing with multi-omics techniques to advance the development of cold-resistant cultivars and cultivation practices. This study offers a scientific foundation for research on cucumber cold tolerance and proposes potential solutions to agricultural challenges in the context of global climate change. Full article

This study presents a comparative techno-economic and environmental assessment of three leading stationary energy storage technologies: lithium-ion batteries, lead-acid batteries, and hydrogen systems (electrolyzer–tank–fuel cell). The analysis integrates Life Cycle Assessment (LCA) and Levelized Cost of Storage (LCOS) to provide a holistic evaluation. The LCA covers the full cradle-to-grave stages, while LCOS accounts for capital and operational expenditures, efficiency, and cycling frequency. The results indicate that lithium-ion batteries achieve the lowest LCOS (120–180 EUR/MWh) and high round-trip efficiency (90–95%), making them optimal for short- and medium-duration storage. Lead-acid batteries, though characterized by low capital expenditures (CAPEX) and high recyclability (>95%), show limited cycle life and lower efficiency (75–80%). Hydrogen systems remain costly (>250 EUR/MWh) and less efficient (30–40%), yet they demonstrate clear advantages for long-term and seasonal storage, particularly under scenarios with “green” hydrogen production and reduced CAPEX. These findings provide practical guidance for policymakers, investors, and industry stakeholders in selecting appropriate storage solutions aligned with decarbonization and sustainability goals. Full article

Hydrogen and oxygen isotopes in atmospheric water vapor (δ_v) and precipitation (δ_p or δ_r) were continuously measured using a laser-based water isotope spectrometer in Guangzhou, southeastern China, from March 2016 to February 2018. The measurements were conducted to investigate the variations in water isotopes in the hydrological cycle under the subtropical monsoon climate. The isotopic composition ranged from −24.4‰ to −11.1‰ for δ¹⁸O in water vapor (δ¹⁸O_v) and from −11.5‰ to 2.3‰ for δ¹⁸O in precipitation (δ¹⁸O_r). The values of δ_v and δ_r were enriched during the dry season and depleted during the wet season, exhibiting systematic seasonal variation. A negative correlation was observed between monthly δ_v and precipitation amount, indicating that the values of δ_v exhibits an ‘amount effect’. However, a corresponding amount effect was not observed in the values of δ_r. The mean difference between δ_v and δ_r was −9.7‰ for δ¹⁸O and −76‰ for δD, suggesting that equilibrium fractionation is the dominant process during precipitation. The local meteoric vapor line (LMVL) for Guangzhou (δD = 6.6δ¹⁸O − 6.4) exhibited a slope similar to that of the equilibrium local meteoric vapor line (ELMVL) but with an intercept difference of 8.6. This difference in intercepts can be attributed to the vertical profile of δ_v. The δD-q (q refers to water vapor concentration) relationship is useful for identifying water vapor sources and tracking isotopic changes during atmospheric transport and precipitation. The local water vapor was found to originate primarily from the mixing of oceanic air masses. Data points falling between the oceanic source mixing line and the Rayleigh curve likely reflect post-condensation processes, such as raindrop re-evaporation or mixing with surrounding ambient vapor. Short periods of heavy precipitation were observed to cause severe depletion in δ_v, resulting in values falling below the Rayleigh curve. Full article

Condition-Based Maintenance Plus (CBM+) has been widely adopted in aerospace and mechanical systems, but its application to single-board computers (SBCs) remains difficult due to scarce failure data and subtle degradation signatures. This study investigates CBM+ for the MVME6100 SBC using accelerated life testing (ALT) to generate degradation trajectories and capture precursor signals. Temperature–humidity cycling and vibration tests were performed, while CPU temperature, memory temperature, and output voltage were continuously monitored. Under stable operation, signals followed ambient variations and showed little statistical drift, making degradation visually indistinguishable. However, precursors emerged before failure: CPU temperature exhibited abnormal behavior during thermal cycling, while vibration stress induced communication noise and irregular thermal behavior. These findings indicate that thermal responses provide reliable precursors for electronic degradation. To evaluate data-driven detection, two neural approaches were applied: an Autoencoder (AE) trained only on normal data and a Long Short-Term Memory (LSTM) network trained on both normal and faulty datasets. The Autoencoder reliably detected anomalies via reconstruction error, while the LSTM accurately classified health states and reproduced lifecycle progression. Together, the results demonstrate that precursor-informed CBM+ is feasible for SBCs and that a hybrid AE–LSTM framework enhances prognostics and health management in mission-critical electronics. Full article

In Yucatán state, Mexico, Triatoma dimidiata (Latreille, 1811) is the primary vector of Trypanosoma cruzi, the parasite that causes Chagas disease. The vector population presents diverse forms and colorations. Therefore, this study was designed to determine the morphotypes of T. dimidiata based on the taxonomy of the body and external genitalia. Between March 2023 and April 2025, 902 triatomines from 15 municipalities were examined. Three main morphotypes were characterized (I to III). Morphotype II was the most abundant (62.86%) and most distributed in the study area (12 of 15 municipalities), with a notable presence in forests and caves. Morphotypes I and III were found primarily outside houses and in chicken coops. Within the characterized specimens of T. dimidiata sensu lato, morphotype II displays more prominent morphological and structural characteristics. They are smaller compared to morphotypes I and III. In morphotype II, the spiracles are covered by a black spot that extends from the connexival plate to the urosternites. Males had short and robust parameres. The median process of the pygophore is long and slender compared to morphotypes I and III. The female tergite VIII has six sides. The taxonomy should be complemented by a study of the life cycle of each morphotype and analysis of its genome. Full article