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Prediction of urban heat island intensity based on multiple linear regression and deep learning
The rapid urbanization process has led to many prominent environmental issues in urban areas, resulting from a drastic change in land use. The Urban Heat Island (UHI) effect is of particular concern because it has a significant impact on the livability of cities. Therefore, exploring and studying the intensity of UHI and its future distribution have significant practical implications. In this study, the CNN-LSTM-Attention model was constructed to predict four remote sensing spectral indices, and combined with the CA-Markov model to predict land use change. The relationship between four different spectral indices and the intensity of UHI was analyzed by a multiple linear regression model (R 2 = 0.7468, RMSE = 0.0546), and the UHI intensity and distribution in 2025 were predicted and analyzed. The results show that by 2025, the proportion of built-up area will continue to increase by 1.37 percentage points, which will lead to a more intense and concentrated UHI effect, and the proportion of heat island area will increase by 2.97 percentage points. The study shows that increasing vegetation area and water area can effectively alleviate the impact of UHI. Local government departments formulate reasonable policies based on survey results, reduce over-radiation, improve urban livability, and promote sustainable development.
Melatonin-induced resistance response activation against Fusarium oxysporum f. sp. lycopersici development in tomatoes
Minimally invasive coronary artery bypass grafting (MINI-CABG): Protocol for a pilot randomized controlled trial comparing minimally invasive versus conventional coronary surgery
Introduction Coronary artery bypass grafting (CABG) via sternotomy remains the standard of care for multivessel coronary disease. Minimally invasive cardiac surgery for coronary artery bypass grafting (MICS-CABG) is an evolving technique with the potential to reduce surgical trauma and promote faster recovery without compromising outcomes. This protocol describes a pilot randomized controlled trial comparing MICS-CABG and conventional CABG in multivessel patients in a high-volume tertiary center. Methods and analysis This is a single-center, prospective, randomized controlled pilot trial. A total of 100 patients with multivessel coronary artery disease will be randomized (1:1) to undergo either conventional CABG via median sternotomy or MICS-CABG through a left anterior thoracotomy. The sample size of 100 patients (50 per group) was defined based on feasibility and statistical precision. This allows estimation of an expected 8% major adverse cardiovascular and cerebrovascular events (MACCE) rate with a 95% confidence interval half-width of ±5% in each group. These data will inform sample size calculations for a future phase III trial. The primary outcomes are safety and feasibility. Feasibility will be assessed by the successful completion of the planned minimally invasive coronary revascularization strategy. Safety will be evaluated through the occurrence of MACCE within 30 days postoperatively. Secondary outcomes include operative time, mechanical ventilation time, conversion rate to sternotomy, bleeding volume, atrial fibrillation, postoperative ICU and hospital length of stay, and patient-reported quality of life (EQ-5D-5L) 6 months postoperatively. The trial is ongoing at this moment. Ethics and dissemination The protocol was approved by the Institutional Research Ethics Committee under que number CAAE: 54175921.8.0000.0068. Results will be disseminated through peer-reviewed publications and scientific conferences. Trial registration number ClinicalTrials.gov NCT06794359 .
Molecular characterization of bovine leukemia virus detected in dairy cattle herds from the Emirate of Abu Dhabi, United Arab Emirates
Integrating bioactivity and molecular simulations to explore the pharmacological landscape of Lagerstroemia speciosa leaf extract
Natural resources are vital for identifying novel treatments for noncommunicable diseases and multidrug-resistant pathogens. Lagerstroemia speciosa (L. speciosa) Linn. is traditionally used to manage conditions like diabetes, cancer, and oxidative stress-related diseases. Current studies frequently lack comprehensive bioactivity assessments coupled with molecular simulation analyses of L. speciosa leaf extracts. We aimed to assess the phytochemical profiling, bioactivity, in silico pharmacological properties, and molecular interactions of L. speciosa leaf extracts prepared using organic solvents. Methanol (5.1) and ethanol (4.3) extracts yielded the highest concentrations of total phenolics, flavonoids, and proanthocyanidins due to their high polarity indices. Antioxidant activities were robustly evaluated using DPPH, ABTS, superoxide, and nitric oxide assays, with the ethanol extract demonstrating significant free radical scavenging (IC 50 = 75.53 µg/mL for DPPH). The methanol extract exhibited broad-spectrum antibacterial activity against multidrug-resistant strains, including Escherichia coli and Staphylococcus aureus , alongside moderate antifungal activity against Candida albicans and Saccharomyces cerevisiae . The brine shrimp lethality assay revealed moderate cytotoxicity (LC 50 = 601.8 µg/mL), suggesting potential anticancer properties likely mediated by bioactive compounds such as ellagic acid and gallic acid. Methanol and ethanol extracts of L. speciosa significantly inhibited α-amylase and α-glucosidase compared to standard acarbose, indicating substantial antidiabetic potential by delaying carbohydrate digestion and reducing postprandial glucose levels. Complementary molecular docking and ADME pharmacokinetic studies provided in silico support for the observed antioxidant, antimicrobial, antidiabetic, and chemopreventive activities. The compelling evidence collectively suggests that L. speciosa extracts are a promising source of bioactive compounds.
Efficacy of zinc oxide nanoparticles in chemical castration of male Wistar rats
Vapor-liquid equilibrium of water-hydrogen mixtures: A review of experimental data and modeling with a Cubic-Plus-Association Equation-of-State
Interest in subsurface hydrogen storage and geological hydrogen exploration has grown in recent years. These processes generally involve two-phase, multi-component species transport, e.g. for hydrogen and water, and require accurate phase behavior models under varying temperatures and pressures. We compile experimental data spanning 0–200 ∘ C and up to 400 bar, revealing non-ideal H 2 – H 2 O behavior, such as a non-monotonic solubility trend with temperature. To model vapor-liquid equilibrium (VLE) compositions, we use the Cubic-Plus-Association (CPA) equation-of-state (EoS), which effectively captures hydrogen bonding effects. A single temperature-dependent binary interaction coefficient allows accurate reproduction of experimental data across all phases. In contrast, the cubic Peng-Robinson (PR) EoS lacks key molecular interactions and performs poorly, especially for the vapor phase. We also provide a polynomial parameterization of VLE compositions for easy use by hydrogen energy stakeholders. Our results offer a robust framework for hydrogen storage modeling and practical applications.
Changes in biochemical compositions and salinity tolerance responses of different bread wheat varieties cultivated in an arid and semi-arid climate
Abstract The present study aimed to investigate the differential responses of several wheat cultivars under saline conditions through two complementary experiments, a laboratory-based Petri dish test and a field trial. Therefore, the effects of salinity levels (control, 4, 8, and 12 dS·m −1 ) were firstly studied on seed germination indices and some growth-related parameters of six bread wheat cultivars/new promising lines (e.g., cultivars of Chamran-2, Mehrgan, Marvdasht, and Narin, as well as new promising lines of MS-89-13 and MS-90-13) using a factorial based on the completely randomized design in the Petri test for ten days in three replications. Subsequently, different responses of the superior cultivars/lines selected were evaluated under both normal and saline field conditions through a combined analysis using a randomized complete block design, conducted over the 2020–2021 and 2021–2022 growing seasons with three replications. The Petri data showed that salinity levels negatively influenced germination indices, with the highest germination percentage, optimal T50 values, longest shoot length, and highest leaf protein content observed under the control (non-saline) treatment across all cultivars. Among cultivars, the Chamran-2 cultivar achieved the highest germination percentage, shoot length, and leaf protein content, and the lowest T50 value. Additionally, the minimum values of root length and root length stress tolerance index traits were observed for the interaction of MS-89-13 promising line × 12dS·m −1 salinity level. Field experiment data revealed that the highest values for plant height, 1000-grain weight, grain and biological yields, pigment contents, grain protein, wet gluten, and gluten index were recorded in plants grown under normal conditions during the second year of the study. Chamran-2, and then Mehregan had more proper conditions and had longer plants, heavier grain weight, and higher grain and biological yields. However, the maximum values for wet gluten and gluten index were obtained for Mehregan and Narin cultivars, respectively. The highest straw yield was obtained under the Chamran-2 cultivar × Normal farm × Second year interaction. The highest catalase activity was recorded for saline conditions and the first year of the experiment, and the highest superoxide dismutase activity was observed for the Narin cultivar × Saline conditions × Second year interaction. Eventually, considering the predominant characteristics of the field experiments, Chamran-2 and Mehrgan cultivars can be cultivated in the southern regions of Iran and similar areas as a reference.
Exploration of risk factors for the incidence of knee osteoarthritis in rural areas of northern China and the establishment of a prediction model
Objective This study sought to identify knee osteoarthritis (KOA) contributing factors and develop a preliminary forecasting model for its development. Methods Participants were systematically invited to complete an exhaustive medical questionnaire designed to capture relevant health and demographic information. Following data collection, univariate analyses were conducted to assess the significance of the variables obtained from the questionnaire. To delineate the association between identified risk factors and the occurrence of KOA, a binary logistic regression model was utilized. The reliability of the model was evaluated through internal validation, encompassing both calibration and discrimination analyses. Calibration was quantified using the Hosmer–Lemeshow χ² statistic to assess the model’s goodness of fit, while discrimination was gauged utilizing the receiver operating characteristic (ROC) curve, providing a comprehensive evaluation of the model’s predictive accuracy. Results In the present study, a total of 445 cases were analyzed, with 266 cases employed for model development and 179 cases reserved for internal validation. Univariate analysis revealed significant statistical differences between the two groups with respect to several variables, including family history of KOA, heating methods, stair usage, anxiety and depression, toilet type, and the frequency of consumption of vegetables, fruits, red meat, and dairy products. Binary logistic regression analysis identified advanced age, lower educational level, use of a squat toilet, family history of KOA, and psychological conditions such as anxiety and depression as significant risk factors for the development of KOA. Furthermore, a moderate predictive value was observed for incident KOA based on a combination of factors, including age, gender, weight, height, family history of KOA, toilet type, mode of transportation, dairy product consumption, and emotional state. Conclusions Our findings indicate that, in addition to established risk factors such as age, gender, height, and weight, lifestyle and dietary habits also play a pivotal role in the etiology of KOA. These factors not only serve as potential risk markers but also exhibit predictive utility for the onset of KOA, suggesting a comprehensive approach to prevention and intervention strategies.
From U to mnm⁵Se²U: tuning base pairing preferences through 2-chalcogen and 5-methylaminomethyl modifications
“Finally my turn to write my story”: A convergent mixed methods study exploring the perceptions and experiences of emerging adults who aged out of foster care in Canada
Background Each year, thousands of young Canadians ‘age out’ of foster care on or before their 19 th birthday. This abrupt transition to independence coincides with emerging adulthood (ages 18–29), a developmental period associated with transformational changes, including new or worsening mental health challenges. Objective The purpose of this study was to understand how emerging adults, who aged out of foster care in Canada, navigated their transition to independence, and specifically, how experiences of structural violence encountered pre- and post-emancipation may have influenced their mental health and capacity for positive adaptation. Participants and Setting 203 emerging adults from across Canada took part in the quantitative arm of this study, with a subsample of 31 participants enrolled in the qualitative arm. Virtual methods (online survey and video conferencing) supported remote participation. Methods A convergent mixed methods design involved concurrent quantitative and qualitative data collection and analysis, followed by integration of emergent findings. The quantitative arm of the study consisted of an electronic questionnaire including sociodemographic characteristics, foster care histories, and ten validated measures. The qualitative arm involved virtual semi-structured interviews regarding participants’ transition to independence upon aging out of care, including experiences and perceptions of structural violence, mental health challenges, and positive adaptation. Results Correlation analyses and regression modelling revealed relationships between and among structural violence, mental health challenges, and positive adaptation in this population. Nine qualitative themes uncovered the contextual nuances of participants’ transition to independence. Two joint displays were developed to visually represent the integration of quantitative and qualitative data. Conclusions By exploring this clinical issue from a combined socioecological, temporal, and intersectional perspective, key findings reflect its complexity, nuance, and transformative capacity. Integrated data may suggest approaches for future development of interventions to address the unique mental health care needs of this population.
Mixed-mode control of multiphase interleaved parallel circuit for pulsed laser diode driving
Mapping the mortality-to-incidence ratios of Alzheimer’s Disease and Related Dementias (ADRDs): Evidence from the South Carolina Alzheimer’s disease registry
Introduction Mortality-to-incidence ratios (MIRs) are useful in assessing disease burdens and illustrating disparities. Unlike cancer, MIRs have not been applied to ADRDs. Therefore, we estimated and mapped the MIRs for ADRDs to show disparities in South Carolina. Methods Using data from the South Carolina Alzheimer’s Disease Registry (2017−2021), ADRD MIRs were calculated by demographic and geospatial characteristics. To account for the influence of the COVID-19 pandemic, data from 2015 to 2019 were also examined. MIRs were calculated as age-adjusted mortality rates divided by age-adjusted incidence rates. Results Overall, Black people and rural individuals consistently experienced higher MIRs, with the COVID-19 pandemic increasing this disparity gap. MIRs greater than 1.00 were only observed among Black people. The MIR for 31 out of 46 counties exceeded the state average. Discussion Estimating and mapping ADRDs has aided in identifying specific areas with the greatest burden of ADRD in South Carolina for targeting interventions.
New candidate gene mutations in astrocytoma with seizures
An algorithm for drug retrieval based on robot-grasping detection constraints and DDPG autonomous learning
When the medicine-picking robot grasps drugs, its flexibility and accuracy in grasping detection mainly depend on the precision of visual guidance for the robot. The result of grasping detection directly determines whether the grasping task can be successfully completed. This study aims to enable a faster learning speed for the robot, reduce the search space for the grasping pose of the medicine-picking robot, and improve the grasping accuracy of the robot in unstructured environments. For this purpose, a self-learning DDPG grasping algorithm based on detection constraints is proposed and applied in automated pharmacy detection. The algorithm primarily consists of two steps. First, it extracts candidate grasping areas by analyzing the boundaries of the medicine. Second, with the aid of deep reinforcement learning, it inputs images with candidate grasping areas into an autonomous learning network, conducts adaptive noise exploration and perturbation in the search space, detects the optimal grasping point of the medicine from the image in real time, feeds it back to the medicine-fetching robot, adjusts the grasping pose through autonomous learning, and controls the robot to complete the training grasping. Experiments demonstrate that this method achieves a minimum of 15% improvement in grab detection accuracy compared with the four other grab detection methods. Within the confidence interval, it can achieve a grab success rate of 95%, which verifies the feasibility and effectiveness of this method.
Deep learning denoising enables rapid SEM imaging under charging conditions for FE SEM, CD SEM, and review SEM
Global, regional, and national burden of pulmonary arterial hypertension from 1990 to 2021 and projection to 2050: A systematic analysis for the global burden of disease study 2021
Background Pulmonary arterial hypertension (PAH) is a progressive and incurable syndrome characterized by pulmonary vascular remodeling. Although targeted therapies have advanced, prognosis remains poor, underscoring the need for comprehensive epidemiological evaluations to guide public health strategies and resource allocation. Methods This study assessed the global, regional, and national burden of PAH from 1990 to 2021 using data from the Global Burden of Disease Study (GBD) 2021. Metrics included prevalence, incidence, mortality, and disability-adjusted life years (DALYs), with stratified assessments by geography, gender, age, and socio-demographic index (SDI). Results In 2021, the global PAH burden comprised 191,808 prevalent cases, 43,251 incident cases, 22,021 deaths, and 642,104 DALYs. Age-standardized rates declined consistently over the 32-year period. Population growth was the prominent contributor of the PAH burden, with higher rates in females and older age groups. Prevalence, mortality, and DALYs decreased with higher SDI, whereas incidence showed an inverse trend. The disparity between high and low SDI countries widened, with the slope index of inequality increasing from −5.21 in 1990 to −1.52 in 2021. Predictions revealed that further declines in age-standardized rates of prevalence, mortality, and DALYs from 2022 to 2025, but a rise in incidence. Conclusions A decline in the age-standardized rates of PAH was observed, whereas a persisting high absolute disease burden was evident, clustering among women, older populations, and low-SDI regions.
EXOSC3 knockdown induces G1/S phase arrest to suppress hepatocellular carcinoma cell proliferation
A 38-plex PCR MALDI-TOF MS-based assay to detect SNPs common in elite athletes
There is great demand for a novel technique to facilitate the rapid identification of multiple single-nucleotide polymorphisms (SNPs) prevalent in elite athletes. Case-control and genome-wide association studies (GWAS) have been conducted to investigate an individual’s likelihood for success in various sports, revealing several putative loci associated with elite athletic status. However, it remains challenging to detect multiple such SNPs simultaneously with the aim of examining their influence on specific physical fitness characteristics, such as muscle power, strength, or endurance. The aim of the present study is to develop a 38-plex PCR amplification assay, integrated with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), for the identification of 38 SNPs linked to elite athletic performance and assess its quantitative performance metrics and high-throughput capabilities within a single 38-plex reaction. The SNPs were chosen based on their high prevalence among elite power athletes, potential influence on muscle power production, and suitability for multiplex PCR amplification. The developed method simultaneously detected the targeted SNPs in a single tube, using a minimum DNA concentration of 10 ng/μL and achieving a total sample call rate of 93.13%. With further research, this new protocol—which integrates the specificity of multiplex PCR and the sensitivity of MALDI-TOF MS—may offer a unique opportunity to deepen our understanding of the genetic basis of physical fitness and may have prospective applications in research initiatives exploring genetic factors that influence athletic performance, e.g., the Speed Gene Study.