Search Results (6,404)

Purinergic P2 receptors are crucial in energy utilization and cellular signaling, making them key targets for stroke therapies. This study examines the temporal mRNA expression of all P2 receptors in rats and mice. Both species exhibited a common subset of P2X and P2Y receptors with elevated expression following cerebral ischemia and reperfusion (I/R), highlighting conserved mechanisms across these species. The receptors with upregulated expression in both species were P2X3, P2X4, P2X7, P2Y2, and P2Y6. While these similarities were observed, notable differences in receptor expression emerged between rats and mice. Rats exhibited a broader receptor profile, with five additional receptors (P2X1, P2Y1, P2Y12, P2Y13, and P2Y14) significantly upregulated compared to only two receptors (P2X2 and P2Y4) in mice, highlighting species-specific regulation of receptor expression distinct from the shared receptors. Following cerebral I/R, P2Y12 was the most upregulated receptor in rats, while P2Y2 was the most upregulated in mice. These findings reveal both conserved and species-specific changes in P2 receptor expression following cerebral I/R. Targeting purinergic receptors, particularly those conserved and upregulated in response to stroke, may represent a promising therapeutic approach. Full article

More than three decades ago, the Supreme Court of the United States (SCOTUS) ruled that individuals who are not competent (alternatively referred to by the Court as insane) at the time of their scheduled execution cannot be put to death. Despite the years that have passed since the Court’s decision and the literal life-or-death stakes involved, competency for execution (CFE) remains underexplored in the psychological, psychiatric, and legal literature. A number of important legal and ethical issues that arise when a person on death row maintains they are not competent to be executed are still unresolved even after the landmark Supreme Court cases such as Ford v. Wainwright (1986), Panetti v. Quarterman (2007), and Madison v. Alabama (2019). In this first-of-its-kind descriptive study, we analyzed the demographic and case characteristics of the 28 successful Ford claimants—individuals in the United States who have been found to be incompetent to be executed and compared them to the general death row population and homicide cases nationwide. Our findings reveal some similarities but also some differences between these claimants and the general death row population and homicide cases: the successful Ford claimants are exclusively male (in keeping with the general prison population on death row), relatively older, and underrepresented among White and Latinx inmates (i.e., Black claimants are more successful than their White and Latinx counterparts at evading execution). Nearly all (96%) suffer from schizophrenia, with 79% experiencing psychiatric comorbidity, yet only 54% received any significant treatment before or after the criminal offense. The claimants’ cases also involve a higher proportion of child victims, male family members, and female non-family member victims, as well as more multiple-victim cases (not indiscriminate) and fewer intraracial homicides. Fewer victims are male, and more are female. However, the cases do not align with typical male-on-male violent crimes or femicide patterns, such as those involving sexual or domestic violence. Additionally, systematic psycho-legal deficiencies are prevalent, including a low rate of mental health evidence (61%) presented at trials and some cases lacking psychiatric involvement in CFE evaluations. Temporal influence and drastic state variations on CFE evaluation are also noted. Although the small sample size limits generalizability, this small-scale descriptive study offers a number of important insights into the complexities of CFE decisions and lays the groundwork for future research and policy development. Full article

Within-field soil physical and chemical heterogeneity may affect spatio-temporal crop performance. Managing this heterogeneity can contribute to improving resource use and crop productivity. A simulation experiment based on comprehensive soil and crop data collected at the patchCROP landscape laboratory in Tempelberg, Brandenburg, Germany, an area characterized by heterogeneous soil conditions, was carried out to quantify the impact of within-field soil heterogeneities and their interactions with interannual weather variability on crop yield variability in summer and winter crops. Our hypothesis was that crop–soil water holding capacity interactions vary depending on the crop, with some crops being more sensitive to water stress conditions. Daily climate data from 1990 to 2019 were collected from a nearby station, and crop management model inputs were based on the patchCROP management data. A previously validated agroecosystem model was used to simulate crop growth and yield for each soil auger profile over the 30-year period. A total of 49 soil auger profiles were classified based on their plant available soil water capacity (PAWC), and the seasonal rainfall by crop was also classified from lowest to highest. The results revealed that the spatial variability in crop yield was higher than the temporal variability for most crops, except for sunflower. Spatial variability ranged from 17.3% for rapeseed to 45.8% for lupine, while temporal variability ranged from 10.4% for soybean to 36.8% for sunflower. Maize and sunflower showed a significant interaction between soil PAWC and seasonal rainfall, unlike legume crops lupine and soybean. As for winter crops, the interaction was also significant, except for wheat. Grain yield variations tended to be higher in years with low seasonal rainfall, and crop responses under high seasonal rainfall were more consistent across soil water categories. The simulated results can contribute to cropping system design for allocating crops and resources according to soil conditions and predicted seasonal weather conditions. Full article

To tackle the challenges arising from missing real-time measurement data and dynamic changes in network topology in optimizing and controlling distribution networks, this study proposes a data-driven collaborative optimization strategy tailored for multi-area clusters. Firstly, the distribution network is clustered based on electrical distance modularity and power balance indicators. Next, a collaborative optimization model for multiple area clusters is constructed with the objectives of minimizing node voltage deviations and active power losses. Then, a locally observable Markov decision model within the clusters is developed to characterize the relationship between the temporal operating states of the distribution network and the decision-making instructions. Using the Actor–Critic framework, the cluster agents are trained while considering the changes in cluster boundaries due to topology variations. A Critic network based on an attention encoder is designed to map the dynamically changing cluster observations to a fixed-dimensional space, enabling agents to learn control strategies under topology changes. Finally, case studies show the effectiveness and superiority of the proposed method. Full article

Background: Thyroid cancer is a rising concern in children and adolescents, with unique biological behaviors compared to adults. This study aimed to explore the epidemiological trends, pathological features, and regional disparities of thyroid cancer in this population using data from the Global Burden of Disease (GBD) 2021. Methods: Data on thyroid cancer incidence and mortality from 1990 to 2021 were extracted for individuals under 20 years old. The estimated annual percentage change (EAPC) was calculated to evaluate temporal trends. The Sociodemographic Index (SDI) was applied to assess regional variations. Future trends were projected using a Bayesian age–period–cohort model. Results: From 1990 to 2021, the global incidence of thyroid cancer in children and adolescents increased significantly, with an EAPC of 1.17%. Low-SDI regions exhibited the highest rise in incidence (EAPC: 2.19%), while high-SDI regions experienced a slight decline (EAPC: −0.69%). Mortality decreased globally (EAPC: −0.27%), with notable reductions in high- and middle-SDI regions but stable or increasing rates in low-SDI regions. Females consistently exhibited higher incidence rates across all SDI levels, while males in high-SDI regions showed higher mortality rates. Future projections suggest a steady decline in incidence and mortality rates through 2050. Conclusions: The increasing incidence and persistent mortality disparities of thyroid cancer in children and adolescents highlight the need for targeted public health interventions. Regions with low socioeconomic development require prioritized strategies to address this growing burden. These findings provide crucial insights for early diagnosis, treatment optimization, and global health policy formulation. Full article

Optimizing soybean yield remains a crucial challenge in meeting global food security demands. In this study, we report a comprehensive genetic analysis of yield-related traits in soybeans using a recombinant inbred line (RIL) population derived from crosses between ‘Qihuang 34’ (GH34) and ‘Dongsheng 16′ (DS16). Phenotypic analysis across two years (2023–2024) revealed significant variations between parental lines. Through high-density genetic mapping with 6297 SLAF markers spanning 2945.26 cM across 20 chromosomes, we constructed a genetic map with an average marker distance of 0.47 cM and 99.17% of gaps under 5 cM. QTL analysis identified ten significant loci across both years: in 2023, we detected six QTLs, including a major main stem node number (MSNN) QTL on chromosome 19 (LOD = 22.59, PVE = 24.57%), two seed number (SN) QTLs on chromosomes 14 and 18 (LOD = 2.52–2.85, PVE = 7.35% combined), and one pod number (PN) QTL on chromosome 20 (LOD = 4.68, PVE = 5.85%). The 2024 analysis revealed four major QTLs, notably a cluster on chromosome 19 harboring significant loci for MSNN (LOD = 37.92, PVE = 43.59%), PN (LOD = 18.16, PVE = 23.02%), and SN (LOD = 15.24, PVE = 19.59%). Within the stable chromosome 19 region, we identified seventeen candidate genes involved in crucial developmental processes. Gene expression analysis revealed distinct temporal patterns between parental lines during vegetative and reproductive stages, with GH34 showing dramatically higher expression of key reproductive genes Glyma.19G201300 and Glyma.19G201400 during the R1 stage. Our findings provide new insights into the genetic architecture of soybean stem node development and yield components, offering multiple promising targets for molecular breeding programs aimed at crop improvement. Full article

The northern slope of the Tianshan Mountains city cluster (NSTM), as a key urban agglomeration for the development of western China, has experienced rapid regional economic development and high population concentration since the twenty-first century. Accompanied by the increase in human activities in the NSTM, it has significantly altered the land use structure, leading to varying levels of habitat disturbance and degradation. In this paper, based on the land use and land cover (LULC) of NSTM from 2000 to 2020. The InVEST model was employed to assess habitat quality, revealing notable spatial and temporal variations. A geoprobe was further employed to explore the key drivers of the spatially distributed pattern of habitat quality in the research region. The results show that (1) from 2000 to 2020, the NSTM was largely characterized by grassland, unused land, and cropland in terms of land use, with a notable expansion of cropland and construction land; (2) the overall habitat quality in the study area is poor, with a clear spatial distribution pattern of high in the south and low in the north, with a predominance of low grades, and a trend of decreasing and then increasing is shown in the temporal direction; (3) under the influence of rapid urbanization in the region, the degradation degree of habitat quality on the NSTM shows a distinct radial structure, with high degradation in the middle and low degradation at the edges, and shows the trend of “increase-decrease-increase” over time; and (4) the results of the geodetector show that altitude and land use type have the greatest influence on habitat quality on the NSTM, indicating that the habitat quality of the research region is primarily influenced by the type of land use. Full article

Inland lakes and reservoirs are critical components of global freshwater resources. However, traditional water level monitoring stations are costly to establish and maintain, particularly in remote areas. As an alternative, satellite altimetry has become a key tool for lake water level monitoring. Nevertheless, conventional radar altimetry techniques face accuracy limitations when monitoring small water bodies. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), equipped with a single-photon counting lidar system, offers enhanced precision and a smaller ground footprint, making it more suitable for small-scale water body monitoring. However, the water level data obtained from the ICESat-2 ATL13 inland water surface height product are limited in quantity, while the lake water level accuracy derived from the ATL08 product is relatively low. To overcome these challenges, this study proposes a Spatial Distribution-Based Hierarchical Clustering for Photon-Counting Laser altimeter (SD-HCPLA) for enhanced water level extraction, validated through experiments conducted at the Danjiangkou Reservoir. The proposed method first employs Landsat 8/9 imagery and the Normalized Difference Water Index (NDWI) to generate a water mask, which is then used to filter ATL03 photon data within the water body boundaries. Subsequently, a Minimum Spanning Tree (MST) is constructed by traversing all photon points, where the vertical distance between adjacent photons replaces the traditional Euclidean distance as the edge length, thereby facilitating the clustering and denoising of the point cloud data. The SD-HCPLA algorithm successfully obtained 41 days of valid water level data for the Danjiangkou Reservoir, achieving a correlation coefficient of 0.99 and an average error of 0.14 m. Compared with ATL08 and ATL13, the SD-HCPLA method yields higher data availability and improved accuracy in water level estimation. Furthermore, the proposed algorithm was applied to extract water level data for five lakes and reservoirs in Hubei Province from 2018 to 2023. The temporal variations and inter-correlations of water levels were analyzed, providing valuable insights for regional ecological environment monitoring and water resource management. Full article

Spike structures appear in several phenomena, whereas spike trains (STs) are of particular importance, since they can carry temporal encoding of information. Regarding the STs of the biological neuron type, several models have already been proposed. While existing models effectively simulate spike generation, they fail to capture the dynamics of high-frequency spontaneous membrane potential fluctuations observed during relaxation intervals between consecutive spikes, dismissing them as random noise. This is eventually an important drawback because it has been shown that, in real data, these spontaneous fluctuations are not random noise. In this work, we suggest an ST production mechanism based on the appropriate coupling of two specific intermittent maps, which are nonlinear first-order difference equations. One of these maps presents small variation in low amplitude values and, at some point, bursts to high values, whereas the other presents the inverse behavior, i.e., from small variation in high values, bursts to low values. The suggested mechanism proves to be able to generate the above-mentioned spontaneous membrane fluctuations possessing the associated dynamical properties observed in real data. Moreover, it is shown to produce spikes that present spike threshold, sharp peak and the hyperpolarization phenomenon, which are key morphological characteristics of biological spikes. Furthermore, the inter-spike interval distribution is shown to be a power law, in agreement with published results for ST data produced by real biological neurons. The use of the suggested mechanism for the production of other types of STs, as well as possible applications, are discussed. Full article

While existing building energy prediction methods have advanced significantly, they face fundamental challenges in simultaneously modeling complex spatial–temporal relationships between buildings and integrating dynamic weather patterns, particularly in dense urban environments where building interactions significantly impact energy consumption patterns. This study presents an advanced deep learning system combining temporal graph neural networks with weather data parameters to enhance prediction accuracy across diverse building types through innovative spatial–temporal modeling. This approach integrates LSTM layers with graph convolutional networks, trained using energy consumption data from 150 commercial buildings over three years. The system incorporates spatial relationships through a weighted adjacency matrix considering building proximity and operational similarities, while weather parameters are integrated via a specialized neural network component. Performance evaluation examined normal operations, data gaps, and seasonal variations. The results demonstrated a 3.2% mean absolute percentage error (MAPE) for 15 min predictions and a 4.2% MAPE for 24 h forecasts. The system showed robust data recovery, maintaining 95.8% effectiveness even with 30% missing values. Seasonal analysis revealed consistent performance across weather conditions (MAPE: 3.1–3.4%). The approach achieved 33.3% better prediction accuracy compared to conventional methods, with 75% efficiency across four GPUs. These findings demonstrate the effectiveness of combining spatial relationships and weather parameters for building energy prediction, providing valuable insights for energy management systems and urban planning. The system’s performance and scalability make it particularly suitable for practical applications in smart building management and urban sustainability. Full article

Evapotranspiration (ET) and water availability (WA) are critical components of the global water cycle. Although the effects of ecological restoration on ET and WA have been widely investigated, quantifying the impacts of multiple environmental factors on plant water consumption and regional water balance in dryland areas remains challenging. In this study, we investigated the spatial and temporal trends of ET and WA and isolated the contributions of vegetation restoration and climate change to variations in ET and WA in the Beijing–Tianjin Sand Source Region (BTSSR) in Northern China from 2001 to 2021, using the remote sensing-based Priestley–Taylor-Jet Propulsion Laboratory (PT-JPL) model and scenario simulation experiments. The results indicate that the estimated ET was consistent with field observations and state-of-the-art ET products. The annual ET in the BTSSR increased significantly by 1.28 mm yr⁻¹ from 2001 to 2021, primarily driven by vegetation restoration (0.78 mm yr⁻¹) and increased radiation (0.73 mm yr⁻¹). In contrast, the drier climate led to a decrease of 0.56 mm yr⁻¹ in ET. In semiarid areas, vegetation and radiation were the dominant factors driving the variability of ET, while in arid areas, relative humidity played a more critical role. Furthermore, reduced precipitation and increased plant water consumption resulted in a decline in WA by −0.91 mm yr⁻¹ during 2001–2021. Climate factors, rather than vegetation greening, determined the WA variations in the BTSSR, accounting for 77.6% of the total area. These findings can provide valuable insights for achieving sustainable ecological restoration and ensuring the sustainability of regional water resources in dryland China under climate change. This study also highlights the importance of simultaneously considering climate change and vegetation restoration in assessing their negative impacts on regional water availability. Full article

Eutrophication and algal blooms have frequently occurred in Chaohu Lake. Water parameters interact with eutrophication and algal blooms. However, there are few studies on the spatial–temporal characteristics of water parameters in the Chaohu Lake Basin. To assess the water quality of Chaohu Lake and its seven surrounding rivers, 132 samples from 33 sites were collected seasonally from September 2019 to July 2020, and 14 physicochemical parameters were detected. Our results showed that urban rivers had the highest nutrients, chemical oxygen demand (COD_Mn, 6.30 ± 0.80 mg/L), five-day biological oxygen demand (BOD₅, 4.51 ± 0.42 mg/L), and chlorophyll a concentration (Chl a, 54.88 ± 39.81 μg/L); forested rivers had higher water transparency (137.83 ± 18.52 cm), lowest nutrients, COD_Mn (4.02 ± 0.20 mg/L), BOD₅ (1.42 ± 0.14 mg/L), and Chl a (7.18 ± 1.41 μg/L); and agricultural and mixed rivers intermediate. Generally, the water quality was “good” and “light-eutrophic” according to the water quality index and trophic level index. The water quality order from good to worst in the season was spring > autumn and summer > winter. These results implied that urban rivers are still the main source of eutrophic nutrients in Chaohu Lake, and the control of urban pollutants is still the core of water quality management in Chaohu Lake. Full article

The application of machine learning models for sign language recognition (SLR) is a well-researched topic. However, many existing SLR systems focus on widely used sign languages, e.g., American Sign Language, leaving other underrepresented sign languages such as Norwegian Sign Language (NSL) relatively underexplored. This work presents a preliminary system for recognizing NSL gestures, focusing on numbers 0 to 10. Mediapipe is used for feature extraction and Long Short-Term Memory (LSTM) networks for temporal modeling. This system achieves a testing accuracy of 95%, aligning with existing benchmarks and demonstrating its robustness to variations in signing styles, orientations, and speeds. While challenges such as data imbalance and misclassification of similar gestures (e.g., Signs 3 and 8) were observed, the results underscore the potential of our proposed approach. Future iterations of the system will prioritize expanding the dataset by including additional gestures and environmental variations as well as integrating additional modalities. Full article

This study aims to determine the optimal frequency for monitoring airborne pollutants in densely populated urban areas to effectively capture their temporal variations. While environmental organizations worldwide typically update air quality data hourly, there is no global consensus on the ideal monitoring frequency to adequately resolve pollutant (particulate matter) time series. By applying temporal variogram analysis to particulate matter (PM) data over time, we identified specific measurement intervals that accurately reflect fluctuations in pollution levels. Using January 2023 air quality data from the Joppa neighborhood of Dallas, Texas, USA, temporal variogram analysis was conducted on three distinct days with varying ${\mathrm{PM}}_{2.5}$ (particulate matter of size ≤ $2.5 \mathsf{\mu }\mathrm{m}$ in diameter) pollution levels. For the most polluted day, the optimal sampling interval for ${\mathrm{PM}}_{2.5}$ was determined to be 12.25 s. This analysis shows that highly polluted days are associated with shorter sampling intervals, highlighting the need for highly granular observations to accurately capture variations in PM levels. Using the variogram analysis results from the most polluted day, we trained machine learning models that can predict the sampling time using meteorological parameters. Feature importance analysis revealed that humidity, temperature, and wind speed could significantly impact the measurement time for ${\mathrm{PM}}_{2.5}$. The study also extends to the other size fractions measured by the air quality monitor. Our findings highlight how local conditions influence the frequency required to reliably track changes in air quality. Full article

The long-term spatial–temporal variation in shoot blight of larch in China has not yet been clearly defined, and the mechanisms behind its long-distance spread remain unknown. This study, based on the historical occurrence dataset of shoot blight of larch in China, used spatial statistical analysis to describe the spatial changes in the disease across five stages since 1973. Subsequently, the study utilized Geo Detector and Random Forest models to investigate the relationship between the spread and occurrence of shoot blight of larch and seven influencing factors. The results revealed the following: (1) The spread of shoot blight of larch in China exhibits significant directionality, with the affected regions distributed along a northeast–southwest axis, and the epicenter of the spread is shifting southwestward; (2) Shandong and Jilin provinces served as the initial introduction points for shoot blight of larch, with most infected counties in other provinces experiencing outbreaks between 1989 and 1996, accompanied by a noticeable spread to neighboring provinces; (3) the occurrence of shoot blight of larch demonstrates a significant positive spatial clustering effect, forming a monocentric “core–periphery” structure centered in Liaoning Province, where kernel density values decrease gradually outward from the core. Geo Detector identified “seedling planting area” as a potential spatial driving factor for the disease. These findings underscore the critical influence of the combined effects of human activities and natural factors in shaping the spatiotemporal distribution patterns of shoot blight of larch. Full article