Rice (Oryza sativa L.) is a staple food for more than half of the global population but faces escalating yield losses from abiotic stresses, notably submergence, drought, salinity, heavy metals, cold, and heat. These stresses act at different developmental stages, altering growth, physiology, and grain quality through common bottlenecks in photosynthesis, water and ion balance, and reproductive development. This review provides a consolidated stress-wise synthesis of morpho-physiological injury and adaptive traits, associated biochemical responses (ROS dynamics, antioxidant enzymes, osmolyte accumulation, carbohydrate metabolism), and molecular regulation (ABA/ethylene/GA/BR crosstalk, Ca2+ signaling, and transcriptional networks including DREB, NAC, MYB, and WRKY). The key genetic hubs such as SUB1A (submergence), qDTY1.1 (drought), Saltol (salinity), COLD1 (cold), HSF-HSP (heat), and HMA3/ZIP1/NIP2 (heavy metals) are highlighted as central to stress tolerance. This review emphasizes integrated management approaches, nutrient and water regimes, post-stress recovery inputs, seed priming, soil amendments, microbial interventions, and nano-enabled solutions that align with physiological and molecular responses to enhance stress resilience. This article provides a comprehensive framework to guide breeding, agronomic management, and future research toward multi-stress tolerance and yield stability in rice under climate change by linking stage specific injury with mechanistic pathways and actionable strategies.
N-acetyl-L-leucine (NALL), a stereospecific derivative of the essential amino acid L-leucine, has attracted increasing attention as a candidate therapy for neurological disorders. Preclinical and clinical studies suggest both symptomatic and neuroprotective effects. This review summarizes evidence on the mechanisms of action, preclinical findings, and clinical outcomes of NALL, and discusses its therapeutic prospects in neurological health and disease. A structured literature search of PubMed, Scopus, and Google Scholar was conducted to identify preclinical and clinical studies evaluating N-acetyl-L-leucine (NALL) in neurological disorders. Preclinical studies demonstrated that NALL enhances mitochondrial bioenergetics, reduces oxidative stress, restores lysosomal and autophagic function, and modulates neuroinflammation. In rodent models, NALL improved motor coordination, neuronal survival, and recovery following traumatic brain injury. Clinical trials in Niemann-Pick disease type C and GM2 gangliosidoses showed improvements in motor function, quality of life, and disease progression, with sustained benefit during extension phases. Preliminary evidence also suggests possible roles in multiple sclerosis, ataxia telangiectasia, Parkinson’s disease, and multiple sulfatase deficiency. NALL was consistently well tolerated with a favorable safety profile. NALL shows multimodal mechanisms and promising translational potential for both rare and common neurological disorders. While early evidence is encouraging, larger randomized trials are required to confirm efficacy, define optimal dosing, and determine its place in neurological therapeutics.
Soil organic carbon (SOC) is a critical component of global carbon cycling and a key regulator of soil CO2 emission. However, the effects of agricultural activities, particularly tillage, on SOC sequestration are not fully understood. Here, we conducted a comprehensive mega-analysis of 24 individual meta-analyses to assess how conservation tillage practices, including no-till (NT), reduced tillage (RT), and mixed NT + RT, affect SOC sequestration. Overall, all conservation tillage types significantly increased SOC stocks, with RT showing the highest increase by 13.42
Growing literature on the experiences of Black students in science, technology, engineering, and mathematics (STEM) at Historically Black Colleges or Universities (HBCUs) has illustrated HBCUs' success in graduating Black students in STEM. However, little research has examined the intersection of race and gender and psychological factors that may influence students' commitment to obtaining a STEM degree and/or career. The purpose of the present study was to identify whether gender moderates the impact of Africultural coping strategies (cognitive emotional debriefing and spiritual-centered) and psychological distress on commitment to persist in STEM among Black STEM students at-tending HBCUs. Using the intersectionality theoretical framework, we administered an online survey via Qualtrics to collect data on the coping strategies, distress, and STEM commitment from 463 Black women and 139 Black men STEM students across several HBCUs. The results indicated that gender was not a significant moderator: there were no significant interactions when predicting short-term commitment (while controlling for college-level courses), long-term commitment, nor career commit-ment to STEM. However, there was a significant main effect of spiritual-centered coping in predicting greater long-term intentions to commit to STEM. There was also a significant main effect of gender on career commitment, with Black men having greater intentions to commit to a STEM career rela-tive to Black women. This research will contribute to the limited research on gender differences in the retention of Black STEM students at HBCUs, using measures of Africultural coping strategies and psychological distress.
ABSTRACT Biochar is a promising soil amendment for enhancing soil organic carbon (SOC), but accurately predicting its effect under diverse environmental conditions remains challenging due to complex, nonlinear interactions among biochar properties, soil characteristics, climate, and management practices. To address this research gap, we developed an ensemble machine learning (ML) framework, combining Extremely Randomized Trees (ExtraTrees), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost) regressors, to model SOC responses to biochar application using a globally curated dataset of 800 field observations. The ensemble model showed strong predictive performance (R2 = 0.86, RMSE = 0.11) and generalized well across a wide range of conditions. Shapley Additive exPlanations (SHAP) analysis identified biochar addition rates, crop types, soil type, and soil pH were the most influential predictors of SOC changes. The most effective biochar application rate was about 40 t/ha, and the saturation point was 121.7 t/ha. Partial dependence plots revealed nonlinear and threshold effects of pyrolysis temperature, initial SOC levels, and nitrogen content. To facilitate practical application, we also developed a user‐friendly graphical interface for SOC prediction under various biochar‐soil‐climate scenarios. This work highlights the predictive power and interpretability of ML tools in digital soil carbon modeling and supports data‐driven strategies for optimizing biochar use in climate‐smart agriculture.
