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In addition to human activities, this study found that topography is also an important factor affecting land surface temperature (LST). In this paper, based on Landsat 8 OLI/TIRS remote sensing images, a radiative transfer model was adopted to retrieve the LST, and a maximum likelihood method was used to remove artificial environmental interference factors, such as water bodies and built-up lands. This paper aims to analyze the influence of topographic factors, such as elevation, slope, aspect and shaded relief, on the LST of Hangzhou. By means of a statistical analysis, we obtained the quantitative relationship between these factors and constructed a multiple linear regression model of terrain factors and LST. The research revealed the following findings: (1) in the study area, elevation and slope are negatively correlated with LST, and all the factors have linear relationships with LST. (2) The relationship between aspect and LST is not significant, and high values of LST are found on the southern, southeastern and southwestern slopes; the lowest values are found on the northern slopes. (3) There is a significant linear relationship between the values of the shaded relief map and LST, and the more shadows there are, the lower the LST value will be. (4) After comprehensive analysis of the influence of the abovementioned topographic factors on the LST, it is found that shaded relief has the greatest contribution and is positively correlated with LST. The influence of shaded relief on surface thermal environment should be paid more attention in the process of surface thermal environment work. The assessment of the influence degree of shaded relief and surface thermal environment should be the premise and basis for many other studies.

Light provides the necessary energy for plant photosynthesis, which allows plants to produce organic matter and energy conversion, during plant growth and development. Light provides material energy to plants as the basis for cell division and differentiation, chlorophyll synthesis, tissue growth and stomatal movement, and light intensity, photoperiod, and light quality play important roles in these processes. There are several regulatory mechanisms involved in sugar metabolism in plants, and light, as one of the regulatory factors, affects cell wall composition, starch granules, sucrose synthesis, and vascular bundle formation. Similarly, sugar species and genes are affected in the context of light-regulated sugar metabolism. We searched the available databases and found that there are fewer relevant reviews. Therefore, this paper provides a summary of the effects of light on plant growth and development and sugar metabolism, further elaborates on the mechanisms of light effects on plants, and provides some new insights for a better understanding of how plant growth is regulated under different light conditions.

Secondary metabolites are low-molecular-weight organic compounds produced by plants under specific conditions. While they are not directly involved in fundamental growth and developmental processes, they play crucial roles in plant defense, protection, and regulation. These compounds mainly include phenolics, terpenoids, alkaloids, flavonoids, and others. Light, as a key environmental factor regulating the synthesis of plant secondary metabolites, influences their production and accumulation through multidimensional regulatory mechanisms. Different light qualities activate or suppress specific metabolic pathways via signal transduction networks mediated by specialized photoreceptors. Light intensity dynamically modulates secondary metabolite accumulation by affecting photosynthetic efficiency, while photoperiod coordinates metabolic rhythms through circadian clock genes. These light responsive mechanisms constitute a chemical defense strategy that enables plants to adapt to their environment, while also providing critical targets for the directed regulation of medicinal components and functional nutrients. This study provides a review of recent research on the effects of light on plant secondary metabolites, aiming to deepen the understanding of the molecular mechanisms underlying light-regulated secondary metabolism. The findings may offer an insight for enhancing bioactive compounds in medicinal plants and developing functional agricultural products.

Global nitrogen (N) enrichment promotes soil organic carbon (SOC) accumulation but often causes biodiversity loss in plants and soil microbes, creating a central challenge for achieving co-benefits of carbon accumulation and biodiversity conservation. The extent to which biodiversity contributes to SOC accumulation, and how these trade-offs can be mitigated under N enrichment remains poorly understood. Here, we conduct a global meta-analysis of N enrichment experiments encompassing 2141 observations from 275 studies to evaluate the contributions of plant, bacterial, and fungal diversity to SOC accumulation and to determine associated ecological thresholds. Our findings indicate that biodiversity, especially in plant communities, is the most important factor for SOC accumulation. We identify three distinct N thresholds characterized by different ecological responses: biodiversity loss accelerates at 50 kg N ha⁻¹ yr⁻¹, soil degradation persists at 200 kg N ha⁻¹ yr⁻¹, and SOC accumulation declines beyond this point. Given that the promoting effects of bacterial, fungal, and plant diversity on SOC diminish or even reverse at 50, 67, and 74 kg N ha⁻¹ yr⁻¹, respectively, we suggest limiting fertilization to around 50 kg N ha⁻¹ yr⁻¹, as the trade-off between biodiversity loss and SOC accumulation under N enrichment is potentially minimized. Nitrogen from human activities can increase soil carbon but often reduces biodiversity. This study finds that biodiversity helps drive soil carbon gains and identifies nitrogen thresholds that limit biodiversity loss while maintaining these gains.

Increasing the concentration of oxygen vacancies in ceria-based materials to solve the bottleneck of their applications in various fields has always been a research hotspot. In this paper, ceria-based cerium–oxygen–sulfur (Ce-O-S) composites that were composed of CeO2, Ce4O4S3, and Ce2(SO4)3 were synthesized by a precipitation method. The compositional, structural, morphological, and light response characteristics of prepared Ce-O-S composites were investigated by various characterization techniques. The molar ratio of oxygen vacancies to lattice oxygen can reach a maximum of 1.83 with Ce-O-S composites. The band gap values of the Ce-O-S composites were less than 3.00 eV, and the minimum value was 2.89 eV (at pH 12), which successfully extended the light response range from the ultraviolet light region to the short-wave blue light region. The remarkable light response performance of Ce-O-S composites can be mainly attributed to the high proportion of oxygen vacancy. Moreover, the higher proportion of oxygen vacancies can be attributed to the doping of Ce (+3) and S (−2) in the lattice of CeO2, and the synergistic effect of CeO2, Ce4O4S3, and Ce2(SO4)3. Moreover, the ceria-based Ce-O-S composites with rich oxygen vacancy in this research can be applied in light blocking, photocatalysis, and other related fields.

Fine-grained hyperspectral mineral classification remains challenging due to spectral homogeneity among minerals with different morphologies, severe spectral mixing from intergrowth, and high dimensionality. Existing methods rely on spectral separability assumptions, which become insufficient when spectral differences are subtle and spatial–structural ambiguity is high. To address these limitations, we propose S3AM-ECA-3DCNN, a spectral–structural collaborative feature learning framework. It uses a 3DCNN backbone to jointly model spectral–spatial features with progressive spectral downsampling. The spectral-similarity-based spatial attention module (S3AM) performs spatial purification by suppressing interference from spectrally mixed neighboring regions, and the efficient channel attention (ECA) module adaptively recalibrates discriminative spectral bands to enhance fine-grained representation. This establishes a spatial-first, channel-second collaborative optimization paradigm. To improve generalization under limited training samples, adaptive global pooling and a lightweight classification head are employed to reduce model complexity and mitigate overfitting. Experiments on a 146-class hyperspectral mineral dataset (covering silicates, carbonates, and sulfates) show that the framework achieves 93.424% overall accuracy, 91.099% average accuracy, and a Kappa coefficient (×100) of 93.368, outperforming mainstream methods. It significantly reduces misclassification among spectrally similar but morphologically distinct minerals, demonstrating strong robustness and discriminative capability for large-scale fine-grained tasks.

Genomic analysis of 5,303 Chinese women with recurrent major depressive disorder (MDD) enables the identification and replication of two genome-wide significant loci contributing to risk of MDD on chromosome 10: one near the SIRT1 gene; the other in an intron of the LHPP gene. Genetic risk factors for depression This genomic analysis of more than 5,000 Chinese women with recurrent major depressive disorder (MDD) has identified and replicated two genome-wide significant loci contributing to risk of MDD on chromosome 10. One is near the sirtuin1 ( SIRT1 ) gene, and the other is in an intron of the phospholysine phosphohistidine inorganic pyrophosphate phosphatase ( LHPP ) gene. The authors suggest that the association close to SIRT1 may implicate abnormalities in mitochondria as risk factors for the disease. Major depressive disorder (MDD), one of the most frequently encountered forms of mental illness and a leading cause of disability worldwide 1 , poses a major challenge to genetic analysis. To date, no robustly replicated genetic loci have been identified 2 , despite analysis of more than 9,000 cases 3 . Here, using low-coverage whole-genome sequencing of 5,303 Chinese women with recurrent MDD selected to reduce phenotypic heterogeneity, and 5,337 controls screened to exclude MDD, we identified, and subsequently replicated in an independent sample, two loci contributing to risk of MDD on chromosome 10: one near the SIRT1 gene ( P  = 2.53 × 10 −10 ), the other in an intron of the LHPP gene ( P = 6.45 × 10 −12 ). Analysis of 4,509 cases with a severe subtype of MDD, melancholia, yielded an increased genetic signal at the SIRT1 locus. We attribute our success to the recruitment of relatively homogeneous cases with severe illness.

Background New therapies are urgently needed for Alzheimer’s disease (AD). Sodium oligomannate (GV-971) is a marine-derived oligosaccharide with a novel proposed mechanism of action. The first phase 3 clinical trial of GV-971 has been completed in China. Methods We conducted a phase 3, double-blind, placebo-controlled trial in participants with mild-to-moderate AD to assess GV-971 efficacy and safety. Participants were randomized to placebo or GV-971 (900 mg) for 36 weeks. The primary outcome was the drug-placebo difference in change from baseline on the 12-item cognitive subscale of the Alzheimer’s Disease Assessment Scale (ADAS-cog12). Secondary endpoints were drug-placebo differences on the Clinician’s Interview-Based Impression of Change with caregiver input (CIBIC+), Alzheimer’s Disease Cooperative Study-Activities of Daily Living (ADCS-ADL) scale, and Neuropsychiatric Inventory (NPI). Safety and tolerability were monitored. Results A total of 818 participants were randomized: 408 to GV-971 and 410 to placebo. A significant drug-placebo difference on the ADAS-Cog12 favoring GV-971 was present at each measurement time point, measurable at the week 4 visit and continuing throughout the trial. The difference between the groups in change from baseline was − 2.15 points (95% confidence interval, − 3.07 to − 1.23; p  < 0.0001; effect size 0.531) after 36 weeks of treatment. Treatment-emergent adverse event incidence was comparable between active treatment and placebo (73.9%, 75.4%). Two deaths determined to be unrelated to drug effects occurred in the GV-971 group. Conclusions GV-971 demonstrated significant efficacy in improving cognition with sustained improvement across all observation periods of a 36-week trial. GV-971 was safe and well-tolerated. Trial registration ClinicalTrials.gov, NCT0229391 5. Registered on November 19, 2014

Background Whether healthy older people can benefit from cognitive training (CogTr) remains controversial. This study explored the benefits of CogTr in community dwelling, healthy, older adults and compared the effects of single-domain with multi-domain CogTr interventions. Methods A randomized, controlled, 3-month trial of CogTr with double-blind assessments at baseline and immediate, 6-month and 12-month follow-up after training completion was conducted. A total of 270 healthy Chinese older people, 65 to 75 years old, were recruited from the Ganquan-area community in Shanghai. Participants were randomly assigned to three groups: multi-domain CogTr, single-domain CogTr, and a wait-list control group. Twenty-four sessions of CogTr were administrated to the intervention groups over a three-month period. Six months later, three booster training sessions were offered to 60% of the initial training participants. The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS, Form A), the Color Word Stroop test (CWST), the Visual Reasoning test and the Trail Making test (TMT) were used to assess cognitive function. Results Multi-domain CogTr produced statistically significant training effects on RBANS, visual reasoning, and immediate and delayed memory, while single-domain CogTr showed training effects on RBANS, visual reasoning, word interference, and visuospatial/constructional score (all P < 0.05). At the 12-month posttest, the multi-domain CogTr showed training effects on RBANS, delayed memory and visual reasoning, while single-domain CogTr only showed effects on word interference. Booster training resulted in effects on RBANS, visual reasoning, time of trail making test, and visuospatial/constructional index score. Conclusions Cognitive training can improve memory, visual reasoning, visuospatial construction, attention and neuropsychological status in community-living older people and can help maintain their functioning over time. Multi-domain CogTr enhanced memory proficiency, while single-domain CogTr augmented visuospatial/constructional and attention abilities. Multi-domain CogTr had more advantages in training effect maintenance. Clinical Trial Registration Chinese Clinical Trial Registry. Registration number: ChiCTR-TRC-09000732.

Mental health care systems worldwide face critical challenges, including limited access, shortages of clinicians, and stigma-related barriers. In parallel, large language models (LLMs) have emerged as powerful tools capable of supporting therapeutic processes through natural language understanding and generation. While previous research has explored their potential, a comprehensive review assessing how LLMs are integrated into mental health care, particularly beyond technical feasibility, is still lacking. This systematic literature review investigates and conceptualizes the application of LLMs in mental health care by examining their technical implementation, design characteristics, and situational use across different touchpoints along the patient journey. It introduces a 3-layer morphological framework to structure and analyze how LLMs are applied, with the goal of informing future research and design for more effective mental health interventions. A systematic literature review was conducted across PubMed, IEEE Xplore, JMIR, ACM, and AIS databases, yielding 807 studies. After multiple evaluation steps, 55 studies were included. These were categorized and analyzed based on the patient journey, design elements, and underlying model characteristics. Most studies assessed technical feasibility, whereas only a few examined the impact of LLMs on therapeutic outcomes. LLMs were used primarily for classification and text generation tasks, with limited evaluation of safety, hallucination risks, or reasoning capabilities. Design aspects, such as user roles, interaction modalities, and interface elements, were often underexplored, despite their significant influence on user experience. Furthermore, most applications focused on single-user contexts, overlooking opportunities for integrated care environments, such as artificial intelligence-blended therapy. The proposed 3-layer framework, which consists of the L1: LLM layer, L2: interface layer, and L3: situation layer, highlights critical design trade-offs and unmet needs in current research. LLMs hold promise for enhancing accessibility, personalization, and efficiency in mental health care. However, current implementations often overlook essential design and contextual factors that influence real-world adoption and outcomes. The review underscores that the self-attention mechanism, a key component of LLMs, alone is not sufficient. Future research must go beyond technical feasibility to explore integrated care models, user experience, and longitudinal treatment outcomes to responsibly embed LLMs into mental health care ecosystems.