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Landslide susceptibility research serves as the primary approach for analyzing the future development of landslides, and could provide the scientific reference for mountainous area development strategic decisions. The accuracy of landslide susceptibility assessment mainly depends on input data and evaluation methods. To address the issues of significant impacts from raw data defects and low spatial resolution in susceptibility assessment results within traditional deep learning evaluation models, this study establishes a physically constrained U-Net model (PCUM). This method selects ten landslide assessment factors, including slope gradient, profile curvature, slope aspect, landform, river distribution density, annual average precipitation, annual average temperature, fault distribution density, lithology, and seismic intensity. Through adjusting the weights of the model’s loss function by imposing explicit physical constraints, the model’s performance was ultimately enhanced. A comparison of the model evaluation results before and after applying the constraints is as follows: the AUC value increased from 0.871 to 0.877, the recall increased from 0.884 to 0.891, and the Kappa coefficient rose from 0.597 to 0.605. Meanwhile, the frequency ratio of the very high susceptibility zone increased from 5.64 to 5.80, while that of the very low susceptibility zone decreased from 4.77 × 10⁻² to 3.23 × 10⁻². Compared with other deep learning models, the evaluation results of PCUM demonstrate the advantage of maintaining spatial resolution consistent with the original input data, better revealing the spatial characteristics of landslide distribution, and higher accuracy. This study focuses on the southeastern region of Tibet and selects five typical regions to investigate the impact of spatial heterogeneity in raw data on the PCUM. The AUC values of the BP neural network model in the five typical regions are 0.905, 0.930, 0.871, 0.877, and 0.920, respectively, with relatively poor performance in typical regions 3 and 4. The AUC values of the residual neural network model are 0.874, 0.921, 0.915, 0.891, and 0.914, respectively, showing poor performance in typical region 1. In contrast, the PCUM achieves AUC values of 0.923, 0.940, 0.935, 0.915, and 0.899 across the five regions, demonstrating robust performance in all typical areas. The results indicate that the physically constrained U-Net model can effectively handle spatial heterogeneity in raw data and exhibits good generalization capabilities. This study may provide an effective reference for the generalizability research of landslide susceptibility assessment models.

The construction of ecological livability plays a crucial role in achieving ecological civilization. During economic development, conflicts between urban populations, resources, and the environment have become increasingly prominent. Although China’s efforts in building ecological livability have made certain achievements, they are still in the early stages, making research in this area very necessary. This study aims to establish an indicator system for the level of ecological livability construction from four aspects: ecology, society, residents’ living conditions, and economy. Based on panel data from 2008 to 2021, the entropy-TOPSIS model was used to evaluate the level of ecological livability construction in Zhangjiajie City. Additionally, the coupling coordination degree model was employed to study the correlation and coordination among different indicators within the system. Furthermore, the obstacle degree model was introduced to explore the main factors affecting the improvement of ecological livability construction levels. The results show that, over the fourteen-year period, the proximity of Zhangjiajie City’s ecological livability construction level decreased from 0.2712 in 2008 to 0.2547 in 2010, then rose to 0.7686 in 2021, reaching a medium coordination level, with the constraining effects among the ecological livability systems weakening. The average wage of employees, the proportion of social security and employment expenditure to fiscal expenditure, the per capita total postal and telecommunications business volume, the per capita number of public library collections, and the proportion of education expenditure to fiscal expenditure have become the main influencing factors of Zhangjiajie City’s ecological livability construction level.

As the largest city in northern China and the capital of China, the rapid increases in Beijing’s water consumption in recent years have made water resources provision an increasing problem. To rationally allocate water resources, it is important to obtain long‐term runoff information in Beijing. In this study we develop a 236‐year chronology of tree‐ring widths based on cores from Pinus tabuliformis from four sampling sites. The resulting regression model reconstructs December–July runoff of the Yongding River in Beijing, with 49.5% of the variance explained, back to 1786 CE. Among the last 236 years, 1868, 1956, 1991, 1998, 2018, and 2021 were extremely high runoff years; and 1900, 1906, 1999, and 2000 were extremely low runoff years. Comparison of the runoff reconstruction results with climate grid data demonstrated a large magnitude of climate change in North China during the study period. Linkage analysis between the reconstructed runoff and large‐scale water vapor indicated that the high runoff years occurred during negative phases of the Pacific Decadal Oscillation, which may be influenced by the East Asian Summer Monsoon. Projections indicate that the flow of the Yongding River will increase in the future. Supported by policies such as the Ecological Water Supply and South‐to‐North Water Diversion, regional vegetation productivity and Yongding River runoff have increased substantially since 2000. Vegetation growth interacts with runoff volume. It is unclear how long these increases will continue. Plain Language Summary As the largest city in northern China and the capital of China, Beijing has faced an increasingly water shortage problem in recent years. The Yongding River is an important river in the Beijing area and plays an important role in the local ecological system. In this study, we reconstructed the December–July runoff changes for the Yongding River using tree‐ring data from four sampling sites over the past 236 years. Under the influence of human water resource regulation, regional vegetation productivity and Yongding River runoff have increased significantly since 2000. Vegetation growth and runoff interact, and it is unknown how long these increases will last. Synoptic climatology analysis indicated that the high runoff years occurred during negative phases of the Pacific Decadal Oscillation, which may be influenced by the East Asian Summer Monsoon. Projections indicate that the flow of the Yongding River will increase in the future. Key Points Reflecting runoff changes in the Beijing area over the past 200 years, including extreme high and low runoff years Possible effects of atmospheric circulation on runoff changes in recent decades, links to the Pacific Decadal Oscillation, etc NDVI changes and trends in Beijing in recent years, as well as future flow changes

The great river systems originating from the Tibetan Plateau are pivotal for the wellbeing of more than half the global population. Our understanding of historical ranges and future changes in water availability for much of Southeast Asia is, however, limited by short observational records and complex environmental factors. Here we present annually resolved and absolutely dated tree ring-based streamflow reconstructions for the Mekong, Salween and Yarlung Tsangpo rivers since 1000 ce, which are supplemented by corresponding model projections until 2100 ce. We show a significant positive correlation between streamflow and dry season vegetation indices over the Indochinese Peninsula, revealing the importance of the Tibetan Water Tower for the functioning and productivity of ecological and societal systems in Southeast Asia. The streamflow variability is associated with low-frequency sea-surface temperature variability in the North Atlantic and North Pacific. We find that streamflow extremes coincide with distinct shifts in local populations that occurred during medieval times, including the occupation and subsequent collapse of Angkor Wat from the eleventh to the sixteenth century. Finally, our projections suggest that future streamflow changes will reach, or even exceed, historical ranges by the end of this century, posing unprecedented risks for Southeast Asia.Reconstructions of Tibetan Plateau streamflow over the last millennia reveal close associations with dry season vegetation and major population shifts in Southeast Asia.

IntroductionRecent studies have indicated the potential of stem cell therapy in combination with cytokines to restore the bone repair via migration and homing of stem cells to the defected area. The present study aimed to investigate the mobilization and recruitment of mesenchymal stem cells (MSCs) in response to SDF-1.Materials and MethodsHerein, the knockout rat model of the bone defect (BD) was treated with the induced membrane technique. Then, wild type Wistar rats and SDF-1-knockout rats were selected for the establishment of BD-induced membrane (BD-IM) models and bone-graft (BG) models. The number of MSCs was evaluated by flow cytometry, along with the expression pattern of the SDF-1/CXCR4 axis as well as osteogenic factors was identified by RT-qPCR and Western blot analyses. Finally, the MSC migration ability was assessed by the Transwell assay.ResultsOur data illustrated that in the induced membrane tissues, the number of MSCs among the BD-IM modeled rats was increased, whereas, a lower number was documented among BG modeled rats. Besides, we found that lentivirus-mediated over-expression of SDF-1 in BG modeled rats could activate the SDF-1/CXCR4 axis, mobilize MSCs into the defect area, and up-regulate the osteogenic proteins.ConclusionsCollectively, our study speculated that up-regulation of SDF-1 promotes the mobilization and migration of MSCs through the activation of the SDF-1/CXCR4 signal pathway.

Background Cognitive decline poses a major public health challenge, while individual trajectories are heterogeneous and modifiable. Social engagement is a potential influnencing factors, but the long-term cognitive implications of sustained engagement in distinct modes remain unclear.To address this gap, this study investigates the association between longitudinal patterns of social interaction and domain-specific cognitive trajectories in older adults. Methods This study analyzed 9-year data (2012–2021) from the English Longitudinal Study of Ageing (ELSA). Cognitive function was assessed across three domains: orientation ( n  = 2394), execution ( n  = 2113), and memory (immediate memory and delayed memory) ( n  = 2415). Participants were classified into four groups based on their longitudinal social engagement patterns: continuous online/no social interaction group, continuous face-to-face group, continuous mixed group, changes group. Growth mixture modeling identified cognitive trajectories, and multinomial logistic regression examined their association with social patterns. Results Three distinct trajectories were identified in each cognitive domain. For orientation and executive function, compared to the “continuous online/no social interaction” group, membership in the “continuous face-to-face”, “continuous mixed”, or “changes” groups were significantly more likely to remain at a stable and high level (all ORs < 1, P  < 0.05). No significant associations were found between social interaction patterns and memory trajectories (immediate memory and delayed memory). Conclusions Face-to-face social interaction was associated with greater stability and less decline in orientation and executive function among older adults. These findings underscore the importance of in-person social interaction for maintaining cognitive health and support personalized, pattern-specific recommendations for cognitive health promotion.

Dietary inflammatory index (DII) was demonstrated to be correlated to quality of life (QoL) in people with osteoarthritis (OA), but there is lack of an exploration of the relationship between the longitudinal trajectories of QoL and DII in OA people. This study aims to investigate the relationship between DII and longitudinal trajectories of QoL, and the moderating effect of physical activity on this relationship in OA people. Data from the Osteoarthritis Initiative database includes baseline measurements and follow-up data at 1, 2, 3, 4, 5, 6, and 8-year intervals for the 12-item Short-Form Health Outcome Survey questionnaire (physical and mental dimensions) and baseline sociodemographic characteristics utilized in this study. Dual Group-Based Trajectory Modeling analysis was employed to identify the profiles of physical and mental QoL trajectories. Multiple logistic regression analysis was used to determine the relationship between DII and QoL profiles. A structural equation model was conducted to assess the moderating effect of physical activity on the relationship between DII and QoL in OA people. A total of 1127 OA people were categorized into three profiles based on their physical and mental QoL: low physical-low mental QoL (14.5%), low physical-high mental QoL (27.1%), and high physical-high mental QoL (58.4%). People with higher DII were more likely to be in the low physical-low mental QoL profile ( OR  = 1.163, P  = 0.014) and low physical-high mental QoL ( OR  = 1.131, P  = 0.013) compared to the high physical-high mental QoL profile. Additionally, a higher level of physical activity could mitigate the relationship between a higher DII and lower QoL ( β  = 0.064, P  = 0.029). A higher DII was associated with a greater likelihood of impaired QoL. Increasing physical activity levels can help mitigate the negative impact.

Exploring the evolution of vegetation cover and its drivers in the Ferghana Basin helps to understand the current ecological status of the Ferghana Basin and to analyze the vegetation changes and drivers, with a view to providing a scientific basis for regional ecological and environmental management and planning. Based on GIMMS NDVI3g and meteorological data, the spatial and temporal evolution characteristics of NDVI were analyzed from multiple perspectives with the help of linear trend and Mann–Kendall (MK) test methods using arcgis and the R language spatial analysis module, combined with partial correlation coefficients and residual analysis methods to analyze the impacts of climate change and human activities on the regional vegetation cover of the Ferghana Basin from 1982 to 2015. NDVI driving forces. The results showed the following: (1) The growing season of vegetation NDVI in the Ferghana Basin showed an increasing trend in the 34-year period, with an increase rate of 0.0044/10a, and the spatial distribution was significantly different, which was high in the central part of the country and low in the northern and southern parts of the country. (2) Temperature and precipitation simultaneously co-influenced the vegetation NDVI growth season, with most of the temperature and precipitation contributing in the spring, most of the temperature in the summer being negatively phased and the precipitation positively correlated, and most of the temperature and precipitation in the fall inhibiting vegetation NDVI growth. (3) The combined effect of climate change and human activities was the main reason for the overall rapid increase and great spatial variations in vegetation NDVI in China, and the spatial distribution of drivers, namely human activities and climate change, contributed 44.6% to vegetation NDVI in the growing season. The contribution of climate change and human activities to vegetation NDVI in the Ferghana Basin was 62.32% and 93.29%, respectively. The study suggests that more attention should be paid to the role of human activities and climate change in vegetation restoration to inform ecosystem management and green development.

ALS is a progressive neurodegenerative disease that has a serious impact on patients and their caregivers. For a long time in the past, ALS was considered a painless disease that was largely ignored by clinicians. Describing the complexity and needs of pain symptoms from the perspective of patients can provide the most intuitive direction for future research. The purpose of our research is to explore the experience of pain symptoms in patients with amyotrophic lateral sclerosis (ALS), provide reference for better understanding of pain symptoms in ALS patients. From April 2023 to May 2023, 27 patients experiencing pain symptoms in Peking University Third Hospital who met the diagnostic criteria of \"Chinese Guidelines for the Diagnosis and Treatment of amyotrophic lateral sclerosis\" were interviewed by means of objective sampling. The content analysis method was used to describe the pain changes since the disease (amyotrophic lateral sclerosis), the factors that aggravate the pain, the measures to cope with the pain and the needs. The interview results included 3 themes and 11 subthemes. (1) Pain is diverse: the type of pain, the time when pain occurs, the change in pain intensity, and the factors that aggravate pain; (2) Individualized pain coping measures: posture adjustment, medication, physical therapy, warmth, emotional regulation; (3) Patients lack of understanding of pain: insufficient source of knowledge, the single orientation of the solution. The nature, location and aggravating factors of pain in amyotrophic lateral sclerosis patients in China are complicated, which should be paid attention to by clinical staff and scientific researchers. The situation of pain management is not optimistic, and the pain of the vast majority of patients has not been effectively alleviated. It is necessary to realize the importance of self-management and care of others in coping with pain, and conduct further research in the future to find a breakthrough in pain relief, so as to strengthen pain intervention in clinical practice.

Understanding changes in water availability is critical for Central Asia; however, long streamflow reconstructions extending beyond the period of instrumental gauge measurements are largely missing. Here, we present a 785-year-long streamflow reconstruction from spruce tree rings from the Tien Shan Mountains. Although an absolute causal relationship can not be established, relatively high streamflow rates coincided roughly with the period of Mongol expansion from 1225 to 1260 CE and the rise of the Timurid Empire from 1361 to 1400 CE. Since overall wetter conditions were further found during the Zunghar Khanate period 1693–1705 CE, we argue that phases of streamflow surplus likely promoted oasis and grassland productivity, which was an important factor for the rise of inner Eurasian steppe empires. Moreover, we suggest that the streamflow variation might be critical for plague outbreaks in Central Asia, and propose several explanations for possible links with Europe’s repeated Black Death pandemics. We demonstrate that 20th-century low streamflow is unprecedented in the past eight centuries and exacerbated the Aral Sea crisis, which is one of the most staggering ecological disasters of the twentieth century.