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Melatonin (N-acetyl-5-methoxytryptamine) is found in ovarian follicular fluid, and its concentration is closely related to follicular health status. Nevertheless, the molecular mechanisms underlying melatonin function in follicles are uncertain. In this study, melatonin concentration was measured in porcine follicular fluid at different stages of health. The melatonin concentration decreased as the follicles underwent atresia, suggesting that melatonin may participate in the maintenance of follicular health. The molecular pathway through which melatonin may regulate follicular development was further investigated. The pro-apoptotic protein BimEL (Bcl-2-interacting mediator of cell death-Extra Long), a key protein controlling granulosa cell apoptosis during follicular atresia, was selected as the target molecule. BimEL was downregulated when porcine granulosa cells were cultured in medium containing 10−9 M melatonin and isolated cumulus oocyte complexes (COCs) or follicle stimulating hormone (FSH). Interestingly, ERK-mediated phosphorylation was a prerequisite for the melatonin-induced decline in BimEL, and melatonin only promoted the ubiquitination of phosphorylated BimEL, and did not affect the activities of the lysosome or the proteasome. Moreover, the melatonin-induced downregulation of BimEL was independent of its receptor and its antioxidant properties. In conclusion, melatonin may maintain follicular health by inducing BimEL ubiquitination to inhibit the apoptosis of granulosa cells.

Gross primary productivity (GPP) is vital for ecosystems and the global carbon cycle, serving as a sensitive indicator of ecosystems’ responses to climate change. However, the impact of future climate changes on GPP in the Tibetan Plateau, an ecologically important and climatically sensitive region, remains underexplored. This study aimed to develop a data-driven approach to predict the seasonal and annual variations in GPP in the Tibetan Plateau up to the year 2100 under changing climatic conditions. A convolutional neural network (CNN) was employed to investigate the relationships between GPP and various environmental factors, including climate variables, CO2 concentrations, and terrain attributes. This study analyzed the projected seasonal and annual GPP from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under four future scenarios: SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5. The results suggest that the annual GPP is expected to significantly increase throughout the 21st century under all future climate scenarios. By 2100, the annual GPP is projected to reach 1011.98 Tg C, 1032.67 Tg C, 1044.35 Tg C, and 1055.50 Tg C under the four scenarios, representing changes of 0.36%, 4.02%, 5.55%, and 5.67% relative to 2021. A seasonal analysis indicates that the GPP in spring and autumn shows more pronounced growth under the SSP3–7.0 and SSP5–8.5 scenarios due to the extended growing season. Furthermore, the study identified an elevation band between 3000 and 4500 m that is particularly sensitive to climate change in terms of the GPP response. Significant GPP increases would occur in the east of the Tibetan Plateau, including the Qilian Mountains and the upper reaches of the Yellow and Yangtze Rivers. These findings highlight the pivotal role of climate change in driving future GPP dynamics in this region. These insights not only bridge existing knowledge gaps regarding the impact of future climate change on the GPP of the Tibetan Plateau over the coming decades but also provide valuable guidance for the formulation of climate adaptation strategies aimed at ecological conservation and carbon management.

Due to the scarcity of observational data and the intricate precipitation–runoff relationship, individually applying physically based hydrological models and machine learning (ML) techniques presents challenges in accurately predicting floods within data-scarce glacial river basins. To address this challenge, this study introduces an innovative hybrid model that synergistically harnesses the strengths of multi-source remote sensing data, a physically based hydrological model (i.e., Spatial Processes in Hydrology (SPHY)), and ML techniques. This novel approach employs MODIS snow cover data and remote sensing-derived glacier mass balance data to calibrate the SPHY model. The SPHY model primarily generates baseflow, rain runoff, snowmelt runoff, and glacier melt runoff. These outputs are then utilized as extra inputs for the ML models, which consist of Random Forest (RF), Gradient Boosting (GDBT), Long Short-Term Memory (LSTM), Deep Neural Network (DNN), Support Vector Machine (SVM) and Transformer (TF). These ML models reconstruct the intricate relationship between inputs and streamflow. The performance of these six hybrid models and SPHY model is comprehensively explored in the Manas River basin in Central Asia. The findings underscore that the SPHY-RF model performs better in simulating and predicting daily streamflow and flood events than the SPHY model and the other five hybrid models. Compared to the SPHY model, SPHY-RF significantly reduces RMSE (55.6%) and PBIAS (62.5%) for streamflow, as well as reduces RMSE (65.8%) and PBIAS (73.51%) for floods. By utilizing bootstrap sampling, the 95% uncertainty interval for SPHY-RF is established, effectively covering 87.65% of flood events. Significantly, the SPHY-RF model substantially improves the simulation of streamflow and flood events that the SPHY model struggles to capture, indicating its potential to enhance the accuracy of flood prediction within data-scarce glacial river basins. This study offers a framework for robust flood simulation and forecasting within glacial river basins, offering opportunities to explore extreme hydrological events in a warming climate.

Accurately simulating glacier mass balance (GMB) data is crucial for assessing the impacts of climate change on glacier dynamics. Since physical models often face challenges in comprehensively accounting for factors influencing glacial melt and uncertainties in inputs, machine learning (ML) offers a viable alternative due to its robust flexibility and nonlinear fitting capability. However, the effectiveness of ML in modeling GMB data across diverse glacier types within High Mountain Asia has not yet been thoroughly explored. This study addresses this research gap by evaluating ML models used for the simulation of annual glacier-wide GMB data, with a specific focus on comparing maritime glaciers in the Niyang River basin and continental glaciers in the Manas River basin. For this purpose, meteorological predictive factors derived from monthly ERA5-Land datasets, and topographical predictive factors obtained from the Randolph Glacier Inventory, along with target GMB data rooted in geodetic mass balance observations, were employed to drive four selective ML models: the random forest model, the gradient boosting decision tree (GBDT) model, the deep neural network model, and the ordinary least-square linear regression model. The results highlighted that ML models generally exhibit superior performance in the simulation of GMB data for continental glaciers compared to maritime ones. Moreover, among the four ML models, the GBDT model was found to consistently exhibit superior performance with coefficient of determination (R2) values of 0.72 and 0.67 and root mean squared error (RMSE) values of 0.21 m w.e. and 0.30 m w.e. for glaciers within Manas and Niyang river basins, respectively. Furthermore, this study reveals that topographical and climatic factors differentially influence GMB simulations in maritime and continental glaciers, providing key insights into glacier dynamics in response to climate change. In summary, ML, particularly the GBDT model, demonstrates significant potential in GMB simulation. Moreover, the application of ML can enhance the accuracy of GMB modeling, providing a promising approach to assess the impacts of climate change on glacier dynamics.

Lung cancer is one of the most common cancers around the world, with a high mortality rate. Despite substantial advancements in diagnoses and therapies, the outlook and survival of patients with lung cancer remains dismal due to drug tolerance and malignant reactions. New interventional treatments urgently need to be explored if natural compounds are to be used to reduce toxicity and adverse effects to meet the needs of lung cancer clinical treatment. An internalizing arginine-glycine-aspartic acid (iRGD) modified by a tumour-piercing peptide liposome (iRGD-LP-CUR-PIP) was developed via co-delivery of curcumin (CUR) and piperine (PIP). Its antitumour efficacy was evaluated and validated via in vivo and in vitro experiments. iRGD-LP-CUR-PIP enhanced tumour targeting and cellular internalisation effectively. In vitro, iRGD-LP-CUR-PIP exhibited enhanced cellular uptake, suppression of tumour cell multiplication and invasion and energy-independent cellular uptake. In vivo, iRGD-LP-CUR-PIP showed high antitumour efficacy, mainly in terms of significant tumour volume reduction and increased weight and spleen index. Data showed that iRGD peptide has active tumour targeting and it significantly improves the penetration and cellular internalisation of tumours in the liposomal system. The use of CUR in combination with PIP can exert synergistic antitumour activity. This study provides a targeted therapeutic system based on natural components to improve antitumour efficacy in lung cancer.

Insulin-like growth factor-1 (IGF-1) is an intra-ovarian growth factor that plays important endocrine or paracrine roles during ovarian development. IGF-1 affects ovarian function and female fertility through reducing apoptosis of granulosa cells, yet the underlying mechanism remains poorly characterized. Here, we aimed to address these knowledge gaps using porcine primary granulosa cells and examining the anti-apoptotic mechanisms of IGF-1. IGF-1 prevented the granulosa cell from apoptosis, as shown by TUNEL and Annexin V/PI detection, and gained the anti-apoptotic index, the ratio of Bcl-2/Bax. This process was partly mediated by reducing the pro-apoptotic BimEL (Bcl-2 Interacting Mediator of Cell Death-Extra Long) protein level. Western blotting showed that IGF-1 promoted BimEL phosphorylation through activating p-ERK1/2, and that the proteasome system was responsible for degradation of phosphorylated BimEL. Meanwhile, IGF-1 enhanced the Beclin1 level and the rate of LC3 II/LC3 I, indicating that autophagy was induced by IGF-1. By blocking the proteolysis processes of both proteasome and autophagy flux with MG132 and chloroquine, respectively, the BimEL did not reduce and the phosphorylated BimEL protein accumulated, thereby indicating that both proteasome and autophagy pathways were involved in the degradation of BimEL stimulated by IGF-1. In conclusion, IGF-1 inhibited porcine primary granulosa cell apoptosis via degradation of pro-apoptotic BimEL. This study is critical for us to further understand the mechanisms of follicular survival and atresia regulated by IGF-1. Moreover, it provides a direction for the treatment of infertility caused by ovarian dysplasia, such as polycystic ovary syndrome and the improvement of assisted reproductive technology.

Ice avalanches are one of the most devastating mountain hazards, and can pose a great risk to the security of the surrounding area. Although ice avalanches have been widely observed in mountainous regions around the world, only a few ice avalanche events have been studied comprehensively, due to the lack of available data. In this study, in response to the recent catastrophic rock-ice avalanche (7 February 2021) at Chamoli in the India Himalaya, we used high-resolution satellite images and found that this event was actually a glacier-rock landslide, where the collapse of the rock-ice body was caused by the sliding of the bedrock beneath the glacier, for which the source area and volume loss were about 2.89×10 5 m 2 and 2.46×10 7 m 3 , respectively, corresponding to an average elevation change of about -85 m. Furthermore, visual analysis of the dense time-series satellite images shows that the overall downward sliding of the collapsed rock-ice body initiated around the summer of 2017, and thereafter exhibited clear seasonality (mainly in summer). Meteorological analysis reveals a strong rainfall anomaly in the initiation period of the sliding and a remarkable winter warming anomaly in the 40 days before the collapse. Comparisons of multi-temporal digital elevation models (DEMs) further suggest that the glacier geometry in the collapsed areas was likely changing (i.e., accelerated surface thinning in the lower part of the glaciers and insignificant change in the upper part), which is consistent with the region-wide climate warming. Finally, by combining the above findings and a geomorphic analysis, we conclude that the rock-ice avalanche event was mainly caused by the joint effects of climate and weather changes acting on a steeply sloping and fracture-prone geological condition. The findings of this study provide new and valuable evidence for the study of slope/glacier instability at high altitudes. This study also highlights that, for the Himalaya and other high mountain ranges, there is an urgent need to identify the glaciers that have a high risk of ice avalanches.

Understanding kinase action requires precise quantitative measurements of their activity in vivo. In addition, the ability to capture spatial information of kinase activity is crucial to deconvolute complex signaling networks, interrogate multifaceted kinase actions, and assess drug effects or genetic perturbations. Here we develop a proteomic kinase activity sensor technique (ProKAS) for the analysis of kinase signaling using mass spectrometry. ProKAS is based on a tandem array of peptide sensors with amino acid barcodes that allow multiplexed analysis for spatial, kinetic, and screening applications. We engineered a ProKAS module to simultaneously monitor the activities of the DNA damage response kinases ATR, ATM, and CHK1 in response to genotoxic drugs, while also uncovering differences between these signaling responses in the nucleus, cytosol, and replication factories. Furthermore, we developed an in silico approach for the rational design of specific substrate peptides expandable to other kinases. Overall, ProKAS is a versatile system for systematically and spatially probing kinase action in cells. Understanding kinase action requires precise quantitative and spatial measurements of their activity in vivo. Here the authors develop a proteomic kinase activity sensor technique (ProKAS) enabling multiplexed spatial, kinetic, and screening analyses of kinase activities via mass spectrometry.

Pyroptosis, a form of programmed cell death, has been implicated in autoimmune diseases (ADs) pathogenesis. However, its role in primary Sjögren's syndrome (pSS) remains unclear. This study aims to identify pyroptosis-related gene (PRG) signatures in pSS. Three datasets were obtained from the Gene Expression Omnibus (GEO) database to analyze the gene expression profiles in pSS. Differentially expressed genes (DEGs) were intersected with PRGs to identify pyroptosis-related DEGs (PRDEGs). Functional enrichment was assessed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Key genes were identified using the least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM) analyses. A diagnostic model was constructed using logistic regression. mRNA-microRNA (miRNA) and mRNA-transcription factor (TF) interaction networks were constructed. Immune cell infiltration (ICI) was analyzed using cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORTs) and single-sample gene set enrichment analysis (ssGSEA). Experimental validation was performed in nonobese diabetic (NOD)/ShiLtj mice using reverse transcription quantitative polymerase chain reaction (RT-qPCR), western blotting, and immunofluorescence and was further validated using immunofluorescence in pSS patient samples. A total of 489 DEGs were identified, of which 22 were pyroptosis-related. GO/KEGG analysis revealed enrichment in immune response regulation, pyroptosis, and the positive regulation of receptor signaling pathways. LASSO and SVM analyses identified eight key genes ( , platelet-endothelial cell adhesion molecule-1 [ ], , , , absent in melanoma 2 [ ], , and macrophage-expressed gene 1 [ ]), which were incorporated into a diagnostic model that demonstrated strong discriminatory performance in both combined and validation datasets. Experimental validation confirmed significant increased expressions of , , , and in the salivary glands from both NOD mice and pSS patients. PECAM1, IFI16, AIM2, and MPEG1 were identified as PRG signatures and potential biomarkers in pSS, providing novel insights into pSS pathogenesis.

Due to climate warming, the glaciers of the Tibetan Plateau have experienced rapid mass loss and the patterns of glacier changes have exhibited high spatiotemporal heterogeneity, especially in interior areas. As the largest ice field within the Tibetan Plateau, the Puruogangri Ice Field has attracted a lot of attention from the scientific community. However, relevant studies that are based on satellite data have mainly focused on a few periods from 2000–2016. Long-term and multiperiod observations remain to be conducted. To this end, we estimated the changes in the glacier area and mass balance of the Puruogangri Ice Field over five subperiods between 1975 and 2021, based on multisource remote sensing data. Specifically, we employed KH-9 and Landsat images to estimate the area change from 1975 to 2021 using the band ratio method. Subsequently, based on KH-9 DEM, SRTM DEM, Copernicus DEM and ZY-3 DEM data, we evaluated the glacier elevation changes and mass balance over five subperiods during 1975–2021. The results showed that the total glacier area decreased from 427.44 ± 12.43 km2 to 387.87 ± 11.02 km2 from 1975 to 2021, with a decrease rate of 0.86 km2 a−1. The rate of area change at a decade timescale was −0.74 km2 a−1 (2000–2012) and −1.00 km2 a−1 (2012–2021). Furthermore, the rates at a multiyear timescale were −1.23 km2 a−1, −1.83 km2 a−1 and −0.42 km2 a−1 for 2012–2015, 2015–2017 and 2017–2021, respectively. In terms of the glacier mass balance, the region-wide results at a two-decade timescale were −0.23 ± 0.02 m w.e. a−1 for 1975–2000 and −0.29 ± 0.02 m w.e. a−1 for 2000–2021, indicating a sustained and relatively stable mass loss over the past nearly five decades. After 2000, the loss rate at a decade timescale was −0.04 ± 0.01 m w.e. a−1 for 2000–2012 and −0.17 ± 0.01 m w.e. a−1 for 2012–2021, indicating an increasing loss rate over recent decades. It was further found that the mass loss rate was −0.12 ± 0.02 m w.e. a−1 for 2012–2015, −0.03 ± 0.01 m w.e. a−1 for 2015–2017 and −0.40 ± 0.03 m w.e. a−1 for 2017–2021. These results indicated that a significant portion of the glacier mass loss mainly occurred after 2017. According to our analysis of the meteorological measurements in nearby regions, the trends of precipitation and the average annual air temperature both increased. Combining these findings with the results of the glacier changes implied that the glacier changes seemed to be more sensitive to temperature increase in this region. Overall, our results improved our understanding of the status of glacier changes and their reaction to climate change in the central Tibetan Plateau.