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Abstract Vegetation growth may adapt to climate warming by adjusting the relationship between photosynthetic capacity and temperature. However, changes in the optimal temperature for ecosystem productivity during recent decades of warming remain uncertain. Here we provide empirical evidence that global optimal temperature increased at a rate of 0.017 ± 0.002 °C y−1 from 1982 to 2016, using multiple datasets of satellite-derived productivity and climate variables. Model simulations show that the optimal temperature will increase by 0.027 ± 0.001 °C y−1 until the end of 21st century. The global increasing optimal temperature is consistent with increasing mean air temperatures and model simulations further confirm the key role of temperature in regulating changes in optimal temperature, while being co-regulated by other factors, such as CO2 and precipitation. These results suggest that vegetation is acclimating to warming and that the negative impacts of climate change on ecosystem productivity may be less severe than previously thought.

The quantification of net primary productivity (NPP) plays a crucial role in ensuring food security. Previous studies have established a noteworthy correlation between the near-infrared radiance of vegetation (NIRv,Rad) and NPP. However, limited research has been conducted to investigate the impact of drought on the relationship between NPP and NIRv,Rad. To address this gap, we constructed four distinct water stress gradients (well-watered, mild, moderate, and severe) to assess the effectiveness of NIRv,Rad in monitoring drought-induced variations in NPP for winter wheat. The results indicated that NIRv,Rad exhibited higher sensitivity to water stress compared to traditional vegetation indices (VIs) like the NIR reflectance of vegetation (NIRv,Ref), enhanced vegetation index-2 (EVI2), and normalized difference vegetation index (NDVI), which provided confidence in the capacity of NIRv,Rad as a tool for drought monitoring. NIRv,Rad can accurately estimate NPP at both hourly and daily scales under different water stresses. This advantage was more pronounced at hourly scales compared to NIRv,Ref, EVI2, and NDVI. NIRv,Rad proved to be a superior indicator of NPP compared to VIs under water stresses. Our work introduces a novel approach for accurately and easily estimating NPP under different water stress levels and time scales.

Abstract Background The impact of diabetes mellitus (DM) on adverse outcomes in hip fracture patients is unclear. Furthermore, no review has synthesized evidence on this subject. Therefore, the current study was designed to answer the following research question: Does DM increase the risk of mortality and major systemic complications in patients with hip fractures? Methods PubMed, Embase, and Google Scholar were searched from 1st January 2000 to 1st August 2021 for studies comparing DM and non-DM patients with hip fractures. Outcomes of interest were pooled using risk ratios (RR). The study was registered on PROSPERO (CRD42021268525). Results Sixteen studies were included. Meta-analysis revealed a statistically significant increased risk of mortality in diabetics as compared to non-diabetics after 1 year (RR: 1.24 95% CI 1.08, 1.43 I2 = 62% p = 0.003). Pooled analysis of eight studies reporting adjusted mortality outcomes also demonstrated similar results (RR: 1.17 95% CI 1.09, 1.25 I2 = 74% p

The impacts of drought on the terrestrial gross primary production (GPP) are the most intense and widespread in all extreme climate events. Solar-induced chlorophyll fluorescence (SIF) is considered as a direct representative of actual vegetation photosynthesis and has better performance in monitoring vegetation conditions than greenness-based vegetation indices (VIs) during drought events. Based on the spatially downscaled SIF (SIFds), VIs and GPP products, we explored the potential of SIFds to monitor drought effects on GPP in winter wheat. First, the spatiotemporal dynamics of hydrometeorological factors and vegetation variables in winter wheat during drought events were observed. Then, the SIFds—GPP relationships in different phenological stages were examined in the rainfed area. Finally, the drought-induced GPP losses in different phenological stages were evaluated by scaling SIFds to GPP based on the linear SIFds–GPP relationship in the rainfed area. Results showed that SIFds could capture the spatiotemporal dynamics of drought-induced GPP variations in winter wheat during drought events, and it could quantify accurately the drought-induced GPP losses, with higher sensitivities to GPP changes during the vigorous growing periods. Our study reveals the applicability of SIFds to achieve regional agricultural drought detection and drought-induced GPP loss assessment, which can provide some help for crop adaptation management.

Glioblastoma (GBM) is one of the most aggressive forms of cancer. Although IDH1 mutation indicates a good prognosis and a potential target for treatment, most GBMs are IDH1 wild-type. Identifying additional molecular markers would help to generate personalized therapies and improve patient outcomes. Here, we used our recently developed metabolic modeling method (genome-wide precision metabolic modeling, GPMM) to investigate the metabolic profiles of GBM, aiming to identify additional novel molecular markers for this disease. We systematically analyzed the metabolic reaction profiles of 149 GBM samples lacking IDH1 mutation. Forty-eight reactions showing significant association with prognosis were identified. Further analysis indicated that the purine recycling, nucleotide interconversion, and folate metabolism pathways were the most robust modules related to prognosis. Considering the three pathways, we then identified the most significant GBM type for a better prognosis, namely N+P−. This type presented high nucleotide interconversion (N+) and low purine recycling (P−). N+P−-type exhibited a significantly better outcome (log-rank p = 4.7 × 10−7) than that of N−P+. GBM patients with the N+P−-type had a median survival time of 19.6 months and lived 65% longer than other GBM patients. Our results highlighted a novel molecular type of GBM, which showed relatively high frequency (26%) in GBM patients lacking the IDH1 mutation, and therefore exhibits potential in GBM prognostic assessment and personalized therapy.

Drought is one of the most devastating disasters and a serious constraint on agricultural development. The reflectance-based vegetation indices (VIs), such as Normalized Difference Vegetation Index (NDVI), have been widely used for drought monitoring, but there is a lag in the response of VIs to the changes of photosynthesis induced by drought. Solar-induced chlorophyll fluorescence (SIF) is closely related to photosynthesis of vegetation and can capture changes induced by drought timely. This study investigated the capability of SIF for drought monitoring. An intelligent irrigation control system (IICS) utilizing the Internet of Things was designed and constructed. The soil moisture of the experiment plots was controlled at 60–80% (well-watered, T1), 50–60% (mild water stress, T2), 40–50% (moderate water stress, T3) and 30–40% (severe water stress, T4) of the field water capacity using the IICS based on data collected by soil moisture sensors. Meanwhile, SIF, NDVI, Normalized Difference Red Edge (NDRE) and Optimized Soil Adjusted Vegetation Index (OSAVI) were collected for a long time series using an automated spectral monitoring system. The differences in the responses of SIF, NDVI, NDRE and OSAVI to different drought intensities were fully analyzed. This study illustrates that the IICS can realize precise irrigation management strategies and the construction of regulated deficit irrigation treatments. SIF significantly decreased under mild stress, while NDVI, NDRE and OSAVI only significantly decreased under moderate and severe stress, indicating that SIF is more sensitive to drought. This study demonstrates the excellent ability of SIF for drought monitoring and lays the foundation for the future application of SIF in agricultural drought monitoring.