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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.

Solar-induced chlorophyll fluorescence (SIF) from ground, airborne, and satellite-based observations has been increasingly used in drought monitoring recently due to its close relationship with photosynthesis. SIF emissions respond rapidly to droughts, relative to the widely used vegetation indices (VIs), thus indicating their potential for early drought monitoring. The response of SIF to droughts can be attributed to the confounding effects of both the physiology and canopy structure. In order to reduce the reabsorption and scattering effects, the total emitted SIF (SIFtot) was proposed and served as a better tool to estimate GPP compared with the top-of-canopy SIF (SIFtoc). However, the response time and response magnitude of SIFtot to droughts and its relationships with the environmental parameters and soil moisture (SM) (i.e., the knowledge of drought monitoring using SIFtot) remains unclear. Here, the continuous ground data of F760toc (SIFtoc at 760 nm) from a nadir view that was downscaled to F760tot (SIFtot at 760 nm), NIRv, and the NDVI, SM, meteorological, and crop growth parameters were measured from four winter wheat plots with different intensities of drought (well-watered, moderate drought, severe drought, and extreme drought) over 2 months. The results indicated that F760tot was more closely correlated with the SM than the VIs at short time lags but weaker at longer time lags. The daily mean values of F760tot and NIRv were able to distinguish the differences between different drought levels, and F760tot responded quickly to the onset of drought, especially for the moderate drought intensity. These findings demonstrated that F760tot has potential for early drought monitoring and may contribute to mitigating the risk of agricultural drought.

Colon adenocarcinoma (COAD) represents a major public health issue due to its high incidence and mortality. As different histological subtypes of COAD are related to various survival outcomes and different therapies, finding specific targets and treatments for different subtypes is one of the major demands of individual disease therapy. Interestingly, as these different subtypes show distinct metabolic profiles, it may be possible to find specific targets related to histological typing by targeting COAD metabolism. In this study, the differential expression patterns of metabolism-related genes between COAD (n = 289) and adjacent normal tissue (n = 41) were analyzed by one-way ANOVA. We then used weighted gene co-expression network analysis (WGCNA) to further identify metabolism-related gene connections. To determine the critical genes related to COAD metabolism, we obtained 2,114 significantly differentially expressed genes (DEGs) and 12 modules. Among them, we found the hub module to be significantly associated with histological typing, including non-mucin-producing colon adenocarcinoma and mucin-producing colon adenocarcinoma. Combining survival analysis, we identified glycerophosphodiester phosphodiesterase 1 (GDE1) as the most significant gene associated with histological typing and prognosis. This gene displayed significantly lower expression in COAD compared with normal tissues and was significantly correlated with the prognosis of non-mucin-producing colon adenocarcinoma (p = 0.0017). Taken together, our study showed that GDE1 exhibits considerable potential as a novel therapeutic target for non-mucin-producing colon adenocarcinoma.

Solar-induced chlorophyll fluorescence (SIF) is closely related to vegetation photosynthesis, and is considered as a direct and non-invasive indicator of the functional status of photosynthetic machinery; the normalized difference vegetation index (NDVI) can also reflect photosynthetic characteristics. Both are related to surface soil moisture (SSM), which is susceptible to drought, and phenology plays an important role in these vegetation-moisture relationships. Based on a variety of gridded SIF, NDVI, and SSM datasets obtained from satellite sensors, we presented the detailed relationships of SSM with SIF and NDVI in different phenological stages. Results showed that SIF and NDVI were significantly correlated with SSM in different phenological stages, especially during the maturity and senescence stages. For different vegetation types, SIF was more sensitive than NDVI to SSM anomalies in grasslands and forests during the maturity stage and rainfed croplands during the senescence stage. These relationships were regulated by precipitation and mean temperature, and decreased precipitation had the most significant impact when accompanied by increased temperature during the maturity stage or decreased temperature during the senescence stage. Our findings shed light on the role that phenology has in the relationships of SSM with SIF and NDVI on a large scale, which can further improve the understanding of vegetation-moisture relationships.

At present, convolutional neural networks (CNN) have been widely used in building extraction from remote sensing imagery (RSI), but there are still some bottlenecks. On the one hand, there are so many parameters in the previous network with complex structure, which will occupy lots of memories and consume much time during training process. On the other hand, low-level features extracted by shallow layers and abstract features extracted by deep layers of artificial neural network cannot be fully fused, which leads to an inaccurate building extraction from RSI. To alleviate these disadvantages, a dense residual neural network (DR-Net) was proposed in this paper. DR-Net uses a deeplabv3+Net encoder/decoder backbone, in combination with densely connected convolution neural network (DCNN) and residual network (ResNet) structure. Compared with deeplabv3+net (containing about 41 million parameters) and BRRNet (containing about 17 million parameters), DR-Net contains about 9 million parameters; So, the number of parameters reduced a lot. The experimental results for both the WHU Building Dataset and Massachusetts Building Dataset, DR-Net show better performance in building extraction than other two state-of-the-art methods. Experiments on WHU building data set showed that Intersection over Union (IoU) increased by 2.4% and F1 score increased by 1.4%; in terms of Massachusetts Building Dataset, IoU increased by 3.8% and F1 score increased by 2.9%.

It has been demonstrated that solar-induced chlorophyll fluorescence (SIF) is linearly related to the primary production of photosynthesis (GPP) in various ecosystems. However, it is unknown whether such linear relationships have been established in senescent crops. SIF and GPP can be expressed as the products of absorbed photosynthetically active radiation (APAR) with the SIF yield and photosystem II (PSII) operating efficiency, respectively. Thus, the relationship between SIF and GPP can be represented by the relationship between the SIF yield and PSII operating efficiency when the APAR has the same value. Therefore, we analyzed the relationship between the SIF yield and the PSII operating efficiency to address the abovementioned question. Here, diurnal measurements of the canopy SIF (760 nm, F760) of soybean and sweet potato were manually measured and used to calculate the SIF yield. The PSII operating efficiency was calculated from measurements of the chlorophyll fluorescence at the leaf level using the FluorImager chlorophyll fluorescence imaging system. Meanwhile, field measurements of the gas exchange and other physiological parameters were also performed using commercial-grade devices. The results showed that the SIF yield was not linearly related to the PSII operating efficiency at the diurnal scale, reflecting the nonlinear relationship between SIF and GPP. This nonlinear relationship mainly resulted from the heterogeneity and diurnal dynamics of the PSII operating efficiency and from the intrinsic diurnal changes in the maximum efficiency of the PSII photochemistry and the proportion of opened PSII centers. Intensifying respiration was another factor that complicated the response of photosynthesis to the variation in environmental conditions and negatively impacted the relationship between the SIF yield and the PSII operating efficiency. The nonlinear relationship between the SIF yield and PSII efficiency might yield errors in the estimation of GPP using the SIF measurements of senescent crops.

Timely and accurate prediction of large-scale crop yields is critical for national food security. Solar-induced chlorophyll fluorescence (SIF), an indicator of photosynthesis, has emerged as a promising predictor of crop yields. However, it remains unclear to what extent satellite-based SIF data can predict crop yields at the regional scale compared to the newly proposed Near-Infrared Reflectance of Vegetation (NIRv). Using multiple statistical machine learning (ML) methods, this study investigated the predictive abilities of SIF and NIRv by combining climate data to predict winter wheat yields in five provinces in the North China Plain (NCP). Results showed that: (a) SIF outperformed NIRv in predicting winter wheat yields. However, in the Extreme Gradient Boosting (XGB) model, SIF’s predictive performance was better than that of the combination of SIF and NIRv, indicating that combining SIF and NIRv could not completely enhance SIF’s predictive performance. (b) Random Forest (RF) and XGB models were significantly better than the other models in yield prediction; specifically, the RF model had high stability. The results highlighted the benefits of combining multiple sources of data and revealed the advantages of RF and XGB models in crop yield prediction in the major grain production region.