Global diversity and biogeography of potential phytopathogenic fungi in a changing world

Phytopathogenic fungi threaten global food security but the ecological drivers of their global diversity and biogeography remain unknown. Here, we construct and analyse a global atlas of potential phytopathogenic fungi from 20,312 samples across all continents and major oceanic island regions, eleven land cover types, and twelve habitat types. We show a peak in the diversity of phytopathogenic fungi in mid-latitude regions, in contrast to the latitudinal diversity gradients observed in aboveground organisms. Our study identifies climate as an important driver of the global distribution of phytopathogenic fungi, and our models suggest that their diversity and invasion potential will increase globally by 2100. Importantly, phytopathogen diversity will increase largely in forest (37.27-79.12%) and cropland (34.93-82.51%) ecosystems, and this becomes more pronounced under fossil-fuelled industry dependent future scenarios. Thus, we recommend improved biomonitoring in forests and croplands, and optimised sustainable development approaches to reduce potential threats from phytopathogenic fungi

Multisource High-Resolution Remote Sensing Image Vegetation Extraction with Comprehensive Multifeature Perception

High-resolution remote sensing image-based vegetation monitoring is a hot topic in remote sensing technology and applications. However, when facing large-scale monitoring across different sensors in broad areas, the current methods suffer from fragmentation and weak generalization capabilities. To address this issue, this paper proposes a multisource high-resolution remote sensing image-based vegetation extraction method that considers the comprehensive perception of multiple features. First, this method utilizes a random forest model to perform feature selection for the vegetation index, selecting an index that enhances the otherness between vegetation and other land features. Based on this, a multifeature synthesis perception convolutional network (MSCIN) is constructed, which enhances the extraction of multiscale feature information, global information interaction, and feature cross-fusion. The MSCIN network simultaneously constructs dual-branch parallel networks for spectral features and vegetation index features, strengthening multiscale feature extraction while reducing the loss of detailed features by simplifying the dense connection module. Furthermore, to facilitate global information interaction between the original spectral information and vegetation index features, a dual-path multihead cross-attention fusion module is designed. This module enhances the differentiation of vegetation from other land features and improves the network’s generalization performance, enabling vegetation extraction from multisource high-resolution remote sensing data. To validate the effectiveness of this method, we randomly selected six test areas within Anhui Province and compared the results with three different data sources and other typical methods (NDVI, RFC, OCBDL, and HRNet). The results demonstrate that the MSCIN method proposed in this paper, under the premise of using only GF2 satellite images as samples, exhibits robust accuracy in extraction results across different sensors. It overcomes the rapid degradation of accuracy observed in other methods with various sensors and addresses issues such as internal fragmentation, false positives, and false negatives caused by sample generalization and image diversity

A Mechanistic Model for Estimating Rice Photosynthetic Capacity and Stomatal Conductance from Sun-Induced Chlorophyll Fluorescence

Enhancing the photosynthetic rate is one of the effective ways to increase rice yield, given that photosynthesis is the basis of crop productivity. At the leaf level, crops’ photosynthetic rate is mainly determined by photosynthetic functional traits including the maximum carboxylation rate (Vcmax) and stomatal conductance (gs). Accurate quantification of these functional traits is important to simulate and predict the growth status of rice. In recent studies, the emerging sun-induced chlorophyll fluorescence (SIF) provides us an unprecedented opportunity to estimate crops’ photosynthetic traits, owing to its direct and mechanistic links to photosynthesis. Therefore, in this study, we proposed a practical semimechanistic model to estimate the seasonal Vcmax and gs time-series based on SIF. We firstly generated the coupling relationship between the open ratio of photosystem II (qL) and photosynthetically active radiation (PAR), then estimate the electron transport rate (ETR) based on the proposed mechanistic relationship between SIF and ETR. Finally, Vcmax and gs were estimated by linking to ETR based on the principle of evolutionary optimality and the photosynthetic pathway. Validation with field observations showed that our proposed model can estimate Vcmax and gs with high accuracy (R2 > 0.8). Compared to simple linear regression model, the proposed model could increase the accuracy of Vcmax estimates by >40%. Therefore, the proposed method effectively enhanced the estimation accuracy of crops’ functional traits, which sheds new light on developing high-throughput monitoring techniques to estimate plant functional traits, and also can improve our understating of crops’ physiological response to climate change

The potential of satellite FPAR product for GPP estimation: An indirect evaluation using solar-induced chlorophyll fluorescence

Satellite remotely sensed fraction of photosynthetically active radiation (FPAR) products are widely used in land-surface monitoring and modeling, especially for estimating global terrestrial photosynthetic activity through light use efficiency (LUE) models. PAR absorbed by active chlorophyll (APAR_(chl)) is directly linked to vegetation photosynthesis and can be used to estimate ecosystem gross primary production (GPP). Previous studies have demonstrated that solar induced chlorophyll fluorescence has very tight relationship with APAR_(chl) at various ecosystems. Therefore, the solar angle normalized SIF (nSIF) is directly related to the fraction of PAR absorbed by chlorophyll (FPAR_(chl)). This paper intercompared six space FPAR products from Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), Copernicus Global Land Service (CGLS), Multi-angle Imaging SpectroRadiometer (MISR), Earth Polychromatic Imaging Camera (EPIC) and Ocean and Land Colour Instrument (OLCI). Their potential relationships with FPAR_(chl) were indirectly evaluated with both spaceborne (Orbiting Carbon Observatory-2, OCO-2 and TROPOspheric Monitoring Instrument, TROPOMI) and airborne (Chlorophyll Fluorescence Imaging Spectrometer, CFIS) nSIF data as well as in situ GPP measurements. Our results show that these FPAR products are different in terms of amplitudes and seasonal variations across biomes. Among six FPAR products, OLCI FPAR shows the best relationships with TROPOMI nSIF740, OCO-2 nSIF757, and CFIS nSIF755. The coefficient of determination (R²) for the relationship between OLCI FPAR and TROPOMI nSIF740 is 0.79 ± 0.17 on a global average. APAR calculated from OLCI also exhibits the best relationship (R² = 0.79) with in situ GPP over 25 flux towers

Short-term outcomes of perioperative glucocorticoid administration in patients undergoing liver surgery: a systematic review and meta-analysis of randomised controlled trials

Objective The purpose of this systematic review and meta-analysis was to investigate whether glucocorticoid might be beneficial after hepatectomy.Design Systematic review and meta-analysis.Data sources PubMed, Embase, Cochrane Library and Web of Science.Eligibility criteria We included studies assessing the efficacy of perioperative glucocorticoid administration in patients undergoing liver surgery.Data extraction and synthesis Four data bases were retrieved for all randomised controlled trials. We considered postoperative complications, hospital stay and postoperative chemistry evaluations as outcomes. Pooled effects of dichotomic variables were expressed as relative risk (RR) with a 95% CI. The mean difference was used for continuous variables and an inverse variance statistical method was adopted.Results Fourteen studies with 1205 patients were included. Lower risk of overall complications was associated with glucocorticoid (RR, 0.77; 95% CI 0.64 to 0.92), while no difference was found in hospital stay (RR, 0.02; 95% CI −0.47 to 0.51). There were also improvements in postoperative chemistry evaluations including interleukin 6 on day 1 and 3, C reactive protein on day 1, 2 and 3, international normalised ratio on day 2, total bilirubin on day 1, 2, 3 and 5, albumin on day 1.Conclusion Current evidence indicated that perioperative glucocorticoid administration for patients undergoing hepatectomy reduced the risk of overall complications with inhibited postoperative inflammatory response and improved postoperative liver function.PROSPERO registration number CRD42022307533

The potential of satellite FPAR product for GPP estimation: An indirect evaluation using solar-induced chlorophyll fluorescence

Satellite remotely sensed fraction of photosynthetically active radiation (FPAR) products are widely used in land-surface monitoring and modeling, especially for estimating global terrestrial photosynthetic activity through light use efficiency (LUE) models. PAR absorbed by active chlorophyll (APAR_(chl)) is directly linked to vegetation photosynthesis and can be used to estimate ecosystem gross primary production (GPP). Previous studies have demonstrated that solar induced chlorophyll fluorescence has very tight relationship with APAR_(chl) at various ecosystems. Therefore, the solar angle normalized SIF (nSIF) is directly related to the fraction of PAR absorbed by chlorophyll (FPAR_(chl)). This paper intercompared six space FPAR products from Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), Copernicus Global Land Service (CGLS), Multi-angle Imaging SpectroRadiometer (MISR), Earth Polychromatic Imaging Camera (EPIC) and Ocean and Land Colour Instrument (OLCI). Their potential relationships with FPAR_(chl) were indirectly evaluated with both spaceborne (Orbiting Carbon Observatory-2, OCO-2 and TROPOspheric Monitoring Instrument, TROPOMI) and airborne (Chlorophyll Fluorescence Imaging Spectrometer, CFIS) nSIF data as well as in situ GPP measurements. Our results show that these FPAR products are different in terms of amplitudes and seasonal variations across biomes. Among six FPAR products, OLCI FPAR shows the best relationships with TROPOMI nSIF740, OCO-2 nSIF757, and CFIS nSIF755. The coefficient of determination (R²) for the relationship between OLCI FPAR and TROPOMI nSIF740 is 0.79 ± 0.17 on a global average. APAR calculated from OLCI also exhibits the best relationship (R² = 0.79) with in situ GPP over 25 flux towers

Shape Matters: Impact of Mesoporous Silica Nanoparticle Morphology on Anti-Tumor Efficacy

A nanoparticle’s shape is a critical determinant of its biological interactions and therapeutic effectiveness. This study investigates the influence of shape on the performance of mesoporous silica nanoparticles (MSNs) in anticancer therapy. MSNs with spherical, rod-like, and hexagonal-plate-like shapes were synthesized, with particle sizes of around 240 nm, and their other surface properties were characterized. The drug loading capacities of the three shapes were controlled to be 47.46%, 49.41%, and 46.65%, respectively. The effects of shape on the release behaviors, cellular uptake mechanisms, and pharmacological behaviors of MSNs were systematically investigated. Through a series of in vitro studies using 4T1 cells and in vivo evaluations in 4T1 tumor-bearing mice, the release kinetics, cellular behaviors, pharmacological effects, circulation profiles, and therapeutic efficacy of MSNs were comprehensively assessed. Notably, hexagonal-plate-shaped MSNs loaded with PTX exhibited a prolonged circulation time (t1/2 = 13.59 ± 0.96 h), which was approximately 1.3 times that of spherical MSNs (t1/2 = 10.16 ± 0.38 h) and 1.5 times that of rod-shaped MSNs (t1/2 = 8.76 ± 1.37 h). This research underscores the significance of nanoparticles’ shapes in dictating their biological interactions and therapeutic outcomes, providing valuable insights for the rational design of targeted drug delivery systems in cancer therapy

The epidemiological trends of biliary tract cancers in the United States of America

Abstract Background Biliary tract cancers (BTCs) are a series of heterogeneous malignancies that are broadly grouped based on the anatomical site where they arise into subtypes including intrahepatic cholangiocarcinoma (ICC), extrahepatic cholangiocarcinoma (ECC), gallbladder cancer (GBC), and ampulla of Vater cancer (AVC). Methods and Results The present study provides an overview of the epidemiology of the various BTCs based on data from the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) database from 2000 to 2018. Distinct differences in both incidence and mortality rates were observed for these BTCs as a function of age, sex, ethnicity, and calendar year. In 2018, BTCs emerged as the fifth most prevalent form of alimentary tract cancer in the USA. While the incidence and mortality of ICC appear to be increasing, the incidence rates of GBC, ECC, and AVC have remained stable, as have the corresponding mortality rates. The most common and deadliest BTCs in 2018 were ICC and GBC among males and females, respectively. The ethnic groups exhibiting the highest incidence rates of these different BTCs were American Indians and Alaska Natives for GBC, and Asian and Pacific Islanders for ICC, ECC, and AVC. The incidence of all of these forms of BTC rose with age. There were some variations in BTCs in terms of staging, locoregional surgical treatments, adjuvant therapies, and prognostic outcomes from 2000 to 2018. Conclusions The epidemiological characteristics, staging, locoregional surgical treatments, adjuvant therapies, and prognostic outcomes were distinct for each of these BTCs