Multi-decadal trends in global terrestrial evapotranspiration and its components

Evapotranspiration (ET) is the process by which liquid water becomes water vapor and energetically this accounts for much of incoming solar radiation. If this ET did not occur temperatures would be higher, so understanding ET trends is crucial to predict future temperatures. Recent studies have reported prolonged declines in ET in recent decades, although these declines may relate to climate variability. Here, we used a well-validated diagnostic model to estimate daily ET during 1981–2012, and its three components: transpiration from vegetation (Et), direct evaporation from the soil (Es) and vaporization of intercepted rainfall from vegetation (Ei). During this period, ET over land has increased significantly (p < 0.01), caused by increases in Et and Ei, which are partially counteracted by Es decreasing. These contrasting trends are primarily driven by increases in vegetation leaf area index, dominated by greening. The overall increase in Et over land is about twofold of the decrease in Es. These opposing trends are not simulated by most Coupled Model Intercomparison Project phase 5 (CMIP5) models, and highlight the importance of realistically representing vegetation changes in earth system models for predicting future changes in the energy and water cycle

Reduced Intrinsic Non-Radiative Losses Allow Room-Temperature Triplet Emission from Purely Organic Emitters

Persistent luminescence from triplet excitons in organic molecules is rare, as fast non-radiative deactivation typically dominates over radiative transitions. This work demonstrates that the substitution of a hydrogen atom in a derivative of phenanthroimidazole with an N-phenyl ring can substantially stabilize the excited state. This stabilization converts an organic material without phosphorescence emission into a molecular system exhibiting efficient and ultralong afterglow phosphorescence at room temperature. Results from systematic photophysical investigations, kinetic modeling, excited-state dynamic modeling, and single-crystal structure analysis identify that the long-lived triplets originate from a reduction of intrinsic non-radiative molecular relaxations. Further modification of the N-phenyl ring with halogen atoms affects the afterglow lifetime and quantum yield. As a proof-of-concept, an anticounterfeiting device is demonstrated with a time-dependent Morse code feature for data encryption based on these emitters. A fundamental design principle is outlined to achieve long-lived and emissive triplet states by suppressing intrinsic non-radiative relaxations in the form of molecular vibrations or rotations

Clinical and virological characteristics of coexistent hepatitis B surface antigen and antibody in treatment-naive children with chronic hepatitis B virus infection

Serological pattern of simultaneous positivity for hepatitis B surface antigen (HBsAg) and antibody against HBsAg (anti-HBs) is considered a specific and atypical phenomenon among patients with chronic hepatitis B virus (HBV) infection, especially in pediatric patients. Unfortunately, there is limited understanding of the clinical and virological characteristics among children having chronic HBV infection and the coexistence of HBsAg and anti-HBs. Hence, our objective was to determine the prevalence of coexistent HBsAg and anti-HBs and to explore the associated clinical and virological features in this patient population. The researchers conducted a retrospective cohort study on the 413 pediatric patients with chronic HBV infection from December 2011 to June 2022. The patients were stratified into two groups based on their anti-HBs status. Demographic, serum biochemical and virological parameters of two group were compared. Of the total 413 enrolled subjects, 94 (22.8%) were tested positive for both HBsAg and anti-HBs. Patients with anti-HBs were younger and demonstrated significantly higher ratio of albumin to globulin (A/G), elevated serum levels of alanine transaminase (ALT), lower ratio of aspartate transaminase (AST)/ALT (AST/ALT) and reduced serum levels of globulin, HBsAg and HBV DNA, Additionally, these patients were more likely to show coexistent HBeAg and anti-HBe when compared to patients without anti-HBs. The results of multivariate logistical analysis revealed that AST/ALT, serum levels of globulin and HBsAg were negatively associated with coexistence of HBsAg and anti-HBs. Our data demonstrated a considerable prevalence of coexisting HBsAg and anti-HBs in pediatric patients. Children with this specific serological pattern were commonly of a younger age, seemly predisposing them to early liver impairment and lower HBV replication activity

Induction of Epidermolysis Bullosa Acquisita in Mice by Passive Transfer of Autoantibodies from Patients

Epidermolysis bullosa acquisita (EBA) is an autoimmune sub-epidermal blistering disease characterized by autoantibodies to type VII (anchoring fibril) collagen. To date, however, direct evidence for a pathogenic role of human EBA autoantibodies has not been demonstrated. In this study, we affinity-purified anti-type VII collagen antibodies from EBA patients' sera and then injected them into adult hairless immunocompetent mice. Mice injected with EBA autoantibodies developed skin fragility, blisters, erosions, and nail loss on their paws - all features of EBA patients. By clinical, histological, immunological, and ultrastructural parameters, the induced lesions were reminiscent of human EBA. Histology showed bullous lesions with an epidermal-dermal separation. IgG and C3 deposits were observed at the epidermal-dermal junction. All mice had serum antibodies that labeled the dermal side of salt-split human skin like EBA sera. Direct immunogold electron microscopy specifically localized deposits of human IgG to anchoring fibrils. (Fab')(2) fragments generated from EBA autoantibodies did not induce disease. We conclude that EBA human patient autoantibodies cause sub-epidermal blisters and induce EBA skin lesions in mice. These passive transfer studies demonstrate that human EBA autoantibodies are pathogenic. This novel EBA mouse model can be used to further investigate EBA autoimmunity and to develop possible therapies

Prediction of recurrence of ischemic stroke within 1 year of discharge based on machine learning MRI radiomics

PurposeThis study aimed to investigate the value of a machine learning-based magnetic resonance imaging (MRI) radiomics model in predicting the risk of recurrence within 1 year following an acute ischemic stroke (AIS).MethodsThe MRI and clinical data of 612 patients diagnosed with AIS at the Second Affiliated Hospital of Nanchang University from March 1, 2019, to March 5, 2021, were obtained. The patients were divided into recurrence and non-recurrence groups according to whether they had a recurrent stroke within 1 year after discharge. Randomized splitting was used to divide the data into training and validation sets using a ratio of 7:3. Two radiologists used the 3D-slicer software to label the lesions on brain diffusion-weighted (DWI) MRI sequences. Radiomics features were extracted from the annotated images using the pyradiomics software package, and the features were filtered using the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Four machine learning algorithms, logistic regression (LR), Support Vector Classification (SVC), LightGBM, and Random forest (RF), were used to construct a recurrence prediction model. For each algorithm, three models were constructed based on the MRI radiomics features, clinical features, and combined MRI radiomics and clinical features. The sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC) were used to compare the predictive efficacy of the models.ResultsTwenty features were selected from 1,037 radiomics features extracted from DWI images. The LightGBM model based on data with three different features achieved the best prediction accuracy from all 4 models in the validation set. The LightGBM model based solely on radiomics features achieved a sensitivity, specificity, and AUC of 0.65, 0.671, and 0.647, respectively, and the model based on clinical data achieved a sensitivity, specificity, and AUC of 0.7, 0.799, 0.735, respectively. The sensitivity, specificity, and AUC of the LightGBM model base on both radiomics and clinical features achieved the best performance with a sensitivity, specificity, and AUC of 0.85, 0.805, 0.789, respectively.ConclusionThe ischemic stroke recurrence prediction model based on LightGBM achieved the best prediction of recurrence within 1 year following an AIS. The combination of MRI radiomics features and clinical data improved the prediction performance of the model

Label-Free, Visual Detection of Small Molecules Using Highly Target-Responsive Multimodule Split Aptamer Constructs

Colorimetric aptamer-based sensors offer a simple means of on-site or point-of-care analyte detection. However, these sensors are largely incapable of achieving naked-eye detection, because of the poor performance of the target-recognition and signal-reporting elements employed. To address this problem, we report a generalizable strategy for engineering novel multimodule split DNA constructs termed CBSAzymes that utilize a cooperative binding split aptamer (CBSA) as a highly target-responsive bioreceptor and a new, highly active split DNAzyme as an efficient signal reporter. CBSAzymes consist of two fragments that remain separate in the absence of target, but effectively assemble in the presence of the target to form a complex that catalyzes the oxidation of 2,2\u27-azino-bis(3-ethylbenzthiazoline)-6-sulfonic acid, developing a dark green color within 5 min. Such assay enables rapid, sensitive, and visual detection of small molecules, which has not been achieved with any previously reported split-aptamer-DNAzyme conjugates. In an initial demonstration, we generate a cocaine-binding CBSAzyme that enables naked-eye detection of cocaine at concentrations as low as 10 μM. Notably, CBSAzyme engineering is straightforward and generalizable. We demonstrate this by developing a methylenedioxypyrovalerone (MDPV)-binding CBSAzyme for visual detection of MDPV and 10 other synthetic cathinones at low micromolar concentrations, even in biological samples. Given that CBSAzyme-based assays are simple, label-free, rapid, robust, and instrument-free, we believe that such assays should be readily applicable for on-site visual detection of various important small molecules such as illicit drugs, medical biomarkers, and toxins in various sample matrices

Overexpression of glutathione synthetase gene improving redox homeostasis and chicken infectious bursal disease virus propagation in chicken embryo fibroblast DF-1

Abstract Infectious bursal disease (IBD) of chickens is an acute, high-contact, lytic infectious disease caused by infectious bursal disease virus (IBDV). The attenuated inactivated vaccine produced by DF-1 cells is an effective control method, but the epidemic protection demands from the world poultry industry remain unfulfilled. To improve the IBDV vaccine production capacity and reduce the economic losses caused by IBDV in chicken, cellular metabolic engineering is performed on host cells. In this study, when analyzing the metabolomic after IBDV infection of DF-1 cells and the exogenous addition of reduced glutathione (GSH), we found that glutathione metabolism had an important role in the propagation of IBDV in DF-1 cells, and the glutathione synthetase gene (gss) could be a limiting regulator in glutathione metabolism. Therefore, three stable recombinant cell lines GSS-L, GSS-M, and GSS-H (gss gene overexpression with low, medium, and high mRNA levels) were screened. We found that the recombinant GSS-M cell line had the optimal regulatory effect with a 7.19 ± 0.93-fold increase in IBDV titer. We performed oxidative stress and redox status analysis on different recombinant cell lines, and found that the overexpression of gss gene significantly enhanced the ability of host cells to resist oxidative stress caused by IBDV infection. This study established a high-efficiency DF-1 cells system for IBDV vaccine production by regulating glutathione metabolism, and underscored the importance of moderate gene expression regulation on the virus reproduction providing a way for rational and precise cell engineering. Graphical Abstrac

Rapper: A Parameter-Aware Repair-in-Memory Accelerator for Blockchain Storage Platform

&lt;p&gt;Blockchain storage platforms reward storage nodes for keeping user-uploaded data for a certain amount of time. These storage nodes are unstable and can go online or offline unpredictably at any time, leading to potential data loss. To prevent data loss, blockchain storage platforms adopt erasure codes on user-uploaded encrypted data. Data repair processes will be performed to recover the lost data. However, the data repair processes heavily rely on time-consuming erasure coding algorithms, mainly consisting of vector-matrix multiplications. The emerging processing-in-memory technique can efficiently speed up the processing of vector-matrix multiplications. It can be integrated into blockchain storage platforms to solve the data repair issue.&lt;/p&gt;&lt;p&gt;This paper presents Rapper, a parameter-aware repair-inmemory accelerator for blockchain storage platforms. Rapper utilizes the computing power of emerging processing-in-memory architecture so that data repair processes can be processed in a parallel manner and the overall efficiency can be improved significantly. Specifically, at the hardware level, the ReRAM memory is reorganized into our proposed double bank, XRU, XGroup, and ReRAM crossbars structure. At the software level, a parallel decoding/encoding strategy is proposed to fully exploit the internal parallelism of ReRAM. We also propose an adaptive parameter-aware mapping to handle various sizes of stripes. To demonstrate the viability of the proposed technique, a representative blockchain storage project Storj is adopted as the default storage infrastructure. Experimental results show that Rapper can achieve a 1.96× speedup on average compared to the representative scheme.&nbsp;&lt;/p&gt

Rapper: A Parameter-Aware Repair-in-Memory Accelerator for Blockchain Storage Platform

&lt;p&gt;Blockchain storage platforms reward storage nodes for keeping user-uploaded data for a certain amount of time. These storage nodes are unstable and can go online or offline unpredictably at any time, leading to potential data loss. To prevent data loss, blockchain storage platforms adopt erasure codes on user-uploaded encrypted data. Data repair processes will be performed to recover the lost data. However, the data repair processes heavily rely on time-consuming erasure coding algorithms, mainly consisting of vector-matrix multiplications. The emerging processing-in-memory technique can efficiently speed up the processing of vector-matrix multiplications. It can be integrated into blockchain storage platforms to solve the data repair issue.&lt;/p&gt;&lt;p&gt;This paper presents Rapper, a parameter-aware repair-inmemory accelerator for blockchain storage platforms. Rapper utilizes the computing power of emerging processing-in-memory architecture so that data repair processes can be processed in a parallel manner and the overall efficiency can be improved significantly. Specifically, at the hardware level, the ReRAM memory is reorganized into our proposed double bank, XRU, XGroup, and ReRAM crossbars structure. At the software level, a parallel decoding/encoding strategy is proposed to fully exploit the internal parallelism of ReRAM. We also propose an adaptive parameter-aware mapping to handle various sizes of stripes. To demonstrate the viability of the proposed technique, a representative blockchain storage project Storj is adopted as the default storage infrastructure. Experimental results show that Rapper can achieve a 1.96× speedup on average compared to the representative scheme.&nbsp;&lt;/p&gt

A new method of “one blow and two washes” for the analysis of stain categories and content of down raw materials

The down raw materials collected from the slaughterhouses generally adhered with an excessive amount of dust and complex stains which hinder them to be utilized directly for down textiles processing. Water washing is an important core process of down raw material processing for dust removal, decontamination, and sterilization of down fibers. However, there is no standard method to analyze the stain category and content of down raw materials, so it can only rely on traditional experience to wash down, which may cause problems including insufficient or excessive washing, and serious damage to the quality of down fibers. Thus, this study has been specifically designed and developed a method dedicated to categorizing stain types and content of down raw materials. This novel design process is named as “one blow and two washes”, which is composed of three steps to analyze dust content, water-soluble stains, and grease stains in down raw material more accurately. In this experiment, the stain analysis of six different sources of down raw materials was carried out, scanning electron microscope (SEM) and super depth of field 3D microscope system were used to characterize down samples. The results showed that the “one blow and two washes” method could accurately be used to distinguish stain categories of different sources of down raw material with a small average variance value and a coefficient of variation less than 12%, indicating that this experimental method has high reliability and good reproducibility. These research results will provide an important basis for the design of the washing agent dosage and process parameters for raw down, which is expected to achieve the precise washing process of raw down and improve the quality of down