Vegetation net primary productivity in urban areas of China responded positively to the COVID-19 lockdown in spring 2020.

To prevent the spread of COVID-19, China implemented large-scale lockdown measures in early 2020, resulting in a marked reduction in human activities over a short period. Studies have explored environmental changes during lockdowns, lacking analysis of response of net primary productivity (NPP) to lockdowns, especially for diverse vegetation types. Correlation between NPP and impact factors during lockdowns remains unclear. Through Google Earth Engine, we evaluated spatial-temporal changes in spring NPP at multiple scales during lockdown period (LD, 2020) compared with unlocked period (UL, 2017-2019) by remote sensing data in urban areas of China. Changes in four impact factors, aerosol optical depth (AOD) and photosynthetically active radiation (PAR) (via remote sensing data), alongside temperature (TEM) and precipitation (PRE) (via meteorological data) were explored. Additionally, geodetector, a valuable statistical tool for detecting the driving ability of various elements, was employed to explore the underlying causes of vegetation changes during LD. In the spring of LD: 1) National urban NPP generally increased (+6.50 %), notably in Northeast China (NE), North China (N) and East China (E). Besides, overall urban AOD decreased (-3.64 %), notably in N and Central China (C). National urban PAR increased (+2.7 %), particularly in C and Northwest China (NW). However, overall urban TEM (-0.06 %) and PRE (-1.21 %) changed negatively. 2) NPP in all three vegetation types in urban areas enhanced, with change rates: croplands > forests > grasslands. Evident enhancements occurred in the forests and croplands in N, and the grasslands in NE. 3) Through geodetector, during LD, AOD (q = 0.223) and TEM (q = 0.272) emerged as the dominant factors for NPP. Compared with UL, the explanatory power of AOD and PAR on NPP increased during LD. This study provides valuable insights into understanding the effects of short-term human activities on vegetation productivity, offering reference for the formulation of ecological and environmental policies.

Protocol to analyze immune cells in the tumor microenvironment by transcriptome using machine learning.

Immunotherapy is a promising strategy to treat cancer. Here, we present a protocol for analyzing the transcriptome-based phenotypic alterations and immune cell infiltration in the tumor microenvironment. We describe steps for integrating single-cell RNA sequencing (scRNA-seq) data, comparing phenotypes and origins of mononuclear phagocytes, inferring the differentiation trajectory and infiltration process, and identifying infiltration-associated genes using machine learning. We then detail procedures for exploring the impact of these genes in prognosis through the integrated microarray and bulk RNA-seq data to obtain potential drug targets. For complete details on the use and execution of this protocol, please refer to Liao et al.1.

An efficient convolutional neural network-based diagnosis system for citrus fruit diseases.

Introduction: Fruit diseases have a serious impact on fruit production, causing a significant drop in economic returns from agricultural products. Due to its excellent performance, deep learning is widely used for disease identification and severity diagnosis of crops. This paper focuses on leveraging the high-latitude feature extraction capability of deep convolutional neural networks to improve classification performance. Methods: The proposed neural network is formed by combining the Inception module with the current state-of-the-art EfficientNetV2 for better multi-scale feature extraction and disease identification of citrus fruits. The VGG is used to replace the U-Net backbone to enhance the segmentation performance of the network. Results: Compared to existing networks, the proposed method achieved recognition accuracy of over 95%. In addition, the accuracies of the segmentation models were compared. VGG-U-Net, a network generated by replacing the backbone of U-Net with VGG, is found to have the best segmentation performance with an accuracy of 87.66%. This method is most suitable for diagnosing the severity level of citrus fruit diseases. In the meantime, transfer learning is applied to improve the training cycle of the network model, both in the detection and severity diagnosis phases of the disease. Discussion: The results of the comparison experiments reveal that the proposed method is effective in identifying and diagnosing the severity of citrus fruit diseases identification.

CHD6 promotes broad nucleosome eviction for transcriptional activation in prostate cancer cells.

Despite being a member of the chromodomain helicase DNA-binding protein family, little is known about the exact role of CHD6 in chromatin remodeling or cancer disease. Here we show that CHD6 binds to chromatin to promote broad nucleosome eviction for transcriptional activation of many cancer pathways. By integrating multiple patient cohorts for bioinformatics analysis of over a thousand prostate cancer datasets, we found CHD6 expression elevated in prostate cancer and associated with poor prognosis. Further comprehensive experiments demonstrated that CHD6 regulates oncogenicity of prostate cancer cells and tumor development in a murine xenograft model. ChIP-Seq for CHD6, along with MNase-Seq and RNA-Seq, revealed that CHD6 binds on chromatin to evict nucleosomes from promoters and gene bodies for transcriptional activation of oncogenic pathways. These results demonstrated a key function of CHD6 in evicting nucleosomes from chromatin for transcriptional activation of prostate cancer pathways.

HTO/Cellulose Aerogel for Rapid and Highly Selective Li+ Recovery from Seawater.

To achieve rapid and highly efficient recovery of Li+ from seawater, a series of H2TiO3/cellulose aerogels (HTO/CA) with a porous network were prepared by a simple and effective method. The as-prepared HTO/CA were characterized and their Li+ adsorption performance was evaluated. The obtained results revealed that the maximum capacity of HTO/CA to adsorb Li+ was 28.58 ± 0.71 mg g-1. The dynamic k2 value indicated that the Li+ adsorption rate of HTO/CA was nearly five times that of HTO powder. Furthermore, the aerogel retained extremely high Li+ selectivity compared with Mg2+, Ca2+, K+, and Na+. After regeneration for five cycles, the HTO/CA retained a Li+ adsorption capacity of 22.95 mg g-1. Moreover, the HTO/CA showed an excellent adsorption efficiency of 69.93% ± 0.04% and high selectivity to Li+ in actual seawater. These findings confirm its potential as an adsorbent for recovering Li+ from seawater.

Intestinal Microbiota and Liver Diseases: Insights into Therapeutic Use of Traditional Chinese Medicine.

Liver disease is a leading cause of global morbidity and mortality, for which inflammation, alcohol use, lipid metabolic disorders, disturbance to bile acid metabolism, and endotoxins are common risk factors. Traditional Chinese Medicine (TCM) with its "holistic approach" is widely used throughout the world as a complementary, alternative therapy, due to its clinical efficacy and reduced side effects compared with conventional medicines. However, due to a lack of reliable scientific evidence, the role of TCM in the prevention and treatment of liver disease remains unclear. Over recent years, with the rapid development of high-throughput sequencing, 16S rRNA detection, and bioinformatics methodology, it has been gradually recognized that the regulation of intestinal microbiota by TCM can play a substantial role in the treatment of liver disease. To better understand how TCM regulates the intestinal microbiota and suppresses liver disease, we have reviewed and analyzed the results of existing studies and summarized the relationship and risk factors between intestinal microbiota and liver disease. The present review summarizes the related mechanisms by which TCM affects the composition and metabolites of the intestinal microbiome.

Highly Efficient, Stable, and Recyclable Hydrogen Manganese Oxide/Cellulose Film for the Extraction of Lithium from Seawater.

The extraction of lithium from seawater has attracted much interest as a means to meet increasing demand for lithium with the rapid expansion of the electric vehicle and electronics markets. Herein, a renewable and recyclable hydrogen manganese oxide (HMO)-modified cellulose film was developed and investigated toward the extraction of lithium from lithium-containing aqueous solutions. The porous film was characterized, and its extraction efficacy and selectivity toward lithium from an aqueous solution (ppm level) and seawater (ppb level) were investigated. The HMO/cellulose film exhibited a higher Li+ adsorption capacity (21.6 mg g-1 HMO) than HMO/polymer (e.g., poly(vinyl chloride) or poly(vinylidene fluoride)) films, which have been examined in the literature for lithium extraction, because of its multidimensional porosity and hydrophilicity. The kinetics analysis based on a pseudo-second-order model indicated that the Li+ extraction rate of the HMO/cellulose film was 3 times higher than that achieved by the HMO particle alone (i.e., 0.075; cf. 0.023 g mg-1 h-1). Furthermore, the HMO/cellulose film displayed high selectivity for Li+ when exposed to seawater-the extraction of Li+ reached 99%, whereas that of the other ions present in seawater (i.e., Sr2+, K+, and Ca2+) was <4%. In addition, the adsorption capacity and mechanical strength of the HMO/cellulose film remained stable even after eight adsorption-desorption cycles. The present findings demonstrate the potential of the present HMO/cellulose film for the recovery of Li+ from seawater or wastewater.

Jie-Du-Hua-Yu Granules Promote Liver Regeneration in Rat Models of Acute Liver Failure: miRNA-mRNA Expression Analysis.

PURPOSE: Jie-Du-Hua-Yu (JDHY) granules are a traditional Chinese medicine with known therapeutic effects for the treatment of acute liver failure (ALF). This study explored the potential molecular mechanism(s) of JDHY granules in promoting liver regeneration and preventing ALF. METHODS: Rat models of ALF were constructed through administration of D-galactosamine (D-GalN) (600 mg/kg) and lipopolysaccharides (LPS) (20 µg/kg). Rats were gavaged with JDHY granules, and serum and liver samples were collected at 12 h post-D-GalN/LPS administration. The degree of liver injury was evaluated through hepatic pathology and alanine/aspartate aminotransferase (ALT/AST) activity. miRNA chips were used to detect the miRNA expression profiles of rat models. Bioinformatics analysis was used to identify the biological processes and cell signaling pathways mediating the therapeutic effects of JDHY. Real-time PCR (RT-PCR) and western blotting were used to validate the data. RESULTS: JDHY granules could effectively decrease the levels of ALT and AST, relieve D-GalN/LPS-induced liver injury, and improve hepatic function. JDHY granules were found to regulate the expression of 20 miRNAs and 19 mRNAs, which influenced 21 biological processes and 9 signaling pathways. Upon analysis of the therapeutic mechanism(s) governing the effects of JDHY granules on liver regeneration, enhanced DNA replication and an improved cholesterol metabolic ratio were identified. JDHY granules were also found to increase the expression of MCM3, CDK4, and TC, confirming the involvement of these pathways. Moreover, JDHY granules were found to promote hepatocyte mitosis and inhibit the progression of ALF. CONCLUSION: JDHY granules protect against D-GalN/LPS-induced ALF in rats by promoting liver regeneration through enhanced DNA replication and an improved cholesterol metabolic ratio.