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The structure and electrochemical performance of lithium (Li) metal degrade quickly owing to its hostless nature and high reactivity, hindering its practical application in rechargeable high energy density batteries. In order to enhance the electrochemical reversibility of metallic Li, we designed a Li/Li

Modern drug discovery typically faces large virtual screens from huge compound databases where multiple docking tools are involved for meeting various real scenes or improving the precision of virtual screens. Among these tools, AutoDock Vina and its numerous derivatives are the most popular and have become the standard pipeline for molecular docking in modern drug discovery. Our recent Vina-GPU method realized 14-fold acceleration against AutoDock Vina on a piece of NVIDIA RTX 3090 GPU in one virtual screening case. Further speedup of AutoDock Vina and its derivatives with GPUs is beneficial to systematically push their popularization in large-scale virtual screens due to their high benefit-cost ratio and easy operation for users. Thus, we proposed the Vina-GPU 2.0 method to further accelerate AutoDock Vina and the most common derivatives with new docking algorithms (QuickVina 2 and QuickVina-W) with GPUs. Caused by the discrepancy of their docking algorithms, our Vina-GPU 2.0 adopts different GPU acceleration strategies. In virtual screening for two hot protein kinase targets RIPK1 and RIPK3 from the DrugBank database, our Vina-GPU 2.0 reaches an average of 65.6-fold,1.4-fold and 3.6-fold docking acceleration against the original AutoDock Vina, QuickVina 2 and QuickVina-W while ensuring their comparable docking accuracy. In addition, we develop a friendly and installation-free graphical user interface (GUI) tool for their convenient usage. The codes and tools of Vina-GPU 2.0 are freely available at https://github.com/DeltaGroupNJUPT/Vina-GPU-2.0, coupled with explicit instructions and examples.

Emotion recognition based on brain-computer interface (BCI) has attracted important research attention despite its difficulty. It plays a vital role in human cognition and helps in making the decision. Many researchers use electroencephalograms (EEG) signals to study emotion because of its easy and convenient. Deep learning has been employed for the emotion recognition system. It recognizes emotion into single or multi-models, with visual or music stimuli shown on a screen. In this article, the convolutional neural network (CNN) model is introduced to simultaneously learn the feature and recognize the emotion of positive, neutral, and negative states of pure EEG signals single model based on the SJTU emotion EEG dataset (SEED) with ResNet50 and Adam optimizer. The dataset is shuffle, divided into training and testing, and then fed to the CNN model. The negative emotion has the highest accuracy of 94.86% fellow by neutral emotion with 94.29% and positive emotion with 93.25% respectively. With average accuracy of 94.13%. The results showed excellent classification ability of the model and can improve emotion recognition.

The study of runoff under the influence of human activities is a research hot spot in the field of water science. Land-use change is one of the main forms of human activities and it is also the major driver of changes to the runoff process. As for the relationship between land use and the runoff process, runoff yield theories pointed out that the runoff yield capacity is spatially heterogeneous. The present work hypothesizes that the distribution of the runoff yield can be divided by land use, which is, areas with the same land-use type are similar in runoff yield, while areas of different land uses are significantly different. To prove it, we proposed a land-use-based framework for runoff yield calculations based on a conceptual rainfall–runoff model, the Xin’anjiang (XAJ) model. Based on the framework, the modified land-use-based Xin’anjiang (L-XAJ) model was constructed by replacing the yielding area (f/F) in the water storage capacity curve of the XAJ model with the area ratio of different land-use types (L/F; L is the area of specific land-use types, F is the whole basin area). The L-XAJ model was then applied to the typical cultivated–urban binary land-use-type basin (Taipingchi basin) to evaluate its performance. Results showed great success of the L-XAJ model, which demonstrated the area ratio of different land-use types can represent the corresponding yielding area in the XAJ model. The L-XAJ model enhanced the physical meaning of the runoff generation in the XAJ model and was expected to be used in the sustainable development of basin water resources.

A unified strategy for the biomimetic total synthesis of the spiroindimicin family of natural products was reported. Key transformations include a one-pot two-enzyme-catalyzed oxidative dimerization of L-tryptophan/5-chloro-L-tryptophan to afford the bis-indole precursors chromopyrrolic acid/5',5''-dichloro-chromopyrrolic acid, and regioselective C3'-C2'' and C3'-C4'' bond formation converting a common bis-indole skeleton to two skeletally different natural products, including (±)-spiroindimicins D and G with a [5,5] spiro-ring skeleton, and (±)-spiroindimicins A and H with a [5,6] spiro-ring skeleton, respectively.

Chemical phosphorus removal (CPR) is being increasingly adopted in wastewater treatment plants (WWTPs) to enhance P elimination to comply with stringent discharge limits. However, strategies to recover P enriched in the produced waste activated sludge (WAS) are not well developed. In this study, we investigated the release of P in WAS from three WWTPs employing Al salt enhanced CPR by alkaline treatment. We also monitored P mobilization by tracking the dynamics of P fractions and species, the dissolution of major metals, and sludge cell integrities as pH was altered. The level of aqueous total phosphorus (TPaq) in the sludge increased significantly to >200 mg/L (from 800 mg/L in this work).

Metallic sodium (Na) has been regarded as one of the most attractive anodes for Na-based rechargeable batteries due to its high specific capacity, low working potential, and high natural abundance. However, several important issues hinder the practical application of the metallic Na anode, including its high reactivity with electrolytes, uncontrolled dendrite growth, and poor processability. Metal nitrates are common electrolyte additives used to stabilize the solid electrolyte interphase (SEI) on Na anodes, though they typically suffer from poor solubility in electrolyte solvents. To address these issues, a Na/NaNO3 composite foil electrode was fabricated through a mechanical kneading approach, which featured uniform embedment of NaNO3 in a metallic Na matrix. During the battery cycling, NaNO3 was reduced by metallic Na sustainably, which addressed the issue of low solubility of an SEI stabilizer. Due to the supplemental effect of NaNO3, a stable SEI with NaNxOy and Na3N species was produced, which allowed fast ion transport. As a result, stable electrochemical performance for 600 h was achieved for Na/NaNO3||Na/NaNO3 symmetric cells at a current density of 0.5 mA cm-2 and an areal capacity of 0.5 mAh cm-2. A Na/NaNO3||Na3V2(PO4)2O2F cell with active metallic Na of ∼5 mAh cm-2 at the anode showed stable cycling for 180 cycles. In contrast, a Na||Na3V2(PO4)2O2F cell only displayed less than 80 cycles under the same conditions. Moreover, the processability of the Na/NaNO3 composite foil was also significantly improved due to the introduction of NaNO3, in contrast to the soft and sticky pure metallic Na. Mechanical kneading of soft alkali metals and their corresponding nitrates provides a new strategy for the utilization of anode stabilizers (besides direct addition into electrolytes) to improve their electrochemical performance.

Floods are one of the main natural disaster threats to the safety of people’s lives and property. Flood hazards intensify as the global risk of flooding increases. The control of flood disasters on the basin scale has always been an urgent problem to be solved that is firmly associated with the sustainable development of water resources. As important nonengineering measures for flood simulation and flood control, the hydrological and hydraulic models have been widely applied in recent decades. In our study, on the basis of sufficient remote-sensing and hydrological data, a hydrological (Xin’anjiang (XAJ)) and a two-dimensional hydraulic (2D) model were constructed to simulate flood events and provide support for basin flood management. In the Chengcun basin, the two models were applied, and the model parameters were calibrated by the parameter estimation (PEST) automatic calibration algorithm in combination with the measured data of 10 typical flood events from 1990 to 1996. Results show that the two models performed well in the Chengcun basin. The average Nash–Sutcliffe efficiency (NSE), percentage error of peak discharge (PE), and percentage error of flood volume (RE) were 0.79, 16.55%, and 18.27%, respectively, for the XAJ model, and those values were 0.76, 12.83%, and 11.03% for 2D model. These results indicate that the models had high accuracy, and hydrological and hydraulic models both had good application performance in the Chengcun basin. The study can a provide decision-making basis and theoretical support for flood simulation, and the formulation of flood control and disaster mitigation measures in the basin.

Background music has been increasingly affecting people's lives. The research on the influence of background music on working memory has become a hot topic in brain science. In this paper, an improved electroencephalography (EEG) experiment based on n-back paradigm was designed. Fifteen university students without musical training were randomly selected to participate in the experiment, and their behavioral data and the EEG data were collected synchronously in order to explore the influence of different types of background music on spatial positioning cognition working memory. The exact low-resolution brain tomography algorithm (eLORETA) was applied to localize the EEG sources and the cross-correlation method was used to construct the cortical brain function networks based on the EEG source signals. Then the characteristics of the networks under different conditions were analyzed and compared to study the effects of background music on people's working memory. The results showed that the difference of peak periods after stimulated by different types of background music were mainly distributed in the signals of occipital lobe and temporal lobe (背景音乐日益影响着人们的生活，研究背景音乐对工作记忆的影响成为脑科学的热点。本文设计了基于 n-back 范式的改进的脑电实验，随机招募 15 名未接受过音乐专业训练的在校大学生作为被试，同步采集被试的行为学数据和头皮脑电数据，以探究不同类型背景音乐对于空间认知工作记忆的影响。采用精确的低分辨率层析成像方法（eLORETA）方法进行脑电溯源，针对溯源后的信号，利用互相关方法构建皮层脑功能网络，并对不同音乐类型下脑网络的特征属性进行分析对比。结果显示，不同音乐类型下刺激后峰值期的差异主要分布在枕叶与颞叶（

Herbicide-resistant weeds pose a considerable threat to agriculture, but their resistance mechanisms are poorly understood. Differential gene expression analysis of a weed subjected to herbicide treatment is a key step toward more mechanistic studies. Such an analysis, often involving quantitative real-time PCR (qPCR), requires suitable reference genes as internal controls. In this study, we identified optimal reference genes in the noxious weed, Japanese foxtail. This weed has evolved resistance to acetyl-coenzyme A carboxylase (ACCase) inhibitors. We analyzed the stability of eight commonly used candidate reference genes (glyceraldehyde-3-phosphate dehydrogenase [GAPDH]; ubiquitin [UBQ]; capsine phosphatase [CAP]; beta-tubulin [TUB]; eukaryotic initiation factor 4a [EIF4A]; elongation factor-1 alpha [EF1]; 18S ribosomal RNA [18S]; 25S ribosomal RNA [25S]) from root, stem, and leaf tissue of plants that were either resistant or sensitive to ACCase inhibitors, with or without herbicide stress, using qPCR. The results were further ranked and analyzed using geNorm, NormFinder, and BestKeeper software. These analyses identified EF1 and UBQ in roots, EF1, TUB, CAP, and 18S in stems, and EF1, GAPDH, and 18S in leaves as suitable references for qPCR normalization. We have identified a set of reference genes that can be used to study herbicide resistance mechanisms in Japanese foxtail.

Japanese foxtail is a grass weed in eastern China. This weed is controlled by fenoxaprop-P-ethyl, one of the most common acetyl-CoA carboxylase (ACCase)-inhibiting herbicides. Some Japanese foxtail populations have developed resistance to fenoxaprop-P-ethyl, owing to target-site mutations (amino acid substitutions) located within the carboxyl transferase domain of ACCase. In the present study, three mutations were detected in three fenoxaprop-P-ethyl–resistant Japanese foxtail populations: Ile-1781-Leu in JCJT-2, Ile-2041-Asn in JZJR-1, and Asp-2078-Gly in JCWJ-3. Two copies ofACCase(Acc1-1andAcc1-2) were identified, but mutations were detected only inAcc1-1. The derived cleaved amplified polymorphic sequence (dCAPS) method detected these mutations successfully in Japanese foxtail. The mutation frequencies in JCJT-2, JZJR-1, and JCWJ-3 were approximately 98%, 92%, and 87%, respectively. Different cross-resistance patterns to ACCase inhibitors were found in the three resistant populations. JCJT-2 (Ile-1781-Leu) and JZJR-1 (Ile-2041-Asn) showed cross-resistance to haloxyfop-R-methyl, clodinafop-propargyl, and pinoxaden, but were susceptible to clethodim. JCWJ-3 (Asp-2078-Gly) showed cross-resistance to all tested ACCase-inhibiting herbicides.

In natural farmland ecosystems, cadmium (Cd) pollution in rice has attracted increasing attention because of its high toxicity, relative mobility, and high water solubility. This study aims to develop a spectral index for detecting Cd stress in rice on a regional scale. Three experimental sites are selected in Zhuzhou City, Hunan Province. The hyperspectral data, chlorophyll (Chl) content, leaf area index, average leaf angle, Cd concentration in soil, and Sentinel-2A images from 2017 and 2018 are collected. A new spectral index sensitive to Cd stress in rice is established based on the global sensitivity analysis of the radiative transfer model PROSPECT + SAIL (commonly called PROSAIL) model with the auxiliary of the field-measured data. The heavy metal Cd stress-sensitive spectral index (HCSI) is devised as an indicator of the degree of Cd stress in rice. Results indicate that (1) the HCSI developed based on Chl is a good indicator of rice damage caused by Cd stress, that is, low values of HCSI occur in rice subject to relatively high pollution, (2) compared with common spectral indices, such as red-edge position and red-edge Chl index, HCSI is more sensitive to Chl content with higher Pearson correlation coefficients with respect to Chl content, ranging from 0.85 to 0.95, (3) HCSI is successfully applied in Sentinel-2A images from the two different years of monitoring rice Cd stress on a regional scale. Cd stress levels in rice stabilized, and the largest area percentage of each pollution levels of Cd decreased in the following order: No pollution (i.e., 40%), low pollution (i.e., 35%), and high pollution (i.e., 25%). This study indicates that a combination of simulation data from the PROSAIL model and measured data appears to be a promising method for establishing a sensitivity spectral index to heavy metal stress, which can accurately detect regional Cd stress in crops.

Due to natural change and special human destruction, the area of wetland is greatly decreasing. To recover the wetland ecological system has become an urgent task that must be faced. Water plays an important role in the course of wetland recovery. With Yongnian wetland as a case study, the ecological function method was used, based on structures and functions of the wetland ecological system, the ecological water requirement was divided into four parts: water requirement of wetland vegetation, water requirement of wetland soil, water requirement of wildlife habitats and water requirement of recharging ground water. According to the indexes that were presented in the paper, each part was classified to three grades, respectively. According to the formulas that were given in the paper, all kinds of ecological water requirement of Yongnian wetland were calculated; the results showed that the least ecological water requirement is 1.256 × 108 m3. The proper ecological water requirement is between 3.041 × 108 m3 and 4.301 × 108 m3, and the most ecological water requirement is 6.350 × 108 m3.

The rice-cropping system in southern China experienced a major change in the past few decades. Rice-cropping systems not only affect the comprehensive utilization intensity of agricultural resources, food security, and the ecological environment but also reveal the conditions of the agricultural policy, regional economy, and rural labor force. Consequently, accurate detection of rice-cropping systems is an essential issue. We present a method for classifying rice-cropping systems using a pretrained convolutional neural network (CNN) that involves extracting deep features of spatial and spectral trajectories. The study area is a major rice-growing region located in Zhuzhou City, Hunan Province, China. Multitemporal Sentinel-2 satellite images were collected to obtain time series curves of texture (spatial trajectories) and the first derivative vegetation index (spectral trajectories) as inputs of the hierarchical classification model. The classification results of land cover and rice-cropping systems were acquired using the pretrained CNN, and the method achieved overall accuracy of 94.78% and 94.87%, respectively. This method was then compared with the support vector machine (SVM). The accuracy of rice-cropping systems using the pretrained CNN was 7.11% higher than that of the SVM. The adaptability of the model was also investigated using the time series curve in another year with relatively insufficient data. The model obtained satisfactory performance with the overall accuracy of land cover types and rice-cropping systems of 93.52% and 93.23%, respectively. We suggest that the pretrained CNN can improve the accuracy of rice-cropping system mapping by extracting deep features of spatial and spectral trajectories during the rice growth cycle and such method may be applied in other fine crop classifications.