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Early warning of flash droughts is crucial to mitigate their adverse impacts on agriculture, ecosystems, and water resources. In recent years, advances in weather forecasting have been significant, paving the way for the development of reliable flash drought early-warning systems. Based on these recent developments, we present the operational, global-scale Flash Drought Viewer, Index, and Early Warning System (FD-VIEWS), which combines a deep learning hybrid version of the Global Land Evaporation Amsterdam Model (GLEAM, Koppa et al. 2022) with high-resolution ensemble meteorological forecasts from the Multi-Source Weather product (MSWX, Beck et al. 2022). Based on probabilistic forecasts of evaporative stress, FD-VIEWS diagnoses flash droughts using the Standardized Evaporation Stress Ratio (SESR) proposed by Christian et al. (2019) and further developed by Gou et al. (2022). The early-warning system predicts not only onset, continuation, and termination, but also estimates intensification rate and drought severity. FD-VIEWS is evaluated on its ability to predict flash droughts globally over a 10-day forecast horizon. The evaluation of FD-VIEWS reveals a high skill in predicting flash drought onset and termination; the onset forecast skill is higher in arid regions, whereas the termination forecast skill is higher in humid areas. Overall, FD-VIEWS shows potential in improving our understanding of flash drought predictability and its drivers, and enables more effective water management.––––––––––––––––––––––––––Beck, H. E., van Dijk, A. I., Larraondo, P. R., McVicar, T. R., Pan, M., Dutra, E., Miralles, D. G., 2022: MSWX: Global 3-Hourly 0.1° Bias-Corrected Meteorological Data Including Near-Real-Time Updates and Forecast Ensembles. Bulletin of the American Meteorological Society, 103 (3), E710-E732.Christian, J. I., Basara, J. B., Otkin, J. A., Hunt, E. D., Wakefield, R. A., Flanagan, P. X., Xiao, X., 2019: A Methodology for Flash Drought Identification: Application of Flash Drought Frequency across the United States. Journal of Hydrometeorology, 20 (5), 833-846.Gou, Q., Zhu, Y., Lü, H., Horton, R., Yu, X., Zhang, H., Wang, X., Su, J., Liu, E., Ding, Z., Wang, Z., Yuan, F., 2022: Application of an improved spatio-temporal identification method of flash droughts. Journal of Hydrology, 604, 127224.Koppa, A., Rains, D., Hulsman, P., Poyatos, R., Miralles, D. G., 2022: A deep learning-based hybrid model of global terrestrial evaporation. Nature Communications, 13 (1), 1912.

The specific biological niche inside host plants allows endophytes to regulate plant disease resistance effectively. However, there have been few reports on the role of active metabolites from endophytes in inducing host disease resistance.

Endophytes can benefit the growth and stress resistance of host plants by secreting bioactive components. Thiamine is an essential vitamin involved in many metabolic pathways and can only be synthesized by microbes and plants. In this study, we found that thiamine could inhibit the development of the phytopathogen

The Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) V06 product has been widely studied, but the errors and the source of the errors within IMERG over diverse climate regions still need to be quantified. To this end, the final run gauge-calibrated IMERG V06 (V06C) and uncalibrated IMERG V06 (V06UC) products are comprehensively evaluated here against 2088 precipitation gauges acquired between March 2014 and June 2018 over China. Moreover, V06C and V06UC rainfall estimates are compared against the Precipitation Estimation from Remotely Sensed Imagery using Artificial Neural Networks (PERSIANN)-Climate Data Record (CDR) and the Climate Prediction Center morphing technique (CMORPH) gauge-satellite blended (BLD) products. Continuous statistical indices and two error decomposition schemes are used to quantify their performance. Key results are as follows. (1) Except for V06UC’s relatively high underestimation over the Tibetan Plateau (TP) and high overestimation over Xinjiang (XJ), Northeastern China (DB), and Northern China (HB) and CDR’s severe overestimation over TP, all four satellite-based precipitation products can generally capture the spatial pattern of precipitation over China. Moreover, the satellite-based precipitation estimates agree better with gauge observations over humid regions than over semi-humid, semi-arid, and arid regions. (2) All the statistical indicators show that CDR has the worst performance, whereas BLD is the best precipitation product. As for the two IMERG products, V06C has improved V06UC’s precipitation estimate. Results show that the gauge calibration algorithm (GCA) used in IMERG has active effect in terms of r, POD, and CSI. (3) Within all subregions, all four satellite-based precipitation products demonstrate their worst performance over the arid XJ region which exhibits the highest FAR and lowest POD and CSI values among all regions. (4) In terms of intensity distribution, for summer over China, the four satellite-based precipitation products generally overestimate the frequency of moderate precipitation and light precipitation events (42 mm/day). (5) The relative bias ratio (RBR) analysis shows that the contribution of missed precipitation tends to be lower over wetter regions. In addition, for the same climate region, the contribution of missed precipitation is clearly lower in summer than in winter. In summer, false precipitation dominates the total error, whereas missed and false precipitation are the two leading error sources in winter. Future algorithm refinement efforts should focus on decreasing FAR in summer and winter and improving missed snow events during the winter.

Drought and salinity are major environmental stresses that impair crop growth and productivity worldwide. Improving drought and salt tolerance of crops with microbial mutualists is an effective and environmentally sound strategy to meet the demands of the ever-growing world population. In the present study, we found that the Streptomyces albidoflavus OsiLf-2, a moderately salt-tolerant endophytic actinomycete, produced abundant osmolytes, including proline, polysaccharides, and ectoine. Inoculation with OsiLf-2 increased the osmotic-adjustment ability of the rice host by increasing the proline content (by 250.3% and 49.4%) and soluble sugar (by 20.9% and 49.4%) in rice under drought and salt conditions, relative to the uninoculated control. OsiLf-2 increased stress responses in the rice host at the physiological and biochemical levels (photosynthesis efficiency, osmolytes and antioxidant content), and the gene level (osmolytes synthesis, stress-responsive and ion-transport related genes), raising rice yields under both greenhouse and saline–alkaline soil conditions. The use of endophytic actinomycetes offers a promising biotechnological approach to developing stress-tolerant plants.

Purpose The effects of endophytic actinobacterial strains, Microbispora sp. CP56, Actinomadura sp. CP84B, Streptomyces spp. CP200B and CP21A, on the chickpea-Mesorhizobium symbiosis, were investigated in planta, with the aim of revealing the underlying mechanisms of action. Methods The actinobacterial endophytes were co-inoculated with Mesorhizobium ciceri onto chickpea seedlings to study the effect on plant growth parameters, nodulation development and grain yield. The role of actinobacterial exudates on rhizobial growth was investigated, as was the role of root exudates of actinobacteria-colonized plants on the expression of rhizobial nod factors and biofilm formation. Changes in expression of plant flavonoids and bacterial N-fixation genes resulting from actinobacterial co-inoculation were assessed using qPCR. Results Application of actinobacterial endophytes, together with M. ciceri, showed growth promotion of chickpea with an increase in root nodule number and weight. Enhanced nodulation was accompanied by increases in total plant nitrogen, larger total plant weight and a 2–3-fold increase in grain yield. Factors associated with this tripartite symbiosis are promotion of rhizobial growth, earlier nodule formation, increased secondary root formation, up-regulated expression of genes related to flavonoid synthesis and nif genes. In addition, exudates of chickpea roots colonised with actinobacteria increased nodulation-related biological processes - rhizobial chemotaxis, biofilm formation and nod gene expression. Conclusion These endophytic actinobacteria positively affect many aspects of the chickpea-Mesorhizobium symbiosis resulting in increases in grain yield. Similar improvements recorded in chickpea growing in potted field soils, shows the potential to enhance chickpea production in the field.

Graph Convolutional Networks (GCNs) are widely applied in classification tasks by aggregating the neighborhood information of each sample to output robust node embedding. However, conventional GCN methods do not update the graph during the training process so that their effectiveness is always influenced by the quality of the input graph. Moreover, previous GCN methods lack the interpretability to limit their real applications. In this paper, a novel personalized diagnosis technique is proposed for early Alzheimer’s Disease (AD) diagnosis via coupling interpretable feature learning with dynamic graph learning into the GCN architecture. Specifically, the module of interpretable feature learning selects informative features to provide interpretability for disease diagnosis and abandons redundant features to capture inherent correlation of data points. The module of dynamic graph learning adjusts the neighborhood relationship of every data point to output robust node embedding as well as the correlations of all data points to refine the classifier. The GCN module outputs diagnosis results based on the learned inherent graph structure. All three modules are jointly optimized to perform reliable disease diagnosis at an individual level. Experiments demonstrate that our method outputs competitive diagnosis performance as well as provide interpretability for personalized disease diagnosis.

Camellia oleifera (C. oleifera) is a unique edible oil crop in China cultivated in the hilly southern mountains. Although C. oleifera is classified as a drought-tolerant tree species, drought remains the main factor limiting the growth of C. oleifera in summer and autumn. Using endophytes to improve crop drought tolerance is one effective strategy to meet our growing food crop demand. In this study, we showed that endophyte Streptomyces albidoflavus OsiLf-2 could mitigate the negative impact of drought stress on C. oleifera, thus improving seed, oil, and fruit quality. Microbiome analysis revealed that OsiLf-2 treatment significantly affected the microbial community structure in the rhizosphere soil of C. oleifera, decreasing both the diversity and abundance of the soil microbe. Likewise, transcriptome and metabolome analyses found that OsiLf-2 protected plant cells from drought stress by reducing root cell water loss and synthesizing osmoregulatory substances, polysaccharides, and sugar alcohols in roots. Moreover, we observed that OsiLf-2 could induce the host to resist drought stress by increasing its peroxidase activity and synthesizing antioxidants such as cysteine. A multi-omics joint analysis of microbiomes, transcriptomes, and metabolomes revealed OsiLf-2 assists C. oleifera in resisting drought stress. This study provides theoretical and technical support for future research on endophytes application to enhance the drought resistance, yield, and quality of C. oleifera.

Root-zone soil moisture (RZSM) is crucial for water resource management, drought monitoring and sub-seasonal flood climate forecast. RZSM is not directly observable from space but various model-derived RZSM products are available at the global scale and are widely used. In this paper, a comprehensive quantitative evaluation of eight RZSM products is made over the Huai river basin (HRB) in China. A direct validation is performed using observations from 58 in situ soil moisture stations from 1 April 2015 to 31 March 2020. Attention is drawn to the potential factors increasing uncertainties of model-generated RZSM, such as errors on atmospheric forcings (precipitation, air temperature), soil properties, and model parameterizations. Results indicate that the Global Land Data Assimilation System Catchment Land Surface Model (GLDAS_CLSM) performs best among all RZSM products with the highest correlation coefficient (R) and lowest unbiased root-mean square error (ubRMSE): 0.503 and 0.031 m3 m−3, respectively. All RZSM products tend to overestimate the in situ soil moisture values, except for the Soil Moisture and Ocean Salinity (SMOS) L4 product, which underestimates RZSM. The underestimated SMOS L3 SSM associated with low physical surface temperature triggers the underestimation of RZSM in SMOS L4. The RZSM overestimation by other products can be explained by the overestimation of precipitation amount, precipitation event frequency (drizzle effects) and by the underestimation of air temperature. Besides, the overestimation of the soil clay content and the underestimation of the soil sand content in different LSMs leads to larger soil moisture values. The intercomparison of the eight RZSM products shows that MERRA-2 and SMAP L4 RZSM are the most correlated with one another. These products are based on the same LSM and on the same surface meteorological forcing generated from the National Aeronautics and Space Administration (NASA) GEOS-5. In addition, model parameterizations in different LSMs vary considerably, affecting the transfer and exchange of water and heat in the vadose zone.

With the trend of people expressing opinions and emotions via images online, increasing attention has been paid to affective analysis of visual content. Traditional image affective analysis mainly focuses on single-label classification, but an image usually evokes multiple emotions. To this end, emotion distribution learning is proposed to describe emotions more explicitly. However, most current studies ignore the ambiguity included in emotions and the elusive correlations with complex visual features. Considering that emotions evoked by images are delivered through various visual features, and each feature in the image may have multiple emotion attributes, this paper develops a novel model that extracts multiple features and proposes an enhanced fuzzy k-nearest neighbor (EFKNN) to calculate the fuzzy emotional memberships. Specifically, the multiple visual features are converted into fuzzy emotional memberships of each feature belonging to emotion classes, which can be regarded as an intermediate representation to bridge the affective gap. Then, the fuzzy emotional memberships are fed into a fully connected neural network to learn the relationships between the fuzzy memberships and image emotion distributions. To obtain the fuzzy memberships of test images, a novel sparse learning method is introduced by learning the combination coefficients of test images and training images. Extensive experimental results on several datasets verify the superiority of our proposed approach for emotion distribution learning of images.

The functional connectomic profile is one of the non-invasive imaging biomarkers in the computer-assisted diagnostic system for many neuro-diseases. However, the diagnostic power of functional connectivity is challenged by mixed frequency-specific neuronal oscillations in the brain, which makes the single Functional Connectivity Network (FCN) often underpowered to capture the disease-related functional patterns. To address this challenge, we propose a novel functional connectivity analysis framework to conduct joint feature learning and personalized disease diagnosis, in a semi-supervised manner, aiming at focusing on putative multi-band functional connectivity biomarkers from functional neuroimaging data. Specifically, we first decompose the Blood Oxygenation Level Dependent (BOLD) signals into multiple frequency bands by the discrete wavelet transform, and then cast the alignment of all fully-connected FCNs derived from multiple frequency bands into a parameter-free multi-band fusion model. The proposed fusion model fuses all fully-connected FCNs to obtain a sparsely-connected FCN (sparse FCN for short) for each individual subject, as well as lets each sparse FCN be close to its neighbored sparse FCNs and be far away from its furthest sparse FCNs. Furthermore, we employ the $\ell _{{1}}$ -SVM to conduct joint brain region selection and disease diagnosis. Finally, we evaluate the effectiveness of our proposed framework on various neuro-diseases, i.e., Fronto-Temporal Dementia (FTD), Obsessive-Compulsive Disorder (OCD), and Alzheimer’s Disease (AD), and the experimental results demonstrate that our framework shows more reasonable results, compared to state-of-the-art methods, in terms of classification performance and the selected brain regions. The source code can be visited by the url https://github.com/reynard-hu/mbbna .

In this study, the canonical correlation analysis algorithm (CCA) is used to fuse the two-dimensional wind field retrieved from the single-Doppler weather radar, the three-dimensional wind field retrieved from the dual-Doppler weather radars, the observations from the ground automatic weather stations and the meteorological reanalysis data in three hail episodes (“0625” episode in Beijing, “0330” and “0801” episodes in Guangdong). During the hail episode in Beijing on June 25, 2020, an evident and long-lasting three-body scatter spike was observed, which played an important role in the hail identification and warning. In the three-dimensional wind field retrieved from the dual-Doppler weather radars, there is horizontal convergence of northeasterly and northwesterly winds and that of northwesterly and southeasterly winds in the low-level strong echo area, and there are obvious updrafts in the vertical wind field structure. Such a circulation configuration is favorable for the development and maintenance of hail storm. The multi-source data fusion of the wind fields can effectively improve the identification of the low-level convergence. The data fusion for the other two hail episodes (“0330” and “0801” episodes in Guangdong) yields the same conclusion. It is revealed that the dual-radar fusion performs better than the single-radar fusion in the identification of the meso-γ scale vortices. It can visually illustrate the characteristics of the cyclonic convergent flow fields which is more consistent with the near-surface observation. It can be concluded that the multi-source data fusion technique is practicable in the three severe convection processes.

Unsupervised feature selection (UFS) is a popular technique of reducing the dimensions of high-dimensional data. Previous UFS methods were often designed with the assumption that the whole information in the data set is observed. However, incomplete data sets that contain unobserved information can be often found in real applications, especially in industry. Thus, these existing UFS methods have a limitation on conducting feature selection on incomplete data. On the other hand, most existing UFS methods did not consider the sample importance for feature selection, i.e., different samples have various importance. As a result, the constructed UFS models easily suffer from the influence of outliers. This article investigates a new UFS method for conducting UFS on incomplete data sets to investigate the abovementioned issues. Specifically, the proposed method deals with unobserved information by using an indicator matrix to filter it out the process of feature selection and reduces the influence of outliers by employing the half-quadratic minimization technique to automatically assigning outliers with small or even zero weights and important samples with large weights. This article further designs an alternative optimization strategy to optimize the proposed objective function as well as theoretically and experimentally prove the convergence of the proposed optimization strategy. Experimental results on both real and synthetic incomplete data sets verified the effectiveness of the proposed method compared with previous methods, in terms of clustering performance on the low-dimensional space of the high-dimensional data.

The soil moisture active/passive (SMAP) mission represents a significant advance in measuring soil moisture from satellites. However, its large spatial-temporal data gaps limit the use of its values in near-real-time (NRT) applications. Considering this, the study uses NRT operational metadata (precipitation and skin temperature), together with some surface parameterization information, to feed into a random forest model to retrieve the missing values of the SMAP L3 soil moisture product. This practice was tested in filling the missing points for both SMAP descending (6:00 AM) and ascending orbits (6:00 PM) in a crop-dominated area from 2015 to 2019. The trained models with optimized hyper-parameters show the goodness of fit (R

The cartoon animation industry has developed into a huge industrial chain with a large potential market involving games, digital entertainment, and other industries. However, due to the coarse-grained classification of cartoon materials, cartoon animators can hardly find relevant materials during the process of creation. The polar emotions of cartoon materials are an important reference for creators as they can help them easily obtain the pictures they need. Some methods for obtaining the emotions of cartoon pictures have been proposed, but most of these focus on expression recognition. Meanwhile, other emotion recognition methods are not ideal for use as cartoon materials. We propose a deep learning-based method to classify the polar emotions of the cartoon pictures of the "Moe" drawing style. According to the expression feature of the cartoon characters of this drawing style, we recognize the facial expressions of cartoon characters and extract the scene and facial features of the cartoon images. Then, we correct the emotions of the pictures obtained by the expression recognition according to the scene features. Finally, we can obtain the polar emotions of corresponding picture. We designed a dataset and performed verification tests on it, achieving 81.9% experimental accuracy. The experimental results prove that our method is competitive.

Spectral clustering is widely applied in real applications, as it utilizes a graph matrix to consider the similarity relationship of subjects. The quality of graph structure is usually important to the robustness of the clustering task. However, existing spectral clustering methods consider either the local structure or the global structure, which can not provide comprehensive information for clustering tasks. Moreover, previous clustering methods only consider the simple similarity relationship, which may not output the optimal clustering performance. To solve these problems, we propose a novel clustering method considering both the local structure and the global structure for conducting nonlinear clustering. Specifically, our proposed method simultaneously considers (i) preserving the local structure and the global structure of subjects to provide comprehensive information for clustering tasks, (ii) exploring the nonlinear similarity relationship to capture the complex and inherent correlation of subjects and (iii) embedding dimensionality reduction techniques and a low-rank constraint in the framework of adaptive graph learning to reduce clustering biases. These constraints are considered in a unified optimization framework to result in one-step clustering. Experimental results on real data sets demonstrate that our method achieved competitive clustering performance in comparison with state-of-the-art clustering methods.

As a typical semiarid farming-pastoral ecotone sensitive to the environmennt, the Plain of West Liaohe Basin (WLBP) is currently experiencing drastic environmental changes. To identify how environmental change affect vegetation in the WLBP, we analysed spatiotemporal variation characteristics of Ecological environment factors based on monthly and annual air temperature (T), precipitation (P) and Normalized Difference Vegetation Index (NDVI) from 1982 to 2015. And the correlations between them were investigated by correlation analysis (Simple correlation, partial correlation and complex correlation) at temporal and spatial scale. The results showed that: (1) the vegetation growth of the WLBP showed ameliorated trend, with a change rate of 0.004/yr.; (2) P was more sensitive to NDVI than T; (3) and the influence of hydrothermal changes on vegetation growth was more significant than that of the change of single climate factors at time scales; (4) the effects of anthropogenic factors on vegetation change were 75.07% (1982–1993) and 98.08% (1994–2015), respectively. At the temp-special scales, P&T and land use type change (LUCC) were the main climatic and anthropogenic factors that affect vegetation changes, respectively.

This paper proposes a new multi-view classification method by taking three sample constraints into account to automatically assign large weights to important samples and small weights to unimportant samples. To do this, we first demonstrate that different samples have different contributions to the classification models, and then propose to consider sample weight, class weight, and view weight, to overcome the influence of different levels of noise. Specifically, the sample weight for every data point is obtained by penalizing an $$\ell _{2,1}$$ -norm loss on its estimation error to reduce the influence of the sample-level noise, the class weight for each class is obtained by considering the misclassification cost as well as imbalance class to overcome the influence of class-level noise, and the view weight for each view is obtained by penalizing a squared root operator on the estimation error of each view to reduce the influence of view-level noise. In particular, our proposed sample constraints can be easily embedded in previous multi-view learning models. Experimental results on simulated and real data sets showed that our proposed method was superior to the state-of-the-art classification methods in terms of classification performance of cost-sensitive learning.

The West Liaohe Plain (WLP) is a typical crop-pastoral ecotone of the semiarid area in Northwestern, China. Land use/cover change (LUCC) of the WLP might endanger this ecosystem, triggering long-lasting environmental concerns. LUCC data for China (1980-2020) and hydrometeorological data were analyzed to reveal factors contributing to change and explore sustainable development opportunities. The results show that characteristics of the main land-use types in the WLP have changed significantly, especially cultivated land area, which increased by 15.2% and 6.79% during the periods 1980-1995 and 2000-2020, respectively. Response relationships were observed due to natural (precipitation, temperature, and runoff) and anthropogenic (economy) factors and LUCC. Between 2000 and 2020, the impact of anthropogenic factors on cultivated land was stronger than on grassland at the class and landscape level, using the landscape indices which were selected, including percent of landscape (PLAND), number of patches (NP), largest patch index (LPI), and Shannon's evenness index (SHEI). Expansion of cultivated land from 1990 to 1995 was not only related to anthropogenic factors but also to hydrologicalclimatic factors. The results of this study have the potential to influence sustainable land resource development.

Graph convolutional network (GCN) outputs powerful representation by considering the structure information of the data to conduct representation learning, but its robustness is sensitive to the quality of both the feature matrix and the initial graph. In this article, we propose a novel multigraph fusion method to produce a high-quality graph and a low-dimensional space of original high-dimensional data for the GCN model. Specifically, the proposed method first extracts the common information and the complementary information among multiple local graphs to obtain a unified local graph, which is then fused with the global graph of the data to obtain the initial graph for the GCN model. As a result, the proposed method conducts the graph fusion process twice to simultaneously learn the low-dimensional space and the intrinsic graph structure of the data in a unified framework. Experimental results on real datasets demonstrated that our method outperformed the comparison methods in terms of classification tasks.

In recent years, due to the intensification of human activities, water ecological problems are gradually increasing. As the third largest freshwater lake in China, Lake Taihu is an important drinking water source for several densely populated cities in China. The prominent water ecological problem in this area is mainly Cyanobacteria Bloom. Cyanobacterial blooms have been erupting which have affected local residents' drinking water and caused losses to the national economy. Based on the interpretation results of MODIS data in the Lake Taihu region from 2004 to 2014, this paper analyzes the main driving factors of cyanobacterial bloom are phosphorus and potassium through the correlation analysis of nitrogen, phosphorus, potassium and cyanobacteria area, normalizes nutrient, and identifies that the water level of Lake Taihu is the influencing factor of cyanobacterial bloom. A Lake Taihu cyanobacteria bloom hazard function is constructed to quantitatively assess the losses (economic losses) caused by cyanobacterial blooms from 2001 to 2012, supporting for cyanobacteria control management in Lake Taihu.

The increasing tendency of people expressing opinions via images online has motivated the development of automatic assessment of sentiment from visual contents. Based on the observation that visual sentiment is conveyed through many visual elements in images, we put forward to tackle visual sentiment analysis under multiple instance learning (MIL) formulation. We propose a deep multiple clustered instance learning formulation, under which a deep multiple clustered instance learning network (DMCILN) is constructed for visual sentiment analysis. Specifically, the input image is converted into a bag of instances through visual instance generation module, which is composed of a pre-trained convolutional neural network (CNN) and two adaptation layers. Then, a fuzzy c-means routing algorithm is introduced for generating clustered instances as semantic mid-level representation to bridge the instance-to-bag gap. To explore the relationships between clustered instances and bags, we construct an attention based MIL pooling layer for representing bag features. A multi-head mechanism is integrated to form MIL ensembles, which enables to weigh the contribution of each clustered instance in different subspaces for generating more robust bag representation. Finally, we conduct extensive experiments on several datasets, and the experimental results verify the feasibility of our proposed approach for visual sentiment analysis.

Conventional spectral clustering is generally divided into two main steps, 1) constructing a reliable spectral representation by similarity matrix or Laplacian matrix; 2) performing K-means clustering on the spectral representation. In this paper, we propose a novel spectral clustering algorithm to improve clustering performance by separately improving these two steps in a same learning framework. Specifically, we first utilize two dimensionality reduction methods to learn the robust low-dimensional spectral representation, and a hypergraph structure to make the spectral representation keep the high-order relation of original data. Furthermore, a spectral rotation clustering method is embedded into the spectral representation learning model to conduct one-step clustering, which effectively reduces the deviation of clustering and obtains a reliable clustering performance. Besides, an effective optimization algorithm is further proposed to solve the objective problem to have a fast convergence. Experimental analysis on real data sets showed that our proposed clustering method outperformed the classical and state-of-the-art spectral clustering methods in terms of four frequently-used clustering metrics.

Since the data in each view may contain distinct information different from other views as well as has common information for all views in multi-view learning, many multi-view clustering methods have been designed to use these information (including the distinct information for each view and the common information for all views) to improve the clustering performance. However, previous multi-view clustering methods cannot effectively detect these information so that difficultly outputting reliable clustering models. In this article, we propose a fuzzy, sparse, and robust multi-view clustering method to consider all kinds of relations among the data (such as view importance, view stability, and view diversity), which can effectively extract both distinct information and common information as well as balance these two kinds of information. Moreover, we devise an alternating optimization algorithm to solve the resulting objective function as well as prove that our proposed algorithm achieves fast convergence. It is noteworthy that existing multi-view clustering methods only consider a part of the relations, and thus are a special case of our proposed framework. Experimental results on synthetic datasets and real datasets show that our proposed method outperforms the state-of-the-art clustering methods in terms of evaluation metrics of clustering such as clustering accuracy, normalized mutual information, purity, and adjusted rand index.

Spectral feature selection (SFS) is superior to conventional feature selection methods in many aspects, by extra importing a graph matrix to preserve the subspace structure of data. However, the graph matrix of classical SFS that is generally constructed by original data easily outputs a suboptimal performance of feature selection because of the redundancy. To address this, this paper proposes a novel feature selection method via coupling the graph matrix learning and feature data learning into a unified framework, where both steps can be iteratively update until achieving the stable solution. We also apply a low-rank constraint to obtain the intrinsic structure of data to improve the robustness of learning model. Besides, an optimization algorithm is proposed to solve the proposed problem and to have fast convergence. Compared to classical and state-of-the-art feature selection methods, the proposed method achieved the competitive results on twelve real data sets.

Previous feature selection methods equivalently consider the samples to select important features. However, the samples are often diverse. For example, the outliers should have small or even zero weights while the important samples should have large weights. In this paper, we add a self-paced regularization in the sparse feature selection model to reduce the impact of outliers for conducting feature selection. Specifically, the proposed method automatically selects a sample subset which includes the most important samples to build an initial feature selection model, whose generalization ability is then improved by involving other important samples until a robust and generalized feature selection model has been established or all the samples have been used. Experimental results on eight real datasets show that the proposed method outperforms the comparison methods.

Identification and characterization of a novel bacterial carbohydrate esterase (PaCes7) with application potential for lignocellulose and pesticide degradation. PaCes7 was identified from the lignocellulolytic bacterium, Pantoea ananatis Sd-1 as a new carbohydrate esterase. Recombinant PaCes7 heterologously expressed in Escherichia coli showed a clear preference for esters with short-chain fatty acids and exhibited maximum activity towards α-naphthol acetate at 37 °C and pH 7.5. Purified PaCes7 exhibited its catalytic activity under mesophilic conditions and retained more than 40% activity below 30 °C. It displayed a relatively wide pH stability from pH 6–11. Furthermore, the enzyme was strongly resistant to Mg2+, Pb2+, and Co2+ and activated by K+ and Ca2+. Both P. ananatis Sd-1 and PaCes7 could degrade the pesticide carbaryl. Additionally, PaCes7 was shown to work in combination with cellulase and/or xylanase in rice straw degradation. The data suggest that PaCes7 possesses promising biotechnological potential.

Background Various high-energy tasks in the construction industry can lead to craniocerebral injuries. Construction industry–associated penetrating craniocerebral injuries due to metal foreign bodies have unique characteristics. However, no norms exist for removing metal foreign bodies and preventing secondary trauma. This study aimed to explore the characteristics and treatment of construction industry–associated penetrating craniocerebral injuries due to metal foreign bodies. Methods Data of patients who suffered from penetrating injuries due to metal foreign bodies and were treated in the Zhongshan People's Hospital from 2001 to 2021 were collected based on the causes of injuries to explore disease characteristics and therapeutic effects. Results A total of six patients with penetrating craniocerebral injuries due to metal foreign bodies, who underwent surgeries, were included in the study. Five patients recovered well after the surgery, and one patient died. In four patients, intracranial infection complicated the course after surgery, and two patients had delayed intracranial hematoma. Conclusion Patients with construction industry–associated penetrating craniocerebral injuries due to metal foreign bodies are prone to coma and intracranial vascular injuries. Early surgical removal and prevention of intracranial infection are key to achieving good therapeutic effects.

Support Vector Machine (SVM) has been widely applied in real application due to its efficient performance in the classification task so that a large number of SVM methods have been proposed. In this paper, we present a novel SVM method by taking the dynamic graph learning and the self-paced learning into account. To do this, we propose utilizing self-paced learning to assign important samples with large weights, learning a transformation matrix for conducting feature selection to remove redundant features, and learning a graph matrix from the low-dimensional data of original data to preserve the data structure. As a consequence, both the important samples and the useful features are used to select support vectors in the SVM framework. Experimental analysis on four synthetic and sixteen benchmark data sets demonstrated that our method outperformed state-of-the-art methods in terms of both binary classification and multi-class classification tasks.

The Integrated Multi‐satellitE Retrievals for Global Precipitation Measurement mission (IMERG) aims to deliver the “best” precipitation estimation from space and has attracted much attention. The Version 05 of IMERG products including the near‐real‐time “Early” and “Late” run products (IMERG‐E and IMERG‐L, respectively), and the post‐real‐time “Final” run IMERG product (IMERG‐F) are assessed at both national and basin scales against gauge observations over Mainland China for a 4‐year period (from April 2014 to March 2018). As control products for comparison, their predecessor Tropical Rainfall Measurement Mission (TRMM) Multi‐satellite Precipitation Analysis (TMPA) products (i.e., TMPA‐RT and TMPA‐V7) are also employed. Components analysis confirms the best performance of IMERG‐F among the five SPEs in three different categories. All five SPEs feature increasing bias and root mean square difference (RMSD) with increasing daily gauge total precipitation, and such issue is less pronounced for IMERG‐F—as evidenced by the lowest bias and RMSD across all precipitation rates. Besides, compared to TMPA, IMERG products exhibit better accuracy in detecting real precipitation evens, especially for light‐to‐medium rain (

The hardware-in-the-loop (HIL) employs the real controller and true signal transmission, which is efficient to test control algorithms for the situation that is inconvenient to conduct experiments....

Microbial antagonists and their bioactive metabolites provide one of the best alternatives to chemical pesticides to control crop disease for sustainable agriculture and global food security. The rice endophyte Streptomyces hygroscopicus OsiSh-2, with remarkable antagonistic activity towards the rice blast fungus Magnaporthe oryzae, was reported in our previous study. The present study deciphered the possible direct interaction mode of OsiSh-2 against M. oryzae. An in vitro antibiotic assay for OsiSh-2 culture filtrate revealed strong suppression of mycelial growth, conidial germination and appressorial formation of M. oryzae. Meanwhile, severe morphological and internal abnormalities in M. oryzae hyphae were observed under a scanning electron microscope and transmission electron microscope. Foliar treatment of rice seedlings by OsiSh-2 culture filtrate in the greenhouse and in the field showed 23.5% and 28.3% disease reduction, respectively. Correspondingly, OsiSh-2 culture filtrate could induce disorganized chitin deposition in the cell wall and lowered ergosterol content in the cell membrane of M. oryzae. Additionally, cell wall integrity pathway activation, large cell electrolytes release, reactive oxygen species accumulation and tricarboxylic acid cycle-related enzyme activity changes were found in M. oryzae. All these results suggested that the direct antagonistic activity of OsiSh-2 against M. oryzae may be attributed to damaging the integrity of the cell wall and membrane and disrupting mitochondrial function in the pathogen.