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Drought is one of the significant natural disasters that has a profound impact on human societies, particularly in arid places such as Balochistan, Pakistan. Geographic information system and remote sensing has played a major role in predicting the effect of drought events and mitigate. Therefore, the purpose of this study was firstly to evaluate the spatiotemporal patterns of drought in Balochistan, Pakistan, utilizing MODIS based satellite data and validate the PMD stations data with CHIRPS data. Secondly the objective of this research to quantify the influence of drought on vegetation anomalies and comparison between droughts patterns with vegetation response. Drought conditions in Balochistan by integrating remote sensing (RS) drought indices (RSDI).RSDI was calculated through Hargreaves method using monthly data. The following remaining indices were the main focus of the study i.e., Standardized Precipitation Evapotranspiration Index (SPEI), Vegetation Condition Index (VCI), Vegetation Health Index (VHI), Temperature Vegetation Dryness Index (TVDI), and Precipitation Condition Index (PCI). These indices offered differing perspectives, emphasizing the value of a comprehensive strategy. Approximately 60% of the area was significantly affected by drought conditions, with SPEI values for the period being less than -1.5.SPEI and TVDI performed better in identifying droughts. TVDI values ranged from 0.63 to 0.88, indicating agricultural dryness. For instance, the East experienced a severe drought between 2001 and 2022, according to SPEI. Significant drought events occurred in 2001, 2004, 2009, 2014, and 2022, allowing comparative analysis. TVDI proved more effective than VCI in predicting drought. RDI and TVDI localized drought better than PCI. SPEI, RDI, and TVDI contributed significantly to understanding drought (73.63%, 74.15%, and 72.30% respectively). Considering diverse indices is vital for long-term drought mitigation strategies. RDI, especially valuable with limited temperature data, aids in understanding drought dynamics. This analysis aids in predicting future droughts and mitigating agricultural losses in Balochistan, informing decision-making and adaptive measures. [ABSTRACT FROM AUTHOR]

Ferroptosis is an iron-dependent cell death that was discovered recently. For beneficial microbes to establish mutualistic relationships with hosts, precisely controlled cell death in plant cells is necessary. However, whether ferroptosis is involved in the endophyte‒plant system is poorly understood. Here, we reported that endophytic Streptomyces hygroscopicus OsiSh-2, which established a sophisticated and beneficial interaction with host rice plants, caused ferroptotic cell death in rice characterized by ferroptosis- and immune-related markers. Treatments with ferroptosis inhibitors and inducers, different doses of OsiSh-2, and the siderophore synthesis-deficient mutant ΔcchH revealed that only moderate ferroptosis induced by endophytes is essential for the establishment of an optimal symbiont to enhance plant growth. Additionally, ferroptosis involved in a defence-primed state in rice, which contributed to improved resistance against rice blast disease. Overall, our study provides new insights into the mechanisms of endophyte‒plant interactions mediated by ferroptosis and suggests new directions for crop yield promotion. Ferroptosis is of great importance in plant immunity including microbe-plant interactions. Here, the authors unveil that a regulated ferroptosis process is involved in the mutualistic symbiosis and disease resistance of endophyte OsiSh-2 and host rice. [ABSTRACT FROM AUTHOR]

Music Emotion Recognition (MER) has attracted much interest in the past decades. Many deep learning methods have been applied to this field recently. However, the previous methods for MER mostly utilized simple convolutional layers to extract features from the original audio signals, in which representative emotion-related features cannot be extracted. In this paper, we propose a novel method named Modularized Composite Attention Network (MCAN) for continuous MER. A sample reconstruction technique is proposed to enhance the stability of the network. Specifically, a feature augmentation module is constructed to extract salient features and we design a weighted attention module to control the focus of the whole network. Furthermore, a style embedding module is introduced to enhance the detail processing capability of the network. We conduct experiments on two datasets, that is, the benchmark dataset DEAM and the newly proposed dataset PMEmo. The superior results prove the effectiveness of our proposed MCAN. Especially qualitative analyses are given to for explaining the performance of our model. [ABSTRACT FROM AUTHOR]

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 ℓ 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Previous methods of multi-view clustering focused on the improvement of clustering effectiveness by detecting common information of all views and individual information for every view, but they ignore the following issues, i.e., the initialization sensitivity, the cluster number determination, and the influence of outliers. However, either single-view clustering or multi-view clustering often suffers from above issues. In this paper, we propose a robust self-tuning multi-view clustering to introduce a sum-of-norm loss function to explore the issue of initialization sensitivity, design a sum-of-norm regularization to automatically determine the cluster number, and employ robust statistics techniques to reduce influence of outliers. Furthermore, we propose an effective alternating optimization method to solve the resulting objective function and then theoretically prove its convergence. Experimental results on both synthetic and real data sets demonstrated that our proposed multi-view clustering method outperformed the state-of-the-art clustering methods, in terms of four clustering evaluation metrics. [ABSTRACT FROM AUTHOR]

Spectral clustering is a popular graph-based clustering method which is widely applied to pattern recognition and image segmentation. Traditional spectral clustering usually involves two separate processes: graph learning and graph-based clustering, making such a two-step strategy easily output sub-optimal performance and moreover, noisy data and imbalanced clusters could make existing clustering methods hard to meet practical necessity. To this end, one-step spectral clustering coupled with a balanced constraint is proposed to jointly optimize the robust low-dimensional representation, the spectral rotation and the cluster indicator in a unified learning framework. Specifically, dimensionality reduction is conducted by combining subspace learning and feature selection to obtain a robust low-dimensional representation. Besides, a spectral rotation mechanism is used to produce one-step clustering for reducing clustering biases, and a balanced constraint is utilized to regularize the clustering result to generate clusters with similar sizes. Moreover, an iterative optimization method is put forward to fast solve the proposed objective function. Experimental results on ten benchmark datasets compared to state-of-the-art spectral clustering methods showed that our proposed clustering method could output the balanced clusters and the competitive clustering performance simultaneously. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objective: Identification and characterization of a novel bacterial carbohydrate esterase (PaCes7) with application potential for lignocellulose and pesticide degradation. Results: 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. Conclusions: The data suggest that PaCes7 possesses promising biotechnological potential. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Server workload in the form of cloud-end clusters is a key factor in server maintenance and task scheduling. How to balance and optimize hardware resources and computation resources should thus receive more attention. However, we have observed that the disordered execution of running application and batching seriously cuts down the efficiency of the server. To improve the workload prediction accuracy, this paper proposes an approach using the long short-term memory (LSTM) encoder-decoder network with attention mechanism. First, the approach extracts the sequential and contextual features of the historical workload data through the encoder network. Second, the model integrates the attention mechanism into the decoder network, through which the prediction for batch workloads can be carried out. Third, experiments carried out on Alibaba and Dinda workload traces dataset demonstrate that our method achieves state-of-the-art performance in mixed workload prediction in cloud computing environment. Furthermore, we also propose a scroll prediction method, which splits a long prediction sequence into several small sequences to monitor and control prediction accuracy. This work helps to dynamically guide the configuration for workload balancing. [ABSTRACT FROM AUTHOR]

Previous spectral feature selection methods generate the similarity graph via ignoring the negative effect of noise and redundancy of the original feature space, and ignoring the association between graph matrix learning and feature selection, so that easily producing suboptimal results. To address these issues, this paper joints graph learning and feature selection in a framework to obtain optimal selected performance. More specifically, we use the least square loss function and an ℓ2,1-norm regularization to remove the effect of noisy and redundancy features, and use the resulting local correlations among the features to dynamically learn a graph matrix from a low-dimensional space of original data. Experimental results on real data sets show that our method outperforms the state-of-the-art feature selection methods for classification tasks. [ABSTRACT FROM AUTHOR]

Spectral clustering is an important component of clustering method, via tightly relying on the affinity matrix. However, conventional spectral clustering methods 1). equally treat each data point, so that easily affected by the outliers; 2). are sensitive to the initialization; 3). need to specify the number of cluster. To conquer these problems, we have proposed a novel spectral clustering algorithm, via employing an affinity matrix learning to learn an intrinsic affinity matrix, using the local PCA to resolve the intersections; and further taking advantage of a robust clustering that is insensitive to initialization to automatically generate clusters without an input of number of cluster. Experimental results on both artificial and real high-dimensional datasets have exhibited our proposed method outperforms the clustering methods under comparison in term of four clustering metrics. [ABSTRACT FROM AUTHOR]

Feature selection is an important preprocessing step for dealing with high dimensional data. In this paper, we propose a novel unsupervised feature selection method by embedding a subspace learning regularization (i.e., principal component analysis (PCA)) into the sparse feature selection framework. Specifically, we select informative features via the sparse learning framework and consider preserving the principal components (i.e., the maximal variance) of the data at the same time, such that improving the interpretable ability of the feature selection model. Furthermore, we propose an effective optimization algorithm to solve the proposed objective function which can achieve stable optimal result with fast convergence. By comparing with five state-of-the-art unsupervised feature selection methods on six benchmark and real-world datasets, our proposed method achieved the best result in terms of classification performance. [ABSTRACT FROM AUTHOR]

Rice blast caused by Magnaporthe oryzae severely impacts global rice yield stability. The rice endophyte Streptomyces sporocinereus OsiSh-2, with strong antagonistic activity towards M. oryzae, has been reported in our previous study. To decipher the model of the antagonistic action of OsiSh-2 towards M. oryzae, we compared the iron-capturing abilities of these two strains. The cultivation of OsiSh-2 and a M. oryzae strain under iron-rich and iron-starved conditions showed that M. oryzae depended more on iron supplementation for growth and development than did OsiSh-2. Genomic analysis of the S. sporocinereus and M. oryzae species strains revealed that they might possess different iron acquisition strategies. The actinobacterium OsiSh-2 is likely to favor siderophore utilization compared to the fungus M. oryzae. In addition, protein annotations found that OsiSh-2 contains the highest number of the siderophore biosynthetic gene clusters among the 13 endophytic actinomycete strains and 13 antifungal actinomycete strains that we compared, indicating the prominent siderophore production potential of OsiSh-2. Additionally, we verified that OsiSh-2 could excrete considerably more siderophores than Guy11 under iron-restricted conditions and displayed greater Fe3+-reducing activity during iron-supplemental conditions. Measurements of the iron mobilization between the antagonistic OsiSh-2 and Guy11 showed that the iron concentration is higher around OsiSh-2 than around Guy11. In addition, adding iron near OsiSh-2 could decrease the antagonism of OsiSh-2 towards Guy11. Our study revealed that the antagonistic capacity displayed by OsiSh-2 towards M. oryzae was related to the competition for iron. The highly efficient iron acquisition system of OsiSh-2 may offer valuable insight for the biocontrol of rice blast. [ABSTRACT FROM AUTHOR]

Objective: To identify and characterize a novel bacterial pyranose 2-oxidase (P2Ox) and investigate its potential use in lignin degradation applications.Results: A new bacterial P2Ox (PaP2Ox) enzyme was identified in the lignocellulolytic bacterium Pantoea ananatis Sd-1. The PaP2Ox open reading frame was cloned, and the encoded protein was heterologously expressed in an Escherichia coli expression system. Unlike another reported bacterial P2Ox enzyme, the purified PaP2Ox exhibits a homotetrameric spatial conformation that is similar to fungal P2Oxs, with each subunit having a molecular mass of 65 kDa. The recombinant PaP2Ox exhibits maximum activity at 50 °C and pH 6.5 with d-glucose as its preferred substrate. In addition, this enzyme was shown to work in combination with bacterial laccase in lignin degradation.Conclusions: The bacterial enzyme PaP2Ox has potential use in ligninolytic systems and shows promising value in industrial biotechnological applications. [ABSTRACT FROM AUTHOR]

Table 6 Effect of endophytic actinobacteria (CP strains) on the nodulation and growth of chickpea in Turretfield soil that contains rhizobia and in Lameroo soil that has been co-inoculated with I M. ciceri i strain CC1192. Correction to: Revealing the underlying mechanisms mediated by endophytic actinobacteria to enhance the rhizobia - chickpea (Cicer arietinum L.) symbiosis Seeds for N + and N- control plants and Rhizobium only treatments were coated with 0.3% xanthan gum. [Extracted from the article]

Real life data and information often have different ways to obtain. For example, in computer vision, we can describe an objective by different types, such as text, video and picture. And even from variety of angles. These different descriptors of the same object are usually called multi-view data. In ordinarily, dimensional reduction methods usually include feature selection and subspace learning, respectively, can have better interpretative capability and stabilizing performance, and now are very prevalent method for high-dimensional data. However, it is usually not considering the relationship among class indicators, so the performance of regression model is not very ideal. In this paper, we simultaneously consider feature selection, low-rank selection, and subspace learning into a unified framework. Specifically, under the framework of linear regression model, we first use the low-rank constraint to feature selection which considers two aspects of information inherent in data. The low-rank constraint takes the correlation of response variables into account, then embed an ℓ -norm regularizer to consider the correlation among variety of class indicators, and feature vectors and their corresponding response variables. Meanwhile, we take LDA algorithm which belong to the subspace learning to further adjust relevant feature selection results into account. Lastly, we conducted experiments on several real multi-views image sets and corresponding experimental consequences also validated the furnished method outperformed all comparison algorithms. [ABSTRACT FROM AUTHOR]

since amount of unlabeled and high-dimensional datasets need to be preprocessed, unsupervised learning plays a more and more important role in machine learning field. This paper proposed a novel unsupervised feature selection algorithm that can select informative features from dataset without label, by mixing two sparse representation and self-representation loss function into a unified framework. That is, we use self-representation loss function to represent every feature with remainder features and achieve minimum reconstruction mirror, and then utilize l -norm regularization term and l -norm regularization term simultaneously to enforce coefficient matrix to be sparse, such that filter redundant and irrelative features in order to conduct feature selection, where l -norm regularization can enforce group sparsity while l -norm regularization enforce element sparsity. By this way that utilize both of sparse representation terms, we can choose representative features more accurately. At final, we feed reduced data into support vector machine (SVM) to conduct classification accuracy, which is main assessment criteria to validate performance of algorithm. Extensive experiments on synthetic datasets and real-world datasets have exhibited that our proposed method outperform most of common-used methods, such as PCA, LPP and so on. [ABSTRACT FROM AUTHOR]

This paper proposed a novel feature selection method that includes a self-representation loss function, a graph regularization term and an $${l_{2,1}}$$ -norm regularization term. Different from traditional least square loss function which focuses on achieving the minimal regression error between the class labels and their corresponding predictions, the proposed self-representation loss function pushes to represent each feature with a linear combination of its relevant features, aim at effectively selecting representative features and ensuring the robustness to outliers. The graph regularization terms include two kinds of inherent information, i.e., the relationship between samples (the sample-sample relation for short) and the relationship between features (the feature-feature relation for short). The feature-feature relation reflects the similarity between two features and preserves the relation into the coefficient matrix, while the sample-sample relation reflects the similarity between two samples and preserves the relation into the coefficient matrix. The $${l_{2,1}}$$ -norm regularization term is used to conduct feature selection, aim at selecting the features, which satisfies the characteristics mentioned above. Furthermore, we put forward a new optimization method to solve our objective function. Finally, we feed reduced data into support vector machine (SVM) to conduct classification on real datasets. The experimental results showed that the proposed method has a better performance comparing with state-of-the-art methods, such as k nearest neighbor, ridge regression, SVM and so on. [ABSTRACT FROM AUTHOR]

As a waste-management endonuclease, DNase I has been suggested to be one of the deoxyribonucleases responsible for DNA fragmentation during apoptosis. We report here an alternative fluorescence method for DNase I assay with high accuracy and sensitivity by applying a DNA/GO (graphene oxide) probe. The method with a detection limit of 1 U mL was then applied to investigate the effects of external factors including antibiotics and heavy metal ions on DNase I. The results demonstrated that gentamicin sulfate was a strong inhibitor with an IC value of 0.57 ± 0.12 mM. The investigated heavy metal ions showed an inhibitory effect on DNase I activity in a concentration dependent manner with IC values of 0.04 μg/mL (Hg), 0.10 μg/mL (Pb), 1.35 μg/mL (Cd), 1.20 μg/mL (As), and 1.80 μg/mL (Cu). Finally, the new method was applied to detect DNase levels in complicated tumor tissue and cell samples and the results showed that DNase levels increased in tumor tissues compared with that of adjacent tissue. From the above results, we conclude that the method can be widely used for high - throughput assay of DNase I in biological samples as well as drug screening in vitro. [Figure not available: see fulltext.] [ABSTRACT FROM AUTHOR]

Objectives: Isolation and identification of a novel laccase (namely Lac4) with various industrial applications potentials from an endophytical bacterium. Results: Endophyte Sd-1 cultured in rice straw showed intra- and extra-cellular laccase activities. Genomic analysis of Sd-1 identified four putative laccases, Lac1 to Lac4. However, only Lac4 contains the complete signature sequence of laccase and shares at most 64 % sequence identity with other characterized bacterial multi-copper oxidases. Recombinant Lac4 can oxidize non-phenolic and phenolic compounds under acidic conditions and at 30-50 °C; K values of Lac4 for ABTS at pH 2.5 and for guaiacol at pH 4.5 were 1 ± 0.15 and 6.1 ± 1.7 mM, respectively. The activity of Lac4 was stimulated by 0.8 mM Cu and 5 mM Fe. In addition, Lac4 could decolorize various synthetic dyes and exhibit the degradation rate of 38 % for lignin. Conclusions: The data suggest that Lac4 possesses promising biotechnological potentials. [ABSTRACT FROM AUTHOR]

Six global climate models (GCMs) from Coupled Model Intercomparison Project Phase 5 under three Respectively Concentration Pathways (RCP2.6, RCP4.5 and RCP8.5) were used to assess the impact of climate change on streamflow for the Huangnizhuang catchment (HNZ) in China. Change factor method was used for bias correction between GCM outputs and observations and the SWAT model was used to simulate the hydrological processes. The results indicated that the SWAT model performed well in the study catchment with a monthly Nash-Sutcliffe efficiency (NS) of 0.93 and 0.91 and daily NS of 0.63 and 0.68 for calibration and validation periods respectively. Their corresponding relative errors were −2.2 and 8.9, and −2.6 and 8.5 % respectively. The ensemble of multi-GCMs projected an increase of precipitation in the middle and end of twenty-first century over the HNZ, ranging from −2.4 to 9 %. However, streamflow is likely to decline in the future, ranging −6.9 to 0.8 %, mainly due to an increase of evapotranspiration in a warming world, as air temperature shows steadily increases for all the GCMs and RCPs. Average monthly streamflow from six GCMs are likely to increase in August and September but decline from October to June. The associated uncertainties of the reported results were also discussed. It includes, but is not limit to, different GCMs, emissions scenarios, downscaling techniques as well as hydrological simulations. The results of this study can inform planning of long-term basin water management strategies taking into account global change scenarios. [ABSTRACT FROM AUTHOR]

This study aims to identify the parameters that are most important in controlling the Noah land surface model (LSM), the analysis of parameter interactions, and the evaluation of the performance of parameter optimization using the parameter estimation software PEST. We found it necessary to analyze parameter sensitivity in order to properly simulate hydrological variables such as latent heat flux in the Huaihe River Basin, China. The parameters under study in the Noah LSM link thermodynamic and hydrological parts into a complete model. To our knowledge, this parameter interaction in the Noah LSM has never been studied before. There are, however, several studies concerning the influence of vegetation types and climate conditions on parameter sensitivity of the Noah LSM. Three sensitivity analysis methods, the including local sensitivity analysis method SENSAN, regional sensitivity analysis, and Sobol's method, were tested. Five experimental sites in the Huaihe River Basin were chosen to perform the simulations. The results show that the Noah LSM parameter sensitivities were impacted by the choice of the analysis method. The local method SENSAN often produced significant differences in results compared to the two global methods. The parameter interactions investigated made a significant contribution towards elucidating how one process influences another in the Noah LSM. The results show that parameters were not transferable solely based on vegetation types but also rely on climate conditions. According to the sensitivity analysis results, four sensitive parameters were chosen to be optimized using the PEST method. PEST is a widely used method for estimating parameters in models. Root-mean-square error was used to evaluate the effect of the optimization. Generally in all sites, the optimized parameters values perform better than the original parameter values. [ABSTRACT FROM AUTHOR]

Main conclusion: Heterologous expression of a fungal NADP(H)-GDH gene ( MgGDH ) from Magnaporthe grisea can improve dehydration stress tolerance in rice by preventing toxic accumulation of ammonium. Glutamate dehydrogenase (GDH; EC 1.4.1.2 and EC 1.4.1.4) may act as a stress-responsive enzyme in detoxification of high intracellular ammonia and production of glutamate for proline synthesis under stress conditions. In present study, a fungal NADP(H)-GDH gene ( MgGDH) from Magnaporthe grisea was over-expressed in rice ( Oryza sativa L. cv. ' kitaake'), and the transgenic plants showed the improvement of tolerance to dehydration stress. The kinetic analysis showed that His-TF- MgGDH preferentially utilizes ammonium to produce l-glutamate. Moreover, the affinity of His-TF- MgGDH for ammonium was dramatically higher than that of His-TF- OsGDH for ammonium. Over-expressing MgGDH transgenic rice plants showed lower water-loss rate and higher completely close stomata than the wild-type plants under dehydration stress conditions. In transgenic plants, the NADP(H)-GDH activities were markedly higher than those in wild-type plants and the amination activity was significantly higher than the deamination activity. Compared with wild-type plants, the transgenic plants accumulated much less NH but higher amounts of glutamate, proline and soluble sugar under dehydration stress conditions. These results indicate that heterologous expression of MgGDH can prevent toxic accumulation of ammonium and in return improve dehydration stress tolerance in rice. [ABSTRACT FROM AUTHOR]

Glutamate dehydrogenase (GDH) tends to have a lower affinity for ammonium than glutamine synthetase (GS) in higher plants. Consequently, nitrogen is mostly assimilated as ammonium by the GS/glutamate synthase pathway which requires 2-oxoglutarate (2-OG) as carbon skeletons. In contrast, the NADP(H)-dependent GDH in fungi has a higher affinity for ammonium than that in higher plants and plays a more significant part in ammonium assimilation. We isolated an NADP(H)-GDH gene ( PcGDH) from the fungus Pleurotus cystidiosus and heterologously expressed it in rice ( Oryza sativa L.). Alterations in nitrogen assimilation, growth, metabolism, and grain yield were observed in the transgenic plants. An investigation of the kinetic properties of the purified recombinant protein demonstrated that the amination activity (7.05 ± 0.78 μmoL min mg soluble protein) of PcGDH was higher than the deamination activity (3.36 ± 0.42 μmoL min mg soluble protein) and that the K value for ammonium ( K = 3.73 ± 0.23 mM) was lower than that for the glutamate ( K = 15.97 ± 0.31 mM), indicating that the PcGDH tends to interconvert 2-OG and glutamate. Examination of the activity of NADP(H)-GDH in control and transgenic lines demonstrated that NADP(H)-GDH activity in the transgenic lines was markedly higher than that in the control lines; in particular, the amination activity was significantly higher than the deamination activity in shoots of the transgenic lines. The results of the hydroponics experiment revealed that shoot and root length, fresh weight, chlorophyll content, nitrogen content, and amino acid levels (glutamate, glutamine, and total amino acids) were elevated in transgenic lines in comparison with those of the control line under different nitrogen conditions at seedling stage. The 1,000-grain weight and the panicle number in transgenic lines were considerably augmented in the field condition, yet the filled grain rate dropped slightly and there was no apparent change in the grain yield. The levels of glutelin and prolamine in the transgenic seeds were considerably higher than those in control seeds. In conclusion, these results demonstrate that heterologous expression of P. cystidiosus GDH ( PcGDH) could improve nitrogen assimilation and growth in rice. [ABSTRACT FROM AUTHOR]

The influence of the dynamic Stark effect on the dissociation of CsI is theoretically studied by the time-dependent wave-packet method. After a pump pulse induces a dissociating wave packet that propagates through both the ionic channel X0( Cs( S) + I( S)) and the covalent channel A0( Cs( S) + I( P)), a Stark pulse is applied to control the diabatic-dissociation dynamics. The first-order non-resonant non-perturbative dynamic Stark effect gives control over the break up, the dissociation probabilities in the two channels being controlled by the delay between the pump and Stark pulses. With a 720-fs delay, the dissociation probability through channel A is greatly enhanced. [ABSTRACT FROM AUTHOR]

The full-length cDNA encoding a glutamate dehydrogenase (GDH) which catalyzes the reaction of reductive amination of α-oxoglutarate (α-OG) to glutamate (the anabolic activity) and the reverse reaction of oxidative deamination of glutamate (the catabolic activity) was isolated from Sclerotinia sclerotiorum, we designated it as SsGDH. Bioinformatics analysis revealed that SsGDH had a typical GDH spatial structure and extensive homology with other fungal or bacteria GDHs. To evaluate its function in rice, rice ( Oryza sativa L. cv. ' kitaake' ) was transformed with SsGDH in a binary vector construct by Agrobacterium-mediated transformation. Transgenic rice plants showed that transcripts and proteins of SsGDH accumulated at higher levels and GDH enzymatic activity was obviously higher in transgenic rice plants compared with the non-transformant rice plants (CK), though phenotype including plant height, fresh weight and dry weight became slightly weaker compared with CK under 50, 500 and 5,000 μM nitrogen gradient nutrient solution treatment (NHNO as a nitrogen source) after introducing SsGDH into rice. For enzymatic activity assay in vitro, recombinant His- SsGDH protein was expressed in Escherichia coli BL21 (DE3) and purified by Ni-NTA agarose. Results suggested that recombinant His- SsGDH protein had GDH activity using ammonium, α-OG, and l-glutamate separately as a substrate at two different concentrations, especially the affinity for ammonium was very high, and its Km value was only 0.28 ± 0.03 mM, indicating that SsGDH can assimilate more ammonium into rice. According to previous reports, transgenic plants expressing fungal or bacteria GDHs might show improved herbicide resistance. Basta resistance test showed that SsGDH expression in rice can significantly enhanced their tolerance to Basta than CK. In conclusion, our results may provide some clues for further investigation on nitrogen utilization via introducing exogenous GDHs from lower organisms into rice. [ABSTRACT FROM AUTHOR]

A novel fructosyltransferase ( AoFT) capable of synthesizing sucrose 6-acetate (S6A) from sucrose and glucose 6-acetate has been purified to homogeneity from Aspergillus oryzae ZZ-01. Its molecular mass was ~50 kDa by SDS-PAGE; optimal activity was at 45 °C and it was stable from pH 4.5 to 7.5 with an optimum pH of 6. Mg, K (5 mM), propanol, toluene (50 %, v/v), Tween 20 or Triton X-100 (1 %, w/v) increased the transfructosylation activity by 20, 17, 17, 10, 25 and 20 %, respectively. An overall conversion of 32 % was achieved under optimal conditions over 24 h. This is the first report that the purified and characterized the fructosyltransferase from Aspergillus capable of synthesis of S6A from sucrose and glucose 6-acetate. [ABSTRACT FROM AUTHOR]

Five downscaling techniques, namely the statistical downscaling model, the automated statistical downscaling method, the change factor (CF) method, the advanced CF method, the Weather generator (LarsWG5) method, are applied to the upstream basin of the Huaihe River. Changes in regional climate scenarios and hydrology variables are compared in future periods to investigate the uncertainty associated with the downscaling techniques. Paired-sample T test is applied to evaluation the significant of the difference of the means between the observed data and the downscaled data in the future. The Xinanjiang rainfall-runoff model is employed to simulate the rainfall-runoff relation. The results demonstrate that the downscaling techniques utilized herein predict an increased tendency in the future. The increases range of maximum temperature (T) is between 3.7 and 4.7 °C until the time period of 2070-2099 (2080s). While, the increases range of minimum temperature (T) is between 2.8 and 4.9 °C until 2080s. The research presented herein determined that there is an increase predicted for the peaks over threshold (discussed in the paper) and a decrease predicted for the peaks below the threshold (discussed in the paper) in the future, which illustrates that the temperature would rise gradually in the future. Precipitation changes are not as obvious as temperatures changes and tend to be influence by the season. Most downscaling techniques predict increases, and others indict decreases. The annual mean precipitation range changes between 3.2 and 53.3 %, and moreover, these changes vary from season to season. [ABSTRACT FROM AUTHOR]

Wheat straw is one of the major lignocellulosic plant residues in many countries including China. An attractive alternative is the utilization of wheat straw for bioethanol production. This article mainly studies a simple one-step wet milling with Penicillium simplicissimum and weak acid to hydrolysis of wheat straw. The optimal condition for hydrolysis was ball milling 48 h in citrate solvent (pH = 4) with P. simplicissimum H5 at the speed of 500 rpm and the yield of sugar increased with increased milling time. Corresponding structure transformations before and after milling analyzed by X-ray diffraction, transmission Fourier transform infrared spectroscopy, and environmental scanning electron microscopy clearly indicated that this combined treatment could be attributed to the crystalline and chemical structure changes of cellulose in wheat straw during ball milling. This combined treatment of ball milling, mild acid, and fungus hydrolysis enabled the conversion of the wheat straw. Compared with traditional method of ball milling, this work showed a more simple, novel, and environmentally friendly way in mechanochemical treatment of wheat straw. [ABSTRACT FROM AUTHOR]

In this work, the femtosecond time-resolved photoelectron spectra and the coupling between the AΣ and BΠ states of the NO molecule in a strong laser field have been investigated by the time-dependent wave packet method. We demonstrate that the weak coupling between the AΣ and BΠ states of NO plays a key role on the peak centered at 0.37 eV of the photoelectron spectra in the 2+1' channel. [ABSTRACT FROM AUTHOR]

Moderate leaf rolling is useful in improving photosynthetic efficiency and grain yields. Receptor-like cytoplasmic kinases (RLCKs) play important roles in plant growth and development. However, little is known about their functions in rice leaf morphogenesis. Here, we report the isolation and characterization of LRRK1 (leaf rolling receptor-like cytoplasmic kinase 1), an RLCK gene involved in the regulation of leaf rolling. LRRK1 was mainly localized at the plasma membrane and was phosphorylated in vivo. Overexpression of LRRK1 in rice reduced the size of bulliform cells at the adaxial cell layers, which caused in turn adaxially rolled leaves. However, deficiency of LRRK1 in the lrrk1 mutant did not result in a detectable visual phenotype. LRRK1 could upregulate the expression of negative regulators but downregulate the expression of positive regulators of bulliform cell development. These results indicate that LRRK1 is a negative regulator involved in the bulliform cell development. Furthermore, the panicle numbers in LRRK1-overexpressing plants increased significantly compared with the wild-type plants under a rational close planting condition. Taken together, these findings suggest that LRRK1 plays an important role in regulating leaf rolling and is a promising candidate gene for breeding rice with ideal plant architecture and improved grain yield. [ABSTRACT FROM AUTHOR]