Inhibiting the Formation of Toxic HCN Induced by C3H6 Coexistence and Enhancing C3H6 Resistance of Cu-Zeolite in the NH3-SCR Reaction.

The presence of light hydrocarbons (HCs) in diesel exhaust, specifically C3H6, significantly affects the performance of the state-of-the-art Cu-SSZ-13 zeolite NH3-SCR catalysts. It also leads to the formation of highly toxic HCN, posing risks to the environment and human health. In this work, the highly toxic HCN formation is inhibited, and the C3H6 resistance of Cu-SSZ-13 is improved by secondary metal modification via doping with rare earth/transition metal elements. Upon introduction of C3H6, the activity of Cu-SSZ-13 significantly decreases at medium-high temperatures. This is primarily due to the competitive reaction between C3H6 and NH3, which compete for the NH3 reductant required in the NH3-SCR reaction, resulting in the production of HCN. The unfavorable effect is alleviated on the modified catalysts due to their enhanced oxidation capabilities toward C3H6 and the HCHO intermediate, facilitating the complete oxidation of C3H6 to COx. This inhibits the undesirable partial oxidation reaction between C3H6 and NH3, thereby improving the activity of Cu-SSZ-13 at medium to high temperatures and significantly reducing the formation of highly toxic HCN.

Simultaneous determination of four fungicide residues in figs using liquid chromatography tandem mass spectrometry.

Dissipative behavior and final residue levels of difenoconazole, prochloraz, propiconazole, and pyraclostrobin in figs were investigated using field trials and laboratory assays. A three-factor, three-level orthogonal test was designed to optimize the pretreatment conditions of the method. A method was established using high-performance liquid chromatography tandem mass spectrometry for the determination of difenoconazole, prochloraz, propiconazole, and pyraclostrobin residues in figs. The limit of quantification for all four targets in figs was 0.002 mg/kg. Difenoconazole, prochloraz, propiconazole, and pyraclostrobin are readily digestible pesticides in figs with half-lives of 6.4, 6.2, 4.8, and 7.9 days, respectively. Residues of difenoconazole, prochloraz, propiconazole, and pyraclostrobin in figs were below the European Union established residue levels of 0.1, 0.03, 0.01, and 0.02 mg/kg, respectively, at day 7 after application. Pyraclostrobin, propiconazole, difenoconazole, and prochloraz were applied twice at doses of 75, 125, 150, and 200 mg a.i./kg at 7-day intervals, and the residues of the four fungicides in figs were acceptable 7 days after the last application. Therefore, the safety interval can be set at 7 days for 70% difenoconazole-prochloraz wettable powder and 40% pyraclostrobin-propiconazole aqueous emulsion according to the protocol.

The Degradation and Migration of Cyclanilide in Soil and Sediment in China.

The environmental fate of a plant growth regulator cyclanilide was studied in this paper. The degradation, adsorption, and migration behaviors of cyclanilide were detailly measured in the laboratory. The results showed that the DT50 of cyclanilide degradation in the Jiangxi red, Taihu paddy, Changshu wushan, Shaanxi tide, and Dongbei black soils was 42.3 d, 31.9 d, 14.4 d, 30.4 d as well as 27.4 d under aerobic conditions and 32.3 d, 37.4 d, 29.3 d, 48.9 d as well as 27.0 d under water anaerobic conditions, respectively, with the main metabolite being 2,4-dichloroaniline (2,4-D). The DT50 of 2,4-D ranged from 5.26 to 27.1 days under aerobic conditions, and from 10.6 to 54.1 days under anaerobic conditions. The adsorption of cyclanilide by the soils was well fitted by the empirical linear adsorption isotherm, and the adsorption constant (Kd, H) values in the Jiangxi red, Taihu paddy, Changshu wushan, Shaanxi tide, and Dongbei black soils were 7.08, 4.49, 4.05, 3.20, and 1.41, respectively. The results of a mobility test showed that cyclanilide had strong mobility in the most test soils. Furthermore, soil pH is the dominant element affecting the adsorption of cyclanilide in the soils. Under aerobic environment, the DT50 of total cyclanilide in river and lake water-sediment systems were 30.7 d and 34.0 d, respectively; under anaerobic environment, their DT50 were 30.8 d and 31.4 d, respectively. In water-sediment systems, 2,4-D mainly exists in aqueous phase and the DT50 ranged from 5.23 to 8.76 days. This work demonstrated that cyclanilide has the potential risk to contaminate environment and attention should be paid to its application.

[Spatial Distribution, Speciation, and Ecological Risk Assessment of Heavy Metals in Surface Sediments of Dongjiang Lake, Hunan Province].

To understand the heavy metal pollution status of Dongjiang Lake, the contents and species of heavy metals in the surface sediments were investigated during September 2021, and the heavy metal pollution level and potential ecological risk were evaluated. The results showed that Cd, Pb, As, Cu, Zn, Ni, and Cr contents were in the range of 0.40-34.1, 14.8-1688, 6.99-1155, 6.89-280, 26.2-1739, 6.29-55.4, and 23.3-44.8 mg·kg-1, respectively, with extremely uneven spatial distributions. The highest contents of Cd, Pb, As, Zn, Cu, and Ni were found in the site adjacent to Yaogangxian tungsten ore. The proportion of metal species with bioavailability was high, in which Cd in acid-soluble species was 46.7%-71.5% and Pb in reducible species was 46.8%-67.0%. The bioavailable species of Cu, Zn, Ni, and Cr were 35%-68%, 42%-72%, 26%-51%, and 6%-30%, respectively, although they primarily existed in residual species. According to the geo-accumulation index (Igeo), there was a moderate or extreme pollution status of Cd in all sites, moderate or extreme pollution status of Pb in 90% of sites, and moderate pollution status of As, Cu, and Zn in 30% of sites. The ecological risk factor (Eri) of Cd showed high potential ecological risk in all sites with significantly high potential ecological risk in 80% of sites. Moreover, As and Pb had significantly high potential ecological risk, and Cu had moderate potential ecological risk in S7, which was adjacent to Yaogangxian tungsten ore. There was a high total potential ecological risk in all sites and significantly high potential ecological risk in 50% of sites. Therefore, the surface sediments of Dongjiang Lake were under the combined pollution of Cd, Pb, As, Zn, and Cu with high bioavailability and high total potential ecological risk.

Prediction of subcellular location apoptosis proteins with ensemble classifier and feature selection.

Apoptosis proteins have a central role in the development and the homeostasis of an organism. These proteins are very important for understanding the mechanism of programmed cell death. The function of an apoptosis protein is closely related to its subcellular location. It is crucial to develop powerful tools to predict apoptosis protein locations for rapidly increasing gap between the number of known structural proteins and the number of known sequences in protein databank. In this study, amino acids pair compositions with different spaces are used to construct feature sets for representing sample of protein feature selection approach based on binary particle swarm optimization, which is applied to extract effective feature. Ensemble classifier is used as prediction engine, of which the basic classifier is the fuzzy K-nearest neighbor. Each basic classifier is trained with different feature sets. Two datasets often used in prior works are selected to validate the performance of proposed approach. The results obtained by jackknife test are quite encouraging, indicating that the proposed method might become a potentially useful tool for subcellular location of apoptosis protein, or at least can play a complimentary role to the existing methods in the relevant areas. The supplement information and software written in Matlab are available by contacting the corresponding author.

[Prediction of G-protein-coupled receptor classes with pseudo amino acid composition].

G-protein-coupled receptors (GPCRs), the largest family of cell surface receptors, play an important role in the production of therapeutic drugs. The functions of GPCRs are closely related to their classification and subclassification. It is difficult to obtain the spatial structure of GPCRs sequence by experimental approaches. It is highly desired to develop powerful tools and effective algorithms for classifying the family of GPCRs. In this study, based on the concept of pseudo amino acid composition (PseAA) originally introduced by Chou, approximate entropy (ApEn) of protein sequence as an additional characteristic is used to construct PseAA. A 21-D (dimensional) PseAA is formulated to represent the sample of a protein. Fuzzy K nearest neighbors (FKNN) classifier is adopted as prediction engine. The datasets in low homology are used to validate the performance of the proposed method. Compared with prior works, the successful rates achieved of our research are the highest. The test results indicate that the novel approach can play the role of a compliment to many of the existing methods, which promises to be a useful tool for GPCRs function prediction.

Using Chou's pseudo amino acid composition based on approximate entropy and an ensemble of AdaBoost classifiers to predict protein subnuclear location.

The knowledge of subnuclear localization in eukaryotic cells is essential for understanding the life function of nucleus. Developing prediction methods and tools for proteins subnuclear localization become important research fields in protein science for special characteristics in cell nuclear. In this study, a novel approach has been proposed to predict protein subnuclear localization. Sample of protein is represented by Pseudo Amino Acid (PseAA) composition based on approximate entropy (ApEn) concept, which reflects the complexity of time series. A novel ensemble classifier is designed incorporating three AdaBoost classifiers. The base classifier algorithms in three AdaBoost are decision stumps, fuzzy K nearest neighbors classifier, and radial basis-support vector machines, respectively. Different PseAA compositions are used as input data of different AdaBoost classifier in ensemble. Genetic algorithm is used to optimize the dimension and weight factor of PseAA composition. Two datasets often used in published works are used to validate the performance of the proposed approach. The obtained results of Jackknife cross-validation test are higher and more balance than them of other methods on same datasets. The promising results indicate that the proposed approach is effective and practical. It might become a useful tool in protein subnuclear localization. The software in Matlab and supplementary materials are available freely by contacting the corresponding author.

Using the concept of Chou's pseudo amino acid composition to predict apoptosis proteins subcellular location: an approach by approximate entropy.

The function of protein is closely correlated with it subcellular location. Prediction of subcellular location of apoptosis proteins is an important research area in post-genetic era because the knowledge of apoptosis proteins is useful to understand the mechanism of programmed cell death. Compared with the conventional amino acid composition (AAC), the Pseudo Amino Acid composition (PseAA) as originally introduced by Chou can incorporate much more information of a protein sequence so as to remarkably enhance the power of using a discrete model to predict various attributes of a protein. In this study, a novel approach is presented to predict apoptosis protein solely from sequence based on the concept of Chou's PseAA composition. The concept of approximate entropy (ApEn), which is a parameter denoting complexity of time series, is used to construct PseAA composition as additional features. Fuzzy K-nearest neighbor (FKNN) classifier is selected as prediction engine. Particle swarm optimization (PSO) algorithm is adopted for optimizing the weight factors which are important in PseAA composition. Two datasets are used to validate the performance of the proposed approach, which incorporate six subcellular location and four subcellular locations, respectively. The results obtained by jackknife test are quite encouraging. It indicates that the ApEn of protein sequence could represent effectively the information of apoptosis proteins subcellular locations. It can at least play a complimentary role to many of the existing methods, and might become potentially useful tool for protein function prediction. The software in Matlab is available freely by contacting the corresponding author.

[Protein secondary structure prediction based on maximum entropy model].

Secondary structure prediction plays an important role in function prediction of protein. In this paper, maximum entropy model is used to predict protein secondary structure. We build feature function sets based on the influential factors which are crucial to the states of secondary structure of residues in protein sequence. Multi-factors are taken into account in the model, including charge of amino acids, conformational parameter for the states of secondary structure, short and long ranges of interaction of residues in sequence. As such, multi-source information is integrated into a single probability model by the method. Compared with the reported methods, our method gets a higher accuracy rate in predicting protein secondary structure. The results demonstrate that the proposed method is practical.

[Protein structural class prediction with binary tree-based support vector machines].

A new mutil-classification method based on binary tree SVM (BT-SVM) is presented to predict protein structural class. The protein sequence, which is represented by 26-D vector, is used as input vector. BT-SVM method resolves unclassifiable regions for multiclass problems which can not be solved by SVM. Self-consistency and cross validation test are used to verify the performance of the proposal method on two benchmark datasets. Satisfactory test results demonstrate that the new method is promising. The Jackknife results of the new method are compared with the existing results on the same datasets. The results of the new method are almost the same as the ones of the best exiting method. It illuminates that the new method has good prediction performance and it will become a useful tool in protein structure class prediction.

Prediction of protein structure classes with pseudo amino acid composition and fuzzy support vector machine network.

It is a critical challenge to develop automated methods for fast and accurately determining the structures of proteins because of the increasingly widening gap between the number of sequence-known proteins and that of structure-known proteins in the post-genomic age. The knowledge of protein structural class can provide useful information towards the determination of protein structure. Thus, it is highly desirable to develop computational methods for identifying the structural classes of newly found proteins based on their primary sequence. In this study, according to the concept of Chou's pseudo amino acid composition (PseAA), eight PseAA vectors are used to represent protein samples. Each of the PseAA vectors is a 40-D (dimensional) vector, which is constructed by the conventional amino acid composition (AA) and a series of sequence-order correlation factors as original introduced by Chou. The difference among the eight PseAA representations is that different physicochemical properties are used to incorporate the sequence-order effects for the protein samples. Based on such a framework, a dual-layer fuzzy support vector machine (FSVM) network is proposed to predict protein structural classes. In the first layer of the FSVM network, eight FSVM classifiers trained by different PseAA vectors are established. The 2nd layer FSVM classifier is applied to reclassify the outputs of the first layer. The results thus obtained are quite promising, indicating that the new method may become a useful tool for predicting not only the structural classification of proteins but also their other attributes.

Prediction of protein subcellular location using hydrophobic patterns of amino acid sequence.

The function of eukaryotic protein is closely correlated with its subcellular location. The number of newly found protein sequences entering into data banks is rapidly increasing with the success of human genome project. It is highly desirable to predict a protein subcellular automatically from its amino acid sequence. In this paper, amino acid hydrophobic patterns and average power-spectral density (APSD) are introduced to define pseudo amino acid composition. The covariant-discriminant predictor is used to predict subcellular location. Immune-genetic algorithm (IGA) is used to find the fittest weight factors which are very important in this method. As such, high success rates are obtained by both self-consistency test (86%) and jackknife test (73%). More than 80% predictive accuracy is achieved in independent dataset test. The results demonstrate that the proposed method is practical. And, the method illuminates that the protein subcellular location can be predicted from its surface physio-chemical characteristic of protein folding.

Remote data retrieval for bioinformatics applications: an agent migration approach.

Some of the approaches have been developed to retrieve data automatically from one or multiple remote biological data sources. However, most of them require researchers to remain online and wait for returned results. The latter not only requires highly available network connection, but also may cause the network overload. Moreover, so far none of the existing approaches has been designed to address the following problems when retrieving the remote data in a mobile network environment: (1) the resources of mobile devices are limited; (2) network connection is relatively of low quality; and (3) mobile users are not always online. To address the aforementioned problems, we integrate an agent migration approach with a multi-agent system to overcome the high latency or limited bandwidth problem by moving their computations to the required resources or services. More importantly, the approach is fit for the mobile computing environments. Presented in this paper are also the system architecture, the migration strategy, as well as the security authentication of agent migration. As a demonstration, the remote data retrieval from GenBank was used to illustrate the feasibility of the proposed approach.

A novel motif discovery algorithm for identifying protein families.

Discovering a protein motif is an important research topic in both bioinformatics and protein sciences. This paper presents a novel motif discovery algorithm which is capable of finding a motif set to represent a protein family. The algorithm involves an abstraction method of important features, a location-sensitive connection approach to link two features, and a repeated connection procedure to generate a motif set. The novel algorithm is applied to discovering motifs in 21 ligase subfamilies. The results show that the obtained motifs are able to represent the characteristics of the subfamilies effectively. The proposed algorithm could become a potential useful tool for protein family prediction.

Using maximum entropy model to predict protein secondary structure with single sequence.

Prediction of protein secondary structure is somewhat reminiscent of the efforts by many previous investigators but yet still worthy of revisiting it owing to its importance in protein science. Several studies indicate that the knowledge of protein structural classes can provide useful information towards the determination of protein secondary structure. Particularly, the performance of prediction algorithms developed recently have been improved rapidly by incorporating homologous multiple sequences alignment information. Unfortunately, this kind of information is not available for a significant amount of proteins. In view of this, it is necessary to develop the method based on the query protein sequence alone, the so-called single-sequence method. Here, we propose a novel single-sequence approach which is featured by that various kinds of contextual information are taken into account, and that a maximum entropy model classifier is used as the prediction engine. As a demonstration, cross-validation tests have been performed by the new method on datasets containing proteins from different structural classes, and the results thus obtained are quite promising, indicating that the new method may become an useful tool in protein science or at least play a complementary role to the existing protein secondary structure prediction methods.

Prediction protein structural classes with pseudo-amino acid composition: approximate entropy and hydrophobicity pattern.

Compared with the conventional amino acid (AA) composition, the pseudo-amino acid (PseAA) composition as originally introduced for protein subcellular location prediction can incorporate much more information of a protein sequence, so as to remarkably enhance the power of using a discrete model to predict various attributes of a protein. In this study, based on the concept of PseAA composition, the approximate entropy and hydrophobicity pattern of a protein sequence are used to characterize the PseAA components. Also, the immune genetic algorithm (IGA) is applied to search the optimal weight factors in generating the PseAA composition. Thus, for a given protein sequence sample, a 27-D (dimensional) PseAA composition is generated as its descriptor. The fuzzy K nearest neighbors (FKNN) classifier is adopted as the prediction engine. The results thus obtained in predicting protein structural classification are quite encouraging, indicating that the current approach may also be used to improve the prediction quality of other protein attributes, or at least can play a complimentary role to the existing methods in the relevant areas. Our algorithm is written in Matlab that is available by contacting the corresponding author.