[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.

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.

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.

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.

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.