Detection of microplastics in domestic and fetal pigs' lung tissue in natural environment: A preliminary study.

Microplastics (MPs) are ubiquitous in the environment. However, it is unclear whether MPs are present in mammalian lungs through inhalation, and if so, could be possibly found in fetal tissues. In this study, we aim to determine the presence and characteristics of particles in domestic and fetal pig lung tissue in the natural environment. Specimens from the lungs of domestic pigs (n = 10) and fetal pigs that already died in matrix during vaginal birth from the non-contaminated area (n = 10) were obtained from farmers' nearby sludge treatment plant. These specimens were compressed between two glass microscope slides, which were examined under polarized light microscopy. In addition, Agilent 8700 LDIR Chemical imaging system (LDIR) was used to determine the quantitative and qualitative characteristics of MPs. According to the polarized light microscope survey of domestic pig lungs, we observed an average of 12 particles/g, which was more than the 6 particles/g observed in fetal pig lungs, which ranged in size from 115.14 µm to 1370.43 µm. All the observed MP particles were fiber in shape. LDIR indicated an average of 180 particles/g of domestic pig lungs, ranging in size from 20.34 µm to 916.36 µm, which was twice as many MPs observed in fetal pig lungs. Furthermore, the compositions of MPs were different between them. LDIR indicated that polyamide (PA) was the most common polymer identified in domestic pig lungs (46.11%), while polycarbonate (PC) was the most common polymer in fetal pig lungs (32.99%). These findings confirmed the presence of MPs in the lung tissue of both domestic and fetal pigs in the natural environment, but the main characteristics differed. This fact indicated the increasing risk of MPs to human respiratory tract is increasing. Further research should be conducted to entirely estimate the specific exposure level on humans and offspring.

DEEPSMP: A deep learning model for predicting the ectodomain shedding events of membrane proteins.

Membrane proteins play essential roles in modern medicine. In recent studies, some membrane proteins involved in ectodomain shedding events have been reported as the potential drug targets and biomarkers of some serious diseases. However, there are few effective tools for identifying the shedding event of membrane proteins. So, it is necessary to design an effective tool for predicting shedding event of membrane proteins. In this study, we design an end-to-end prediction model using deep neural networks with long short-term memory (LSTM) units and attention mechanism, to predict the ectodomain shedding events of membrane proteins only by sequence information. Firstly, the evolutional profiles are encoded from original sequences of these proteins by Position-Specific Iterated BLAST (PSI-BLAST) on Uniref50 database. Then, the LSTM units which contain memory cells are used to hold information from past inputs to the network and the attention mechanism is applied to detect sorting signals in proteins regardless of their position in the sequence. Finally, a fully connected dense layer and a softmax layer are used to obtain the final prediction results. Additionally, we also try to reduce overfitting of the model by using dropout, L2 regularization, and bagging ensemble learning in the model training process. In order to ensure the fairness of performance comparison, firstly we use cross validation process on training dataset obtained from an existing paper. The average accuracy and area under a receiver operating characteristic curve (AUC) of five-fold cross-validation are 81.19% and 0.835 using our proposed model, compared to 75% and 0.78 by a previously published tool, respectively. To better validate the performance of the proposed model, we also evaluate the performance of the proposed model on independent test dataset. The accuracy, sensitivity, and specificity are 83.14%, 84.08%, and 81.63% using our proposed model, compared to 70.20%, 71.97%, and 67.35% by the existing model. The experimental results validate that the proposed model can be regarded as a general tool for predicting ectodomain shedding events of membrane proteins. The pipeline of the model and prediction results can be accessed at the following URL: http://www.csbg-jlu.info/DeepSMP/.

Computational Analysis of Specific MicroRNA Biomarkers for Noninvasive Early Cancer Detection.

Cancer is a complex disease residing in various tissues of human body, accompanied with many abnormalities and mutations in genomes, transcriptome, and epigenome. Early detection plays a crucial role in extending survival time of all major cancer types. Recent advances in microarray and sequencing techniques have given more support to identifying effective biomarkers for early detection of cancer. MicroRNAs (miRNAs) are more and more frequently used as candidates for biomarkers in cancer related studies due to their regulation of target gene expression. In this paper, the comparative analysis is used to discover miRNA expression patterns in cancer versus normal samples on early stage of eight prevalent cancer types. Our work focuses on the specific miRNAs biomarkers identification and function analysis. Several identified miRNA biomarkers in this paper are matched well with those reported in existing researches, and most of them could serve as potential candidate indicators for clinical early diagnosis applications.

A feature selection method based on multiple kernel learning with expression profiles of different types.

BACKGROUND: With the development of high-throughput technology, the researchers can acquire large number of expression data with different types from several public databases. Because most of these data have small number of samples and hundreds or thousands features, how to extract informative features from expression data effectively and robustly using feature selection technique is challenging and crucial. So far, a mass of many feature selection approaches have been proposed and applied to analyse expression data of different types. However, most of these methods only are limited to measure the performances on one single type of expression data by accuracy or error rate of classification. RESULTS: In this article, we propose a hybrid feature selection method based on Multiple Kernel Learning (MKL) and evaluate the performance on expression datasets of different types. Firstly, the relevance between features and classifying samples is measured by using the optimizing function of MKL. In this step, an iterative gradient descent process is used to perform the optimization both on the parameters of Support Vector Machine (SVM) and kernel confidence. Then, a set of relevant features is selected by sorting the optimizing function of each feature. Furthermore, we apply an embedded scheme of forward selection to detect the compact feature subsets from the relevant feature set. CONCLUSIONS: We not only compare the classification accuracy with other methods, but also compare the stability, similarity and consistency of different algorithms. The proposed method has a satisfactory capability of feature selection for analysing expression datasets of different types using different performance measurements.

A novel filter feature selection method for paired microarray expression data analysis.

In recent years, a large amount of microarray data sets are produced with tens of thousands of genes. Feature selection has become a very sharp tool to select the informative genes. However, few feature selection methods consider the effect of paired samples, which are much more considered in the experiments of these years. Here, we propose a new feature selection method for paired microarray data sets analysis. It uses the fold change instead of the subtraction in the original approach, measures the statistical significant using the q-value of False Discovery Rate (FDR) and also decreases the influence of redundant genes. We compare the proposed method with another six existing methods in predict performance, stability of gene lists, functional stability and functional enrichment analysis using six kinds of paired cancer data sets. Comparison results show that our proposed method achieves better effectiveness, stability and consistency when it is applied to paired data sets.

A computational method for prediction of saliva-secretory proteins and its application to identification of head and neck cancer biomarkers for salivary diagnosis.

Human saliva is rich in proteins, which have been used for disease detection such as oral diseases and systematic diseases. In this paper, we present a computational method for predicting secretory proteins in human saliva based on two sets of human proteins from published literatures and public databases. One set contains known proteins which can be secreted into saliva, and the other contains the proteins that are deemed to be not extracellular secretion. The protein features with discerning power between two sets were firstly gathered. Then a classifier was trained based on the identified features to predict whether a protein was saliva-secretory one or not. The average values of the sensitivity, specificity, precision, accuracy, and Matthews correlation coefficient value by 10-fold cross validation repeated 100 times were 80.67%, 90.56%, 90.09%, 85.53%, and 0.7168, respectively. These results indicated that our selected features are informative. We applied the classifier for prediction saliva-secretory proteins out of all human proteins, if a known biomarker was likely to enter into saliva, and the potential salivary biomarkers for head and neck squamous cell carcinoma. We also compared the top 1000 proteins predicted by computational methods in different kind of fluids. This work provided a useful tool for effectively identifying the salivary biomarkers for various human diseases and facilitate the development of salivary diagnosis.

Operon prediction by Markov clustering.

The prediction of operons is a critical step for the reconstruction of biochemical and regulatory networks at the whole genome level. In this paper, a novel operon prediction model is proposed based on Markov Clustering (MCL). The model employs a graph-clustering method by MCL for prediction and does not need a classifier. In the cross-species validation, the accuracies of E. coli K12, Bacillus subtilis and P. furiosus are 92.1, 86.9 and 87.3%, respectively. Experimental results show that the proposed method has a powerful capability of operon prediction. The compiled program and test data sets are publicly available at http://ccst.jlu.edu.cn/JCSB/OPMC/.

Prokaryotic phylogenies inferred from whole-genome sequence and annotation data.

Phylogenetic trees are used to represent the evolutionary relationship among various groups of species. In this paper, a novel method for inferring prokaryotic phylogenies using multiple genomic information is proposed. The method is called CGCPhy and based on the distance matrix of orthologous gene clusters between whole-genome pairs. CGCPhy comprises four main steps. First, orthologous genes are determined by sequence similarity, genomic function, and genomic structure information. Second, genes involving potential HGT events are eliminated, since such genes are considered to be the highly conserved genes across different species and the genes located on fragments with abnormal genome barcode. Third, we calculate the distance of the orthologous gene clusters between each genome pair in terms of the number of orthologous genes in conserved clusters. Finally, the neighbor-joining method is employed to construct phylogenetic trees across different species. CGCPhy has been examined on different datasets from 617 complete single-chromosome prokaryotic genomes and achieved applicative accuracies on different species sets in agreement with Bergey's taxonomy in quartet topologies. Simulation results show that CGCPhy achieves high average accuracy and has a low standard deviation on different datasets, so it has an applicative potential for phylogenetic analysis.

A novel multi-stage feature selection method for microarray expression data analysis.

With the development of genome research, finding method to classify cancer and detect biomarkers efficiently has become a challenging problem. In this paper, a novel multi-stage method for feature selection is proposed which considers all kinds of genes in the original gene set. The method eliminates the irrelevant, noisy and redundant genes and selects a subset of relevant genes at different stages. The proposed method is examined on microarray datasets of Leukemia, Prostate, Colon, Breast, Nervous and DLBCL by different classifiers and the best accuracies of the method in these datasets are 100%, 98.04%, 100%, 89.74%, 100% and 98.28%, respectively.

A study of health effects of long-distance ocean voyages on seamen using a data classification approach.

BACKGROUND: Long-distance ocean voyages may have substantial impacts on seamen's health, possibly causing malnutrition and other illness. Measures can possibly be taken to prevent such problems from happening through preparing special diet and making special precautions prior or during the sailing if a detailed understanding can be gained about what specific health effects such voyages may have on the seamen. METHODS: We present a computational study on 200 seamen using 41 chemistry indicators measured on their blood samples collected before and after the sailing. Our computational study is done using a data classification approach with a support vector machine-based classifier in conjunction with feature selections using a recursive feature elimination procedure. RESULTS: Our analysis results suggest that among the 41 blood chemistry measures, nine are most likely to be affected during the sailing, which provide important clues about the specific effects of ocean voyage on seamen's health. CONCLUSIONS: The identification of the nine blood chemistry measures provides important clues about the effects of long-distance voyage on seamen's health. These findings will prove to be useful to guide in improving the living and working environment, as well as food preparation on ships.