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This study examines the concentrations and population-normalized mass loads (PNML) of five phthalate esters (PAEs) and four metabolites (mPAEs) in 390 sewage sludge samples collected from two municipal wastewater treatment plants in Beijing between July 2020 and June 2023, amidst the COVID-19 pandemic. Through GC/MS analysis, the compounds were simultaneously quantified, with peak concentrations in 2020. Bis(2-ethylhexyl) phthalate (DEHP) and mono(2-ethyl-5-oxohexyl) phthalate emerged as predominant PAE and mPAE congeners with concentrations of 78.7 µg/g dw and 259 µg/g dw, respectively. DEHP and monobenzyl phthalate had the highest median PNML among PAEs and mPAEs, respectively, at 128 µg/inhabitant/day and 798 µg/inhabitant/day. Linear regression models revealed a positive association between PNML of PAEs and five public health and social measures aimed at mitigating the COVID-19 pandemic. This research contributes to the expanding body of literature by emphasizing the role of wastewater-based epidemiology as a vital tool for monitoring community-level exposure to environmental contaminants., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

In drug discovery and development, the direct target identification of bioactive small molecules plays a significant role for understanding the mechanism of action, predicting the side effects, and rationally designing more potent compounds. However, due to the complicated regulatory processes in a cell together with thousands of biomacromolecules, target identification is always the major obstacle. New methods and technologies are continuously invented to tackle this problem. Nevertheless, the mainly used tools possess several disadvantages. High synthetic skills are typically required to laboriously synthesize a probe for protein enrichment. To detect the ligand-protein interaction by analyzing proteins' responses to proteolytic or thermal treatment, costly and precise instruments are always necessary. Therefore, convenient and practical techniques are urgently needed. Over the past decades, a strategy using native compounds without the requirement of chemical modification, also termed Native-compound-Coupled Affinity Matrix (NCAM), is developing continuously. Two practical tactics based on "label-free" compounds have been invented and used, that is Photo-cross-linked Small-molecule Affinity Matrix (PSAM) and Native-compound-Coupled CNBr-activated Beads (NCCB). Presently, we will elucidate the characteristics, coupling mechanism, advantages and disadvantages, and future prospect of NCAM in specific target identification and validation., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Allergic rhinitis (AR) and adenoid hypertrophy (AH) are common nasal diseases in children. Studies have shown that heavy metals are environmental risk factors for nasal diseases, and the pathogenic mechanisms may be related to dysregulation of nasal mucosal microbiota. However, it is unclear how heavy metal exposure relates to the nasal mucosal microbiota in nasal diseases. Therefore, we explored serum metal exposure levels and nasal mucosal microbiota composition in children with different nasal disease, and further studied the potential correlation between metal exposure and disease-related taxa. There were 64 children recruited for this study. The 23 metals concentrations in serum were measured by inductively coupled plasma mass spectrometry, and nasal mucosal bacteria was identified by 16S rRNA sequencing. Nasal diseases (AR and AH) in children were associated with alterations in the abundance and diversity of the nasal mucosal microbiota. The nasal microbiota of children with AR showed lower diversity, while the microbiota of children with AH showed higher diversity. Linear discriminant analysis Effect Size showed 108 differentially abundant taxa between AR and control groups, 35 differentially abundant taxa among large adenoid, moderate adenoid and small adenoid groups. The serum zinc concentration was negatively correlated with Pielou's eveness index and Simpson's Index in children classified by adenoid size. The spearman correlation analysis showed that multiple disease-related taxa were closely associated with metal concentrations in serum. Our findings may support a link between metal exposure and the diversity and composition of nasal bacteria in children with nasal disease, which present new evidence for the effects of metals on children health., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Inc.)

G protein-coupled receptors (GPCRs) are the largest drug target family. Unfortunately, applications of GPCRs in cancer therapy are scarce due to very limited knowledge regarding their correlations with cancers. Multi-omics data enables systematic investigations of GPCRs, yet their effective integration remains a challenge due to the complexity of the data. Here, we adopt two types of integration strategies, multi-staged and meta-dimensional approaches, to fully characterize somatic mutations, somatic copy number alterations (SCNAs), DNA methylations, and mRNA expressions of GPCRs in 33 cancers. Results from the multi-staged integration reveal that GPCR mutations cannot well predict expression dysregulation. The correlations between expressions and SCNAs are primarily positive, while correlations of the methylations with expressions and SCNAs are bimodal with negative correlations predominating. Based on these correlations, 32 and 144 potential cancer-related GPCRs driven by aberrant SCNA and methylation are identified, respectively. In addition, the meta-dimensional integration analysis is carried out by using deep learning models, which predict more than one hundred GPCRs as potential oncogenes. When comparing results between the two integration strategies, 165 cancer-related GPCRs are common in both, suggesting that they should be prioritized in future studies. However, 172 GPCRs emerge in only one, indicating that the two integration strategies should be considered concurrently to complement the information missed by the other such that obtain a more comprehensive understanding. Finally, correlation analysis further reveals that GPCRs, in particular for the class A and adhesion receptors, are generally immune-related. In a whole, the work is for the first time to reveal the associations between different omics layers and highlight the necessity of combing the two strategies in identifying cancer-related GPCRs., Competing Interests: Declaration of competing interest All authors have declared that no conflict of interest exist., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Aims: Competing endogenous RNAs (ceRNAs) play essential roles in cancer pathogenesis and those in exosomes have been the promising biomarkers for cancer diagnose and therapy. We aim to identify potential active ceRNA pairs in cancer blood exosomes by combining TCGA and exoRBase., Main Methods: Two strict screening criteria were implemented, including hypergeometric test on the targets predicted by RNA22 for differential miRNAs and Pearson test on the candidate mRNAs and lncRNAs for each cancer. Then2638292, 4925485 and 70669 ceRNAs in blood exosomes are available for colorectal cancer (CRC), hepatocellular carcinoma (HCC) and pancreatic adenocarcinoma (PAAD), respectively., Key Findings: A comprehensive functional analysis on differential miRNAs in cancer blood exosomes indicates that they play important roles in development of cancer by degrading or inhibiting the post-transcription translation level of mRNA or by acting as mediators to regulate the expression of mRNA. Topological and biological functional analysis of ceRNA networks demonstrate that hub ceRNAs involve in cancer-related biological pathways and processes, so as to influence the occurrence and development of cancer and would be the potential biomarkers for three cancers. Finally, we designed a web-accessible database, ExoceRNA Atlas (https://www.exocerna-atlas.com/exoceRNA#/) as a repository of ceRNAs in blood exosomes. It can friendly search, browse and visualize ceRNA networks of the query genes along with giving the detailed functional analysis results. The entire ceRNA data can also be freely downloaded., Significance: ExoceRNA Atlas will serve as a powerful public resource for identifying ceRNAs and greatly deepen our understanding their functions in cancer exosomes., Competing Interests: Declaration of competing interest The authors declare that no conflict of interest exists., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Aims: Currently, using clinicopathological risk factors only is not far from effective to evaluate the risk of disease progression in renal clear cell carcinoma (KIRC) patients. Molecular biomarkers might improve risk stratification of KIRC. DNA methylation occurs the whole process of tumor development and transcriptional disorders are also one of the important characteristics of tumor. Hence, this study aims to develop an effective and independent prognostic signature for KIRC patients by Integrating DNA methylation and gene expression., Main Methods: Difference analysis was conducted on DNA methylation sites and gene expression data. The Spearman's rank correlation and univariate Cox regression analysis were used to screen out the CpG sites that related with RNAs' expression and KIRC patients' overall survival. Then, a five-CpG-based prognostic classifier was established using LASSO Cox regression method., Key Findings: The seven-CpG-based classifier can successfully divide KIRC patients into high-risk from low-risk groups, even after adjustment for standard clinical prognostic factors, such as age, stage, gender and grade. Moreover, the seven-CpG-based signature was more effective as independent prognostic factors than the combined model of these clinical factors. Six differential mRNA genes corresponding to the seven CpG sites are all related to human cancers by functional exploration. The gene functional and pathway enrichment analysis found that genes in immune-related pathways were remarkably different in high and low-risk groups., Significance: The new seven-CpG-based signature could helpfully provide insights into the underlying mechanism of KIRC and may be a powerful independent biomarker for predicting of the survival of KIRC patients., Competing Interests: Declaration of competing interest The authors declare no conflicts of interest., (Copyright © 2020 Elsevier Inc. All rights reserved.)

In this study, the natural attenuation of naphthalene during riverbank infiltration was examined using batch experiments. The results indicated that, as the grain size and the permeability coefficient decreased, the natural attenuation rate of naphthalene increased, and it was highest in loam (62%) and lowest in coarse sand (20%). The half-life of naphthalene was longest in coarse sand (700 d) and shortest in mild clay (250 d). Facultative anaerobes such as Methylophilaceae accounted for about 70% of the total bacteria and played a major role in naphthalene degradation. A high total organic carbon concentration and large specific surface area can promote natural attenuation of naphthalene. Moreover, the adsorption to riverbank sediment in the hyporheic zone and bioremediation by indigenous microorganisms can effectively eliminate naphthalene during river water infiltration to the riverbank aquifer., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Dysregulated and reprogrammed metabolism are one of the most important characteristics of cancer, and exploiting cancer cell metabolism can aid in understanding the diverse clinical outcomes for patients. To investigate the differences in metabolic pathways among patients with acute myeloid leukemia (AML) and differential survival outcomes, we systematically conducted microarray data analysis of the metabolic gene expression profiles from 384 patients available from the Gene Expression Omnibus and Cancer Genome Atlas databases. Pathway enrichment analysis of differentially expressed genes (DEGs) showed that the metabolic differences between low-risk and high-risk patients mainly existed in two pathways: biosynthesis of unsaturated fatty acids and oxidative phosphorylation. Using the gene-pathway bipartite network, 62 metabolic genes were identified from 272 DEGs involved in 88 metabolic pathways. Based on the expression patterns of the 62 genes, patients with shorter overall survival (OS) durations in the training set (hazard ratio (HR)=1.58, p=0.038) and in two test sets (HR=1.69 and 1.56 and p=0.089 and 0.029, respectively) were well discriminated by hierarchical clustering analysis. Notably, the expression profiles of ALAS2, BCAT1, BLVRB, and HK3 showed distinct differences between the low-risk and high-risk patients. In addition, models for predicting the OS outcome of AML from the 62 gene signatures achieved improved performance compared with previous studies. In conclusion, our findings reveal significant differences in metabolic processes of patients with AML with diverse survival durations and provide valuable information for clinical translation., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Protein-protein interactions (PPIs) play essential roles in many biological processes. In protein-protein interaction networks, hubs involve in numbers of PPIs and may constitute an important source of drug targets. The intrinsic disorder proteins (IDPs) with unstable structures can promote the promiscuity of hubs and also involve in many disease pathways, so they also could serve as potential drug targets. Moreover, proteins with similar functions measured by semantic similarity of gene ontology (GO) terms tend to interact with each other. Here, the relationship between hub proteins and drug targets based on GO terms and intrinsic disorder was explored. The semantic similarities of GO terms and genes between two proteins, and the rate of intrinsic disorder residues of each protein were extracted as features to characterize the functional similarity between two interacting proteins. Only using 8 feature variables, prediction models by support vector machine (SVM) were constructed to predict PPIs. The accuracy of the model on the PPI data from human hub proteins is as high as 83.72%, which is very promising compared with other PPI prediction models with hundreds or even thousands of features. Then, 118 of 142 PPIs between hubs are correctly predicted that the two interacting proteins are targets of the same drugs. The results indicate that only 8 functional features are fully efficient for representing PPIs. In order to identify new targets from IDP dataset, the PPIs between hubs and IDPs are predicted by the SVM model and the model yields a prediction accuracy of 75.84%. Further research proves that 3 of 5 PPIs between hubs and IDPs are correctly predicted that the two interacting proteins are targets of the same drugs. All results demonstrate that the model with only 8-dimensional features from GO terms and intrinsic disorder still gives a good performance in predicting PPIs and further identifying drug targets., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Estrogen receptor status and the pathologic response to preoperative chemotherapy are two important indicators of chemotherapeutic sensitivity of tumors in breast cancer, which are used to guide the selection of specific regimens for patients. Microarray-based gene expression profiling, which is successfully applied to the discovery of tumor biomarkers and the prediction of drug response, was suggested to predict the cancer outcomes using the gene signatures differentially expressed between two clinical states. However, many false positive genes unrelated to the phenotypic differences will be involved in the lists of differentially expressed genes (DEGs) when only using the statistical methods for gene selection, e.g. Student's t test, and subsequently affect the performance of the predictive models. For the purpose of improving the prediction of clinical outcomes, we optimized the selection of DEGs by using a combined strategy, for which the DEGs were firstly identified by the statistical methods, and then filtered by a similarity profiling approach that used for candidate gene prioritization. In our study, we firstly verified the molecular functions of the DEGs identified by the combined strategy with the gene expression data generated in the microarray experiments of Si-Wu-Tang, which is a popular formula in traditional Chinese medicine. The results showed that, for Si-Wu-Tang experimental data set, the cancer-related signaling pathways were significantly enriched by gene set enrichment analysis when using the DEG lists generated by the combined strategy, confirming the potentially cancer-preventive effect of Si-Wu-Tang. To verify the performance of the predictive models in clinical application, we used the combined strategy to select the DEGs as features from the gene expression data of the clinical samples, which were collected from the breast cancer patients, and constructed models to predict the chemotherapeutic sensitivity of tumors in breast cancer. After refining the DEG lists by a similarity profiling approach, the Matthew's correlation coefficients of predicting estrogen receptor status and the pathologic response to preoperative chemotherapy with the DEGs selected by the fold change ranking were 0.770 and 0.428, respectively, and were 0.748 and 0.373 with the DEGs selected by SAM, respectively, which were generally higher than those achieved with unrefined DEG lists and those achieved by the candidate models in the second phase of Microarray Quality Control project (0.732 and 0.301, respectively). Our results demonstrated that the strategy of integrating the statistical methods with the gene prioritization methods based on similarity profiling was a powerful tool for DEG selection, which effectively improved the performance of prediction models in clinical applications and can guide the personalized chemotherapy better., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Domains are the structural basis of the physiological functions of proteins, and the prediction of which is an advantageous process on the study of protein structure and function. This article proposes a new complete automatic prediction method, PPM-Dom (Domain Position Prediction Method), for predicting the particular positions of domains in a target protein via its atomic coordinate. The presented method integrates complex networks, community division, and fuzzy mean operator (FMO). The whole sequences are divided into potential domain regions by the complex network and community division, and FMO allows the final determination for the domain position. This method will suffice to predict regions that will form a domain structure and those that are unstructured based on completely new atomic coordinate information of the query sequence, and be able to separate different domains in the same query sequence from each other. On evaluating the performance using an independent testing dataset, PPM-Dom reached 91.41% for prediction accuracy, 96.12% for sensitivity and 92.86% for specificity. The tool bag of PPM-Dom is freely available at http://cic.scu.edu.cn/bioinformatics/PPMDom.zip., (Copyright © 2013 The Authors. Published by Elsevier Ltd.. All rights reserved.)

In this study, we focus on different types of Gram-negative bacterial secreted proteins, and try to analyze the relationships and differences among them. Through an extensive literature search, 1612 secreted proteins have been collected as a standard data set from three data sources, including Swiss-Prot, TrEMBL and RefSeq. To explore the relationships among different types of secreted proteins, we model this data set as a sequence similarity network. Finally, a multi-classifier named SecretP is proposed to distinguish different types of secreted proteins, and yields a high total sensitivity of 90.12% for the test set. When performed on another public independent dataset for further evaluation, a promising prediction result is obtained. Predictions can be implemented freely online at http://cic.scu.edu.cn/bioinformatics/secretPv2_1/index.htm., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Flavin mono-nucleotide (FMN) closely evolves in many biological processes. In this study, a computational method was proposed to identify FMN binding sites based on amino acid sequences of proteins only. A modified Position Specific Score Matrix was used to characterize the local environmental sequence information, and a visible improvement of performance was obtained. Also, the ensemble SVM was applied to solve the imbalanced data problem. Additionally, an independent dataset was built to evaluate the practical performance of the method, and a satisfactory accuracy of 87.87% was achieved. It demonstrates that the method is effective in predicting FMN-binding sites., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

In proteins, the number of interacting pairs is usually much smaller than the number of non-interacting ones. So the imbalanced data problem will arise in the field of protein-protein interactions (PPIs) prediction. In this article, we introduce two ensemble methods to solve the imbalanced data problem. These ensemble methods combine the based-cluster under-sampling technique and the fusion classifiers. And then we evaluate the ensemble methods using a dataset from Database of Interacting Proteins (DIP) with 10-fold cross validation. All the prediction models achieve area under the receiver operating characteristic curve (AUC) value about 95%. Our results show that the ensemble classifiers are quite effective in predicting PPIs; we also gain some valuable conclusions on the performance of ensemble methods for PPIs in imbalanced data. The prediction software and all dataset employed in the work can be obtained for free at http://cic.scu.edu.cn/bioinformatics/Ensemble_PPIs/index.html., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

Signal peptides play a crucial role in various biological processes, such as localization of cell surface receptors, translocation of secreted proteins and cell-cell communication. However, the amino acid mutation in signal peptides, also called non-synonymous single nucleotide polymorphisms (nsSNPs or SAPs) may lead to the loss of their functions. In the present study, a computational method was proposed for predicting deleterious nsSNPs in signal peptides based on random forest (RF) by incorporating position specific scoring matrix (PSSM) profile, SignalP score and physicochemical properties. These features were optimized by the maximum relevance minimum redundancy (mRMR) method. Then, a cost matrix was used to minimize the effect of the imbalanced data classification problem that usually occurred in nsSNPs prediction. The method achieved an overall accuracy of 84.5% and the area under the ROC curve (AUC) of 0.822 by Jackknife test, when the optimal subset included 10 features. Furthermore, on the same dataset, we compared our predictor with other existing methods, including R-score-based method and D-score-based methods, and the result of our method was superior to those of the two methods. The satisfactory performance suggests that our method is effective in predicting the deleterious nsSNPs in signal peptides., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

MicroRNA (miRNA) is the negative regulator of gene expression, also known as guide strand of transient miRNA:miRNA* duplex. It is critical in maintaining the normal physiological processes such as development, differentiation, and apoptosis in many organisms. With increasing miRNA data, it is desirable to design methods to identify guide strand based on machine learning algorithms. In this study, the random forest models based on local sequence-structure features were proposed to identify miRNA in four species. The accuracies achieved were 86.51% for Homo sapiens, 81.66% for Ornithorhynchus anatinus, 82.33% for Mus musculus and 85.71% for Schmidtea mediterranea, respectively. Furthermore, the important analysis of feature elements was carried out by using the conditional feature importance strategy. The analysis results revealed that most of the significant elements were related to guanine-cytosine (GC) base pair. We believed that our method could be beneficial to annotate the function of miRNA and help the further understanding of the RNA interference mechanism., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

A three-stage support vector machine (SVM) was constructed to predict the state of cysteines by fusing sequence information, evolution information and annotation information of protein sequences. The first and second stages were for predicting whether the protein sequences contain disulfide bonds and whether all of the cysteines are involved in disulfide bonds. In the last stage, one SVM was constructed for predicting which cysteines are involved in disulfide bonds, among all these cysteines in proteins. The three SVMs give a good performance and the overall prediction accuracy are 90.05%, 96.36% and 80.00%, respectively, which indicates that the features selected in this work are effective for predicting the state of cysteines. In addition, current methods only paid too much attention to the prediction performance and never showed us how much important the roles of these features played in the prediction. As a result a feature importance measurement designated as F-score function was used to evaluate these features. The result shows that among these protein descriptors; evolution information is the most important feature for representing the disulfide-containing proteins. The prediction software and data sets used in this article are freely available at http://cic.scu.edu.cn/bioinformatics/Predict_Cys.zip., (Copyright © 2010 Elsevier Ltd. All rights reserved.)

Based on the combination of uninformative variable elimination (UVE), bootstrap and mutual information (MI), a simple ensemble algorithm, named ESPLS, is proposed for spectral multivariate calibration (MVC). In ESPLS, those uninformative variables are first removed; and then a preparatory training set is produced by bootstrap, on which a MI spectrum of retained variables is calculated. The variables that exhibit higher MI than a defined threshold form a subspace on which a candidate partial least-squares (PLS) model is constructed. This process is repeated. After a number of candidate models are obtained, a small part of models is picked out to construct an ensemble model by simple/weighted average. Four near/mid-infrared (NIR/MIR) spectral datasets concerning the determination of six components are used to verify the proposed ESPLS. The results indicate that ESPLS is superior to UVEPLS and its combination with MI-based variable selection (SPLS) in terms of both the accuracy and robustness. Besides, from the perspective of end-users, ESPLS does not increase the complexity of a calibration when enhancing its performance., (Copyright © 2010 Elsevier B.V. All rights reserved.)

Protein secretion plays an important role in bacterial lifestyles. Secreted proteins are crucial for bacterial pathogenesis by making bacteria interact with their environments, particularly delivering pathogenic and symbiotic bacteria into their eukaryotic hosts. Therefore, identification of bacterial secreted proteins becomes an important process for the study of various diseases and the corresponding drugs. In this paper, fusing several new features into Chou's pseudo-amino acid composition (PseAAC), two support vector machine (SVM)-based ternary classifiers are developed to predict secreted proteins of Gram-negative and Gram-positive bacteria. For the two types of bacteria, the high accuracy of 94.03% and 94.36% are obtained in distinguishing classically secreted, non-classically secreted and non-secreted proteins by our method. In order to compare the practical ability of our method in identifying bacterial secreted proteins with those of six published methods, proteins in Escherichia coli and Bacillus subtilis are collected to construct the test sets of Gram-negative and Gram-positive bacteria, and the prediction results of our method are comparable to those of existing methods. When performed on two public independent data sets for predicting NCSPs, it also yields satisfactory results for Gram-negative bacterial proteins. The prediction server SecretP can be accessed at http://cic.scu.edu.cn/bioinformatics/secretPV2/index.htm., (Copyright © 2010 Elsevier Ltd. All rights reserved.)

With the aim of developing a nonlinear tool for near-infrared spectral (NIRS) calibration, an applicable algorithm, called MIKPLS, is designed based on the combination of two different strategies, i.e. mutual information (MI) for interval selection and kernel partial least squares (KPLS) for modeling. Due to the ability of capturing linear and nonlinear dependencies between variables simultaneously, mutual information between each candidate variables and target is calculated and employed to induce a continuous wavelength interval, which is subsequently applied to build a parsimonious calibration model for future use by kernel partial least squares. Through the experiments on two datasets, it seems that mutual information (MI)-induced interval selection, followed by KPLS, forms a very simple and practical tool, allowing a prediction model to be constructed using a much-reduced set of neighboring variables, but without any loss of generalizations and with improved prediction performance instead.

This article aims at designing a wavelet alternative to Fourier transform infrared spectra (FTIR). In order to select the most suitable wavelet parameters to perform, several decomposition levels and 53 wavelets were tested by trial and error approach, respectively. The result indicated that discrete meyer wavelet (dmey) associated with its third decomposition level was very efficient for this purpose. On the base of it, a novel library named as Fourier transform infrared wavelet coefficients library (FTIR-WC) has been constructed. Finally, two tools such as library search and structure elucidation were developed to evaluate the capability of the new library system. The results obtained were also compared with those from FTIR library by a variety of indices. The results suggested that the new library performed better but with less volume. This work is expected to propose a novel and practical strategy in infrared spectroscopic analysis.

Living cell is highly responsive to specific chemicals in its environment, such as hormones and molecules in food or aromas. The reason is ascribed to the existence of widespread and diverse signal transduction pathways, between which crosstalks usually exist, thus constitute a complex signaling network. Evidently, knowledge of topology characteristic of this network could contribute a lot to the understanding of diverse cellular behaviors and life phenomena thus come into being. In this presentation, signal transduction data is extracted from KEGG to construct a cellular signaling network of Homo sapiens, which has 931 nodes and 6798 links in total. Computing the degree distribution, we find it is not a random network, but a scale-free network following a power-law of P(K) approximately K(-gamma), with gamma approximately equal to 2.2. Among three graph partition algorithms, the Guimera's simulated annealing method is chosen to study the details of topology structure and other properties of this cellular signaling network, as it shows the best performance. To reveal the underlying biological implications, further investigation is conducted on ad hoc community and sketch map of individual community is drawn accordingly. The involved experiment data can be found in the supplementary material.

Mass spectral classifiers of 16 substructures that are present in basic structures of pesticides have been investigated to assist pesticide residues analysis as well as screening of pesticide lead compounds. Mass spectral data are first transformed into 396 features, and then Genetic Algorithm-Partial Least Squares (GA-PLS) as a feature selection method and Support Vector Machine (SVM) as a validation method are implemented together to get an optimization feature set for each substructure. At last, a statistical method which is AdaBoost algorithm combined with Classification and Regression Tree (AdaBoost-CART) is trained to predict the 16 substructures presence/absence using the optimization mass spectral feature set. It is demonstrated that the optimum feature sets can be used to predict the 16 pesticide substructures presence/absence with mostly 85-100% in recognition success rate instead of the original 396 features.

Simultaneous determination of tin, germanium and molybdenum in food samples has been established by flow injection-charge coupled detector (CCD) diode array detection spectrophotometry with partial least squares (PLS) algorithm. The method was based on the chromogenic reaction of metal ions and salicylflurone in the presence of cetyltrimethyl ammonium bromide. The overlapping spectra of these complexes are collected by CCD diode array detector and the multi-wavelength absorbance data are processed using partial least squares algorithm. The reaction conditions and analytical parameters of flow injection analysis have been investigated. The method was applied to directly determine Ge, Mo and Sn in several food samples after digestion with satisfactory results. The recoveries of spiked samples were 80.0-102.0% for tin, 86.3-92.0% for germanium and 83.2-95.2% for molybdenum, and the relative standard deviations for samples were 4.4-7.8%. Molybdenum in certified reference material of cattle liver was determined by the proposed method (n=8). The differential values between determined and guarantee values were within the given uncertain value ranges (t=1.687, P>0.05 for t-test). The samples of mung bean, kelp and pork liver were analyzed by the proposed method and inductively couple plasma-atomic emission spectroscopy (ICP-AES) method. The determination results of the two methods are in good agreement. The sampling rate is 30 samplesh(-1).