Principal Component Analysis

Arsenic is one of the most common metalloid contaminants in groundwater and it has both acute and chronic toxicity affecting multiple organs. Details of the mechanism of arsenic toxicity are still lacking and profile studies at metabolic level are very limited. Using gas chromatography coupled with mass spectroscopy (GC/MS), we first generated metabolomic profiles from the livers of arsenic-treated zebrafish and identified 34 significantly altered metabolite peaks as potential markers, including four prominent ones: cholic acid, glycylglycine, glycine and hypotaurine. Combined results from GC/MS, histological examination and pathway analyses suggested a series of alterations, including apoptosis, glycogenolysis, changes in amino acid metabolism and fatty acid composition, accumulation of bile acids and fats, and disturbance in glycolysis related energy metabolism. The alterations in glycolysis partially resemble Warburg effect commonly observed in many cancer cells. However, cellula...

Recent findings regarding differences in tinnitus-related neural activity according to onset age have raised a question on possible onset age-related differences in neural substrates of distress. Hence we collected quantitative electroencephalography (qEEG) findings of 28 late-onset tinnitus (LOT) and 29 early-onset tinnitus (EOT) (mean onset age 52.3 and 29.0 years, respectively) participants. According to the tinnitus questionnaire (TQ) score grade, LOTs were then subdivided into 13 high distress (HD; TQ grade 3 or 4) and 15 low distress (LD; TQ grade 1 or 2), while EOTs into 14 HD and 15 LD. Compared to the EOT group, the LOT group demonstrated increased qEEG source-localized activity and functional connectivity primarily in the anterior cingulate cortex (ACC) and parahippocampus. In subgroup comparisons, the ACC was activated more in HD-LOT participants than in LD-LOT participants for the beta 1, beta 2 and gamma frequency bands, while the left orbitofrontal cortex and left dors...

The gut bacteria exert phenotypic traits to the host but the factors which determine the gut bacterial profile (GBP) is poorly understood. This study aimed to understand the effect of ethnicity and geography on GBP of Mongoloid and Proto-Australoid tribes of India. Fecal bacterial diversity was studied in fifteen tribal populations representing four geographic regions (Assam, Telangana, Manipur and Sikkim) by DGGE followed by NGS analysis on Illumina MiSeq platform. Geography and diet had significant effect on GBP of the Indian tribes which was dominated by Prevotella. The effects were more prominent with lower taxonomic levels, indicating probable functional redundancy of the core GBP. A comparison with the worldwide data revealed that GBP of the Indian population was similar to the Mongolian population (Mongolia). The bacterial genera Faecalibacterium, Eubacterium, Clostridium, Blautia, Ruminococcus and Roseburia were found to be core genera in the representative populations of th...

High pressure homogenization (HPH) was tested for inducing autolysis in a commercial strain of Saccharomyces bayanus for winemaking. The effects on cell viability, the release of soluble proteins, glucidic colloids and amino acids in wine-like medium and the volatile composition of the autolysates were investigated after processing, in comparison with thermolysis. HPH seemed a promising technique for inducing autolysis of wine yeasts. One pass at 150MPa was the best operating conditions. Soluble colloids, proteins and free amino acids were similar after HPH and thermolysis, but the former gave a more interesting volatile composition after processing, with higher concentrations of ethyl esters (fruity odors) and lower fatty acids (potential off-flavors). This might allow different winemaking applications for HPH, such as the production of yeast derivatives for wine ageing. In the conditions tested, HPH did not allow the complete inactivation of yeast cells; the treatment shall be optimized before winemaking use.

Evaporation is an essential component of hydrological cycle. Several meteorological factors play role in the amount of pan evaporation. These factors are often related to each other. In this study, a multiple linear regression (MLR) in conjunction with Principal Component Analysis (PCA) was used for modeling of pan evaporation. After the standardization of the variables, independent components were obtained using the (PCA). The series of principal component scores were used as input in multiple linear regression models. This method was applied to four stations in East Azerbaijan Province in the North West of Iran. Mathematical models of pan evaporation were derived for each station. The results showed that the first three components in all four stations account for more than 90% of the data variance. Performance criteria, namely coefficient of determination (R 2) and root mean square error (RMSE), were calculated for models in each station. The results showed that in all the PCA-MLR models, the R 2 value was greater than 0.74 (significant at the 5% level) and the RMSE was less than 0.52 mm per day. In general, the results showed an improvement in the results using combination of PCA and MLR models for pan evaporation estimation.

A large variation in prognosis is observed despite the use of clinical prognostic factors in patients with advanced non-small cell lung cancer (NSCLC). It is likely that this variation is due to the different biological properties of the tumour cells. In this work we aimed to identify gene signature that could predict survival in advanced NSCLC. Total RNA was extracted from five 5 μm-thick sections of the FFPE using the High Pure RNA Paraffin Kit (Roche). RNA amplification was performed using WT-Ovation™ FFPE RNA Amplification System V2 (NuGen). The amplified cDNA was then labelled and hybridised onto Illumina HumanRef-8 v3.0 Expression BeadChips. Microarray data analysis was subsequently performed using Genespring GX version 9.0. Out of 75 FFPE samples, only 32 had sufficient RNA quality and quantity for microarray gene expression analysis. Patients were grouped into long and short survival groups based on the time to cancer-related death. After normalization and filtration, 19,002 genes were selected for differential gene expression analysis. A total of 440 genes differed significantly between the long and short survival groups (ANOVA, p <; 0.05, with Benjamini and Hochberg False Discovery Rate multiple testing correction). Unsupervised Hierarchial Clustering with Pearson correlation and average linkage identified two broad clusters of patients corresponding to the long and short survival. Thirteen genes were selected based on the TTest, 2-fold expression changes, principal components analysis and univariate Cox regression analysis and risk scores were calculated for each patient. These gene signatures were independent predictors of survival. The model was validated with a published microarray data from 130 patients with NSCLC. Using Gene Set Analysis (GSA), we found certain biological processes including metastasis and chemotherapy resistance were up-regulated in the short survival group while TID pathway and MAPKKK cascade were enriched in th- - e long survival group. As the conclusion, there is several distinct gene expression profiles associated with survival of patients with advanced stage NSCLC. Survival outcomes in advanced NSCLC could be predicted based on a 13-gene signature.

The stone marten is a widely distributed mustelid in the Palaearctic region that exhibits variable habitat preferences in different parts of its range. The species is a Holocene immigrant from southwest Asia which, according to fossil remains, followed the expansion of the Neolithic farming cultures into Europe and possibly colonized the Iberian Peninsula during the Early Neolithic (ca. 7,000 years BP). However, the population genetic structure and historical biogeography of this generalist carnivore remains essentially unknown. In this study we have combined mitochondrial DNA (mtDNA) sequencing (621 bp) and microsatellite genotyping (23 polymorphic markers) to infer the population genetic structure of the stone marten within the Iberian Peninsula. The mtDNA data revealed low haplotype and nucleotide diversities and a lack of phylogeographic structure, most likely due to a recent colonization of the Iberian Peninsula by a few mtDNA lineages during the Early Neolithic. The microsatel...

We compared the abilities of Biolog’s GN and ECO plates to distinguish among aerobic and heterotrophic bacterial communities in samples from six aquatic environments. The Biolog system is based on interpreting patterns of sole-carbon substrate utilization indicated by color development in a 96-well microtiter plate. Whether of fresh or saltwater origin, bacterial communities utilized >95% of substrates in both types of plates. Samples from any one environment exhibited similar time courses of average well color development (AWCD) in both GN and ECO plates. Principal component analysis was performed on data sets resulting from combinations of algorithms (AWCD and curve-integration methods) and levels of color development (end-point and set-point approaches). In all cases, the two types of plates demonstrated an equal capacity to discriminate among the heterotrophic expressions of the six microbial communities. Substantial deviation from an anticipated 1:1 correspondence occurred when color development of 25 substrates common to both types of plates was compared. The discrepancies likely are related to the different formulations of low-nutrient media in GN and ECO plates.

A randomization method is developed for the calculation of covariation between multiple variables that are linked nonlinearly to a dependent variable. Covariation is a phenomenon often invoked in the study of movement coordination to capture the fact that in coordinated movement the outcome shows greater than expected consistency from the variability in the component processes. However, in most cases, the problem is that more than two variables covary in a nonlinear fashion, which makes quantification with the bivariate linear covariation and correlation coefficient inapplicable. This paper presents a generalization of the calculation of linear bivariate covariance using a variant of a randomization method that is based on the comparison between the empirically measured variability in the outcome and a covariation-free variability. The latter can be estimated by permuting data sets. A generalized correlation coefficient is derived, and it is shown how errors of estimation can be qua...