protected against cisplatin-induced nephrotoxicity. In additional, PCA, PLS-**DA** and **OPLS**-**DA** scores plots of serum samples showed that the normal control group was clearly separated from the cisplatin-only group and the cisplatin- PNS group, while there was a part overlap between the cisplatin-only group and the cisplatin-PNS group. Moreover, the expression of acetone, n-acetyl glycoprotein signals, pyruvate, lipid and VLDL was lower, while the expression of glucose, valine and tyrosine was higher in the cisplatin-only group and the cisplatin-PNS group than that in the nor- mal control group. Furthermore, lactate expression was higher in the cisplatin-only group than that in the cisplatin- PNS group. Phenylalanine expression was higher while tyrosine expression was lower in the cisplatin-PNS group and the normal control group when compared with the cisplatin-only group. Taken together, these results confirmed that the metabolism of cisplatin-induced nephrotoxicity had a huge change and PNS would be an effective medicine. Its protective mechanism may be associated with correcting the disturbed metabolism of cisplatin-induced nephrotox- icity. And here is the first demonstration about it.

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As reported in details in the experimental section, three different bucket datasets were generated from NMR spectra: BUCKET-1 was obtained within the range 10.0 - 0.5 ppm, BUCKET-2 was obtained within the range 10.0 - 5.6 ppm and BUCKET-3 was the combination of the two previous bucket tables (taking into account only the range 5.0 - 0.5 ppm originating from BUCKET-1 and the whole BUCKET-2). For every bucket table built, multi- variate statistical analyses (unsupervised, PCA, and su- pervised methods, PLS-**DA** and **OPLS**-**DA**) were applied. PCA allowed to obtain a general overview of the natural data grouping. The original datasets were rearranged in a new multivariate coordinate space where the dimensions are ordered by decreasing explained variance in the data. The principal components were displayed as a set of scores (t), which highlight clustering or outliers, and a set of loadings (p), which highlight the influence of input variables on t. In all the models studied, PCA did not show significant trends or clustering with the exception of t2/t4 PCA score plot (Figure 2) for BUCKET-3. It should be noted that this combined bucket table gave also the best results, reported and discussed below, for further statistical analyses. PCA on the monocultivar Cellina and Ogliarola samples with 7 components gave R 2 = 0.755 and Q 2 = 0.525, a weak model but useful for visualiza- tion of data. Looking at the score plot a certain degree of

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component that do not contribute to discrimination be- tween the defined groups [29]. The minimum number of features needed for optimal classification of the **OPLS**-**DA** models was determined by iteratively excluding the vari- ables with low regression coefficients and wide confidence intervals derived from jackknifing combined with low variable importance in the projection (VIP) until max- imum improvement of the quality of the models. The me- tabolite with the lowest P-value (Welch’s means equality t-test) was conserved in the model if the same metabolite was detected in the positive and negative ionization mode for LC-HRMS. The model quality was evaluated after 7-fold cross validation by cumulative R 2 Y (goodness of fit), cumulative Q 2 (goodness of pre- diction), and CV-ANOVA (cross validation-analysis of variance). CV-ANOVA is a diagnostic tool for assessing the reliability of **OPLS**-**DA** models; the returned P-value is indicative of the statistical significance of the fitted model [30]. The contribution of each predictor in the model was evaluated through the variable score con- tribution (i.e. the differences, in scaled units, for all the terms in the model, between the outlying observation and the normal observation, multiplied by the absolute value of the normalized weight) and the importance in the model (VIP). Subsequently, the **OPLS**-**DA** models, as de- scribed above, were fitted on the GC-MS and LC-HRMS data sets, considering the genotypes (i.e. Hubbard Classic, Ross 308) independently.

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In a comparison of low HC vs high HC, 23 putative biomarkers (Table S2) passed the 95% CI filter and showed significant change based on FDR corrected p values, and had an AUC above 0.7. These 23 putative biomarkers were used to rebuild an **OPLS**-**DA** model (Figure S3, supplementary) in order to examine its ability to separate observations based on HC dose. The figure shows separation between observations having either low or high HC doses. Approximately 66% of the variability in metabolites was explained by the model, of which 38.5% was due to HC dose alone with the rest being attributable to other factors related to inter-individual variability. The result for the cross-validation of the model is shown in Figure S4. The validity of the number of orthogonal components in the model was examined using an observed versus predicted plot (Figure S4A), the regression line R 2 = 0.82 indi-

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For the results of the physical-chemical analysis of the experimental diets and biological data, the Student t-test was used, considering p < 0.05 as the minimum probability between means. For the statistical analysis, the software SigmaStat 3.5 was used, and the software Graph Pad Prism 6.0 was used to treat biological data and plot graphs. Qualitative analyses were expressed as medians. We used the Kruskal- Wallis test with Mann-Whitney post test, considering p < 0.05. Prior to chemometric analysis, the 1 H NMR spectra from serum samples were binned and Pareto scaled in the AMIX 3.8 Bruker software. The buckets were built with a simple rectangular form and 0.03 ppm of width, integrated by the sum of absolute intensities, and scaled by the total intensity. The evaluated spectral region was 0.60–5.60 ppm, excluding the noises above 5.60 ppm and the region between 4.30 and 5.15 ppm for the residual water signal. For primary visualization, distribution, and clustering, the principal component analysis (PCA) was applied. Orthogonal projections to latent structures- discriminant analysis (OPSL-**DA**) was further performed as a supervised pattern recognition method, which maximizes the variation between the different groups and identifies variables responsible for the separation. The uncorrelated variation was eliminated using with one orthogonal correction. **OPLS**-**DA** was carried out in the Pirouette 4.0 Infometrix softwarewith a three-fold cross validation procedure (CV, 1/7 of the samples being excluded from calculations in each round) to determine the variation between datasets.

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not appear to play any discriminatory role in CM versus NCM at days 3–4 pi although on day 5 enhanced level of glucose is observed in NCM sera compared to CM. Moreover, serum lactate does not contribute significantly towards segregation of CM and NCM during this period. Thus, glycolysis, its substrate glucose and the end product lactate are not involved in differentiating CM and NCM animals at an earlier stage of infection but later time points and glucose levels indeed seem to be perturbed in CM in comparison with NCM. The possible forma- tion of triglycerides from glucose at day 8–9 pi in CM has already been reported. In this study it seems that such a perturbation is operative from day 5 pi. The negative cor- relation of blood glucose and triglycerides in **OPLS**-**DA** plot at day 5 pi is suggestive of such perturbation.

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Raw (unrarefied) OTU abundance data were imported into R for analyses using Phyloseq [34] (Additional files 5 and 6). A phylogenetic tree was generated using MEGA v7.0 [35] and rooted to a random node using the R pack- age phytools [36]. A pseudocount of 0.001 was added to all OTU abundances to avoid calculating log-ratios involving zeros, and then data was then made compos- itional through isometric log-ratio transformation using the R package philr [37]. Ordination was carried out using the ‘ordinate’ function in Phyloseq, based on Euclidean distances in philr space. Permutational ana- lysis of variance (PERMANOVA) was carried out using the ADONIS command in the R package vegan [38]. **OPLS**-**DA** models were built using the ropls package in R [39]. Alpha diversity was assessed using Phyloseq. Compositional analysis of the microbiota at six taxo- nomic levels was based on isometric log-ratio transform- ation of raw sequence abundances and adjusted for multiple testing using the Benjamini-Hochberg method, carried out using the ANCOM statistical framework [40] in R, with code obtained from the author’s webpage: https://sites.google.com/site/siddharthamandal1985/ research.

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There are many other differences in the univariate data and it is dif- ﬁ cult to rationalise them all. In order to determine if there are subgroups within the samples, a PCA model was ﬁ tted to the 36 samples used to produce the **OPLS**-**DA** model using only the metabolites shown in Table S1 which were signi ﬁ cantly different according to univariate anal- ysis. Hierarchical cluster analysis clearly highlighted a group of 9 SDB samples which were far away from the rest of the samples that did not clearly separate in the PCA plot (Fig. 4). The metabolites which were most signi ﬁ cant in separating this sub-group from the controls in the PCA plot are listed in Table 3 along with P values and ratios de- rived from univariate comparison of these nine samples with the con- trol samples. The brains in this subgroup contain much lower levels of NAAG in comparison with the rest of the SDB group and sorbitol, gluconic acid, and xylitol/ribitol/arabinotol are also higher than in the general cohort. In addition, N-acetylvanilalanine is higher in these samples along with tyrosine and phenylalanine than in the rest of the SDB samples which might indicate a greater degree of aromatic amino acid decarboxylase de ﬁ ciency. However, tryptophan is not signi ﬁ cantly different in this subgroup compared with the rest of the SDB samples although its metabolite tryptamine is elevated. In addition norepineph- rine sulphate and guanosine are signi ﬁ cantly lower in this group com- pared with the rest of the samples.

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ess the quality of the built **OPLS**-**DA** model [14]. The 299-iteration permutation test was ap- plied to check whether there was over-fitting or not. Variable Importance in Projection (VIP) score indicates the relative importance of each metabolite in discriminating different groups. Metabolites with VIP score >1.0 (equivalent to a p-value of less than 0.05) were viewed as dif- ferential metabolites [15]. Secondly, logistic- regression analysis with Akaike’s information criterion (AIC) [16] was used to derive an opti- mal and simplified GC vs. HCs biomarker model. Thirdly, receiver-operating characteristic (ROC)

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Addtitional file 2: Tables S7-S12 and Figures S11–S13. Table 5 shows the identified biological pathways in- volved, based on the most important metabolites found by NMR and GC-MS analyses, in the diagnosis of H1N1 pneumonia based on culture-positive bacterial pneumonia and ventilated ICU control subjects and prognosis of mortality in H1N1 pneumonia. The path- ways have been ordered by their impact values from high to low in Table 5. Table 5 shows more differenti- ated biological pathways involved in the diagnosis of H1N1 from ICU control subjects compared with patients with culture-positive bacterial CAP. This evidence sug- gests that the difference in metabolomic profile between patients with H1N1 pneumonia and ICU control subjects is greater than that of patients with H1N1 pneumonia and culture-positive bacterial pneumonia, based on the **OPLS**- **DA** models (Table 4) as well as on the biological pathways. It is interesting that a variety of biological pathways were found in separation of H1N1 nonsurvivors from survivors on the basis of metabolite changes. More potential net- works of biological pathways were generated through the use of IPA software in the diagnosis and prognosis of mortality studies for both NMR and GC-MS datasets (Table 6).

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ACN, acetonitrile; APOA1, apolipoprotein A1; APOC1, apo- lipoprotein C-I; APOE apolipoprotein E; BBB, blood-brain barrier; BSCB, blood-spinal cord barrier; C7, complement component C7; CFH, complement factor H; CFS, chronic fatigue syndrome; CNS, central nervous system; COL6A3, collagen alpha-3(VI) chain; CSF, cerebrospinal ﬂ uid; DKK3, dickkopf related protein 3; ENPP2, autotaxin; F2, prothrom- bin; FA, formic acid; HPA, Human Protein Atlas; HPX, hemo- pexin; IC-MS, immunocapture mass spectrometry; LPA; lysophosphatidic acid; MS, mass spectrometry; MYH7, myo- sin heavy chain 7; NRX1, neurexin-1; OGN, osteoglycin; **OPLS**-**DA**, orthogonal partial least squares – discriminant analysis; PCA, principal component analysis; PTGDS, pros- taglandin-H2 D-isomerase; S1P, sphingosine-1-phosphate; SCG1, secretogranin-1; SCS, spinal cord stimulation; SOD1, superoxide dismutase 1; TLR4, toll-like receptor 4.

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for sample distribution overview, (2) projection to latent structures by partial least squares (PLS) regression, and (3) orthogonal partial least square discriminant analysis (**OPLS**-**DA**) for the identification of the most discrimi- nant variables that characterize groups. PCA is a tech- nique that transforms the variables in a dataset into a smaller number of new latent variables, known as prin- cipal components. Each new principal component repre- sents a linear combination of original variables, enabling the generation of a compact description of the variation within a given dataset. The **OPLS**-**DA** model maximizes the covariance between the measured data of the X-var- iable (peak intensities in NMR spectra) and the response of the Y-variable (class assignment) within the groups. Useful parameters obtained from the **OPLS**-**DA** model were the variable influence on projection (VIP) scores and coefficients that describe the metabolite influence over all validated components. The model quality was evaluated on the corresponding partial least square dis- criminant analysis (PLS-**DA**) model using a 7-fold cross- validation and permutation test. The generated R 2 and Q 2

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The accuracy of the method is defined by a random displacement. In this investigation, differences of force fields are shown by comparing the calculated energies using AMBER and **OPLS**. In this investigation Hyper-Chem professional release 7.01 is used for most of chemical calculations.

Particularmente, a análise espacial focaliza aspectos específicos concernentes aos dados e aos modelos utilizados. Anselin (1988; 1992) denomina esses aspectos de efeitos espaciais sobre os dados e os divide em dois tipos gerais: a dependência espacial e a heterogeneidade espacial. O primeiro efeito, também conhecido como associação espacial ou autocorrelação espacial, está diretamente relacionado com a primeira lei **da** geografia de Tobler, qual seja, [...] todas as coisas são parecidas, porém, coisas mais próximas se parecem mais que coisas mais distantes (Tobler apud Druck et al., 2004, p. 11). Nesse sentido, pode-se considerar que, em um conjunto de entes geográficos (pontos, linhas ou polígonos), valores similares de uma variável tendem a estar próximos, o que poderia ser um aglomerado ( cluster ). A título de exemplo, veja-se o caso de um bairro metropolitano com altas taxas de criminalidade; provavelmente, os bairros vizinhos também possuem um grande número de crimes. A análise de dependência espacial, portanto, busca verificar essa associação que depende, especialmente, **da** distância. 1 Ressalta-se que a noção de espaço (relativo) pode ir além **da** idéia

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carry out the 5 ns simulations and similar results were obtained with each. Results presented here correspond to model 1. All simulations were carried out using GROMACS 3.2 [8,9], running on a single Fedora Linux system. The **OPLS**-AA force field was used. The peptide was solvated in a box containing approx. 500 water mole- cules [10]. Periodic boundary conditions were employed to eliminate surface effects. Energy minimization with a tolerance of 2000 kJ/mol/nm was carried out using the Steepest Descent method. All bonds were constrained using LINCS [11]. The system was loosely coupled to a

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Com as novas formas de acumulação de capital, de relações de produção e de estatuto jurídico **da** propriedade, todas as práticas populares que se classificavam, seja numa forma silenciosa, cotidiana, tolerada, seja numa forma violenta, na ilegalidade dos direitos, são desviadas à força para a ilegalidade dos bens. O roubo tende a se tornar a primeira das grandes escapatórias à legalidade, nesse movimento que vai de uma sociedade **da** apropriação jurídico-política a uma sociedade **da** apropriação dos meios e produtos do trabalho. Ou pra dizer as coisas de outra maneira: a economia das ilegalidades se reestruturou com o desenvolvimento **da** sociedade capitalista. (FOUCAULT, 2012, p. 84).

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Therefore, an extensive accuracy test of the OPLS-AA force field is performed in Chapter V by comparing the excess free energy of mixing, calculated using thermodynamic i[r]

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Trata-se de um estudo descritivo, exploratório e transversal com abordagem qualitativa de dados, por meio de uma revisão integrativa sobre os aspectos desencadeantes **da** síndrome de burnout em enfermeiros **da** ESF. O levantamento bibliográfico foi realizado entre novembro de 2007 a janeiro de 2013, nas seguintes bases de dados: Base de Dados **da** Enfermagem (BDENF); Literatura Latino-Americana e do Caribe em Ciências de Saúde (LILACS). A revisão integrativa é um método que traz a análise de pesquisas relevantes, onde se tem a síntese de estudos realizados, construindo conclusões a partir dos resultados evidenciados em cada estudo a respeito de uma particular área de investigação. Tal método fornece suporte para a tomada de decisão e melhoria à execução de ações, além de ser uma ferramenta valiosa para os profissionais **da** enfermagem, que por muitas vezes não disponibilizam de tempo suficiente para realizarem a leitura de todo conhecimento científico disponível nos mais diversos periódicos (MENDES; SILVEIRA; GALVÃO, 2008; POMPEO; ROSSI; GALVÃO, 2009).

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Ao seu nível, um processo técnico (produção) pode igualmente ele mesmo vir a ser objecto de reforma, como a alteração, quando não por modificações internas discricionárias, do regime jurídico/simplificação com anulação de procedimentos ou elementos necessários por que se tem constituído ou, até, vir a tornar-se mais acessível ou fácil por recurso à sua disponibilização territorial (princípio **da** procura e não o **da** oferta ou do controlo) ou, ainda, facilitado no suporte por uso de algum meio de troca e de envio e entrega do próprio processo. Por exemplo, na AdP, para lá dessas alterações e das dimensões **da** regularidade e concentração, linearidade e uniformismo ou redução, uma política de reforma **da** actividade do(s) seu(s) sistema(s) de finanças públicas, e controlo dos seus padrões orçamentais e de contabilidades, para lá das consolidações, bem pode passar o aperfeiçoamento dessa sua actividade financeira, nas funções ou tarefas de programação e coordenação **da** mesma, mediante,

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