Samples In Order Research Articles

Evaluation of a Sliding Window mechanism as DataAugmentation over Emotion Detection on Speech

Emotion analysis is an important field of study, with many applications for security, financial, and politician. Despite beinga subjective branch of study, emotion analysis can be simulated by Machine Learning algorithms that are trained for this purpose, throughcataloged audio datasets, they can recognize patterns in these media that could be related to corresponding emotion. Neural NetworkAlgorithms are able to work on the recognition of these emotions, with a focus only on audio, known as Speech Emotion Recognition(SER). Neural Network Algorithms generally obtain unequal averages of referring results such as recognition of emotions when applied todifferent audio datasets. This research evaluates a Data Augmentation method called Slide Window, which generates more data samplesin order to increase the averages of classification rates. The method has been applied to three public datasets: EMO-DB, SAVEE, and RAVEDESS. The experiments have shown effectiveness in the increasing of the recognition rates of about to 11.95% on the EMO-DBbase, 22.76% on SAVEE, and 18.82% on RAVEDESS when compared to other approaches in the literature.

Synthesis and structural characteristics of Co1-xCuxFe2O4 magnetic ferrite nanoparticles using hydrothermal technique

In this work, copper substituted cobalt ferrite nanoparticles withchemical formula Co1-xCuxFe2O4 (x=0, 0.3, and 0.7), has beensynthesized via hydrothermal preparation method. The structure ofthe prepared materials was characterized by X-ray diffraction (XRD).The (XRD) patterns showed single phase spinel ferrite structure.Average crystallite size (D), lattice constant (a), and crystal density(dx) have been calculated from the most intense peak (311).Comparative standardization also performed using smaller averageparticle size (D) on the XRD patterns of as-prepared ferrite samplesin order to select most convenient hydrothermal synthesis conditionsto get ferrite materials with smallest average particle size.

Lead Isotope Systematics in Groundwater: Implications for Source Tracing in Different Aquifer Types

We report Pb isotope measurements for 23 groundwater samples collected at different sites in France located from various types of aquifers (alluvial, sedimentary, karst, sand, volcanic and crystalline lithologies). We have measured the range of Pb isotopic variations in these groundwater samplesin order to test the usefulness of Pb isotopes as a tool for tracing Pb sources in aquifer systems. In these groundwater samples, Pb isotopic ratios ranged from 17.33 to 18.56, from 15.45 to 16.11 and from 37.06 to 40.35, respectively for 206Pb/204Pb, 207Pb/204Pb, 208Pb/204Pb, whereas the Pb concentrations were between 0.023 and 0.160μg/L, and below the 10μg/L maximum allowable concentration of Pb in drinking water. Lead isotope ratios of the study groundwater display very large variations, in particular for the sedimentary aquifers. The Pb isotopic variations werenot related to lead concentrations nor to the type of aquifers. The data indicate that Pb in groundwater is typically originatedfrom the mixing of natural and anthropogenicsources.

The Fundamental Problem of Gibbs Sampler in Mixture Models

The mixture models were rstly studied by Pearson in 1894. These models are strong tools,through which the complicated systems can be analyzed in a wide range of disciplines such as As-tronomy, Economics, Mechanics, etc. although the structure of these models is apparently simple,it is very complicated to obtain maximum likelihood estimators and Bayesian ones in particular andit needs to be approximated in most cases. In this paper, we apply the Gibbs Sampling in order toapproximate the Bayesian Estimator in Mixture models, present the Gibbs algorithms for the familyof exponential distributions and nally, we would show the disadvantage of this algorithm throughan example.

A quantitative proteomic analysis of FFPE melanoma

A quantitative proteomic analysis of FFPE melanoma

Trypsin and reduction method to prepare DNA from formalin‐fixed paraffin‐embedded samples for methylation analysis

In recent years, emerging evidences indicate that epigenetic alteration in the promoter region or the 5’ untranslated region of genes is one of the most common molecular events associated with human malignancies1. To analyze methylation patterns in human malignancies, large numbers of samples are needed. Most of the human issues, however, are in the archived form of formalin-fixed and paraffin embedded (FFPE)blocks. Currently, methylation specific PCR or methylation sequencing requires the usage of DNA samples extracted only from fresh or frozen tissue. DNA extracted from FFPE tissues is of poor quality largely due to containing DNA-DNA and DNA-protein cross-links that survive the DNA extraction process. These cross-links interfere with bisulfite deamination of cytosine, and produce inconsistent, and sometimes, outright false positive results. As a result, there are only sporadic reports of epigenetic analysis of FFPE samples thus far. This greatly limits the usefulness of these samples for epigenetic analysis. A new method that eliminates these cross-links is needed for analyzing the methylation patterns of these samples. In this report, we generate a new method that has significant improvement on reducing the false positive or false negative rate of sodium bisulfite deamination. In this study, we retrieve modifiable genomic DNA by incorporating trypsin digestion, cross-linking reduction, protein striping and DNA denaturing into a four-step process (figure 1a). In the first step, the microdissected FFPE samples are treated with 0.25% trypsin at 37°C for 30 minutes to digest DNA associated proteins and to reduce the complexity of DNA-proteins complex. This process is followed by incubating the sample with lysis buffer (80 mM Tris/HCl pH 6.8, 10 % Mercaptoethanol, 200 mM NaCl, 2 % SDS) in boiling condition for 30 minutes. This step intends to denature protein, to reduce DNA-protein cross-linking, and to remove proteins from genome DNA. The samples are then treated with phenol/chloroform to remove the reduced/dissociated proteins. The DNA is subsequently purified through ammonia acetate/ethanol precipitation. Figure 1 Trypsin and reduction protocol for formalin-fixed and paraffin-embedded samples In order to validate this methodology, we fixed fresh PC-3 cells with formalin and subsequently created a paraffin block containing these PC-3 cells. Five microns thin sections of PC-3 cell block were sliced onto glass slides. The DNA from these sections was extracted using trypsin/reduction protocol. An aliquot of fresh PC-3 cells from the same batch of culture was used as controls to determine the status of gene methylation. The DNA was treated with sodium bisulfite. Methylation PCRs were performed on each DNA preparation using degenerative methylation primers specific for MT1e, MT1h and SOX4. The PCR products from each preparation were ligated into TA cloning vector, and transformed into E.coli. Five colonies from each preparation were analyzed for CpG island methylation status. To evaluate the accuracy of this method, we used the methylation sequencing results from fresh prepared PC-3 cell samples as the standard. When there is a complete concordance between FFPE and fresh samples, the accuracy of the method reaches 100%, and the error rate is 0%. Vice versa, when there is no concordance between FFPE cells and fresh samples, the accuracy will be 0%, and error rate will be 100%. As shown in Table 1, the overall accuracy for trypsin/reduction method is 99.3% for MT1e, 99.4% for MT1h and 100% for SOX4. Trypsin/reduction has only 4 errors in total. To investigate the utility of this method in analyzing methylation status of genes of archived tissues, we selected five samples of FFPE tissues with relatively long storage time (10 to 20 years) and their matched frozen counterpart. Subsequently, we performed methylation PCR and methylation sequencing on these samples as described above. The results from FFPE samples were then compared with those from matched frozen tissues to evaluate the accuracy of the methylation analysis. As shown in table 1, the accuracy rate of cytosine deamination ranges from 97% to 100% for the CpG islands of these 3 genes, appears not to be affected by long storage time. As a result, the trypsin/reduction method presented in the study appears reproducible and reliable for analyzing FFPE archived tissues. In order to compare our method with the existing Heat/alkaline2 and Heat/proteinase K3 methods, we extracted DNA from the FFPE PC3 cell blocks using these methods, and performed methylation analysis on MT1e and MT1h genes. As shown in table 2, the overall error rate of heat/alkaline is 3%, while heat/proteinase K is 9.5%, significantly higher than that of trypsin/reduction method (0.6%, p=0.003 versus heat/alkaline, p=1.7 × 10−12 versus heat/proteinase K). Furthermore, trypsin/reduction method appears to accurately analyze the methylation status of FFPE tissues with long storage time, making this method highly useful in analyzing archived clinical samples. Table 1 Utility of trypsin and reduction method in formalin-fixed and paraffin-embedded tissues Table 2 Detail error rate of methodology

Speaking Stata: Density Probability Plots

Density probability plots show two guesses at the density function of a continuous variable, given a data sample. The first guess is the density function of a specified distribution (e.g., normal, exponential, gamma, etc.) with appropriate parameter values plugged in. The second guess is the same density function evaluated at quantiles corresponding to plotting positions associated with the sample's order statistics. If the specified distribution fits well, the two guesses will be close. Such plots, suggested by Jones and Daly in 1995, are explained and discussed with examples from simulated and real data. Comparisons are made with histograms, kernel density estimation, and quantile–quantile plots.

L Moment and Probability Plot Correlation Coefficient Goodness‐of‐fit Tests for the Gumbel Distribution and Impact of Autocorrelation

This paper analyzes the power of two L moment and the probability plot correlation coefficient (PPCC) goodness‐of‐fit tests for the Gumbel distribution and the impact of autocorrelation. The two L moment tests are the kappa test suggested by Hosking et al. (1985) using biased PWM estimators, and the L‐Cs test suggested by Chowdhury et al. (1991) using unbiased PWM estimators. The generalized extreme value (GEV) distribution with various values of the shape parameter κ was used as the parent distribution. Results show that the L moment‐based tests outperform the PPCC test for independent data, or data with small autocorrelations (ρ ≤ 0.4). For high autocorrelation (ρ = 0.8), all tests are invalid because the type 1 error probability is larger than the target value. An example demonstrates consistency problems with scale and shape parameters estimated using the biased PWM estimators; these cause us to advise against their use and to recommend instead unbiased PWM estimators that employ a sample's order statistics. Overall, this paper provides another endorsement of the use of unbiased L moment estimators for goodness‐of‐fit tests and distribution selection, as well as a recommendation for parameter estimation.