Automatic PRPD Image Recognition of Multiple Simultaneous Partial Discharge Sources in On-Line Hydro-Generator Stator Bars

In this study, a methodology for automatic recognition of multiple simultaneous types of partial discharges (PDs) in hydro-generator stator windings was proposed. All the seven PD sources typical in rotating machines were considered, and up to three simultaneous sources could be identified. The functionality of identifying samples with no valid PDs was also incorporated using a new technique. The data set was composed of phase-resolved partial discharge (PRPD) patterns obtained from on-line measurements of hydro-generators. From an input PRPD, noise and interference were removed with an improved version of an image-based denoising algorithm previously proposed by the authors. Then, a novel image-based algorithm that separates partially superposed PD clouds was proposed, by decomposing the input pattern into two sub-PRPDs containing discharges of different natures. From the sub-PRPDs, one extracts features quantifying the PD distribution over amplitudes and the contour of PD clouds. Those features are fed as inputs to several artificial neural networks (ANNs), each of which solves a part of the classification problem and acts as a block of a larger system. Once trained, ANNs work collaboratively to identify an unknown sample. Good results were obtained, with overall accuracies ranging from 88% to 94.8% for all the considered PD sources.

System Suitability Testing of the Method Developed to Measure the Active Concentration of a Fab Fragment Via Surface Plasmon Resonance

SPR methods are increasingly accepted by the biopharmaceutical industry in quality control (QC), R&D and clinical research. However, there are few reports on method validation and system suitability testing (SST), in particular on concentration determination methods related to the potency of the drug. System compliance must be demonstrated with certain tests in order, for the method validation to be ensured, as agreed by the scientific community and the industry, and specified in the guidelines. To contribute to the alleviation of the deficiency in this matter, we developed a system suitability testing of an SPR-based active concentration measurement method using Fab-fragment as a model. High amounts of anti-FAB1 were captured onto sensor chip surfaces via biotin/neutravidin interaction. The unknown concentration of FAB1 measured via calibration curve dependent concentration measurement. Controls were run between unknown samples to check the reliability of the method. The standard four-parameter logistic equation was used to determine the unknown concentration. CV % of samples and Difference % of controls form standards were calculated from obtained data. The CV % and Diff. % values were less than or equal to 10 % at each parameter. The 100 % level of unknown sample measured as 105 %. Obtained CV % values and repeated analyses showed that the system and method are suitable for the concentration analysis of the FAB1 fragment. The system suitability parameters evaluated in the study can be applied for all SPR analyzes. Resumen. Los métodos SPR (Surface Plamon Resonance) son cada vez más aceptados por la industria biofarmacéutica en el control de calidad (QC), investigación y desarrollo (R & D) e investigación clínica. Sin embargo, existen pocos informes sobre la validación de los métodos y las pruebas de idoneidad del sistema (SST), en particular sobre los métodos de determinación de la concentración relacionados con la potencia del fármaco. La idoneidad del sistema debe demostrarse para garantizar la validación del método, según la normatividad acordada por la comunidad científica y la industria. Con el fin de contribuir al alivio de la deficiencia en este asunto, se desarrolló una prueba de idoneidad del sistema con un método de medición de concentración activa basado en SPR utilizando el fragmento Fab como modelo. Se capturaron grandes cantidades de anti-FAB1 en las superficies del chip sensor mediante la interacción biotina/neutravidina. Se estableció la concentración desconocida de FAB1 a través de la determinación de la concentración dependiente de la curva de calibración. Se ejecutaron controles entre muestras desconocidas para comprobar la fiabilidad del método. Se utilizó una ecuación estándar de cuatro parámetros para determinar la concentración desconocida. A partir de los datos obtenidos se determinó el porcentaje de CV de las muestras y la diferencia respecto a los controles. Los valores de % CV y % Diff. fueron menores o iguales al 10 % en cada parámetro. Los valores de % CV obtenidos y los análisis repetidos mostraron que el sistema y el método son adecuados para el análisis de concentración del fragmento FAB1. Los parámetros de idoneidad del sistema evaluados en el estudio se pueden aplicar a todos los análisis SPR.

A new strategy for the computer-assisted development of reversed-phase liquid chromatography separation methods of unknown sample mixtures

A new strategy for the computer-assisted methods development in the reversed-phase liquid chromatographic separations of unknown sample mixtures has been developed using the latent spectral information in chromatogram raw data files of appropriately designed experiments, rather than resorting to elemental information functions (e.g., the number of peaks in chromatograms or similar criteria). The strategy developed allows unification of the approach for samples of both known and unknown composition and, thus, provide a general strategy for computer-aided tools in the chromatography laboratory. The operation principle of this strategy departs from extracting the spectra of components in the mixture chromatograms by resorting to multivariate curve resolution-alternating least squares (MCR-ALS). This technique allows the estimation of the true spectra for the individual components except when they have identical spectra or are fully overlapped. Thus, a convenient experimental design will try to perform separations of the sample mixture having at least partial resolution of components in some runs. This will allow estimating the spectra of components and, then, assign these components to the peaks in each run chromatogram. In this way, a retention model can be built for each component so computerized optimization process can be developed to provide the chromatographer with the best possible separation programs. Following this approach, strategies for sample mixtures of known and unknown composition are only different in the need of an initial spectrum discovery process for unknown mixtures and therefore a real general approach for the computer-assisted LC methods development is now available for the first time.

Prediction of Female Diabetic Patient in India Using Different Learning Algorithms

Diabetics or Diabetic Mellitus is a metabolic disorder of blood sugar levels in the human body. It is a major non-communicable disease and involved many serious health risk issues. This disease is rapidly increasing in India. It is a chronic condition and it occurs when a body doesn't produce enough insulin hormone to control the blood sugar level. In this study, different variables have been analyzed that cause the diabetics, and different machine learning algorithms are used to predict whether an unknown sample is diabetes or not. For this purpose, PIMA diabetic detection for Female patients was used. Here 10 different classification model is used for prediction. Finally, the detailed performance analysis of the different variables of the PIMA dataset and also the classification model are discussed.

Affordable and time-effective high throughput screening of SARS-CoV-2 variants using Denaturing High-Performance Liquid Chromatography analysis

Mutations in the receptor binding domain region of SARS-CoV-2 have been shown to impact the infectivity, pathogenicity and transmissibility of new variants. Even more worrisome, those mutations have the potential of causing immune escape, undermining the population immunity induced by ongoing mass vaccination programs.The massive parallel sequencing techniques have taken a lead role in the detection strategies of the new variants. Nevertheless, they are still cumbersome and labour-demanding. As a matter of fact, there is an urgent need for novel strategies and techniques aimed at the surveillance of the active emergence and spread of the variants of concern.Two PCRs were designed to target the binding domain region, spanning residues N417 through N501 of the Spike protein. Furthermore, very rapid, affordable and straightforward Denaturing High-Performance Liquid Chromatography screening was set up. The screening consisted of mixing the unknown sample with a standard sample of a known sequence, denaturing at high temperature for two minutes, renaturing for 15 minutes followed by a 2-minute run using the WAVE® DHPLC system to detect the heteroduplexes which invariably originate whenever the unknown sample has a nucleotide difference with respect to the standard used. The workflow was able to readily detect new variants including the P.1 and the B.1.585 strains at a very affordable cost. This approach has the potential of greatly expediting surveillance of the SARS-CoV-2 variants.

ANALISIS KEPERCAYAAN PUBLIK TERHADAP KEBIJAKAN VAKSINASI COVID-19 DI KOTA SURABAYA

Vaccination should be one of the solutions for handling the Covid-19 outbreak, but in fact there are still many people who doubt and even don't believe it, so this creates conflict between the government and the community. The aim of this study is to determine the extent of public confidence in the Covid-19 vaccination policy made by the government so that the acceleration of handling Covid-19 can be achieved. The research method used is descriptive analysis with a quantitative approach and data collection techniques using a questionnaire. The sample in this study was taken from the population of the city of Surabaya. The sample is divided into 5 community clusters, namely students, state civil servants (ASN), health workers, education personnel and the civil society. This research is limited to an unknown sample so that the determination of the number of samples is set to be 100 respondents. The results of this study indicate that the level of public trust from the five clusters in the Covid-19 vaccination policy is in the trust category, which means that the level of public compliance with the Covid-19 vaccination policy is also high. This can be a consideration for the government to increase its efforts to build relationships with the community so that it will gain the trust of the community.

MegaR: an interactive R package for rapid sample classification and phenotype prediction using metagenome profiles and machine learning

Background Diverse microbiome communities drive biogeochemical processes and evolution of animals in their ecosystems. Many microbiome projects have demonstrated the power of using metagenomics to understand the structures and factors influencing the function of the microbiomes in their environments. In order to characterize the effects from microbiome composition for human health, diseases, and even ecosystems, one must first understand the relationship of microbes and their environment in different samples. Running machine learning model with metagenomic sequencing data is encouraged for this purpose, but it is not an easy task to make an appropriate machine learning model for all diverse metagenomic datasets. Results We introduce MegaR, an R Shiny package and web application, to build an unbiased machine learning model effortlessly with interactive visual analysis. The MegaR employs taxonomic profiles from either whole metagenome sequencing or 16S rRNA sequencing data to develop machine learning models and classify the samples into two or more categories. It provides various options for model fine tuning throughout the analysis pipeline such as data processing, multiple machine learning techniques, model validation, and unknown sample prediction that can be used to achieve the highest prediction accuracy possible for any given dataset while still maintaining a user-friendly experience. Conclusions Metagenomic sample classification and phenotype prediction is important particularly when it applies to a diagnostic method for identifying and predicting microbe-related human diseases. MegaR provides various interactive visualizations for user to build an accurate machine-learning model without difficulty. Unknown sample prediction with a properly trained model using MegaR will enhance researchers to identify the sample property in a fast turnaround time.

Analysis of Voice Changes in Anti Forensic Activities Case Study: Voice Changer with Telephone Effect

Voice recordings can be changed in various ways, either intentionally or unintentionally, one of which is by using a voice changer. Reference voice recordings and suspect voice recordings will be more difficult to analyze if suspect voice recordings are changed using a voice changer application under certain effects such as telephone effect. Voice Changer can be one form of activity that can be carried out by anti-forensics, making it difficult for investigators to investigate if the voice recording is changed with telephone effect. This study has two types of recordings, namely the reference voice recording (unknown sample) and suspect voice recording (known sample) that has been changed using a voice changer application with telephone effect. Investigations were carried out based on data results extraction and analysis using pitch, formant, and spectrogram using the Analysis of variance (ANOVA) method and the likelihood ratio method. The results of this study indicate that the application of voice changer can be one form of activity that can be carried out by anti-forensics so that it can be difficult for investigators to conduct investigations on sound recording evidence. This research may help forensic communities, especially investigators to conduct investigations on sound recording.

Using the InsideWood web site: Potentials and pitfalls

The InsideWood web site is a freely accessible resource for research and teaching in wood anatomy and includes a multiple-entry key to aid in wood identification. Its database has over 9400 descriptions of fossil and modern woody dicots, representing over 10 000 species and 200 plant families, and is accompanied by over 50 000 images. The descriptions and the multiple key use the numbered features of the IAWA List of Hardwood Features for Wood Identification. The background for creating this web site, the rationale for how descriptions in the database were created, and the basics for using the multiple-entry key are given. The potentials of the ever-expanding and continuously edited database for microscopic wood identification are enormous. Yet many users experience problems when attempting the identification of an unknown sample. The main reasons for this are (1) erroneous or ambiguous interpretation of the IAWA Hardwood features; (2) incomplete coverage of the infraspecific wood anatomical variation in the literature for numerous entries in the InsideWood database. Against this background, we review all individual features of the IAWA Hardwood List and give their frequency in the database, and we suggest how to use their presence or absence in the multiple-entry key. All this is done with an awareness of the limitations of the IAWA Hardwood List and InsideWood. We give two examples of using InsideWood to try to identify an unknown wood. It cannot be overemphasized that it is necessary to consult reference materials (slides, literature descriptions) to verify the identification of an unknown.

Investigation of Compatibility of SARS-CoV-2 RT-PCR Kits Containing Different Gene Targets During COVID-19 Pandemic

ABSTRACTAimIn the diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), reverse transcriptase-polymerase chain reaction (RT-PCR) technique is often used. We evaluated the compatibility of SARS-CoV-2 RT-PCR kits containing different gene targets during the pandemic.Materials & methodsSamples were tested by Bio-Speddy® (RdRp gene) and Diagnovital® (RdRp+E genes). The correlation between two assays were determined by Deming regression and chi-square heatmap analyses.ResultsDiagnovital PCR kit showed in a constricted range and conveniently exponential amplification curves than Bio-Speedy PCR kit. While the correlation increased when a secondary biomarker was added to the kit.ConclusionIn an unknown sample, using together different PCR kits that target different genes during the pandemic situation may provide a more accurate diagnosis of SARS-CoV-2.