Package For Data Research Articles

A Study on Training Effectiveness and Academic Staffs Performance in Higher Learning Institutions in Zanzibar

Training has become an essential part of today’s successful businesses of any nature and not only for acquiring capabilities but for focusing on the effectiveness with linking to employee performance and retention. This paper examines the training effectiveness and performance of academic staff in higher learning institutions with case observation at the Institute of Public Administration Zanzibar. The study specifically determines the contribution of training outcomes to the level of academic performance of staff in the Institute of Public Administration and examines the trends of training schemes for the academic staff and their influence within the institute. A descriptive survey approach was employed to tackle the target and a survey questionnaire was used as a tool to gather data together with interviews and documentary review to elicit the information from the sample. Fairly representative samples of 115 respondents who make up 10% of the total population were chosen from academic staff trainees, non-academic staff, and students. The qualitative data were subjected to analysis through Statistical Package for Social Science (SPSS) and quantitative data were summarized in tables and charts. The findings revealed that existing staff training has a positive contribution to the academic performance in the institute with 83%. In addition, there is an existence of training opportunities for employees of the Institute but the institute has no specific schedule for staff training. The findings also show that getting on Design Quarterly Staff Training on Specific Institution Needs, Conduct Needs Assessment for Long Term and Short Term Training, Prepare Sufficient Budget for Staff Training, Benchmarking the Performance Outcomes Prior and Post Staff Training, Developing the Measurable Performance Indicators and Conduct Continually Performance Assessment is very important to the Institution academic staffs. These results show that the management of the Institute should set up a training policy that will include a well-designed training schedule for the academic staff. Also, the institute should conduct training Needs Assessments for long-term and short-term training in order to identify the effective training for its staff and design quarterly staff training on specific institutional needs.

Introducing zoid: A mixture model and R package for modeling proportional data with zeros and ones in ecology.

Many ecological data sets are proportional, representing mixtures of constituent elements such as species, populations, or strains. Analyses of proportional data are challenged by categories with zero observations (zeros), all observations (ones), and overdispersion. In lieu of ad hoc data adjustments, we describe and evaluate a zero-and-one inflated Dirichlet regression model, with its corresponding R package (zoid), capable of handling observed data consisting of three possible categories: zeros, proportions, or ones. Instead of fitting the model to observations of single biological units (e.g., individual organisms) within a sample, we sum proportional contributions across units and estimate mixture proportions using one aggregated observation per sample. Optional estimation of overdispersion and covariate influences expand model applications. We evaluate model performance, as implemented in Stan, using simulations and two ecological case studies. We show that zoid successfully estimates mixture proportions using simulated data with varying sample sizes and is robust to overdispersion and covariate structure. In empirical case studies, we estimate the composition of a mixed-stock Chinook salmon (Oncorhynchus tshawytscha) fishery and analyze the stomach contents of Atlantic cod (Gadus morhua). Our implementation of the model as an R package facilitates its application to varied ecological data sets composed of proportional observations.

Continuous Glucose Monitoring Time Series Data Analysis: A Time Series Analysis Package for Continuous Glucose Monitoring Data.

The R package Continuous Glucose Monitoring Time Series Data Analysis (CGMTSA) was developed to facilitate investigations that examine the continuous glucose monitoring (CGM) data as a time series. Accordingly, novel time series functions were introduced to (1) enable more accurate missing data imputation and outlier identification; (2) calculate recommended CGM metrics as well as key time series parameters; (3) plot interactive and three-dimensional graphs that allow direct visualizations of temporal CGM data and time series model optimization. The software was designed to accommodate all popular CGM devices and support all common data processing steps. The program is available for Linux, Windows, and Mac at GitHub.

A novel greedy approach to harmonic summing using GPUs

One-dimensional incoherent harmonic summing is a technique used to improve the sensitivity of Fourier domain search methods. In time-domain radio astronomy, the harmonic sum is a part of the Fourier domain periodicity search, which aims to detect isolated single pulsars. We present a new harmonic sum algorithm based on the greedy approach and an implementation of this on NVIDIA GPUs using the CUDA programming language. The new algorithm determines which samples to add according to short-term gains. We present an evaluation of the sensitivity of the new approach and its performance against the PRESTO harmonic sum a standard used in radio astronomy. The new Greedy harmonic sum has, on average, 20% higher sensitivity whilst it has the same performance as the standard algorithm. This work forms part of the AstroAccelerate project which is a GPU accelerated software package for processing time-domain radio astronomy data.

MHCtools - an R package for MHC high-throughput sequencing data: Genotyping, haplotype and supertype inference, and downstream genetic analyses in non-model organisms.

The major histocompatibility complex (MHC) plays a central role in the vertebrate adaptive immune system and has been of long‐term interest in evolutionary biology. While several protocols have been developed for MHC genotyping, there is a lack of transparent and standardized tools for downstream analysis of MHC data. Here, we present the r package mhctools and demonstrate the use of its functions to (i) assist accurate MHC genotyping from high‐throughput amplicon‐sequencing data, (ii) infer functional MHC supertypes using bootstrapped clustering analysis, (iii) identify segregating MHC haplotypes from family data, and (iv) analyse functional and genetic distances between MHC sequences. We employed mhctools to analyse MHC class I (MHC‐I) amplicon data of 559 great reed warblers (Acrocephalus arundinaceus). We identified 390 MHC‐I alleles which clustered into 14 functional supertypes. A phylogenetic analysis and analyses of positive selection suggested that the MHC‐I alleles belong to several distinct functional groups. We furthermore identified 107 segregating haplotypes among 116 families, and found substantial variation in diversity with 4–21 MHC‐I alleles and 3–13 MHC‐I supertypes per haplotype. Finally, we show that the great reed warbler haplotypes harboured combinations of MHC‐I supertypes with greater functional divergence than observed in simulated populations of possible haplotypes, a result that is in accordance with the divergent allele advantage hypothesis. Our study demonstrates the power of mhctools to support genotyping and analysis of MHC in non‐model species, which we hope will encourage broad implementation among researchers in MHC genetics and evolution.

Improving Contractors’ Participation of Resource Utilization in Construction and Demolition Waste through Government Incentives and Punishments

This paper develops a simulation model for analyzing how government incentives and punishments improve contractors' participation in resource utilization of construction and demolition waste (RUCDW) based on system dynamics theory. The construction industry's long-term objective is to become more sustainable and resource-effective, and as part of this objective, generated construction and demolition waste should be recycled and resource utilized. However, most contractors have little willingness to engage in RUCDW because it increases their costs. The government thus plays a vital role in improving their participation in RUCDW through a range of educational tools such as advertisements, professional training, incentives, and punishments. Among these approaches, incentives and punishments are considered the most effective because they directly change project costs. We use the Vensim software package for numerical simulation and data collected from Suzhou, China are used to demonstrate and validate the developed model. Simulation results show that the government can improve contractors' participation in RUCDW through three kinds of incentives and punishments: (1) subsidizing RUCDW; (2) increasing landfill fees; and 3) issuing fines for illegal dumping. Comprehensive application of multiple policies has a stronger effect than single policies. The established model is therefore a valuable tool for assessing the dynamic effects of government incentives and punishments on RUCDW ahead of implementation, which can provide guidance for policymakers.

Employee Grievances Impacts and Institution Performances: Case Study of Juba City Council, South Sudan

The overall objective of the study is to assess the Impact of employee grievances Impact and Institution performance in Juba City Council, Juba, Central Equatoria State. Data was collected from 50 respondents using Questionnaires. Quantitative data was analyzed using the Statistical Package for Social Sciences (SPSS) and qualitative data was summarized and analyze manually using content analysis. The findings from the study showed that employee grievances exist in different forms with 84% of the respondents indicating that low and imbalance salary create grievances among employee, 88% of respondents indicated that lack of attractive work incentives create employee grievances and 74% indicated that ineffective communication between management and employees causes grievances among employee while 10% declined. 86% of respondents indicated that lack of supervision and management skills among supervisors and managers result to grievances among employee. Furthermore, 64.6% of respondents indicated that unfair treatment of employees by the management. In addition to that 86% of respondents indicated that management failure to pay attention to employee personal problems risk employee's grievances. 70% of respondents indicated that ineffective of delegation of authority to employees by the management causes employee grievances. The findings of the study showed that 92% of respondents indicated that grievances reduce the employee commitment in performance organization's task, 76% of respondents indicated that grievances lead to high turnover of employee in an organization, 88% of respondents indicated that grievances lead to poor quality of produce in an organization. 86% of respondents indicated that recruitment of professional HRM can helps in effective handling of grievances among employees, 86% of respondents indicated that review available remuneration schemes contribute in handling grievances, 78% of participants indicated that reduce bureaucratic procedures in managing employee grievances helps in quick resolution of employee grievances.In conclusion, effective management of employee grievance help management understands the feelings and attitude of the workers concerning the organizations polices practices and rules. In addition, employee grievance management practice helps managers to identify questionable practices and areas of discontents in the organization and provides measures of correcting the questionable or faulty practices or policies. Since most grievances in workplaces are related to performance appraisal, rewards and incentives, salary, and general working conditions, the study recommended that management should ensure that it improves working conditions of her employees.

Comprehensive Genomic and Epigenomic Analyses on Transcriptomic Regulation in Stomach Adenocarcinoma.

Background: DNA methylation (MET)–mediated transcriptomic disturbance and copy number variations (CNVs) exert a significant influence in stimulating the heterogeneous progression of stomach adenocarcinoma (STAD). Nevertheless, the relation of DNA MET with CNVs, together with its impact on tumor occurrence, is still unclear. Methods: The messenger RNA (mRNA) expression (EXP) profiles, DNA MET, and DNA copy numbers, together with STAD mutation data, were collected from the TCGA official data portal. We employed circular binary segmentation algorithm in “DNAcopy.” library of R package for mapping DNA CNV data at genetic level for all samples based on the segmented CNV data. Stable clusters of samples were recognized using negative matrix factorization cluster analysis based on 50 iterations and the “brunet” method using the MET-correlated (METcor) and CNV-correlated (CNVcor) genes. The R package “iCluster” method was utilized to comprehensively analyze the EXP, MET, and DNA CNV profiles. Results: A total of 313 STAD samples were isolated for checking DNA copy numbers and MET and for measuring EXP. In accordance with our results, we discovered obvious co-regulation of CNVcor genes and METcor counterparts. Apart from that, these genes were subject to multi-omics integration. Meanwhile, three subtypes of STAD were detected and confirmed based on independent data. Among them, the subtype with increased aggressiveness was related to decreased mutation frequencies of ARID1A, PIK3CA, ZFHX3, SPECC1, OBSCN, KMT2D, FSIP2, ZBTB20, TTN, and RANBP2, together with the abnormal levels of JPH3, KCNB1, and PLCXD3. Conclusion: According to the results, these aforementioned genes exerted crucial roles in the development of invasive STAD. Our findings on transcriptomic regulation genomically and epigenetically facilitate the understanding of the STAD pathology from different aspects, which help to develop efficient anti-STAD therapy.

The role of IDH1 mutation on gene expression in glioblastoma

Glioblastoma multiforme (GBM) is the most malignant primary brain tumor found in humans. Mutations in isocitrate dehydrogenases 1 (IDH1R132H) was observed in GBM. GBMs with IDH1R132H have longer patient survival compared to GBMs with wild-type IDH1 and identifying the mechanism of this can contribute to the development of a new treatment for GBM. To investigate the role of IDH1R132H on gene expression, we incorporated three expression arrays from GEO including GSE36245, GSE131837, and GSE122586, and one expression array from TCGA which in total investigate gene expression differences between 293 wild type and 41 mutant IDH1 GBM samples. Meta-DE package in R software was applied to screen the differentially expressed genes (DEGs) between IDH1 wild type and IDH1R132H GBMs. DEGs were used to construct co-expression networks by WGCNA package for TCGA data and genes of the detected module were used to build protein-protein interaction (PPI) network. Each of the mentioned expression arrays was analyzed independently using limma package by cutoff |logFC| > 1 and P.value < 0.05, and 13 downregulated and 26 upregulated common genes were identified. By merging the results of limma package and core genes of the PPI network, 10 hub genes including ARAP3, ARHGAP11B, BDNF, CFAP45, CXCL8, MMP9, RHOBTB1, SHH, SYNJ2, and VEGFA were identified. Based on the TCGA database, we tested the prognostic significance of the detected genes using Kaplan-Meier survival analysis. In conclusion, the results suggested that the hub genes may play role in GBM pathogeny.

The lrd package: An R package and Shiny application for processing lexical data.

Recall testing is a common assessment to gauge memory retrieval. Responses from these tests can be analyzed in several ways; however, the output generated from a recall study typically requires manual coding that can be time intensive and error-prone before analyses can be conducted. To address this issue, this article introduces lrd (Lexical Response Data), a set of open-source tools for quickly and accurately processing lexical response data that can be used either from the R command line or through an R Shiny graphical user interface. First, we provide an overview of this package and include a step-by-step user guide for processing both cued- and free-recall responses. For validation of lrd, we used lrd to recode output from cued, free, and sentence-recall studies with large samples and examined whether the results replicated using lrd-scored data. We then assessed the inter-rater reliability and sensitivity and specificity of the scoring algorithm relative to human-coded data. Overall, lrd is highly reliable and shows excellent sensitivity and specificity, indicating that recall data processed using this package are remarkably consistent with data processed by a human coder.

Visually guided preprocessing of bioanalytical laboratory data using an interactive R notebook (pguIMP).

The evaluation of pharmacological data using machine learning requires high data quality. Therefore, data preprocessing, that is, cleaning analytical laboratory errors, replacing missing values or outliers, and transforming data adequately before actual data analysis, is crucial. Because current tools available for this purpose often require programming skills, preprocessing tools with graphical user interfaces that can be used interactively are needed. In collaboration between data scientists and experts in bioanalytical diagnostics, a graphical software package for data preprocessing called pguIMP is proposed, which contains a fixed sequence of preprocessing steps to enable reproducible interactive data preprocessing. As an R‐based package, it also allows direct integration into this data science environment without requiring any programming knowledge. The implementation of contemporary data processing methods, including machine‐learning‐based imputation techniques, ensures the generation of corrected and cleaned bioanalytical data sets that preserve data structures such as clusters better than is possible with classical methods. This was evaluated on bioanalytical data sets from lipidomics and drug research using k‐nearest‐neighbors‐based imputation followed by k‐means clustering and density‐based spatial clustering of applications with noise. The R package provides a Shiny‐based web interface designed to be easy to use for non–data analysis experts. It is demonstrated that the spectrum of methods provided is suitable as a standard pipeline for preprocessing bioanalytical data in biomedical research domains. The R package pguIMP is freely available at the comprehensive R archive network (https://cran.r‐project.org/web/packages/pguIMP/index.html).

SangeranalyseR: Simple and interactive processing of Sanger sequencing data in R

sangeranalyseR is feature-rich, free, and open-source R package for processing Sanger sequencing data. It allows users to go from loading reads to saving aligned contigs in a few lines of R code by using sensible defaults for most actions. It also provides complete flexibility for determining how individual reads and contigs are processed, both at the command-line in R and via interactive Shiny applications. sangeranalyseR provides a wide range of options for all steps in Sanger processing pipelines including trimming reads, detecting secondary peaks, viewing chromatograms, detecting indels and stop codons, aligning contigs, estimating phylogenetic trees, and more. Input data can be in either ABIF or FASTA format. sangeranalyseR comes with extensive online documentation and outputs aligned and unaligned reads and contigs in FASTA format, along with detailed interactive HTML reports. sangeranalyseR supports the use of colorblind-friendly palettes for viewing alignments and chromatograms. It is released under an MIT licence and available for all platforms on Bioconductor (https://bioconductor.org/packages/sangeranalyseR, last accessed February 22, 2021) and on Github (https://github.com/roblanf/sangeranalyseR, last accessed February 22, 2021).

Factors Influencing Sustainability of Rural Community Development Projects in Kilungu Sub-County, Makueni County, Kenya

The challenge facing rural community development projects is lack of sustainability after Donor phase out or before project completion. Community development projects have been executed in rural areas in order to improve livelihoods of people. However, projects often do not meet the intended objectives, despite huge finances spent on the projects. The project beneficiaries may not get benefits, value of involvement and ownership of project. It is sustainability which determines project success and failure of community projects. The study Sought to determine social and management factors influencing sustainability of rural community development projects inKilungu Sub-County of Makueni County. The study employed a descriptive research design and target population included project beneficiaries, managers and management committee members. A sample size of 210 respondents was selected using simple random sampling technique. Questionnaires and interview schedules were used for data collection. Data was analyzed using Statistical Package for Social Sciencesand data was presented using Tables and prose. The results on social factors showed 58% and 56% of respondents agreed gender and education had high influence on sustainability of community development projects while 56.7% strongly disagreed that marital status influenced sustainability of community projects. The findings on management factors showed that 66.7% of respondents had management knowledge and skills to enhance sustainability. However, 53% of respondents indicated that people who were appointed as managers of projects were not competent. The 48.7% of respondents agreed that organizational structure helps in division of activities into manageable units. It was concluded that social and Management factors influence sustainability of rural community projects if the factors were integrated in the project cycle. It was recommended that the two factors should be implemented in the sustainability of community projects. The results are beneficial to Government officials, Non-Governmental Organizations and Managers.

SangeranalyseR: Simple and Interactive Processing of Sanger Sequencing Data in R.

sangeranalyseR is feature-rich, free, and open-source R package for processing Sanger sequencing data. It allows users to go from loading reads to saving aligned contigs in a few lines of R code by using sensible defaults for most actions. It also provides complete flexibility for determining how individual reads and contigs are processed, both at the command-line in R and via interactive Shiny applications. sangeranalyseR provides a wide range of options for all steps in Sanger processing pipelines including trimming reads, detecting secondary peaks, viewing chromatograms, detecting indels and stop codons, aligning contigs, estimating phylogenetic trees, and more. Input data can be in either ABIF or FASTA format. sangeranalyseR comes with extensive online documentation and outputs aligned and unaligned reads and contigs in FASTA format, along with detailed interactive HTML reports. sangeranalyseR supports the use of colorblind-friendly palettes for viewing alignments and chromatograms. It is released under an MIT licence and available for all platforms on Bioconductor (https://bioconductor.org/packages/sangeranalyseR, last accessed February 22, 2021) and on Github (https://github.com/roblanf/sangeranalyseR, last accessed February 22, 2021).

Anxiety among Pregnant Women about Corona Virus Infections during COVID-19 Pandemic at a Tertiary Care Center in Nepal: A Descriptive Cross-sectional Study.

COVID-19 is a beta coronavirus that is transmitted by physical interaction or close contact. This Coronavirus Pandemic has also created stress and anxiety among pregnant women all over the world. The disease was first identified in Wuhan city, China, in late December 2019 and was declared pandemic by the World Health Organization on 11th March 2020. Concern and stress in pregnancy are associated with pre-eclampsia, intrauterine growth restriction, preterm labour, depression etc. Pregnancy is an immune-compromised state and poses a high risk to this risk. This study aims to identify anxiety about the coronavirus infection among pregnant women visiting a tertiary care centre in Kathmandu, Nepal, during this COVID-19 pandemic. This is a descriptive cross-sectional study conducted at Kathmandu Medical College and Teaching Hospital from 15th July 2020 to 30th July 2020 after taking the ethical clearance from the Institutional Review Committee of Kathmandu Medical College (reference no. 207202002). Convenient sampling method was used. All the data were entered in Statistical Package for the Social Sciences data 20.0 and analyzed. Data was presented in frequencies, charts and percentage. Among the total 273 cases, only 2 (0.73%) cases had a score between 25-30 corresponding to moderate to severe anxiety, 21 (7.69%) had a score between 18-24, which corresponds to mild to moderate anxiety and 250 (91.57%) had score 0-17 which corresponds mild status. Most of the participants in the study reported a mild status of anxiety. Very few participants reported moderate to severe anxiety.

Global Sourcing and Its Associated Challenges in the Mining Industry in Ghana

This research investigated the extent, benefits and challenges of Global Sourcing in the Ghana Mining industry. The study looked at the level of outsourcing outside the country, the products that are sourced globally, factors that contribute to the decision to source globally, challenges of global sourcing and above all the benefits that these companies derive from sourcing globally. Three mining companies each representing the large, medium and small-scale mining companies in Ghana were used as case studies. A well-structured questionnaire was sent to these companies to gather primary data. Secondary data from various offices within the supply chain departments of these companies were also used to supplement data obtained from the survey. Using the Statistical Package for Social Sciences (SPSS) software, both the primary and secondary data were analyzed. The analysis included the use of basic descriptive statistics and graphical representations using bar charts, histograms, scatter plots, etc. Even though the literature cites cost as the key factor for Global sourcing, the study showed that for the Ghana mining Industry, product quality is the primary factor. From the study, Quality recorded 45%, Competitive advantage 20%, and combined cost and global outlook recording 16.7% when respondents were asked about the most important factor influencing global sourcing in their respective companies. Differences in the legal frameworks and business environments of foreign sources possess many challenges to the mining companies contrary to popularly observed challenges with delivery and lead time. There are no clearly defined models or strategies for doing global sourcing in the companies as various authors have noted however contrary to the traditional belief that M&M companies conduct Low Cost Country Sourcing (LCCS), the companies under study preferred Best Cost Country Sourcing (BCCS) which recorded 55.2% against 44.8% for both LCCS and BCCS.

Application and Comprehensive Analysis of Neighbor Approximated Information Theoretic Configurational Entropy Methods to Protein-Ligand Binding Cases.

The binding entropy is an important thermodynamic quantity which has numerous applications in studies of the biophysical process, and configurational entropy is often one of the major contributors in it. Therefore, its accurate estimation is important, though it is challenging mostly due to sampling limitations, anharmonicity, and multimodality of atomic fluctuations. The present work reports a Neighbor Approximated Maximum Information Spanning Tree (A-MIST) method for conformational entropy and presents its performance and computational advantage over conventional Mutual Information Expansion (MIE) and Maximum Information Spanning Tree (MIST) for two protein-ligand binding cases: indirubin-5-sulfonate to Plasmodium falciparum Protein Kinase 5 (PfPK5) and P. falciparum RON2-peptide to P. falciparum Apical Membrane Antigen 1 (PfAMA1). Important structural regions considering binding configurational entropy are identified, and physical origins for such are discussed. A thorough performance evaluation is done of a set of four entropy estimators (Maximum Likelihood (ML), Miller-Madow (MM), Chao-Shen (CS), and James and Stein shrinkage (JS)) with known varying degrees of sensitivity of the entropy estimate on the extent of sampling, each with two schemes for discretization of fluctuation data of Degrees of Freedom (DFs) to estimate Probability Density Functions (PDFs). Our comprehensive evaluation of influences of variations of parameters shows Neighbor Approximated MIE (A-MIE) outperforms MIE in terms of convergence and computational efficiency. In the case of A-MIE/MIE, results are sensitive to the choice of root atoms, graph search algorithm used for the Bond-Angle-Torsion (BAT) conversion, and entropy estimator, while A-MIST/MIST are not. A-MIST yields binding entropy within 0.5 kcal/mol of MIST with only 20-30% computation. Moreover, all these methods have been implemented in an OpenMP/MPI hybrid parallel C++11 code, and also a python package for data preprocessing and entropy contribution analysis is developed and made available. A comparative analysis of features of current implementation and existing tools is also presented.

방송 댄스운동 동호인에 대한 긍정심리자본, 운동지속의도, 주관적 운동강도의 관계 분석

Purpose: The purpose of this study is to analyze the relationship among the positive psychological capital, the exercise adherence intention, and the intensity of the subjective exercise of the members of broadcasting dance exercise.BR Method: For the result, frequency analysis, correlation analysis, exploratory factor analysis, reliability analysis, multiple regression analysis, and one-way variance analysis were performed using the SPSS 21.0 statistical analysis package for data obtained from 183 women in the broadcast dance exercise.BR Results: The following conclusions were obtained. First, the positive psychological capital of members of the broadcast dance exercise positively influenced the exercise adherence intention. Second, there was a difference between the positive psychological capital and the exercise adherence intention in the subjective intensity of exercise of the members of the broadcast dance movement. Third, there was a difference in positive psychological capital and exercise adherence intention according to the age classification of the broadcast dance exercise members.BR Conclusion: Therefore, it is confirmed that participation in the broadcast dance exercise positively affects the exercise adherence intention through the effective increase of the positive psychological capital and that the subjective exercise intensity has a significant relationship between the positive psychological capital and the exercise adherence intention.

Pymia: A Python package for data handling and evaluation in deep learning-based medical image analysis

Background and Objective: Deep learning enables tremendous progress in medical image analysis. One driving force of this progress are open-source frameworks like TensorFlow and PyTorch. However, these frameworks rarely address issues specific to the domain of medical image analysis, such as 3-D data handling and distance metrics for evaluation. pymia, an open-source Python package, tries to address these issues by providing flexible data handling and evaluation independent of the deep learning framework.Methods: The pymia package provides data handling and evaluation functionalities. The data handling allows flexible medical image handling in every commonly used format (e.g., 2-D, 2.5-D, and 3-D; full- or patch-wise). Even data beyond images like demographics or clinical reports can easily be integrated into deep learning pipelines. The evaluation allows stand-alone result calculation and reporting, as well as performance monitoring during training using a vast amount of domain-specific metrics for segmentation, reconstruction, and regression.Results: The pymia package is highly flexible, allows for fast prototyping, and reduces the burden of implementing data handling routines and evaluation methods. While data handling and evaluation are independent of the deep learning framework used, they can easily be integrated into TensorFlow and PyTorch pipelines. The developed package was successfully used in a variety of research projects for segmentation, reconstruction, and regression.Conclusions: The pymia package fills the gap of current deep learning frameworks regarding data handling and evaluation in medical image analysis. It is available at https://github.com/rundherum/pymia and can directly be installed from the Python Package Index using pip install pymia.

Effects of hazard analysis and critical control points principles on food safety at national youth service in Nakuru County, Kenya

This study examined the effect of hazard analysis and critical control points principles on food safety at the National Youth Service in Nakuru County; Kenya. Application of Hazard Analysis and Critical Control Point (Hazard Analysis and Critical Control Point, HACCP) principle in food production and service areas is a requirement especially in all food handling stages, Successful implementation of the procedures based on the HACCP principles requires the full cooperation and commitment of food handlers’ requiring employees to undergo training. Food safety plays a significant role in the economic and health development of Nations by safeguarding the nation‘s health, enhancing tourism, hospitality, and international trade, the production, distribution, and consumption of safe food. The scope of the study was limited to NYS Catering unit kitchens in Gilgil College. The findings explain the hygiene and safety practices of other Academic kitchens in Kenya. The study adopted a mixed research methodology, this study adopted a census method; the reason being the total population was 121. The response from the quantitative data from the questionnaires was tabulated, coded, and processed by use of the statistical package for social sciences Data collected was analyzed using descriptive and inferential statistics. From the findings, the R and R2 value representing the simple correlation summary showed that 16.6 percent of HACCP principles can be explained by variance in food safety. ANOVA results showed that the model applied was statistically significant to predict the outcome variable. The study also concludes that food-hygiene operating procedure that is documented and awareness too that food safety inspections by health inspectors help to ensure safe food handling practices are followed.