Flat Files Research Articles

Epiviz File Server: Query, transform and interactively explore data from indexed genomic files.

MotivationGenomic data repositories like The Cancer Genome Atlas, Encyclopedia of DNA Elements, Bioconductor’s AnnotationHub and ExperimentHub etc., provide public access to large amounts of genomic data as flat files. Researchers often download a subset of data files from these repositories to perform exploratory data analysis. We developed Epiviz File Server, a Python library that implements an in situ data query system for local or remotely hosted indexed genomic files, not only for visualization but also data transformation. The File Server library decouples data retrieval and transformation from specific visualization and analysis tools and provides an abstract interface to define computations independent of the location, format or structure of the file. We demonstrate the File Server in two use cases: (i) integration with Galaxy workflows and (ii) using Epiviz to create a custom genome browser from the Epigenome Roadmap dataset.Availability and implementationEpiviz File Server is open source and is available on GitHub at http://github.com/epiviz/epivizFileServer. The documentation for the File Server library is available at http://epivizfileserver.rtfd.io.

The PROFOUND Database for evaluating vegetation models and simulating climate impacts on European forests

Abstract. Process-based vegetation models are widely used to predict local and global ecosystem dynamics and climate change impacts. Due to their complexity, they require careful parameterization and evaluation to ensure that projections are accurate and reliable. The PROFOUND Database (PROFOUND DB) provides a wide range of empirical data on European forests to calibrate and evaluate vegetation models that simulate climate impacts at the forest stand scale. A particular advantage of this database is its wide coverage of multiple data sources at different hierarchical and temporal scales, together with environmental driving data as well as the latest climate scenarios. Specifically, the PROFOUND DB provides general site descriptions, soil, climate, CO2, nitrogen deposition, tree and forest stand level, and remote sensing data for nine contrasting forest stands distributed across Europe. Moreover, for a subset of five sites, time series of carbon fluxes, atmospheric heat conduction and soil water are also available. The climate and nitrogen deposition data contain several datasets for the historic period and a wide range of future climate change scenarios following the Representative Concentration Pathways (RCP2.6, RCP4.5, RCP6.0, RCP8.5). We also provide pre-industrial climate simulations that allow for model runs aimed at disentangling the contribution of climate change to observed forest productivity changes. The PROFOUND DB is available freely as a “SQLite” relational database or “ASCII” flat file version (at https://doi.org/10.5880/PIK.2020.006/; Reyer et al., 2020). The data policies of the individual contributing datasets are provided in the metadata of each data file. The PROFOUND DB can also be accessed via the ProfoundData R package (https://CRAN.R-project.org/package=ProfoundData; Silveyra Gonzalez et al., 2020), which provides basic functions to explore, plot and extract the data for model set-up, calibration and evaluation.

Review: Pierre Koenig: A View from the Archive, by Neil Jackson

Review: <i>Pierre Koenig: A View from the Archive</i>, by Neil Jackson

Monitoring Mobile Information Processing Systems

We describe the implementation of the monitoring for the IT systems at the core of the autonomous driving vehicle. The role of the monitoring is to assist in decision to start the driving cycle and continuous assessment for the fitness to drive the vehicle. The requirements for the monitoring system with the increased resiliency and data replication make it sufficiently different from standard monitoring systems and warrant a unique implementation tuned for the autonomous driving requirements. The monitoring system combines the OS events and real-time measurements of sensor data. The information is stored in flat files for emergency access as well as in a Time Series Data Base (TSDB).

CernVM-FS Container Image Integration

Linux containers have gained widespread use in high energy physics, be it for services using container engines such as containerd/kubernetes, for production jobs using container engines such as Singularity or Shifter, or for development workflows using Docker as a local container engine. Thus the efficient distribution of the container images, whose size usually ranges from a few hundred megabytes to a few tens of gigabytes, is becoming a pressing concern. Because container images show similar characteristics than scientific application stacks, unpacking the images in CernVM-FS can remedy the distribution issues provided that the container engine at hand is able to use such unpacked images from CernVM-FS. In this contribution, we willl report on recent advances in the integration of Singularity, Docker, and containerd with CernVM-FS. We show improvements in the publishing of container images from a Docker registry that rely on new means of directly ingesting image tarballs. Well also show a repository file system structure for storing container images that are optimized for storing both container engines using flat root file systems (Singularity) as well as container engines using layers (containerd, Docker). To evaluate the benefits of our approach, we show concrete use cases and figures for production and development images from LHC experiments stored in the recently created unpacked.cern.ch repository.

Spectral decomposition of the Engineering Strong Motion (ESM) flat file: regional attenuation, source scaling and Arias stress drop

We perform a spectral decomposition of the Fourier amplitude spectra disseminated along with the Engineering Strong Motion (ESM) flat file for Europe and Middle East. We apply a non-parametric inversion schema to isolate source, propagation and site effects, introducing a regionalization for the attenuation model into three domains. The obtained propagation and source components of the model are parametrized in terms of geometrical spreading, quality factor, seismic moment, and corner frequency assuming a ω2 source model. The non-parametric spectral attenuation values show a faster decay for earthquakes in Italy than in the other regions. Once described in terms of geometrical spreading and frequency-dependent quality factor, slopes and breakpoint locations of the piece-wise linear model for the geometrical spreading show regional variations, confirming that the non-parametric models capture the effects of crustal heterogeneities and differences in the anelastic attenuation. Since they are derived in the framework of a single inversion, the source spectra of the largest events which have occurred in Europe in the last decades can be directly compared and the scaling of the extracted source parameters evaluated. The Brune stress drop varies over about 2 orders of magnitude (the 5th, 50th and 95th percentiles of the ∆σ distribution are 0.76, 2.94, and 13.07 MPa, respectively), with large events having larger stress drops. In particular, the 5th, 50th and 95th percentiles for M > 5.5 are 2.87, 6.02, and 23.5 MPa, respectively whereas, for M < 5.5, the same percentiles are 0.73, 2.84, and 12.43 MPa. If compared to the residual distributions associated to a ground motion prediction equation previously derived using the same Fourier amplitude spectra, the source parameter and the empirical site amplification effects correlate well with the inter-event and inter-station residuals, respectively. Finally, we calibrated both non-parametric and parametric attenuation models for estimating the stress drop from the ratio between Arias intensity and significant duration. The results confirm that computing the Arias stress drop is a suitable approach for complementing the seismic moment with information controlling the source radiation at high frequencies for rapid response applications.

Alignparse: A Python package for parsing complex features from high-throughput long-read sequencing.

Summary & PurposeAdvances in sequencing technology have made it possible to generate large numbers of long, high-accuracy sequencing reads. For instance, the new PacBio Sequel platform can generate hundreds of thousands of high-quality circular consensus sequences in a single run (Hebert et al., 2018; Rhoads & Au, 2015). Good programs exist for aligning these reads for genome assembly (Chaisson & Tesler, 2012; Li, 2018). However, these long reads can also be used for other purposes, such as sequencing PCR amplicons that contain various features of interest. For instance, PacBio circular consensus sequences have been used to identify the mutations in influenza viruses in single cells (Russell et al, 2019), or to link barcodes to gene mutants in deep mutational scanning (Matreyek et al., 2018). For such applications, the alignment of the sequences to the targets may be fairly trivial, but it is not trivial to then parse specific features of interest (such as mutations, unique molecular identifiers, cell barcodes, and flanking sequences) from these alignments.Here we describe alignparse, a Python package for parsing complex sets of features from long sequences that map to known targets. Specifically, it allows the user to provide complex target sequences in Genbank Flat File format that contain an arbitrary number of user-defined sub-sequence features (Sayers et al., 2019). It then aligns the sequencing reads to these targets and filters alignments based on whether the user-specified features are present with the desired identities (which can be set to different thresholds for different features). Finally, it parses out the sequences, mutations, and/or accuracy (sequence quality) of these features as specified by the user. The flexibility of this package therefore fulfills the need for a tool to extract and analyze complex sets of features in large numbers of long sequencing reads.

Ontology of Consumer Health Vocabulary: providing a formal and interoperable semantic resource for linking lay language and medical terminology.

The Consumer Health Vocabulary has been an important contribution to the health informatics field since its introduction in 2006. Many studies have utilized the vocabulary for various scientific research to bridge the gap between consumers and health experts. Given the flat file format of the Consumer Health Vocabulary dataset, we developed a SKOS-based ontology of the dataset. As an ontology, this dataset can be semantically linked to other resources to provide consumer-level meaning. In addition with this artifact, we plan to further expand the terminology.

DockNmine, a Web Portal to Assemble and Analyse Virtual and Experimental Interaction Data.

Scientists have to perform multiple experiments producing qualitative and quantitative data to determine if a compound is able to bind to a given target. Due to the large diversity of the potential ligand chemical space, the possibility of experimentally exploring a lot of compounds on a target rapidly becomes out of reach. Scientists therefore need to use virtual screening methods to determine the putative binding mode of ligands on a protein and then post-process the raw docking experiments with a dedicated scoring function in relation with experimental data. Two of the major difficulties for comparing docking predictions with experiments mostly come from the lack of transferability of experimental data and the lack of standardisation in molecule names. Although large portals like PubChem or ChEMBL are available for general purpose, there is no service allowing a formal expert annotation of both experimental data and docking studies. To address these issues, researchers build their own collection of data in flat files, often in spreadsheets, with limited possibilities of extensive annotations or standardisation of ligand descriptions allowing cross-database retrieval. We have conceived the dockNmine platform to provide a service allowing an expert and authenticated annotation of ligands and targets. First, this portal allows a scientist to incorporate controlled information in the database using reference identifiers for the protein (Uniprot ID) and the ligand (SMILES description), the data and the publication associated to it. Second, it allows the incorporation of docking experiments using forms that automatically parse useful parameters and results. Last, the web interface provides a lot of pre-computed outputs to assess the degree of correlations between docking experiments and experimental data.

Flexible Production Systems: Automated Generation of Operations Plans Based on ISA-95 and PDDL

Model-driven engineering (MDE) provides tools and methods for the manipulation of formal models. In this letter, we leverage MDE for the transformation of production system models into flat files that are understood by general purpose planning tools and that enable the computation of plans, i.e., sequences of production steps that are required to reach certain production goals. These plans are then merged back into the production system model, thus enriching the formalized production system knowledge.

Automation of chromatographic peak review and order to result data transfer in a clinical mass spectrometry laboratory

IntroductionMass spectrometry-based assays have increasingly been implemented in clinical laboratories for their multiplexing capacity and high specificity and sensitivity. However, these methods are often associated with labor-intensive and error-prone data-related workflows, due to the volume of data generated that is often manually reviewed and resulted. We aimed to establish a system within our clinical mass spectrometry laboratory to facilitate data ‘flow’ from electronic medical record order to result and to automate processes for chromatogram peak review. The processes and validation are described for a 25-hydroxyvitamin D assay. MethodsAutomating chromatogram review and order to result data transfer required flat file interfacing, file transfers of standardized data formats, barcode scanning, and software for peak processing and review. Validation of the automated workflow involved (1) correlation of quantified results generated by two chromatogram analysis methods: Waters TargetLynx and Indigo Bioautomation ASCENT, (2) manual verification of quality assurance flags applied in ASCENT, and (3) testing data flow and integrity across all the systems from order to result. Efficiency and quality improvements were assessed through calculation of batch review times and rates for autoverification and manual manipulations. ResultsThe correlation of TargetLynx and ASCENT quantitation methods for 25-hydroxyvitamin D2 in patient samples yielded slope of 0.99 (95% CI: 0.989 to 0.996), intercept of 0.46 (95% CI: 0.363 to 0.565), with r = 0.999. The correlation for the D3 fraction showed Deming regression slope of 0.98 (95% CI: 0.969 to 0.989), intercept of 0.06 (95% CI: −0.115 to 0.313), and r = 0.995. Results from both quantitation approaches were also compared to the assigned value in CDC reference samples. The mean bias relative to the CDC was 4.6% for ASCENT and 2.5% for TargetLynx. The median time for chromatogram review of a full 96-well plate of vitamin D results is reduced from approximately 2 h to 14 min and 80% of batches were reviewed within 30 min. Instead of 100% peak review, technologists review only the peaks that have been flagged by the system based on applied rules. Analysis of full plate batches showed that 2–20% of peaks per batch were flagged for manual review. Manipulations made by technologists during chromatogram review were reduced by 75% when using the automated versus manual system. ConclusionsWe describe a system to facilitate data ‘flow’ from electronic order to result and to automate chromatogram peak review in a clinical liquid chromatography mass spectrometry assay for 25-hydroxyvitamin D. This eliminated manual result entry, repetitive transcription, and unnecessary review of high quality data while enabling systematic evaluation of data quality indicators. The new processes were accurate, improved the data review and processing times, and helped to reduce manual manipulations during chromatogram review.

SystemER

Entity Resolution (ER) is the task of identifying different representations of the same real-world object. To achieve scalability and the desired level of quality, the typical ER pipeline includes multiple steps that may involve low-level coding and extensive human labor. We present SystemER, a tool for learning explainable ER models that reduces the human labor all throughout the stages of the ER pipeline. SystemER achieves explainability by learning rules that not only perform a given ER task but are human-comprehensible; this provides transparency into the learning process, and further enables verification and customization of the learned model by the domain experts. By leveraging a human in the loop and active learning, SystemER also ensures that a small number of labeled examples is sufficient to learn high-quality ER models. SystemER is a full-fledged tool that includes an easy to use interface, support for both flat files and semi-structured data, and scale-out capabilities by distributing computation via Apache Spark.

Techniques for Data Extraction from Heterogeneous Sources with Data Security

Data Extraction is the process of mining or fetching relevant information from unstructured data or the heterogeneous sources of data. This paper aims at mining data from three different sources such as online website, flat files and database and the extracted data are even analyzed in terms of precisions, recall and accuracy. In the environment of heterogeneous sources of data, data extraction is one of the crucial issue and therefore considering the present scenario, we can observe that the heterogeneity is expanding widespread. So this paper focus on the different sources for the data extraction and provides a single framework to perform the required tasks. In this paper, healthcare data are considered in order to show the processing starting from data extraction using three different sources to dividing them in to two clusters based on the thresholds value which has been calculated using cosine similarity and finally calculations of parameters like precisions, recall and accuracy for analyzation purpose. Fetching data online is the task in which we cannot fetch simple string from any website. The backend of each page is html and hence this paper focus on extracting that html of the page while mining data from any web server. The webpage contains a lot of html tags and all of these cannot be removed because they are complex tags which cannot be removed by regular expressions. But still 60% filtered data can be attained as demonstrated in this paper as most of the waste html will be removed. While filtration of the data, we should also note that the content containing Google APIs cannot be removed. So filtered data will contain the contents and tags which does not contain Google APIs. In order to provide data security while extraction, the connection string is being used to avoid tampering of data. This paper also focuses on one of the arguable concepts present in the generation of big data which is Data Lake. In originality, the origin about the idea of Data Lake appears from the field of business. An architectural approach which is specially designed in order to store all the data which are potentially relevant in a repository located centrally is referred to as Data Lake. The data which are stored in the central based repository are fetched from the sources belonging to public as well as enterprises and these data are further used for the purpose of organization, discovery of hidden facts, understanding of new concepts, analyzation of stored information etc. Many challenges and concerns related to privacy are faced during the adoption of Data Lake as it is a new concept which brings revolutionization. This paper also highlights some of the issues imposed by Data Lake.

Abstract 2481: Developing analysis platform for pan-cancer study of DNA methylation, mirna and lncrna expression based on tumor subtypes using TCGA data

Abstract Introduction: The Cancer Genome Atlas (TCGA) consortium performed high-throughput sequencing of thousands of tumor and normal tissues across 33 cancer types. This has led to molecular characterization of different cancers and many novel discoveries. Such diverse data offers an excellent opportunity to further address the questions associated with tumor heterogeneity. Systematic exploration of epigenetic features and non-coding gene expression could lead clinicians/cancer researchers towards unearthing new diagnostic biomarkers and therapeutic targets. Previously our group developed a cancer transcriptome web portal, UALCAN [http://ualcan.path.uab.edu, Google: UALCAN)] to study expression and survival profile of protein coding genes among TCGA cancers based on various subgroups and molecular subtypes of cancer. Although many data portals facilitating easy access and in-depth analysis of TCGA data exists, there is need for a user-friendly resource facilitating comprehensive DNA methylation profile and gene expression analysis of lncRNA and miRNA within tumor subgroups based on factors such as stage, tumor grade, age, sex, race and integration with the gene expression. Methods: TCGA Level 3 RNA-seq, miRNA-seq and Illumina Infinium HumanMethylation450K data were downloaded using TCGA-Assembler 2 tool and Genomic Data Commons (GDC). The data were processed, organized based on tumor subtypes using custom R scripts. DNA methylation and gene expression analysis was performed using in-house PERL scripts, while statistical significance was estimated using “Statistics::TTest” module. The analyses results were stored as flat file database and hosted on a web portal developed using Apache2.0, PERL-CGI and HighChartJS JavaScript libraries. Results: The user friendly web portal 1) allows users could obtain list of top over-/under-expressed miRNAs and lncRNAs in each of 33 TCGA cancer 2) facilitates analysis of promoter methylation level and gene expression for given set of miRNA/ lncRNAs in various tumor sub-groups based on individual cancer stages, tumor grade, race, body weight or other clinicopathologic features, 3) enables researchers to download high resolution graphics such as heatmap, boxplots, dotplots as analyses outputs. The data can be downloaded in multiple output formats for use by researchers. Conclusion: The current resource aids cancer researchers in identifying DNA methylation mediated epigenetic modification of gene expression in tumor subgroup specific manner. Furthermore, our platform will facilitate analyses of long non-coding RNAs and microRNAs, thereby help in discovery of novel biomarkers and better understanding of the tumor biology. Citation Format: Darshan Shimoga Chandrashekar, Chad J. Creighton, Israel Ponce-Rodriguez, Sooryanarayana Varambally. Developing analysis platform for pan-cancer study of DNA methylation, mirna and lncrna expression based on tumor subtypes using TCGA data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2481.

Quality of MBSAQIP data: bad luck, or lack of QA plan?

National clinical registries are commonly used in clinical research, quality improvement, and health policy. However, little is known about methodological challenges associated with these registry analyses that could limit their impact and compromise patient safety. This study examined the quality of Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program (MSBASQIP) data to assess its usability potential and improve data collection methodologies. We developed a single flat file (n = 168,093) using five subsets (Main, BMI, Readmission, Reoperation, and Intervention) of the 2015 MBSAQIP Participant User Data File (PUF). Logic and validity tests included (1) individual profiles of patient's body mass index (BMI) changes over time, (2) individual patient care pathways, and (3) correlation analysis between variable pairs associated with the same clinical encounters. 8888 (5.3%) patients did not have postoperative weight/BMI data; 20% of patients had different units for preoperative and postoperative weights. Postoperative weight measurements ranged between - 71 and 132% of preoperative weight. There were 325 (3.7%) hospital readmissions reported on the day of or day after MBS. The self-reporting of "emergency" vs. "planned" interventions did not correlate with the type of procedure and its indication. Up to 20% of data could potentially be unused for analysis due to data quality issues. Our analysis revealed various data quality issues in the 2015 MBSAQIP PUF related to completeness, accuracy, and consistency. Since information on where the surgery was performed is lacking, it is not possible to conclude whether these issues represent data errors, patient outliers, or inappropriate care. Including automated data checks and biomedical informatics oversight, standardized coding for complications, additional de-identified facility and provider information, and training/mentorship opportunities in data informatics for all researchers who get access to the data have been shown to be effective in improving data quality and minimizing patient safety concerns.

IRS county-to-county migration data, 1990‒2010

Background: The county-to-county migration data of the Internal Revenue Service’s (IRS) is an incredible resource for understanding migration in the United States. Produced annually since 1990 in conjunction with the US Census Bureau, the IRS migration data represents 95% to 98% of the tax-filing universe and their dependents, making the IRS migration data one of the largest sources of migration data. However, any analysis using the IRS migration data must process at least seven legacy formats of this public data across more than 2000 data files – a serious burden for migration scholars. Objective: To produce a single, flat data file containing complete county-to-county IRS migration flow data and to make the computer code to process the migration data freely available. Methods: This paper uses R to process more than 2,000 IRS migration files into a single, flat data file for use in migration research. Contribution: To encourage and facilitate the use of this data, we provide a single, standardized, flat data file containing county-to-county one-year migration flows for the period 1990–2010 (containing 163,883 dyadic county pairs resulting in 3.2 million county-year observations totaling over 343 million migrants) and provide the full R script to download, process, and flatten the IRS migration data.

EMBL2checklists: A Python package to facilitate the user-friendly submission of plant and fungal DNA barcoding sequences to ENA

BackgroundThe submission of DNA sequences to public sequence databases is an essential, but insufficiently automated step in the process of generating and disseminating novel DNA sequence data. Despite the centrality of database submissions to biological research, the range of available software tools that facilitate the preparation of sequence data for database submissions is low, especially for sequences generated via plant and fungal DNA barcoding. Current submission procedures can be complex and prohibitively time expensive for any but a small number of input sequences. A user-friendly software tool is needed that streamlines the file preparation for database submissions of DNA sequences that are commonly generated in plant and fungal DNA barcoding.MethodsA Python package was developed that converts DNA sequences from the common EMBL and GenBank flat file formats to submission-ready, tab-delimited spreadsheets (so-called ‘checklists’) for a subsequent upload to the annotated sequence section of the European Nucleotide Archive (ENA). The software tool, titled ‘EMBL2checklists’, automatically converts DNA sequences, their annotation features, and associated metadata into the idiosyncratic format of marker-specific ENA checklists and, thus, generates files that can be uploaded via the interactive Webin submission system of ENA.ResultsEMBL2checklists provides a simple, platform-independent tool that automates the conversion of common DNA barcoding sequences into easily editable spreadsheets that require no further processing but their upload to ENA via the interactive Webin submission system. The software is equipped with an intuitive graphical as well as an efficient command-line interface for its operation. The utility of the software is illustrated by its application in four recent investigations, including plant phylogenetic and fungal metagenomic studies.DiscussionEMBL2checklists bridges the gap between common software suites for DNA sequence assembly and annotation and the interactive data submission process of ENA. It represents an easy-to-use solution for plant and fungal biologists without bioinformatics expertise to generate submission-ready checklists from common DNA sequence data. It allows the post-processing of checklists as well as work-sharing during the submission process and solves a critical bottleneck in the effort to increase participation in public data sharing.

PP165 Content Instead Of Orders: Experiences Of Launching A Knowledge Base

IntroductionIn Hungary, the procedure for health technology assessment of innovative pharmaceutical products allows 13 assessors 43 calendar days to evaluate reimbursement submissions. These short timelines have created a need for smart capacity building, namely, streamlining the scientific evaluation process while making sure that the quality of the critical appraisals remain high. The objective of this study was to present and evaluate the implementation of an online knowledge base to distill community knowledge, and also for management purposes.MethodsThe scope and the content-, functional-, and technical specification was developed, and information technology security requirements were identified during the pre-implementation phase. An existing platform was chosen for adaptation, ensuring that descriptive follow-up data is available on uptake for monitoring purposes. Both the adaptation and maintenance were carried out internally by the Department of Health Technology Assessment at the National Institute of Pharmacy and Nutrition.ResultsThe key requirements identified when developing the specification were searchability, low maintenance need, low operating costs and attractivity for users. An already existing open-source, flat file content management system was chosen for adaptation. In terms of content, a health technology assessment handbook, process documentation, a news bulletin section was created, and corporate identity elements were added. Since the start of the service in September 2018, the number of total daily page downloads to the knowledge base varied between four and 1,193 (average 205 per day), with the assessment handbook topping the overall page visit statistics.ConclusionsThe implementation of this knowledge base enables the Department of Technology Assessment to rely more on the formalized community knowledge when carrying out critical appraisal, while enabling better knowledge and quality management. Uptake remains an issue on the long run, indicating a need for continuous content development.

Application of Database and Data Science Techniques in the Malaysian Breast Cancer Survivorship Cohort STUDY

Background: Breast cancer is one of the leading cause of mortality among women worldwide. The Breast Cancer Resource Centre (BCRC) of University Malaya Medical Centre (UMMC), Kuala Lumpur, Malaysia, started the Malaysian Breast Cancer Survivorship Cohort (MyBCC) study in 2012. Aim: As a further enhancement of the research, the MyBCC database has been developed to conduct the survey in a convenient way, which aims to predict the factors influencing different survival rate among patients from multiethnic origin using data science techniques. Methods: The database comprised of life style related data of the patients including demographic factors, information on other illness, clinical factors, quality of life, psychosocial support, physical activity, work related questions, depression score, family background, type of medication consumed and financial status of the patients. This paper presents an approach to build an automated workflow using the MySQL database management system and PHP, integrated with R and HTML for web display. Results: A relational database comprising 816 breast cancer patients' data were developed for the MyBCC cohort study. This database serves as the backend for the MyBCC application where researchers can register new patients' records, update and view the information of recruited patients by using the system in a more commodious environment than before. Besides, the MyBCC database has been integrated with R programming tool by deploying the RMySQL package to perform audits. A few important analysis using plotly package, leveraging the integration of R with database are presented. Conclusion: In this paper, the development of the MyBCC database is presented, with the aim to automate the manual process of data entry, storage and analysis for performing audits for the breast cancer cohort study. The integration of database with R for automated analysis of data are also shown using examples of predictions that can be generated using functions in R. This fully automated workflow reduces the workload and time taken in performing manual predictions using data sources stored in flat files.

Development and Design Wearable Sensorial System of Low Cost to Determine the Metacarpophalangeal and Interphalangeal Joints Angles

This paper presents the results of a low-cost wearable sensory system design process for the simultaneous capture of the angles of the metacarpophalangeal and interphalangeal joints of the human hand, as well as the characterization of its performance compared with similar commercial systems, The processing subsystem has machine learning algorithms that were compared in order to identify the optimal technique for capturing information corresponding to basic movements such as hold different objects and characterizing displacements at the level of phalanges in the hand. The development was carried out at the hardware level through the manufacture of a glove with flexible resistive sensors of the reference SEN-10264 (it is a sensor with resistive cells that varies its value in ohms depending on the flexion to which it is subjected) two per each finger, for a total of 10 sensors adhered to the surface of the glove, after going through a coupling stage (to avoid large amounts of noise) the signals generated by the sensors are acquired through the data capture module Analogs of a Microcontroller. In terms of software, the Microcontroller was first programmed with a digital filter stage for the signals acquired by the SEN-10264 flex sensor sensors and a machine learning algorithm for the processing of these and in this way obtain the individual metacarpophalangeal and interphalangeal joints angles of the hand evaluated. Finally, obtain the sensor data in terms of angles and export it to a flat text file to be graphed. For a total of 10 people, multiple acquisitions were made with the developed system and in this way, similar patterns were determined in the angles of the metacarpophalangeal and interphalangeal between the test subjects in basic tasks such as hold different types of objects. Finally, the development of the sensor system was validated with two types of wearable sensors: the 5DT Data Glove device and the Optitrack Flex 3 high-speed cameras, these two types of devices have acquisition protocols predetermined by their manufacturers. Given that the variables to obtain are the metacarpophalangeal and interphalangeal joints angles, the first 5DT Data Glove device delivers only one output signal for each finger of the evaluated hand, basically the software of the manufacturer performs a mathematical interpolation and approximations depending on the postures of the fingers, which makes it incapable by means of this technique to determine if a flexion is occurring from the metacarpophalangeal and interphalangeal joints separately, the Optitrack Flex 3 high-speed cameras generate real-time data graphs of the position in 3d and the trajectories of each of the joints of the hand, the problem is that the system performs the capture of data through markers located in the hand and first of all the size of these makes them crash in a task of grip in addition to making the task of characterizing the ag arrest because the viewing angles of the cameras must detect each marker located in the joints of the hand. The individual angles will be used later to carry out a simulation project of a postural biological synergy of some basic object grabs (with a control command generating movement in several degrees of freedom) through the implementation of an artificial intelligence algorithm.