Flat Files Research Articles

ProCogGraph: a graph-based mapping of cognate ligand domain interactions.

Mappings of domain-cognate ligand interactions can enhance our understanding of the core concepts of evolution and be used to aid docking and protein design. Since the last available cognate-ligand domain database was released, the PDB has grown significantly and new tools are available for measuring similarity and determining contacts. We present ProCogGraph, a graph database of cognate-ligand domain mappings in PDB structures. Building upon the work of the predecessor database, PROCOGNATE, we use data-driven approaches to develop thresholds and interaction modes. We explore new aspects of domain-cognate ligand interactions, including the chemical similarity of bound cognate ligands and how domain combinations influence cognate ligand binding. Finally, we use the graph to add specificity to partial EC IDs, showing that ProCogGraph can complete partial annotations systematically through assigned cognate ligands. The ProCogGraph pipeline, database and flat files are available at https://github.com/bashton-lab/ProCogGraph and https://doi.org/10.5281/zenodo.13165851.

Improving Performance of Data Science Applications in Python

Objectives: The objective of this paper is to focus on the need of performance optimization of Data Science applications with an aim to enhance the speed, responsiveness, and effective resource utilization. Methods: This paper identifies the factors which effect the performance of an application and then classifies them into internal and external factors. This study mainly focuses on internal factors such as execution speed and space requirement. It uses code developed in Python as use cases to explore and examine features which improve the performance and effectiveness of Data Science (DS) applications. Findings: This paper provides comprehensive way of improving performance of applications by implementing features of Python like generator, vectorization, profiling, concurrency & parallelism, and caching. It is observed that space requirement reduces with the use of generators in code, leading to better space management. The conventional way of writing code in Python required additional 5.2 MiB (Mebibyte) space, while there was no additional space requirement with the use of generators in the same code. It is further observed that vectorization feature of Python helps in reducing execution time in comparison to the code written without vectorization. This paper further uses parallelism in the code and gets approximately 68% improvement in execution time. The implementation of profiling feature helps in identifying the time and space requirement which can be improved using features like generator, vectorization, concurrency, and parallelism. Other features like handling I/O operations, use of caching and choice between flat file data and database are also deliberated to improve the performance of code. Novelty: This paper highlights the potential of improving performance of DS applications using generator, vectorization, concurrency and parallelism features of Python and examines the effectiveness of the claim through its implementation. Keywords: Data Science, Generator, Vectorization, Parallelism, Profiling

Metadata functional requirements for genomic data practice and curation

Introduction. The rapid accumulation of genomic data and their widespread reuse in clinical and scientific practice demands more effective description and organization of genomic information, which poses new challenges in developing metadata schemas. Based on a previously developed taxonomy of metadata requirements, this paper reports the results from a survey that addresses these new challenges. Method. The survey gathered empirical data from 156 genomics scientists to identify context-sensitive metadata functional models for genome curation. The study further investigated the metadata elements from four well-known genomic metadata schemes against the functional requirements for genome metadata. Analysis. The survey data were employed the statistical package STATA, to produce descriptive statistics, factor analysis, Fisher’s exact test, and related reports. Results. Analysis of the empirical results revealed that genomics scientists recognize specific sets of criteria for metadata needs in the genome-curation context. Twenty one metadata requirements were reduced to five factor constructs. The ranking of these constructs in decreasing order is: portability, reusability, manipulability, sufficiency, interoperability, extensibility, and modularity. The Fisher exact test results revealed that the genomic community required rich context and technical related metadata elements to facilitate data exchanges and experimental operations in genome curation. Conclusion. The findings indicated that genomics scientists developed metadata to meet the needs in genome curation activities related to data wrangling, integrations across platforms and databases, and data reuse. Architectural layout as flat file needs extra administrative metadata to support data sharing and documentation. The resulting metadata requirement model can serve as valuable resources to genome scientists, data curators and administrators for designing metadata schema and developing data-curation policies.

A Survey on Relational Database Based Multi Relational Classification Algorithms

Classification on real world database is an important task in data mining. Many classification algorithms can build model only for data in single flat file as input, whereas most of real-world data bases are stored in multiple tables and managed by relational database systems. As conversion of relational data from multiple tables into a single flat file usually causes many problems, development of multi relational classification algorithms becomes popular area of research interests. Relational database based multi relational classification algorithms aim to build a model that can predict class label of unknown tuple with the help of background table knowledge. This method keeps database in it normalized form without distorting structure of database. This paper presents survey of existing multi relational classification algorithms based on relational database.

Hardness Test Analysis on ST 37 Steel Plate Material and Aluminum Using the Brinell Test Method

One of the most frequently used methods of material testing is hardness testing. The aim of conducting research on material hardness is to determine the hardness value of the material specimens tested, namely ST 37 steel plate and aluminum plate. The test method is to use the Brinell hardness test where the values ​​obtained during testing are from the force load, indenter diameter and pressure trace diameter and the pressure time that has been determined. By using testing tools such as hardness tester, mechanical caliper, indenter, flat file, stopwatch. And the materials used are ST 37 steel plate, aluminum plate and sandpaper. From the test results obtained we can calculate the Brinell hardness value of the test material. The value from the calculation of test data where the highest hardness value using the Brinell method (BHN) for ST 37 steel plate material is 256.992. Meanwhile, the highest hardness value using the Brinell method (BHN) for aluminum plate material is 55,182.

Enabling AI in synthetic biology through Construction File specification

The Construction File (CF) specification establishes a standardized interface for molecular biology operations, laying a foundation for automation and enhanced efficiency in experiment design. It is implemented across three distinct software projects: PyDNA_CF_Simulator, a Python project featuring a ChatGPT plugin for interactive parsing and simulating experiments; ConstructionFileSimulator, a field-tested Java project that showcases ’Experiment’ objects expressed as flat files; and C6-Tools, a JavaScript project integrated with Google Sheets via Apps Script, providing a user-friendly interface for authoring and simulation of CF. The CF specification not only standardizes and modularizes molecular biology operations but also promotes collaboration, automation, and reuse, significantly reducing potential errors. The potential integration of CF with artificial intelligence, particularly GPT-4, suggests innovative automation strategies for synthetic biology. While challenges such as token limits, data storage, and biosecurity remain, proposed solutions promise a way forward in harnessing AI for experiment design. This shift from human-driven design to AI-assisted workflows, steered by high-level objectives, charts a potential future path in synthetic biology, envisioning an environment where complexities are managed more effectively.

Cyclic and torsional fatigue resistance of thermally treated rotary NiTi instruments with different design

Introduction: The aim was to evaluate the cyclic and torsional fatigue resistance among thermally treated NiTi rotary instruments with different design features. Materials and methods: Sixty instruments of three systems were used (n=20): TruNatomy 26.04 (TN 26.04), BassiLogic 25.05 (BL 25.05), and Flat File 25.04 (FF 25.04). The cyclic fatigue test (n=10) was made in an artificial stainless-steel canal at an angle of 60° and a 5-mm radius of curvature at. The torsion test was performed to evaluate the torque (N.cm) and maximum angular deflection until fracture (n=10). The fracture surface of each fragment was examined under a scanning electron microscope. The data were analyzed by Tukey’s test (p<0.05). Results: BL 25.05 and FF 25.04 instruments had a higher number of cycles and time to fracture compared with TN 26.04 (p<0.05). TN 26.04 instruments showed lower torque to fracture. Conclusions: Based on the proposed objectives and the methodology used, TruNatomy 26.04 instruments present lower resistance to cyclic fatigue and torsional fatigue when compared to BassiLogic 25.05 and Flat File 25.04 instruments.

BIGQA: Declarative Big Data Quality Assessment

In the big data domain, data quality assessment operations are often complex and must be implementable in a distributed and timely manner. This article tries to generalize the quality assessment operations by providing a new ISO-based declarative data quality assessment framework (BIGQA). BIGQA is a flexible solution that supports data quality assessment in different domains and contexts. It facilitates the planning and execution of big data quality assessment operations for data domain experts and data management specialists at any phase in the data life cycle. This work implements BIGQA to demonstrate its ability to produce customized data quality reports while running efficiently on parallel or distributed computing frameworks. BIGQA generates data quality assessment plans using straightforward operators designed to handle big data and guarantee a high degree of parallelism when executed. Moreover, it allows incremental data quality assessment to avoid reading the whole dataset each time the quality assessment operation is required. The result was validated using radiation wireless sensor data and Stack Overflow users’ data to show that it can be implemented within different contexts. The experiments show a 71% performance improvement over a 1 GB flat file on a single processing machine compared with a non-parallel application and a 75% performance improvement over a 25 GB flat file within a distributed environment compared to a non-distributed application.

Cyclic and torsional fatigue resistance of a new rotary file on a rotary and reciprocating motion.

This study evaluated the cyclic and torsional fatigue resistance of a new nickel-titanium (Flat File 25.04) instrument on the continuous and reciprocating motion. Sixty instruments of the ProDesign Logic2 25.03 and 25.05 (Easy Equipamentos Odontológicos, Belo Horizonte, Brazil), and MK Flat File 25.04 (n = 20) (MK Life, Porto Alegre, Brazil) were used. For the cyclic fatigue test, an artificial stainless steel simulated canal with an angle of 60° and a radius of curvature of 5 mm located 5 mm from its tip was used. The torque and rotation angle at the instruments' failure on the torsional fatigue test was based on the ISO 3630-1 protocol, in which the 3 mm tip of each instrument was fixed and connected to an electric motor and a load cell. The fractured surface of each fragment was examined by scanning electron microscopy. Data were analyzed using a 1-way analysis of variance and Tukey's test with a significance level of 5%. Flat File 25.04 had lower cyclic fatigue in both kinematics than the Logic instruments (p < .05). Reciprocating motion improved the cyclic fatigue of the tested instruments (p < .05). Flat File 25.04 had similar torque to Logic2 25.05 (p > .05), and both were superior to Logic2 25.03 (p < .05). The angular deflection values were different for the three tested instruments (p < .05), in the decreasing order: Logic2 25.03, 25.05, and Flat File 25.04. Flat File presented good resistance to cyclic and torsional fatigue resistance. Reciprocating motion improved the cyclic fatigue resistance of the instruments and can be considered when using programmable motors. RESEARCH HIGHLIGHTS: Scanning electron microscopy evaluation of different endodontic rotary file and fatigue resistance tests.

An accessible strong-motion dataset (PGA, PGV, and site vS30) of 2022 MS6.8 Luding, China Earthquake

A MS6.8 earthquake occurred on 5th September 2022 in Luding county, Sichuan, China, at 12: 52 Beijing Time (4:52 UTC). We complied a dataset of PGA, PGV, and site vS30 of 73 accelerometers and 791 Micro-Electro-Mechanical System (MEMS) sensors within 300 km of the epicenter. The inferred vS30 of 820 recording sites were validated. The study results show that: (1) The maximum horizontal PGA and PGV reaches 634.1 Gal and 71.1 cm/s respectively. (2) Over 80% of records are from soil sites. (3) The vS30 proxy model of Zhou J et al. is superior than that of Wald and Allen and performs well in the study area. The dataset was compiled in a flat file that consists the information of strong-motion instruments, the strong-motion records, and the vS30 of the recording sites. The dataset is available at https://www.seismisite.net.

Effects of handle shape and texture on the hand-powered tools performance – A review

The performance of hand-powered tools, axil force, total/individual finger force, and subjective discomfort was examined in this study about the handle shape, including its lateral and longitudinal cross-sections, material surface (plastic and rubber), and workpiece orientation (horizontal and vertical). The functioning of hand-powered tools, the force transferred by the tool, and the involvements made by each finger to the overall distribution of grip force, on the other hand, are all impacted by design factors. In the case of a screwdriver, our current study found that cylindrical and reversed double frustum lateral handles, compared to triangular longitudinal cross-sectional handles, double frustum, and cone handles, were less uncomfortable. The newly created ergonomic hand tools for weaving were determined to be usable and acceptable by carpet weavers. In the case of Flat Files, our study finds that the files with handles bent between 50 and 70 degrees demonstrate their superiority.

Geospatial analysis of patients' social determinants of health for health systems science and disparity research.

Geospatial analysis of patients' social determinants of health for health systems science and disparity research.

The cloud data migration idea includes data warehouses that deal with flat files from many sources and are used for data analysis and validation

The cloud data migration idea includes data warehouses that deal with flat files from many sources and are used for data analysis and validation

APOGEE-centric Ananke Simulations in a SciServer SQL Database

Modern galaxy simulations have reached the complexity required to create sophisticated mock catalogs. Ananke is a set of nine mock catalogs constructed from synthetic surveys of three Milky Way-like galaxies from the Latte suite of FIRE simulations. Ananke provides observed quantities for comparison with modern large-scale stellar surveys. In SDSS-IV DR17, mock catalogs for the Apache Point Galactic Evolution Experiment (APOGEE) were built from Ananke synthetic surveys as a Value-Added Catalog, but were only provided as large flat files (∼>10's GB). Here we announce an >40 Tb SQL database for nine APOGEE-specific mock catalogs and describe additions to the data model necessary for effective user queries. The catalogs can be accessed on the free, science platform, SciServer—supported by the Institute for Data Intensive Engineering and Science at the Johns Hopkins University (IDIES); SciServer supports server-side analysis with commonly used coding languages and tools.

BIG DATA PROCESSING FOR WEB SEARCH ENGINE

Crawling, Indexing and Searching are the significant procedure of Search Engine. Web search tool slithered information are so huge or complex and it might be in organized or unstructured arrangement. This enormous informational collection turns out to be Big Data and it is one of the developing advances that have changed the world. This paper talks about Search Engines and its exhibition enhancement by putting away the information in level twofold records and In-memory database. Mass information are put away In-memory databases like REDIS. Enormous Numeric information can be put away in Flat File Binary Format with the end goal that they can be stacked into RAM in a solitary read activity. As run time circle access in level record parallel documents are quicker than customary records, enormous information are put away regarding Binary arrangement which decreases the extra room than other record designs. A few issues, for example, adaptation to internal failure and consistency are likewise all the more testing to deal with in-memory condition. This paper likewise breaks down the difficult issues in the information driven model in the Search Engine Big Data.

Enabling Scalable and Extensible Memory-Mapped Datastores in Userspace

Exascale workloads are expected to incorporate data-intensive processing in close coordination with traditional physics simulations. These emerging scientific, data-analytics and machine learning applications need to access a wide variety of datastores in flat files and structured databases. Programmer productivity is greatly enhanced by mapping datastores into the application process's virtual memory space to provide a unified “in-memory” interface. Currently, memory mapping is provided by system software primarily designed for generality and reliability. However, scalability at high concurrency is a formidable challenge on exascale systems. Also, there is a need for extensibility to support new datastores potentially requiring HPC data transfer services. In this article, we present <i>UMap</i> , a scalable and extensible userspace service for memory-mapping datastores. Through decoupled queue management, concurrency aware adaptation, and dynamic load balancing, <i>UMap</i> enables application performance to scale even at high concurrency. We evaluate <i>UMap</i> in data-intensive applications, including sorting, graph traversal, database operations, and metagenomic analytics. Our results show that <i>UMap</i> as a userspace service outperforms an optimized kernel-based service across a wide range of intra-node concurrency by 1.22-1.9 <inline-formula><tex-math notation="LaTeX">${\times}$</tex-math></inline-formula> . We performed two case studies to demonstrate <i>UMap</i> 's extensibility. First, a new datastore residing in remote memory is incorporated into <i>UMap</i> as an application-specific plugin. Second, we present a persistent memory allocator <i>Metall</i> built atop <i>UMap</i> for unified storage/memory.

Requirements of a data storage infrastructure for effective land administration systems: case study of Victoria, Australia

ABSTRACT Land administration systems are being modernised to streamline the cadastral data lodgement. However, in many jurisdictions, cadastral data are still stored as a flat file. This method of data storage has significant limitations in terms of effective access, management, query, and analysis of cadastral data. Therefore, this study elicited the requirements and proposed an approach to automate the cadastral data storage. The proposed approach was successfully implemented within the land registry organisation in Victoria, Australia and the database management system was rigorously tested. The outcomes can potentially contribute to the implementation of a similar data storage infrastructure in other jurisdictions.

SangeR: the high-throughput Sanger sequencing analysis pipeline.

In the era of next generation sequencing and beyond, the Sanger technique is still widely used for variant verification of inconclusive or ambiguous high-throughput sequencing results or as a low-cost molecular genetical analysis tool for single targets in many fields of study. Many analysis steps need time-consuming manual intervention. Therefore, we present here a pipeline-capable high-throughput solution with an optional Shiny web interface, that provides a binary mutation decision of hotspots together with plotted chromatograms including annotations via flat files. SangeR is freely available at https://github.com/Neuropathology-Giessen/SangeR and https://hub.docker.com/repository/docker/kaischmid/sange_r. Kai.Schmid@patho.med.uni-giessen.de or Daniel.Amsel@patho.med.uni-giessen.de. Supplementary data are available at Bioinformatics online.

Analisis Dynamic ETL Incremental Load untuk Data Integration Datawarehouse

Data integration is a combination of techniques and businesses that are used to collect data from different sources into useful and valuable information ETL process that includes extracting data from various data sources, transforming data to form and calculate data and load data on target storage, to support data warehouse need. Based on organizations and industries that have implemented data warehouse, the problem that generally arises regarding data load is the difficulty in integrating different data sources, how to form data from various data formats into uniform data, how to integrate data delta between data sources and target storage in an incremental load process so that this data synchronization process can be carried out continuously and relatively faster. ETL process requires a platform that can facilitate data integration needs, in order to run this process. SSIS (SQL Server Integration Service) is a Data Integration platform to build an enterprise-level data integration and solutions for data transformation. Integration Service can extract and change data (transform) from various sources such as XML data files, flat files, APIs, and relational data sources, and then load into one or several destination data. According to the problem related to data load, we will examine how the solution model uses SSIS for the ETL process. This paper proposed an ETL Architecture model by completing the ETL process for full &amp; incremental load extraction and the original data layer.

CEBS update: curated toxicology database with enhanced tools for data integration.

The Chemical Effects in Biological Systems database (CEBS) contains extensive toxicology study results and metadata from the Division of the National Toxicology Program (NTP) and other studies of environmental health interest. This resource grants public access to search and collate data from over 10 250 studies for 12 750 test articles (chemicals, environmental agents). CEBS has made considerable strides over the last 5 years to integrate growing internal data repositories into data warehouses and data marts to better serve the public with high quality curated datasets. This effort includes harmonizing legacy terms and metadata to current standards, mapping test articles to external identifiers, and aligning terms to OBO (Open Biological and Biomedical Ontology) Foundry ontologies. The data are made available through the CEBS Homepage (https://cebs.niehs.nih.gov/cebs/), guided search applications, flat files on FTP (file transfer protocol), and APIs (application programming interface) for user access and to provide a bridge for computational tools. The user interface is intuitive with a single search bar to query keywords related to study metadata, publications, and data availability. Results are consolidated to single pages for each test article with NTP conclusions, publications, individual studies, data collections, and links to related test articles and projects available together.