scholarly journals Time distributed data analysis by Cosinor.Online application

2019 ◽  
Author(s):  
Lubos Molcan
Keyword(s):  
Programming Languages ◽  
Data Sets ◽  
Distributed Data ◽  
Web Browsers ◽  
Web Based ◽  
Software Applications ◽  
Physiological Processes ◽  
Wide Range ◽  
Distributed Data Analysis ◽  
Excel File

AbstractPhysiological processes oscillate in time. Circadian oscillations, over approximately 24-h, are very important and among the most studied. To evaluate the presence and significance of 24-h oscillations, physiological time distributed data (TDD) are often set to a cosinor model using a wide range of irregularly updated native apps. If users are familiar with MATLAB, R or other programming languages, users can adjust the parameters of the cosinor model setting based on their needs. Nowadays, many software applications are hosted on remote servers running 24/7. Server-based software applications enable quick analysis of big data sets and run on a wide range of terminal devices using standard web browsers. We created a simple web-based cosinor application, Cosinor.Online. The application code is written in PHP. TDD is handled using a MySQL database and can be copied directly from an Excel file to the webform. Analysis results contain information about setting the 24-h oscillation and a unique ID identifier. The identifier allows users to reopen data and results repeatedly over one month or remove their data from the MySQL database. Our web-based application can be used for a quick and simple inspection of 24-h oscillations of various biological and physiological TDD.

scholarly journals Cloud for Distributed Data Analysis Based on the Actor Model

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Ivan Kholod ◽  
Ilya Petukhov ◽  
Andrey Shorov
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Data Sets ◽  
Distributed Data ◽  
Actor Model ◽  
Confidential Information ◽  
Data Mining Algorithms ◽  
Functional Blocks ◽  
Mining Algorithms ◽  
Distributed Data Analysis

This paper describes the construction of a Cloud for Distributed Data Analysis (CDDA) based on the actor model. The design uses an approach to map the data mining algorithms on decomposed functional blocks, which are assigned to actors. Using actors allows users to move the computation closely towards the stored data. The process does not require loading data sets into the cloud and allows users to analyze confidential information locally. The results of experiments show that the efficiency of the proposed approach outperforms established solutions.

scholarly journals multiSLIDE: a web server for exploring connected elements of biological pathways in multi-omics data

2019 ◽  
Author(s):  
Soumita Ghosh ◽  
Abhik Datta ◽  
Hyungwon Choi
Keyword(s):  
Keyword Search ◽  
Data Sets ◽  
Omics Data ◽  
Web Based ◽  
Molecular Features ◽  
External Data ◽  
Cluster Data ◽  
Wide Range ◽  
Time Basis ◽  
Gene Ontologies

AbstractEmerging multi-omics experiments pose new challenges for exploration of quantitative data sets. We present multiSLIDE, a web-based interactive tool for simultaneous heatmap visualization of interconnected molecular features in multi-omics data sets. multiSLIDE operates by keyword search for visualizing biologically connected molecular features, such as genes in pathways and Gene Ontologies, offering convenient functionalities to rearrange, filter, and cluster data sets on a web browser in a real time basis. Various built-in querying mechanisms make it adaptable to diverse omics types, and visualizations are fully customizable. We demonstrate the versatility of the tool through three example studies, each of which showcases its applicability to a wide range of multi-omics data sets, ability to visualize the links between molecules at different granularities of measurement units, and the interface to incorporate inter-molecular relationship from external data sources into the visualization. Online and standalone versions of multiSLIDE are available at https://github.com/soumitag/multiSLIDE.

scholarly journals A TALE of shrimps: Genome-wide survey of homeobox genes in 120 species from diverse crustacean taxa

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 71
Author(s):  
Wai Hoong Chang ◽  
Alvina G. Lai
Keyword(s):  
Homeobox Genes ◽  
Whole Body ◽  
Model Organisms ◽  
Data Sets ◽  
Crop Species ◽  
Physiological Processes ◽  
Genome Wide ◽  
Wide Range ◽  
Identification And Characterization

The homeodomain-containing proteins are an important group of transcription factors found in most eukaryotes including animals, plants and fungi. Homeobox genes are responsible for a wide range of critical developmental and physiological processes, ranging from embryonic development, innate immune homeostasis to whole-body regeneration. With continued fascination on this key class of proteins by developmental and evolutionary biologists, multiple efforts have thus far focused on the identification and characterization of homeobox orthologs from key model organisms in attempts to infer their evolutionary origin and how this underpins the evolution of complex body plans. Despite their importance, the genetic complement of homeobox genes has yet been described in one of the most valuable groups of animals representing economically important food crops. With crustacean aquaculture being a growing industry worldwide, it is clear that systematic and cross-species identification of crustacean homeobox orthologs is necessary in order to harness this genetic circuitry for the improvement of aquaculture sustainability. Using publicly available transcriptome data sets, we identified a total of 4183 putative homeobox genes from 120 crustacean species that include food crop species, such as lobsters, shrimps, crayfish and crabs. Additionally, we identified 717 homeobox orthologs from 6 other non-crustacean arthropods, which include the scorpion, deer tick, mosquitoes and centipede. This high confidence set of homeobox genes will now serve as a key resource to the broader community for future functional and comparative genomics studies.

scholarly journals multiSLIDE is a web server for exploring connected elements of biological pathways in multi-omics data

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Soumita Ghosh ◽  
Abhik Datta ◽  
Hyungwon Choi
Keyword(s):  
Keyword Search ◽  
Data Sets ◽  
Omics Data ◽  
Web Browser ◽  
Web Based ◽  
Molecular Features ◽  
Cluster Data ◽  
Wide Range ◽  
Or Genes ◽  
Simultaneous Visualization

AbstractQuantitative multi-omics data are difficult to interpret and visualize due to large volume of data, complexity among data features, and heterogeneity of information represented by different omics platforms. Here, we present multiSLIDE, a web-based interactive tool for the simultaneous visualization of interconnected molecular features in heatmaps of multi-omics data sets. multiSLIDE visualizes biologically connected molecular features by keyword search of pathways or genes, offering convenient functionalities to query, rearrange, filter, and cluster data on a web browser in real time. Various querying mechanisms make it adaptable to diverse omics types, and visualizations are customizable. We demonstrate the versatility of multiSLIDE through three examples, showcasing its applicability to a wide range of multi-omics data sets, by allowing users to visualize established links between molecules from different omics data, as well as incorporate custom inter-molecular relationship information into the visualization. Online and stand-alone versions of multiSLIDE are available at https://github.com/soumitag/multiSLIDE.

Knowledge-Based Information Resource Management System for Materials of Fast Reactors

2009 ◽  
Author(s):  
Hsuan-Tsung Sean Hsieh ◽  
Ning Li ◽  
Yitung Chen ◽  
Kenny Kwan ◽  
Jen-Yuan Huang ◽  
...  
Keyword(s):  
Knowledge Discovery ◽  
Gap Analysis ◽  
Technology Development ◽  
Advanced Materials ◽  
Data Sets ◽  
Distributed Data ◽  
Data Generation ◽  
Fast Reactors ◽  
Web Based ◽  
Gen Iv

In the development of advanced fast reactors, materials and coolant/material interactions pose a critical barrier for higher temperature and longer core life designs. For advanced burner reactors (sodium cooled), experience has shown that the qualified structural materials and fuel cladding severely limits the economic performance. In other liquid metal cooled reactor concepts, advanced materials and better understanding and control of coolant and materials interactions are necessary for realizing the potentials. Researches from universities, national laboratories and related industrial participants have been continuously generating invaluable data and knowledge about materials and their interactions with coolants in the past few decades. Under the consideration of cost and time constraints, the paradigm of designing and implementing a successful Gen IV Nuclear Energy Systems can be shifted and updated via the integration of information and internet technologies. Such efforts can be better visualized by implementing collective (centralized or distributed) data storages to serve the community with organized material data sets. Material property data provided by MatWeb.com and the ongoing development of web-based GEN IV material handbook are few examples. From system design perspective, sodium-cooled fast reactor (SFR) proposed in the GEN IV system have been significantly developed. According to the GEN IV ten-year program plan, current R&D work will be pointed to demonstration of the design and safety characteristics, and design optimization. All of those activities follow the path of data generation, analysis, knowledge discovery and finally decision making and implementation. We are proposing to create a modularized web-based information system with models to systematically catalog existing data and guide the new development and testing to acquire new data. Technically speaking, information retrieval and knowledge discovery tools will be implemented for researchers with both information lookup options from material database and technology/development gap analysis from intelligent agent and reporting components. The goal of the system is not only to provide another database, but also to create a sharable and expandable platform-free, location-free online system for research institutes and industrial partners.

scholarly journals Distributed data analysis with ROOT RDataFrame

2020 ◽  
Vol 245 ◽  
pp. 03009
Author(s):  
Vincenzo Eduardo Padulano ◽  
Javier Cervantes Villanueva ◽  
Enrico Guiraud ◽  
Enric Tejedor Saavedra
Keyword(s):  
Data Analysis ◽  
Programming Model ◽  
Distributed Processing ◽  
Distributed Applications ◽  
Distributed Data ◽  
Distributed Environments ◽  
Web Based ◽  
Intuitive Interface ◽  
Distributed Data Analysis

Widespread distributed processing of big datasets has been around for more than a decade now thanks to Hadoop, but only recently higher-level abstractions have been proposed for programmers to easily operate on those datasets, e.g. Spark. ROOT has joined that trend with its RDataFrame tool for declarative analysis, which currently supports local multi-threaded parallelisation. However, RDataFrame’s programming model is general enough to accommodate multiple implementations or backends: users could write their code once and execute it as-is locally or distributedly, just by selecting the corresponding backend. This abstract introduces PyRDF, a new python library developed on top of RDataFrame to seamlessly switch from local to distributed environments with no changes in the application code. In addition, PyRDF has been integrated with a service for web-based analysis, SWAN, where users can dynamically plug in new resources, as well as write, execute, monitor and debug distributed applications via an intuitive interface.

scholarly journals Performance Evaluation of Java Programming Strategies

2021 ◽  
Vol 10 (4) ◽  
pp. 146-159
Author(s):  
Qusay Idrees Sarhan
Keyword(s):  
Programming Languages ◽  
Web Applications ◽  
Experimental Studies ◽  
Experimental Tests ◽  
Performance Improvements ◽  
Java Programming ◽  
Software Applications ◽  
Wide Range ◽  
Significant Performance ◽  
The Impact

Java is one of the most demanding programming languages nowadays and it is used for developing a wide range of software applications including desktop, mobile, embedded, and web applications. Writing efficient Java codes for those various types of applications (which some are critical and time-sensitive) is crucial and recommended best practices that every Java developer should consider. To date, there is a lack of in-depth experimental studies in the literature that evaluate the impact of writing efficient Java programming strategies on the performance of desktop applications in terms of runtime. Thus, this paper aims to perform a variety of experimental tests that have been carefully chosen and implemented to evaluate the most important aspects of desktop efficient Java programming in terms of runtime. The results of this study show that significant performance improvements can be achieved by applying different programming strategies.

Mapping of Protein-Protein Interactions: Web-Based Resources for Revealing Interactomes

2019 ◽  
Vol 26 (21) ◽  
pp. 3890-3910 ◽  
Author(s):  
Branislava Gemovic ◽  
Neven Sumonja ◽  
Radoslav Davidovic ◽  
Vladimir Perovic ◽  
Nevena Veljkovic
Keyword(s):  
Drug Discovery ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Experimental Studies ◽  
Protein Protein Interactions ◽  
Web Based ◽  
Prediction Tools ◽  
Physiological Processes ◽  
Modern Drug ◽  
Ppi Prediction

Background: The significant number of protein-protein interactions (PPIs) discovered by harnessing concomitant advances in the fields of sequencing, crystallography, spectrometry and two-hybrid screening suggests astonishing prospects for remodelling drug discovery. The PPI space which includes up to 650 000 entities is a remarkable reservoir of potential therapeutic targets for every human disease. In order to allow modern drug discovery programs to leverage this, we should be able to discern complete PPI maps associated with a specific disorder and corresponding normal physiology. Objective: Here, we will review community available computational programs for predicting PPIs and web-based resources for storing experimentally annotated interactions. Methods: We compared the capacities of prediction tools: iLoops, Struck2Net, HOMCOS, COTH, PrePPI, InterPreTS and PRISM to predict recently discovered protein interactions. Results: We described sequence-based and structure-based PPI prediction tools and addressed their peculiarities. Additionally, since the usefulness of prediction algorithms critically depends on the quality and quantity of the experimental data they are built on; we extensively discussed community resources for protein interactions. We focused on the active and recently updated primary and secondary PPI databases, repositories specialized to the subject or species, as well as databases that include both experimental and predicted PPIs. Conclusion: PPI complexes are the basis of important physiological processes and therefore, possible targets for cell-penetrating ligands. Reliable computational PPI predictions can speed up new target discoveries through prioritization of therapeutically relevant protein–protein complexes for experimental studies.

Investigating the Efficacy and Cost-Effectiveness of a Web-based Self-help Program for People With Adjustment Problems After an Accident (SelFIT): Protocol for a Randomized Controlled Trial (Preprint)

2020 ◽  
Author(s):  
Julia Hegy ◽  
Noemi Anja Brog ◽  
Thomas Berger ◽  
Hansjoerg Znoj
Keyword(s):  
Health Care ◽  
Psychological Distress ◽  
Cost Effectiveness ◽  
Well Being ◽  
User Friendliness ◽  
Adjustment Problems ◽  
Health Care Consumption ◽  
Web Based ◽  
Self Help ◽  
Wide Range

BACKGROUND Accidents and the resulting injuries are one of the world’s biggest health care issues often causing long-term effects on psychological and physical health. With regard to psychological consequences, accidents can cause a wide range of burdens including adjustment problems. Although adjustment problems are among the most frequent mental health problems, there are few specific interventions available. The newly developed program SelFIT aims to remedy this situation by offering a low-threshold web-based self-help intervention for psychological distress after an accident. OBJECTIVE The overall aim is to evaluate the efficacy and cost-effectiveness of the SelFIT program plus care as usual (CAU) compared to only care as usual. Furthermore, the program’s user friendliness, acceptance and adherence are assessed. We expect that the use of SelFIT is associated with a greater reduction in psychological distress, greater improvement in mental and physical well-being, and greater cost-effectiveness compared to CAU. METHODS Adults (n=240) showing adjustment problems due to an accident they experienced between 2 weeks and 2 years before entering the study will be randomized. Participants in the intervention group receive direct access to SelFIT. The control group receives access to the program after 12 weeks. There are 6 measurement points for both groups (baseline as well as after 4, 8, 12, 24 and 36 weeks). The main outcome is a reduction in anxiety, depression and stress symptoms that indicate adjustment problems. Secondary outcomes include well-being, optimism, embitterment, self-esteem, self-efficacy, emotion regulation, pain, costs of health care consumption and productivity loss as well as the program’s adherence, acceptance and user-friendliness. RESULTS Recruitment started in December 2019 and is ongoing. CONCLUSIONS To the best of our knowledge, this is the first study examining a web-based self-help program designed to treat adjustment problems resulting from an accident. If effective, the program could complement the still limited offer of secondary and tertiary psychological prevention after an accident. CLINICALTRIAL ClinicalTrials.gov NCT03785912; https://clinicaltrials.gov/ct2/show/NCT03785912?cond=NCT03785912&draw=2&rank=1

scholarly journals mtDNAcombine: tools to combine sequences from multiple studies

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Eleanor F. Miller ◽  
Andrea Manica
Keyword(s):  
Sequence Data ◽  
Data Extraction ◽  
Bayesian Skyline Plot ◽  
Model Organisms ◽  
Data Sets ◽  
Data Handling ◽  
Online Database ◽  
Genetic Studies ◽  
Wide Range ◽  
Existing Data

Abstract Background Today an unprecedented amount of genetic sequence data is stored in publicly available repositories. For decades now, mitochondrial DNA (mtDNA) has been the workhorse of genetic studies, and as a result, there is a large volume of mtDNA data available in these repositories for a wide range of species. Indeed, whilst whole genome sequencing is an exciting prospect for the future, for most non-model organisms’ classical markers such as mtDNA remain widely used. By compiling existing data from multiple original studies, it is possible to build powerful new datasets capable of exploring many questions in ecology, evolution and conservation biology. One key question that these data can help inform is what happened in a species’ demographic past. However, compiling data in this manner is not trivial, there are many complexities associated with data extraction, data quality and data handling. Results Here we present the mtDNAcombine package, a collection of tools developed to manage some of the major decisions associated with handling multi-study sequence data with a particular focus on preparing sequence data for Bayesian skyline plot demographic reconstructions. Conclusions There is now more genetic information available than ever before and large meta-data sets offer great opportunities to explore new and exciting avenues of research. However, compiling multi-study datasets still remains a technically challenging prospect. The mtDNAcombine package provides a pipeline to streamline the process of downloading, curating, and analysing sequence data, guiding the process of compiling data sets from the online database GenBank.

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