Software Tools For Data Analysis Research Articles

10Boron-film-based gas detectors at ESS

The development of detectors for the European Spallation Source is an important parallel element to the effort put into the design and construction of the neutron source and instruments. The 10Boron-film-based detector developments that started over a decade ago as basic detector concepts have now reached technical maturity after intense prototyping work and numerous testing campaigns. Several of the ESS beamlines that will soon enter the commissioning phase started welcoming the detector systems built with the 10B-film converter technology. The real-size demonstrators evolved in the last 2–3 years into a diverse suite of gas proportional counters for use in diffraction, reflectometry and small-angle scattering studies in spite of the operational complexity posed by the requirements for large-area coverage, low material budget and robustness for operation in the high-flux and high-radiation environment of ESS. The detectors are now being shipped to ESS or undergoing the last performance and calibration tests before being handed over to the instrument teams for installation in the host beamlines. A common feature of these detector systems is the very large number of readout channels that they are instrumented with in order to fulfill the demanding requirements for sensitivity, spatial resolution and count-rate capability. Across all of the detector types that will operate at ESS, the integration, testing and commissioning of the read-out technologies and software tools for data reduction, calibration and analysis are the focus of the detector and integration teams. These will yield a wealth of knowledge about their operation as well as initial results on the in-situ performance, a very important asset to ensure rapid commissioning of the detectors when neutrons from the ESS source become available. In this paper we will give an overview of the 10B-film-based detector technologies included in the ESS detector suite that are currently facing the transition from the production phase to installation and integration with the other beamline components, which comes with its own specific challenges of both organizational and technical nature.

ProSPyX: software for post-processing images of X-ray ptychography with spectral capabilities.

X-ray ptychography is a coherent diffraction imaging technique based on acquiring multiple diffraction patterns obtained through the illumination of the sample at different partially overlapping probe positions. The diffraction patterns collected are used to retrieve the complex transmittivity function of the sample and the probe using a phase retrieval algorithm. Absorption or phase contrast images of the sample as well as the real and imaginary parts of the probe function can be obtained. Furthermore, X-ray ptychography can also provide spectral information of the sample from absorption or phase shift images by capturing multiple ptychographic projections at varying energies around the resonant energy of the element of interest. However, post-processing of the images is required to extract the spectra. To facilitate this, ProSPyX, a Python package that offers the analysis tools and a graphical user interface required to process spectral ptychography datasets, is presented. Using the PyQt5 Python open-source module for development and design, the software facilitates extraction of absorption and phase spectral information from spectral ptychographic datasets. It also saves the spectra in file formats compatible with other X-ray absorption spectroscopy data analysis software tools, streamlining integration into existing spectroscopic data analysis pipelines. To illustrate its capabilities, ProSPyX was applied to process the spectral ptychography dataset recently acquired on a nickel wire at the SWING beamline of the SOLEIL synchrotron.

Life, death, and statins: association of statin prescriptions and survival in older general practice patients.

This study serves as an exemplar to demonstrate the scalability of a research approach using survival analysis applied to general practice electronic health record data from multiple sites. Collection of these data, the subsequent analysis, and the preparation of practice-specific reports were performed using a bespoke distributed data collection and analysis software tool. Statins are a very commonly prescribed medication, yet there is a paucity of evidence for their benefits in older patients. We examine the relationship between statin prescriptions for general practice patients over 75 and all-cause mortality. We carried out a retrospective cohort study using survival analysis applied to data extracted from the electronic health records of five Australian general practices. The data from 8025 patients were analysed. The median duration of follow-up was 6.48 years. Overall, 52015 patient-years of data were examined, and the outcome of death from any cause was measured in 1657 patients (21%), with the remainder being censored. Adjusted all-cause mortality was similar for participants not prescribed statins versus those who were (HR 1.05, 95% CI 0.92-1.20, P = 0.46), except for patients with diabetes for whom all-cause mortality was increased (HR = 1.29, 95% CI: 1.00-1.68, P = 0.05). In contrast, adjusted all-cause mortality was significantly lower for patients deprescribed statins compared to those who were prescribed statins (HR 0.81, 95% CI 0.70-0.93, P < 0.001), including among females (HR = 0.75, 95% CI: 0.61-0.91, P < 0.001) and participants treated for secondary prevention (HR = 0.72, 95% CI: 0.60-0.86, P < 0.001). This study demonstrated the scalability of a research approach using survival analysis applied to general practice electronic health record data from multiple sites. We found no evidence of increased mortality due to statin-deprescribing decisions in primary care.

A Modification of Magnetic Adaptive Testing: Progressive Method for Nondestructive Inspection of Microstructural Changes in Ferromagnetic Constructional Materials

Magnetic adaptive testing (MAT), thanks to its relative simplicity from the point of view of both hardware and software, appears to be very promising for non-destructive analysis of various ferromagnetic constructional materials used in many industrial applications. In order to make the inspection of tested objects faster, even more straightforward and the processing of data easier, we concentrated our effort both to improve the experimental procedure, based on specific way of the measurement of magnetization curves at piece-wise linear (triangular) exciting field with a constant field rate of change (i.e., slope) as well as to create a universal software tool for MAT data analysis. On contrary to the original implementation of MAT, the hysteresis loops are measured with decreasing maximum field values, starting at sample saturation region; therefore, time consuming sample demagnetization can be skipped completely. In addition, an advanced tool for the processing of experimentally obtained magnetization curves is presented. Using this application allows to find proper non-traditional magnetic parameters (e.g., the differential permeability) being the most sensitive to various types of industrial load (e.g., thermal, mechanical and/or neutron irradiation) and, at the same time, sufficiently correlated with other, traditional magnetic as well as non-magnetic parameters used routinely for the assessment of possible structural changes associated with applied, often long-term, load even on a microscopic scale, prior to any damage manifests on a macroscopic, visible level. The software capabilities were demonstrated on the data representing the material, whose response to acting thermal load is being difficult to analyze, since the dependence of observed parameters (differential permeability) upon defined artificial ageing was rather complicated. Nevertheless, the sensitivity of differential permeability to such a load was found being more than 8 times larger than in case of traditional hysteretic parameter, namely the remanent flux density while the correlation between them was high.

User-Centered Design of a Digitally Enabled Care Pathway in a Large Health System: Qualitative Interview Study.

Major depressive disorder (MDD) is a leading cause of disability worldwide. Management of chronic conditions such as MDD can be improved by enhanced patient engagement, measurement-based care (MBC), and shared decision-making (SDM). A user-centered design approach can improve the understanding of the patient journey and care team workflows and thus aid the development of digital health care innovations optimized for the needs of patients living with MDD and their primary care teams. This study aims to use qualitative research methods for the user-centered design of a digitally enabled MDD care platform, PathwayPlatform, intended to enhance patient engagement, MBC, and SDM. Insights were gathered through 2 stages of qualitative interviews by a study team with expertise in qualitative research and user-centered design methods. Thematic analysis was used to generate an overarching understanding of a set of shared experiences, thoughts, or behaviors across a broad qualitative data set, including transcripts of interviews, to allow both inductive and deductive insights to emerge. Thematic analysis of interviews was supported by Dedoose (SocioCultural Research Consultants, LLC), a qualitative data analysis software tool that enables systematized coding. Findings and insights were presented based on code frequency, salience, and relevance to the research project. In stage 1, interviews were conducted with 20 patients living with MDD and 15 health care providers from September 2018 to January 2019 to understand the experiences with and perceptions about the initial functionality of the Pathway app while also exploring the perceptions about potential additional features and functionality. Feedback about care team workflows and treatment approaches was collected in stage-2 interviews with 36 health care providers at 8 primary care sites. Inductive and deductive thematic analyses revealed several themes related to app functionality, patient-provider engagement, workflow integration, and patient education. Both patients and their care teams perceived the remote tracking of patient-reported outcomes via digital tools to be clinically useful and reliable and to promote MBC and SDM. However, there was emphasis on the need to enhance the flow of real-time data shared with the care team, improve trend visualizations, and integrate the data within the existing clinical workflow and educational programs for patients and their care teams. User feedback was incorporated into the iterative development of the Pathway app. Ongoing communication with patients living with MDD and their care teams provided an opportunity for user-centric developmental iterations of the Pathway Platform. Key insights led to further development of the patient-facing and care team-facing visit preparation features, collaborative goal-setting and goal-tracking features, patient-reported outcome summaries, and trend visualizations. The result is an enhanced digital platform with the potential to improve treatment outcomes and provide patients living with MDD additional support throughout their treatment journey.

Large scale analytics for workload segmentation

As the complexity and size of workload landscapes continue to evolve, traditional tools that are useful for characterizing workloads have gradually become inadequate. Approaches such as scaling studies or isolated analyses of a single workload are gradually becoming insufficient to understand the broad state of the workload universe. To address this problem, we follow a machine learning methodology to leverage large numbers of workload experiments that already exist on Intel. We advanced a workload analysis platform that stores, manages, and facilitates the analysis of workload information using two big data analysis software tools, a top-down workload decision classifier and a workload universe mapping tool, to characterize large-scale workloads. Both analysis tools present a novel way to look at workload data from a higher level and consider these new characteristics at the workload segmentation level. We seek to achieve this goal in alignment with the strategic goals of developing new benchmarks, informing market sizing, and detecting emerging workloads.

PVSMS: A system for quantifying soiling effects and optimizing cleaning schedule in PV solar plants

Soiling of photovoltaic (PV) installations is an efficiency-limiting factor. It induces considerable energy losses and can even make PV projects economically unviable. A priori knowledge about the soiling phenomenon at a given site provides more guidance to PV project owners for effectively designing the solar field and accurately predicting the yield and profitability of the solar project. Soiling information can also improve the performance analysis of operational solar power plants. Recovering soiling losses begins with the installation of a reliable soiling monitoring system, followed by the implementation of optimized soiling mitigation or cleaning policies. In light of these facts, this work aims to provide a reliable and low-cost soiling monitoring station, named PVSMS (Photovoltaic Soiling Monitoring System). The working principle and detailed description of the hardware design are presented. The field validation of the proposed approach was performed by comparing the soiling loss data provided by PVSMS and the soiling loss on a real PV array, and it shows stable operation of the PVSMS and good agreement between the PVSMS data and the soiling losses quantified through the real PV array. A specialized soiling data analysis software tool has been developed to automate the soiling data analysis procedure and to assist operations and maintenance (O&M) teams in deciding on optimal cleaning dates. Furthermore, PVSMS has been used in a feasibility study of a large-scale PV project in a mining site. The result of this application demonstrates that PVSMS can provide valuable insight into soiling behavior and can also be used to optimize cleaning strategy, estimate its related costs and economic benefits.

Case study in New Jersey on perceptions and concerns during COVID-19: Lessons for safety and health during future work-based learning

Background: The New Jersey Safe Schools Program provides training courses to secondary school educational professionals to supervise students in work-based learning. COVID-19 suspended in-person training courses and work-based learning. As worksites reopened, concerns persisted regarding implementation of work-based learning, and safety and health. After site visits, participants completed reflection assignments. Methods: We examined statewide data from a three-day training, "Designing and Implementing Student Training Plans," from 61 participants in spring-summer 2020 regarding concerns for work-based learning during COVID-19. Qualitative analysis on open-ended responses determined emerging themes through inductive, qualitative coding in Microsoft Office spreadsheet software, Excel, followed by import of a spreadsheet-based code book to NVivo, a qualitative data analysis software tool. Results: Data revealed supervisor perspectives pertaining to re-establishing work-based learning programs (33%), COVID-19 safety and reopening measures (24%), student safety and health (17%), obstacles for in-person work-based learning (9%), future work-based learning placements (7%), and online work-based learning opportunities (4%). Conclusion: Data suggested adaptations for future in-person work-based learning experiences; future online student work placements and safety and health trainings; and insights on improving in-school learning and occupational education for students.

Investigation on the Status Quo of Shared Bicycles in Wenzhou and Analysis of Travel Characteristics

In recent years, shared bicycles have developed rapidly across the country as a convenient new mode of travel. It meets the needs of citizens for short-distance travel and has many advantages such as convenience, high flexibility, and low price. This paper takes the survey results of shared bicycle users in Lucheng District, Wenzhou City and the operation of 181 sites within 20 days as the analysis objects, using data analysis software and other tools to analyze the shape characteristics of shared bicycles, including the number of daily use of a single bicycle, running the busiest site and the idlest site, etc. And through the analysis results, the current service system of shared bicycles is evaluated, and suggestions for improving the efficiency of the bicycle service system are given.

Advances in data-independent acquisition mass spectrometry towards comprehensive digital proteome landscape.

The data-independent acquisition mass spectrometry (DIA-MS) has rapidly evolved as a powerful alternative for highly reproducible proteome profiling with a unique strength of generating permanent digital maps for retrospective analysis of biological systems. Recent advancements in data analysis software tools for the complex DIA-MS/MS spectra coupled to fast MS scanning speed and high mass accuracy have greatly expanded the sensitivity and coverage of DIA-based proteomics profiling. Here, we review the evolution of the DIA-MS techniques, from earlier proof-of-principle of parallel fragmentation of all-ions or ions in selected m/z range, the sequential window acquisition of all theoretical mass spectra (SWATH-MS) to latest innovations, recent development in computation algorithms for data informatics, and auxiliary tools and advanced instrumentation to enhance the performance of DIA-MS. We further summarize recent applications of DIA-MS and experimentally-derived as well as in silico spectra library resources for large-scale profiling to facilitate biomarker discovery and drug development in human diseases with emphasis on the proteomic profiling coverage. Toward next-generation DIA-MS for clinical proteomics, we outline the challenges in processing multi-dimensional DIA data set and large-scale clinical proteomics, and continuing need in higher profiling coverage and sensitivity.

Lead-OR: A multimodal platform for deep brain stimulation surgery.

Deep brain stimulation (DBS) electrode implant trajectories are stereotactically defined using preoperative neuroimaging. To validate the correct trajectory, microelectrode recordings (MERs) or local field potential recordings can be used to extend neuroanatomical information (defined by MRI) with neurophysiological activity patterns recorded from micro- and macroelectrodes probing the surgical target site. Currently, these two sources of information (imaging vs. electrophysiology) are analyzed separately, while means to fuse both data streams have not been introduced. Here, we present a tool that integrates resources from stereotactic planning, neuroimaging, MER, and high-resolution atlas data to create a real-time visualization of the implant trajectory. We validate the tool based on a retrospective cohort of DBS patients (N = 52) offline and present single-use cases of the real-time platform. We establish an open-source software tool for multimodal data visualization and analysis during DBS surgery. We show a general correspondence between features derived from neuroimaging and electrophysiological recordings and present examples that demonstrate the functionality of the tool. This novel software platform for multimodal data visualization and analysis bears translational potential to improve accuracy of DBS surgery. The toolbox is made openly available and is extendable to integrate with additional software packages. Deutsche Forschungsgesellschaft (410169619, 424778381), Deutsches Zentrum für Luft- und Raumfahrt (DynaSti), National Institutes of Health (2R01 MH113929), and Foundation for OCD Research (FFOR).

From Evidence Synthesis to Transfer: Results from a Qualitative Case Study with the Perspectives of Participants.

The increase in health research brings challenges to the production, synthesis, and use of research findings in clinical practice. In the case of undergraduate training in nursing, it is necessary to think about the curricular contents and create opportunities to develop skills for learning evidence-based practice. The objective of this study was to analyze nurses’ perspectives regarding the effects of their participation in a project of translation of knowledge into clinical practice during undergraduate nursing education, specifically involving knowledge, attitudes, and competencies related to the use of evidence. This is a qualitative case study grounded in the knowledge-to-action theoretical framework. The participants were 13 nurses who were involved in a project about the translation of knowledge into clinical practice during the last term of their undergraduate course. The data were collected by applying interviews between December 2020 and April 2021. Content analysis was carried out by using the qualitative data analysis software tool webQDA®. The following categories emerged from the content analysis carried out on the material gathered during the interviews: understanding evidence; learning how to use evidence; transferring evidence; adjusting to the context; and observing the advantages of evidence-based practice. Extracurricular activities were perceived as an opportunity to understand what evidence is and observe in loco the advantages of health care for clients, teams, and services. During the project, the participants developed cross-sectional competencies and envisaged changes to their professional activity as a result of changes in their attitude regarding evidence and its use. We concluded that the opportunity to develop evidence-related activities allows for the development of skills and influences the attitude towards evidence-based practice and knowledge use.

A data-independent acquisition (DIA)-based quantification workflow for proteome analysis of 5000 cells

Data independent acquisition (DIA) has emerged as a powerful proteomic technique with exceptional reproducibility and throughput, and has been widely applied to clinical sample analysis. DIA approaches normally rely on project-specific spectral libraries generated by data dependent acquisition (DDA), requiring extensive off-line fractionation and large amounts of input material. In this study, we aimed to explore the utility of DIA for the analysis of samples with limited quantities. We employed three software tools (DIA-NN, Spectronaut, and EncyclopeDIA) for data analysis and generated three types of libraries, including an experiment-specific library built by DDA analysis of off-line fractions, a FASTA sequence database, and a library generated by gas-phase fractionation (GPF), resulting in eight analysis pipelines. Then we systematically compared the performance of the eight strategies by analyzing the DIA data from HEK293T cell tryptic peptides with sample loads of 500 ng, 100 ng, 20 ng, and 4 ng. The results showed that DIA-NN with GPF-based libraries outperformed the others in protein identification and retention time calibration. Next, we further evaluated the optimized workflow by analyzing the proteome alteration in 5000 peripheral blood mononuclear cells (PBMCs) induced by lipopolysaccharide (LPS) stimulation. As a result, 3179 protein groups were quantified, and functional analysis revealed activation of multiple signaling pathways, e. g., endocytosis, NF-kappa B signaling, and T cell receptor signaling. The results showed the practicability of using DIA for scarce samples, and the established workflow of PBMC analysis could be easily adapted for biomarker discovery, immune status evaluation, and drug response monitoring, especially for diseases involved with dysfunction of the immune system.

NVivo.

NVivo. A qualitative data analysis software tool, QSR International, https://www.qsrinternational.com/nvivo-qualitative-data-analysis-software/home; $1,249/user (one-time cost for using specific version indefinitely; upgrades are additional).

ABSTRACTS (BY NUMBER)

Tissue Engineering Part AVol. 28, No. S1 AbstractsFree AccessABSTRACTS (BY NUMBER)These are the abstracts as submitted through the website. Last minute changes, title and presenting changes are not always reflected in this file.Published Online:4 Apr 2022https://doi.org/10.1089/ten.tea.2022.29025.abstractsAboutSectionsPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail Abstract 1 ENABLING TECHNOLOGIES FOR DEVELOPMENT OF BIOARTIFICIAL ORGANSCorresponding author's email: d.stamatialis@utwente.nlIn the coming years, due to the aging of the population and the low availability of donor organs there will be urgent need for bioengineered organs to assist, mimic or replace failing patient organs.These organs could be artificial, based on novel biomaterials and designs, to assist or mimic a patient organ or bioartificial: combining biomaterials and biologics (cells and tissues) to fully replace failing patient organs. In this talk, we will discuss enabling technologies for development of bioartificial organs with specific attention to bioartificial kidney, bioartificial pancreas.References(1) “Innovations in dialysis: Innovations in dialysis membranes for improved kidney replacement therapy”, Nature Reviews Nephrology, I. Geremia, D. Stamatialis (2020), 16(10) p550.2. “Frontiers in hemodialysis: Innovations and technological advances”, C Basile, A. Davenport, S. Mitra, A. Pal, D. Stamatialis, C. Chrysochou, D. Kirmizis, Artificial organs, 45 (2021) p175.Abstract 2 TRANSLATIONAL SYSTEMS BIOLOGY OF INFLAMMATION AND WOUND HEALING: FROM DATA TO MODELS TO DIGITAL TWINSCorresponding author's email: vodovotzy@upmc.eduProperly regulated inflammation is central to homeostasis, but in adequate or overly robust inflammation can lead to disease. Like many biological processes, inflammation and its various manifestations in disease are both multi‐dimensional and multi‐compartmental. Our group has carried out studies aimed at obtaining high‐dimensional, dynamic data on the etiology and progression of various inflammatory processes and diseases in samples derived from cells, animals, and people; creating computational models based on these data as well as on key principles of immunology, as well as developing novel software tools for data aggregation and analysis; and modulating the inflammatory response in an optimal spatial, temporal, and individual‐ / disease‐specific manner. These studies center on various disease states, especially trauma/hemorrhage, infection, wound healing, and liver failure, as well as the application of computational approaches to regenerative medicine, biomarker discovery, and rational drug/device design. Our ultimate goal is to gain a translational systems perspective on the inflammatory response (1,2).Keywords:Inflammation; Mathematical modeling; Systems biology

MAPPRAISER: A massively parallel map-making framework for multi-kilo pixel CMB experiments

Forthcoming cosmic microwave background (CMB) polarized anisotropy experiments have the potential to revolutionize our understanding of the Universe and fundamental physics. The sought-after, tale-telling signatures will be however distributed over voluminous data sets which these experiments will collect. These data sets will need to be efficiently processed and unwanted contributions due to astrophysical, environmental, and instrumental effects characterized and efficiently mitigated in order to uncover the signatures. This poses a significant challenge to data analysis methods, techniques, and software tools which will not only have to be able to cope with huge volumes of data but to do so with unprecedented precision driven by the demanding science goals posed for the new experiments.A keystone of efficient CMB data analysis is solvers of very large linear systems of equations. Such systems appear in very diverse contexts throughout CMB data analysis pipelines, however they typically display similar algebraic structures and can therefore be solved using similar numerical techniques. Linear systems arising in the so-called map-making problem are one of the most prominent and common ones.In this work we present a massively parallel, flexible and extensible framework, comprised of a numerical library, MIDAPACK, and a high level code, MAPPRAISER, which provide tools for solving efficiently such systems. The framework implements iterative solvers based on conjugate gradient techniques: enlarged and preconditioned using different preconditioners. We demonstrate the framework on simulated examples reflecting basic characteristics of the forthcoming data sets issued by ground-based and satellite-borne instruments, executing it on as many as 16,384 compute cores. The software is developed as an open source project freely available to the community at: https://github.com/B3Dcmb/midapack.

PROSPECTIVE SOFTWARE TOOLS FOR DATA ANALYSIS IN BUSINESS

The article presents the results of a research of promising software tools for data analysis that can be used for domestic business, their characteristics, identifying features, advantages and disadvantages of various business intelligence tools, recommendations for implementation. Data analysis tools provide an opportunity to significantly improve the company’s performance, but only when the selection of the tool, its implementation and maintenance take into account all the features of the company’s business processes. Business analytics software tools help you analyze your datasets to make informed management decisions, improve processes, and shape and achieve business goals.To choose the right business intelligence tools, you need to consider the most important factors: visual functionality, flexibility, integration, customization, mobility, learning requirements, pricing, IT support, cloud or local BI deployment, and more. In addition, you need to identify the functions that are most important to your company, will allow you to get a balanced option: meet current needs and anticipate further growth. The main functions offered by BI suppliers: debugging information panels; built-in analytics; functions of joint work; remote access by mobile devices; independent data preparation; advanced analytics, etc. Areas of use of data mining are virtually unlimited, in particular, its use by commercial enterprises that implement projects based on information repositories can gain significant advantages over competitors, as well as in strategic planning. Data is a powerful tool for business transformation, forcing companies to use modern business intelligence tools and seek experts to help understand, decipher and interpret hidden trends in the industry and the market. This is especially true when working with Вig data.

An Automatic CADI’s Ionogram Scaling Software Tool for Large Ionograms Data Analytics

Scale the ionosonde ionograms to produce accurate readings is a professional manual scaling technique. However, there is a high demand for auto-scaling software that can manage a large number of ionograms in order to avoid the time and effort involved in manual scaling as well as human errors. Noise-free, accurate trace identification and precise segmentation are required for the auto-scaling program to work. The Canadian Advanced Digital Ionosonde (CADI) ionograms are processed and auto-scaled using a new model on an open-source (Python) platform in this paper. Filtering the noise, Convolution Neural Network (CNN) based trace detection, layer-wise segmentation, and then extracting the ionospheric features are used to accomplish the scaling accuracy. The investigation uses raw ionogram files generated by the CADI system in Hyderabad, India (Lat: <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$17.47^{\circ }\text{N}$ </tex-math></inline-formula> , Long: <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$78.57^{\circ }\text{E}$ </tex-math></inline-formula> ) between 2014 and 2015. Raw ionograms in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^\ast $ </tex-math></inline-formula> .md4 or <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^\ast $ </tex-math></inline-formula> .md2 file formats can be accepted by the suggested model (Individual or Hourly integrated). The proposed auto-scaling software tool’s individual block performance is examined with several classes of ionograms, and the overall performance is evaluated with a huge set of ionograms obtained during adverse space weather circumstances (16th to 18th March 2015). Univap Digital Ionosonde Data Analysis (UDIDA) software tool was considered for manual scaling. The results of manual scaling are compared with that of proposed scaling software. In fmin and h’f, respectively, the proposed model has a mean absolute error (MAE) of 0.36 MHz and 11.72 km, and a root mean square error (RMSE) of 0.7 MHz and 22.36 km.

EnzymeML-a data exchange format for biocatalysis and enzymology.

EnzymeML is an XML-based data exchange format that supports the comprehensive documentation of enzymatic data by describing reaction conditions, time courses of substrate and product concentrations, the kinetic model, and the estimated kinetic constants. EnzymeML is based on the Systems Biology Markup Language, which was extended by implementing the STRENDA Guidelines. An EnzymeML document serves as a container to transfer data between experimental platforms, modeling tools, and databases. EnzymeML supports the scientific community by introducing a standardized data exchange format to make enzymatic data findable, accessible, interoperable, and reusable according to the FAIR data principles. An application programming interface in Python supports the integration of software tools for data acquisition, data analysis, and publication. The feasibility of a seamless data flow using EnzymeML is demonstrated by creating an EnzymeML document from a structured spreadsheet or from a STRENDA DB database entry, by kinetic modeling using the modeling platform COPASI, and by uploading to the enzymatic reaction kinetics database SABIO-RK.

Metabolomics of Chlorophylls and Carotenoids: Analytical Methods and Metabolome-Based Studies.

Chlorophylls and carotenoids are two families of antioxidants present in daily ingested foods, whose recognition as added-value ingredients runs in parallel with the increasing number of demonstrated functional properties. Both groups include a complex and vast number of compounds, and extraction and analysis methods evolved recently to a modern protocol. New methodologies are more potent, precise, and accurate, but their application requires a better understanding of the technical and biological context. Therefore, the present review compiles the basic knowledge and recent advances of the metabolomics of chlorophylls and carotenoids, including the interrelation with the primary metabolism. The study includes material preparation and extraction protocols, the instrumental techniques for the acquisition of spectroscopic and spectrometric properties, the workflows and software tools for data pre-processing and analysis, and the application of mass spectrometry to pigment metabolomics. In addition, the review encompasses a critical description of studies where metabolomics analyses of chlorophylls and carotenoids were developed as an approach to analyzing the effects of biotic and abiotic stressors on living organisms.