Data Processing Options Research Articles

Spatially Resolved Raman Spectroscopic Investigation of Uranyl Fluoride: A Case Study in the Importance of Instrument Optimization.

Raman spectroscopy is an emerging technique for rapid and nondestructive analysis of nuclear materials for forensic and nonproliferation applications as it is a powerful tool for distinguishing multiple chemical forms of materials with similar stoichiometries. Recent developments in spectroscopic software have enabled rapid data collection with high-speed Raman spectroscopic mapping capabilities. However, some uranium-rich materials are susceptible to degradation in humid air and/or laser-induced phase transformations. To mitigate environmental or measurement-related sample degradation of potential samples of interest, we have taken a systematic approach to define optimized data collection parameters for high-throughput measurements of uranyl fluoride (UO2F2), which is an important intermediate material in the nuclear fuel cycle. First, we systematically describe the influence of optical magnification (5× to 100×), laser power, and exposure time on obtained signal for identical particles of UO2F2 and find that at low laser power and exposure times, comparable signal is obtained regardless of optical magnification. Second, we ensure sample integrity during data collection, and third, collect spectroscopic maps that employ optimized parameters to reduce the time required to obtain spatially resolved spectroscopic information. Reductions of 90% and 99% in measurement times are discussed as they relate to differences in resolving spectroscopic features of particles in identical mapping areas. During this work, we found that additional data processing options were needed and thus developed a customized Python script for importing, processing, analyzing, and visualizing Raman spectroscopic map data.

Agriculture Data Platform – Institutional Data Repository – Selected Aspects

This paper presents selected aspects of a data platform to store agricultural data. It analyses the key user and system requirements for the data platform. The presented aspects were identified through a literature review, interviews and discussions with selected data experts and researchers and future users of the platform. The following issues of the data platform are discussed in the paper: architecture, data types, data source types, metadata, disciplinary interfaces, data sharing, data reusability, Open Science, FAIR data principles, and further data processing options. Part of the knowledge from this article was used in the design and implementation of the Institutional Data Repository called Data Management Platform (DaMP.) CZU (Czech University of Life Sciences Prague) (OPENAI, 2023).

Further Validation of a Self‐administered Smartphone‐based Verbal Learning and Memory Assessment

AbstractBackgroundThe International Shopping List test (ISLT) is a verbal word list learning test set within a realistic shopping list context, supporting ease of cultural adaptation, and addressing content validity and patient relevance. Improvements to and the wide adoption of natural language processing (NLP) software has enabled the possibility for self‐administered tests of verbal learning. LilaTM a self‐administered, smartphone‐application (app) was developed, using the virtual assistant and NLP. We have previously reported acceptability and usability data from qualitative analysis of concept elicitation interviews. We now seek to provide preliminary validation data around its use.MethodA convenience sample of healthy young adults (n = 40), M(SD)age = 35 (16), were invited to install and complete one assessment of the LilaTM app. A usability survey was also administered to explore participants’ experiences. The sample was predominantly female (70%) and of Asian ethnicity (62.5%). Data were collected in Australia, using an English word pool developed for the USA.ResultAcceptability was generally high with N = 38 completing immediate recall rounds and N = 32 completing the delayed recall round. Eight‐two percent found it extremely or somewhat easy to hear and understand what items were on the shopping list and 95% indicated that it was extremely or somewhat easy to understand what they had to do to complete the assessment. Scoring accuracy was 88% at the word item level, with reprocessing to address accent, context setting, and other NLP approaches to improve accuracy underway e.g., homophones biased to the grocery item.ConclusionWhilst acceptability was high, the importance of localization of test stimuli was confirmed as well as the need for additional data processing options to account for accent. Continued optimization of data processing to score participant responses and word item pools to reduce ambiguity is expected to further improve accuracy e.g., accounting for regional accents and consideration of homophones and compound words. However, specific characteristics of speech including in those for whom English may be a second/foreign language will be an area of focus for future development.

Further Validation of a Self‐administered Smartphone‐based Verbal Learning and Memory Assessment

AbstractBackgroundThe International Shopping List test (ISLT) is a verbal word list learning test set within a realistic shopping list context, supporting ease of cultural adaptation, and addressing content validity and patient relevance. Improvements to and the wide adoption of natural language processing (NLP) software has enabled the possibility for self‐administered tests of verbal learning. LilaTM a self‐administered, smartphone‐application (app) was developed, using the virtual assistant and NLP. We have previously reported acceptability and usability data from qualitative analysis of concept elicitation interviews. We now seek to provide preliminary validation data around its use.MethodA convenience sample of healthy young adults (n = 40), M(SD)age = 35 (16), were invited to install and complete one assessment of the LilaTM app. A usability survey was also administered to explore participants’ experiences. The sample was predominantly female (70%) and of Asian ethnicity (62.5%). Data were collected in Australia, using an English word pool developed for the USA.ResultAcceptability was generally high with N = 38 completing immediate recall rounds and N = 32 completing the delayed recall round. Eight‐two percent found it extremely or somewhat easy to hear and understand what items were on the shopping list and 95% indicated that it was extremely or somewhat easy to understand what they had to do to complete the assessment. Scoring accuracy was 88% at the word item level, with reprocessing to address accent, context setting, and other NLP approaches to improve accuracy underway e.g., homophones biased to the grocery item.ConclusionWhilst acceptability was high, the importance of localization of test stimuli was confirmed as well as the need for additional data processing options to account for accent. Continued optimization of data processing to score participant responses and word item pools to reduce ambiguity is expected to further improve accuracy e.g., accounting for regional accents and consideration of homophones and compound words. However, specific characteristics of speech including in those for whom English may be a second/foreign language will be an area of focus for future development.

Aon: a service to augment Alliance Genome Resource data with additional species

ObjectiveCross-species comparative genomics requires access to accurate homology data across the entire range of annotated genes. The Alliance of Genome Resources (AGR) provides an open-source and comprehensive database of homology data calculated using a wide array of algorithms at differing stringencies to elucidate orthologous relationships. However, the current AGR application program interface (API) is limited to five homology endpoints for nine species. While AGR provides a robust resource for several canonical species, its utility can be greatly enhanced by increased filtering and data processing options and incorporating additional species.ResultsHere, we describe a novel API tool, AON, that expands access to the AGR orthology resource by creating a data structure that supports 50 additional endpoints. More importantly, it provides users with a framework for adding bespoke endpoints, custom species, and additional orthology data. We demonstrate AON’s functionality by incorporating the service into the GeneWeaver ecosystem for supporting cross-species data analysis.

How remote sensing choices influence ecosystem services monitoring and evaluation results of ecological restoration interventions

Large-scale ecological restorations are recognized worldwide as an effective strategy to combat environmental degradation and promote sustainability. Remote sensing (RS) imagery, such as obtained from Landsat and Sentinel-2 satellites, can provide spatial, spectral, and temporal information on ecosystem service supply to support monitoring and evaluation of restoration interventions. However, because of the abundance of satellite data and methodological analysis options, choices in data selection and processing options need to be made. This study explored the effect of RS choices on the evaluation of changes in ecosystem services as a result of ecological restoration interventions. Using the ecosystem service of forage provision for wildlife as an example, we used a before-after-control-impact (BACI) analysis to compare how the following choices affected restoration evaluation outcomes: a) different number of control pixels; b) different spatial distribution of control pixels; c) intra-annual image selection; and d) different reference periods. In addition, e) we evaluated the effect of using two different satellite sensor types, using the ecosystem service ‘erosion prevention’ as an example. We explored the effect of these five choices for restoration sites in the Baviaanskloof, South Africa. Results showed that the choice of intra-annual image selection, and the reference period describing the ‘before state’ had a strong effect on the outcomes, often leading to opposite BACI evaluation results. BACI results were less sensitive to choices related to the number of control points in the evaluation. The impact of methodological choices on the BACI outcomes was greater for the less degraded areas of our study site. Satellite sensor choice resulted in similar temporal trajectories of estimated supply. We demonstrated that RS choices have a strong effect on the evaluation results of restoration interventions. Therefore, we recommend that documenting the key RS choices results is essential when communicating restoration evaluation results in order to properly understand, manage and adapt restoration initiatives.

Next generation restoration metrics: Using soil eDNA bacterial community data to measure trajectories towards rehabilitation targets

In post-mining rehabilitation, successful mine closure planning requires specific, measurable, achievable, relevant and time-bound (SMART) completion criteria, such as returning ecological communities to match a target level of similarity to reference sites. Soil microbiota are fundamentally linked to the restoration of degraded ecosystems, helping to underpin ecological functions and plant communities. High-throughput sequencing of soil eDNA to characterise these communities offers promise to help monitor and predict ecological progress towards reference states. Here we demonstrate a novel methodology for monitoring and evaluating ecological restoration using three long-term (>25 year) case study post-mining rehabilitation soil eDNA-based bacterial community datasets. Specifically, we developed rehabilitation trajectory assessments based on similarity to reference data from restoration chronosequence datasets. Recognising that numerous alternative options for microbiota data processing have potential to influence these assessments, we comprehensively examined the influence of standard versus compositional data analyses, different ecological distance measures, sequence grouping approaches, eliminating rare taxa, and the potential for excessive spatial autocorrelation to impact on results. Our approach reduces the complexity of information that often overwhelms ecologically-relevant patterns in microbiota studies, and enables prediction of recovery time, with explicit inclusion of uncertainty in assessments. We offer a step change in the development of quantitative microbiota-based SMART metrics for measuring rehabilitation success. Our approach may also have wider applications where restorative processes facilitate the shift of microbiota towards reference states.

MStractor: R Workflow Package for Enhancing Metabolomics Data Pre-Processing and Visualization.

Untargeted metabolomics experiments for characterizing complex biological samples, conducted with chromatography/mass spectrometry technology, generate large datasets containing very complex and highly variable information. Many data-processing options are available, however, both commercial and open-source solutions for data processing have limitations, such as vendor platform exclusivity and/or requiring familiarity with diverse programming languages. Data processing of untargeted metabolite data is a particular problem for laboratories that specialize in non-routine mass spectrometry analysis of diverse sample types across humans, animals, plants, fungi, and microorganisms. Here, we present MStractor, an R workflow package developed to streamline and enhance pre-processing of metabolomics mass spectrometry data and visualization. MStractor combines functions for molecular feature extraction with user-friendly dedicated GUIs for chromatographic and mass spectromerty (MS) parameter input, graphical quality-control outputs, and descriptive statistics. MStractor performance was evaluated through a detailed comparison with XCMS Online. The MStractor package is freely available on GitHub at the MetabolomicsSA repository.

A Primer on the Analysis of High-Throughput Sequencing Data for Detection of Plant Viruses.

High-throughput sequencing (HTS) technologies have become indispensable tools assisting plant virus diagnostics and research thanks to their ability to detect any plant virus in a sample without prior knowledge. As HTS technologies are heavily relying on bioinformatics analysis of the huge amount of generated sequences, it is of utmost importance that researchers can rely on efficient and reliable bioinformatic tools and can understand the principles, advantages, and disadvantages of the tools used. Here, we present a critical overview of the steps involved in HTS as employed for plant virus detection and virome characterization. We start from sample preparation and nucleic acid extraction as appropriate to the chosen HTS strategy, which is followed by basic data analysis requirements, an extensive overview of the in-depth data processing options, and taxonomic classification of viral sequences detected. By presenting the bioinformatic tools and a detailed overview of the consecutive steps that can be used to implement a well-structured HTS data analysis in an easy and accessible way, this paper is targeted at both beginners and expert scientists engaging in HTS plant virome projects.

Automated integration of extract-based CFD results with AR/VR in engineering education for practitioners

Computational fluid dynamics (CFD) simulations can provide meaningful technical content in engineering education, broad engineering and business. However, computationally demanding data production and complex data processing environments of CFD simulations turn them into esoteric tools for potential non-expert users. This consequently limits applications and communications of CFD simulations and results. Augmented and virtual reality (AR/VR) technologies are opening new gates for visualization and interaction techniques. Despite the many recent attempts, the literature lacks an inclusive system development procedure for CFD simulations with AR/VR. The present study proposes a component-oriented system architecture to generate dedicated workflows for any kind of AR/VR environment supported by CFD simulations. The study further explores the potential of data processing options throughout the preparation of the simulation dataset with AR/VR. An automated data coupling strategy is additionally introduced to ease multiplatform integration. We provide an integration strategy with simple, easy-to-implement, end-to-end, automated and free-to-use utilities that the practitioners can readily pursue.

RETRACTED: Hospital IoT big data and real-time monitoring and nursing intervention for patients with insomnia

Abstract Insomnia is a common problem that has been affecting residents for a long time. This study's main purpose of determining whether insomnia results from greater improvements in sleep using hypnosis than just the educational process of sleep hygiene. Sleep restriction therapy is a weekly session based on the following sleep hygiene education sessions. Provides an important aspect of assessing patients with insomnia and long-term care measures with an existing review of big data processing options. These include various non-drug therapy, drug therapy, cognitive behavioral therapy, the most important treatment for insomnia, benzodiazepines, and Z-drugs melatonin receptor agonist and a selective histamine H1 receptor agonist. A Deep Learning IoT including drugs, origin antagonists, antidepressants, antipsychotics, anticonvulsants and non-selective antihistamine. Many unsupervised data made a valuable tool for large data analysis, where the original data is mainly unmarked and unclassified. Complex patterns from sleeplessness, big data analysis, large amounts of data, semantic indexing, data annotation, and fast information retrieval to simplify identification tasks and some of the important things in the Internet of Things deeply explore the methods be used for problems. However, the deep basic learning array of drug therapy has the above characteristics of insomniac patients, and therapeutic research effects fully characterize its risk/benefit curve. These measures can form the basis of insomnia systems and evidence-based treatment in the clinical setting. Looking at the evidence base in this area and improving many patients' management in many hospitals highlights areas where insomnia needs to provide resources and be investigated.

Digital technology adoption in livestock production with a special focus on ruminant farming

Digitalisation is an integral part of modern agriculture. Several digital technologies are available for different animal species and form the basis for precision livestock farming. However, there is a lack of clarity as to which digital technologies are currently used in agricultural practice. Thus, this work aims to present for the first time the status quo in Swiss livestock farming as an example of a highly developed, small-scale and diverse structured agriculture. In this context, the article focuses on the adoption of electronic sensors and measuring devices, electronic controls and electronic data-processing options and the usage of robotics in ruminant farming, namely, for dairy cattle, dairy goats, suckler cows, beef cattle and meat-sheep. Furthermore, the use of electronic ear tags for pigs and the smartphone usage for barn monitoring on poultry farms was assessed. To better understand the adoption process, farm and farmer’s characteristics associated with the adoption of (1) implemented and (2) new digital technologies in ruminant farming were assessed using regression analyses, which is classified at a 10% adoption hurdle. The results showed clear differences in the adoption rates between different agricultural enterprises, with both types of digital technologies tending to be used the most in dairy farming. Easy-to-use sensors and measuring devices such as those integrated in the milking parlour were more widespread than data processing technologies such as those used for disease detection. The husbandry system further determined the use of digital technologies, with the result that farmers with tie stall barns were less likely to use digital technologies than farmers with loose housing systems. Additional studies of farmers’ determinants and prospects of implementation can help identify barriers in the adoption of digital technologies.

Tutorial: guidelines for the experimental design of single-cell RNA sequencing studies.

Single-cell RNA sequencing is at the forefront of high-resolution phenotyping experiments for complex samples. Although this methodology requires specialized equipment and expertise, it is now widely applied in research. However, it is challenging to create broadly applicable experimental designs because each experiment requires the user to make informed decisions about sample preparation, RNA sequencing and data analysis. To facilitate this decision-making process, in this tutorial we summarize current methodological and analytical options, and discuss their suitability for a range of research scenarios. Specifically, we provide information about best practices for the separation of individual cells and provide an overview of current single-cell capture methods at different cellular resolutions and scales. Methods for the preparation of RNA sequencing libraries vary profoundly across applications, and we discuss features important for an informed selection process. An erroneous or biased analysis can lead to misinterpretations or obscure biologically important information. We provide a guide to the major data processing steps and options for meaningful data interpretation. These guidelines will serve as a reference to support users in building a single-cell experimental framework-from sample preparation to data interpretation-that is tailored to the underlying research context.

Comprehensive evaluation of RNA-seq analysis pipelines in diploid and polyploid species.

BackgroundThe usual analysis of RNA sequencing (RNA-seq) reads is based on an existing reference genome and annotated gene models. However, when a reference for the sequenced species is not available, alternatives include using a reference genome from a related species or reconstructing transcript sequences with de novo assembly. In addition, researchers are faced with many options for RNA-seq data processing and limited information on how their decisions will impact the final outcome. Using both a diploid and polyploid species with a distant reference genome, we have tested the influence of different tools at various steps of a typical RNA-seq analysis workflow on the recovery of useful processed data available for downstream analysis.FindingsAt the preprocessing step, we found error correction has a strong influence on de novo assembly but not on mapping results. After trimming, a greater percentage of reads could be used in downstream analysis by selecting gentle quality trimming performed with Skewer instead of strict quality trimming with Trimmomatic. This availability of reads correlated with size, quality, and completeness of de novo assemblies and with number of mapped reads. When selecting a reference genome from a related species to map reads, outcome was significantly improved when using mapping software tolerant of greater sequence divergence, such as Stampy or GSNAP.ConclusionsThe selection of bioinformatic software tools for RNA-seq data analysis can maximize quality parameters on de novo assemblies and availability of reads in downstream analysis.

Addressing variability in the acoustic startle reflex for accurate gap detection assessment

The acoustic startle reflex (ASR) is subject to substantial variability. This inherent variability consequently shapes the conclusions drawn from gap-induced prepulse inhibition of the acoustic startle reflex (GPIAS) assessments. Recent studies have cast doubt as to the efficacy of this methodology as it pertains to tinnitus assessment, partially, due to variability in and between data sets. The goal of this study was to examine the variance associated with several common data collection variables and data analyses with the aim to improve GPIAS reliability. To study this the GPIAS tests were conducted in adult male and female CBA/CaJ mice. Factors such as inter-trial interval, circadian rhythm, sex differences, and sensory adaptation were each evaluated. We then examined various data analysis factors which influence GPIAS assessment. Gap-induced facilitation, data processing options, and assessments of tinnitus were studied. We found that the startle reflex is highly variable in CBA/CaJ mice, but this can be minimized by certain data collection factors. We also found that careful consideration of temporal fluctuations of the ASR and controlling for facilitation can lead to more accurate GPIAS results. This study provides a guide for reducing variance in the GPIAS methodology – thereby improving the diagnostic power of the test.

Global trends in ocean phytoplankton: a new assessment using revised ocean colour data

ABSTRACTA recent revision of the NASA global ocean colour record shows changes in global ocean chlorophyll trends. This new 18-year time series now includes three global satellite sensors, the Sea-viewing Wide Field of view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS-Aqua), and Visible Infrared Imaging Radiometer Suite (VIIRS). The major changes are radiometric drift correction, a new algorithm for chlorophyll, and a new sensor VIIRS. The new satellite data record shows no significant trend in global annual median chlorophyll from 1998 to 2015, in contrast to a statistically significant negative trend from 1998 to 2012 in the previous version.When revised satellite data are assimilated into a global ocean biogeochemical model, no trend is observed in global annual median chlorophyll. This is consistent with previous findings for the 1998–2012 time period using the previous processing version and only two sensors (SeaWiFS and MODIS). Detecting trends in ocean chlorophyll with satellites is sensitive to data processing options and radiometric drift correction. The assimilation of these data, however, reduces sensitivity to algorithms and radiometry, as well as the addition of a new sensor. This suggests the assimilation model has skill in detecting trends in global ocean colour.Using the assimilation model, spatial distributions of significant trends for the 18-year record (1998–2015) show recent decadal changes. Most notable are the North and Equatorial Indian Oceans basins, which exhibit a striking decline in chlorophyll. It is exemplified by declines in diatoms and chlorophytes, which in the model are large and intermediate size phytoplankton. This decline is partially compensated by significant increases in cyanobacteria, which represent very small phytoplankton. This suggests the beginning of a shift in phytoplankton composition in these tropical and subtropical Indian basins.

Remote sensing training in African conservation

AbstractThe potential of remote sensing (RS) to assist with conservation planning, implementation and monitoring is well described, and particularly relevant in African areas that are inaccessible due to terrain, finances or politics. We provide an African perspective on remote sensing (RS) training for conservation and ecology over the last decade through investigating (1) recent use of RS in African conservation literature, (2) use of RS in African conservation agencies, (3) RS training by African institutions and (4) RS capacity development by ad hoc events. Africa does not produce most of the research using RS in conservation and ecological studies conducted on Africa, with authors with correspondence addresses in the USA predominating (33% of a bibliometric analysis), although South Africa‐based authors constituted 20% (with an increase between 2000 and 2015), Kenya 6% and Tanzania and Ethiopia 4% each. Ideally research should be conducted close to the point of use to ensure relevance and data residence in the country concerned. This is a point for attention, possibly through international funding to increase the capacity of African academic institutions to conduct research using RS to answer conservation questions. Part of this will need to include attention on data and software costs, internet speeds and human capacity. Data costs have been alleviated by free Landsat and MODIS data, and the Copernicus programs, but there is need for higher resolution imagery to be freely available for certain conservation projects. Open Source software may well offer a long‐term solution to software costs. This would require that teaching is realigned to employer requirements, which are shifting in many countries and agencies from proprietary software to Open Source due to licensing costs. Low internet connectivity in many areas of Africa might limit the uptake of new data processing options that require connectivity, although over time these tools may become available to more users. However, human capacity is developing. Of the 72 academic institutions surveyed, a number of conservation programs supplied either tailored RS teaching or used ‘service modules’ to provide RS skills to young graduating conservation professionals, showing a recognition of the importance of RS in conservation in Africa. This study highlights the success of capacity development in Africa, and the increasing use of remote sensing for conservation in Africa.

A Concept of Fuel Tank Calibration Process Automation Within IoT Infrastructure

The Internet-of-Things (IoT) and Cloud technologies today provide new options for remote measurement data collection, storage and processing. Fuel tank calibration process automation by adapting IoT infrastructure model is explored in the article. The impact of geometric and volumetric fuel tank calibration methods upon hardware and communication channels requirements in IoT infrastructure are considered in the paper. A distributed certified metrological laboratory establishment in the field of fuel tank calibration concept is introduced and explained by system architecture view description and sequence diagram of its operation.

Towards a consensus regarding global signal regression for resting state functional connectivity MRI

The number of resting state functional connectivity MRI studies continues to expand at a rapid rate along with the options for data processing. Of the processing options, few have generated as much controversy as global signal regression and the subsequent observation of negative correlations (anti-correlations). This debate has motivated new processing strategies and advancement in the field, but has also generated significant confusion and contradictory guidelines. In this article, we work towards a consensus regarding global signal regression. We highlight several points of agreement including the fact that there is not a single “right” way to process resting state data that reveals the “true” nature of the brain. Although further work is needed, different processing approaches likely reveal complementary insights about the brain's functional organisation.

Mapping Fractional Land Use and Land Cover in a Monsoon Region: The Effects of Data Processing Options

Existing global land use/land cover (LULC) raster maps have limited spatial and thematic resolution relative to the strong heterogeneity of agricultural landscapes. One promising approach to derive more informative maps is using fractional cover instead of hard classification. Here, we evaluate the effect of three key data processing options on the performance of random forest (RF) fractional cover models for moderate resolution imaging spectroradiometer (MODIS) data in a heterogeneous agricultural landscape in a monsoon region: 1) selection of spectral predictor sets [normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), surface reflectance (SR), and all combined (Full)]; 2) time interval (8-day vs. 16-day); and 3) smoothing (no smoothing versus Savitzky–Golay (SG) filter). Model performance was assessed with spatially stratified root-mean-square error (RMSE), Spearman’s rank correlation, and $R^2$ , per LULC type and averaged over all types. We found adequate performance of the best model (avg. $\rho= 0.62$ ) that used all predictors, 8-day interval and no smoothing. Among the different alternatives, the choice of predictors accounted for 36.3% of the variation, smoothing for 19.0%, and time interval for 17.9%. The intrinsic dimensionalities of the spectral predictors were investigated to complement the variable importance analyses. Although predicting LULC fractions for minor types remained difficult, our results suggest that existing satellite products can be a useful source of information about LULC at subpixel level provided the data-processing options are properly chosen.