General Workflow Research Articles

Dengue Prediction in Latin America Using Machine Learning and the One Health Perspective: A Literature Review.

Dengue fever is a serious and growing public health problem in Latin America and elsewhere, intensified by climate change and human mobility. This paper reviews the approaches to the epidemiological prediction of dengue fever using the One Health perspective, including an analysis of how Machine Learning techniques have been applied to it and focuses on the risk factors for dengue in Latin America to put the broader environmental considerations into a detailed understanding of the small-scale processes as they affect disease incidence. Determining that many factors can act as predictors for dengue outbreaks, a large-scale comparison of different predictors over larger geographic areas than those currently studied is lacking to determine which predictors are the most effective. In addition, it provides insight into techniques of Machine Learning used for future predictive models, as well as general workflow for Machine Learning projects of dengue fever.

Characterization of Usage Data with the Help of Data Classifications

Comprehensive data understanding is a key success driver for data analytics projects. Knowing the characteristics of the data helps a lot in selecting the appropriate data analysis techniques. Especially in data-driven product planning, knowledge about the data is a necessary prerequisite because data of the use phase is very heterogeneous. However, companies often do not have the necessary know-how or time to build up solid data understanding in connection with data analysis. In this paper, we develop a methodology to organize and categorize and thus understand use phase data in a way that makes it accessible to general data analytics workflows, following a design science research approach. We first present a knowledge base that lists typical use phase data from a product planning view. Second, we develop a taxonomy based on standard literature and real data objects, which covers the diversity of the data considered. The taxonomy provides 8 dimensions that support classification of use phase data and allows to capture data characteristics from a data analytics view. Finally, we combine both views by clustering the objects of the knowledge base according to the taxonomy. Each of the resulting clusters covers a typical combination of analytics relevant characteristics occurring in practice. By abstracting from the diversity of use phase data into artifacts with manageable complexity, our approach provides guidance to choose appropriate data analysis and AI techniques.

Axial Compressor Map Generation Leveraging Autonomous Self-Training Artificial Intelligence

Abstract The paper describes the study performed by SoftInWay in the scope of the Phase I SBIR project funded by the National Aeronautics and Space Administration (NASA). The project was dedicated to a study of optimization of the variable geometry reset angle schedules with the use of innovative autonomous artificial intelligence (AI) technology. In the scope of the project, an automated compressor performance data generation workflow was developed. Three highly loaded multistage axial compressors were designed. The developed workflow was used to generate the training, validation, and test data sets for all three compressors. Multiple different architectures of artificial neural networks were studied, and parametric models for the representation of performance speedlines were developed. Utilizing the developed approaches, artificial neural networks were trained for all three compressors to predict their performance with a relative error below 3%. The trained neural networks were successfully used in the optimization of the variable inlet guide vanes and variable stator vanes reset angle schedules with a relative error of total-to-total pressure ratio prediction below 2% for most of the points and relative error of total-to-total efficiency prediction below 1% for all the points of the operational line. The capability of the developed AI models to accurately predict the optimal combination of reset angles and efficiency of the axial compressor with multiple vanes controlled independently allowed doing quick evaluations of efficiency and stability margins. The availability of such information enables the opportunity to make technical-economical decisions about the reasonability of implementation of independent variable vanes and their number during engine system analysis.

Towards fully ab initio simulation of atmospheric aerosol nucleation

Atmospheric aerosol nucleation contributes to approximately half of the worldwide cloud condensation nuclei. Despite the importance of climate, detailed nucleation mechanisms are still poorly understood. Understanding aerosol nucleation dynamics is hindered by the nonreactivity of force fields (FFs) and high computational costs due to the rare event nature of aerosol nucleation. Developing reactive FFs for nucleation systems is even more challenging than developing covalently bonded materials because of the wide size range and high dimensional characteristics of noncovalent hydrogen bonding bridging clusters. Here, we propose a general workflow that is also applicable to other systems to train an accurate reactive FF based on a deep neural network (DNN) and further bridge DNN-FF-based molecular dynamics (MD) with a cluster kinetics model based on Poisson distributions of reactive events to overcome the high computational costs of direct MD. We found that previously reported acid-base formation rates tend to be significantly underestimated, especially in polluted environments, emphasizing that acid-base nucleation observed in multiple environments should be revisited.

Designing a Model-Driven Approach Towards Rational Experimental Design in Bioprocess Optimization.

To enable a more rational optimization approach to drive the transition from lab-scale to large industrial bioprocesses, a systematic framework coupling both experimental design and integrated modeling was established to guide the workflow executed from small flask scale to bioreactor scale. The integrated model relies on the coupling of biotic cell factory kinetics to the abiotic bioreactor hydrodynamics to offer a rational means for an in-depth understanding of two-way spatiotemporal interactions between cell behaviors and environmental variations. This model could serve as a promising tool to inform experimental work with reduced efforts via full-factorial in silico predictions. This chapter thus describes the general workflow involved in designing and applying this modeling approach to drive the experimental design towards rational bioprocess optimization.

Realizing FAIR Digital Objects for the German Helmholtz Association of Research Centres

Realizing FAIR Digital Objects for the German Helmholtz Association of Research Centres

The Vitruvian spider: Segmenting and integrating over different body parts to describe ecophenotypic variation.

Understanding what drives the existing phenotypic variability has been a major topic of interest for biologists for generations. However, the study of the phenotype may not be straightforward. Indeed, organisms may be interpreted as composite objects, comprising different ecophenotypic traits, which are neither necessarily independent from each othernor do they respond to the same evolutionary pressures. For this reason, a deep biological understanding of the focal organism is essential for any morphological analysis. The spider genus Dysdera provides a particularly well-suited system for setting up protocols for morphological analyses that encompass a suitof morphological structures in any nonmodel system. This genus has undergone a remarkable diversification in the Canary Islands, where different species perform different ecological roles, exhibiting different levels of trophic specialization or troglomorphic adaptations, which translate into a remarkable interspecific morphological variability. Here, we seek to develop a broad guide, of which morphological characters must be considered, to study the effect of different ecological pressures in spiders and propose a general workflow that will be useful whenever researchers set out to investigate variation in the body plans of different organisms, with data sets comprising a set of morphological traits. We use geometric morphometric methods to quantify variation in different body structures, all of them with diverse phenotypic modifications in their chelicera, prosoma, and legs. We explore the effect of analyzing different combined landmark (LM) configurations of these characters and the degree of morphological integration that they exhibit. Our results suggest that different LM configurations of each of these body parts exhibit a higher degree of integration compared to LM configurations from different structuresand that the analysis of each of these body parts captures different aspects of morphological variation, potentially related to different ecological factors.

Building Interpretable Machine Learning Models to Identify Chemometric Trends in Seabirds of the North Pacific Ocean.

Marine environmental monitoring efforts often rely on the bioaccumulation of persistent anthropogenic contaminants in organisms to create a spatiotemporal record of the ecosystem. Intercorrelation results from the origin, uptake, and transport of these contaminants throughout the ecosystem and may be affected by organism-specific processes such as biotransformation. Here, we explore trends that machine learning tools reveal about a large, recently released environmental chemistry data set of common anthropogenic pollutants measured in the eggs of five seabird species from the North Pacific Ocean. We modeled these data with a variety of machine learning approaches and found models that could accurately determine a range of taxonomic and spatiotemporal trends. We illustrate a general workflow and set of analysis tools that can be used to identify interpretable models which perform nearly as well as state-of-the-art "black boxes." For example, we found shallow decision trees that could resolve genus with greater than 96% accuracy using as few as two analytes and a k-nearest neighbor classifier that could resolve species differences with more than 94% accuracy using only five analytes. The benefits of interpretability outweighed the marginally improved accuracy of more complex models. This demonstrates how machine learning may be used to discover rational, quantitative trends in these systems.

Integrating Micro and Nano Technologies for Cell Engineering and Analysis: Toward the Next Generation of Cell Therapy Workflows.

The emerging field of cell therapy offers the potential to treat and even cure a diverse array of diseases for which existing interventions are inadequate. Recent advances in micro and nanotechnology have added a multitude of single cell analysis methods to our research repertoire. At the same time, techniques have been developed for the precise engineering and manipulation of cells. Together, these methods have aided the understanding of disease pathophysiology, helped formulate corrective interventions at the cellular level, and expanded the spectrum of available cell therapeutic options. This review discusses how micro and nanotechnology have catalyzed the development of cell sorting, cellular engineering, and single cell analysis technologies, which have become essential workflow components in developing cell-based therapeutics. The review focuses on the technologies adopted in research studies and explores the opportunities and challenges in combining the various elements of cell engineering and single cell analysis into the next generation of integrated and automated platforms that can accelerate preclinical studies and translational research.

Perks and pitfalls of city directories as a micro-geographic data source

Historical city directories are rich sources of micro-geographic data. They provide information on the location of households and firms and their occupations and industries, respectively. We develop a generic algorithmic work flow that converts scans of them into geo- and status-referenced household-level data sets. Applying the work flow to our case study, the Berlin 1880 directory, adds idiosyncratic challenges that should make automation less attractive. Yet, employing an administrative benchmark data set on household counts, incomes, and income distributions across more than 200 census tracts, we show that semi-automatic referencing yields results very similar to those from labour-intensive manual referencing. Finally, we discuss how to scale the work flow to other years and cities as well as potential applications in economic history and beyond.

Tutorial on High-Definition Map Generation for Automated Driving in Urban Environments.

High-definition (HD) mapping is a promising approach to realize highly automated driving (AD). Although HD maps can be applied to all levels of autonomy, their use is particularly beneficial for autonomy levels 4 or higher. HD maps enable AD systems to see beyond the field of view of conventional sensors, thereby providing accurate and detailed information regarding a driving environment. An HD map is typically separated into a pointcloud map for localization and a vector map for path planning. In this paper, we introduce two separate but successive HD map generation workflows. Of the several stages involved, the registration and mapping processes are essential for creating the pointcloud and vector maps, respectively. To facilitate the readers’ understanding, the processes of these two stages have been recorded and uploaded online. HD maps are typically generated using open-source software (OSS) tools. CloudCompare and ASSURE, as representative tools, are used in this study. The generated HD maps are validated with localization and path-planning modules in Autoware, which is also an OSS stack for AD systems. The generated HD maps enable environmental-monitoring vehicles to successfully operate at level 4 autonomy.

A Protein Microarray-Based Respiratory Viral Antigen Testing Platform for COVID-19 Surveillance.

High-throughput and rapid screening testing is highly desirable to effectively combat the rapidly evolving COVID-19 pandemic co-presents with influenza and seasonal common cold epidemics. Here, we present a general workflow for iterative development and validation of an antibody-based microarray assay for the detection of a respiratory viral panel: (a) antibody screening to quickly identify optimal reagents and assay conditions, (b) immunofluorescence assay design including signal amplification for low viral titers, (c) assay characterization with recombinant proteins, inactivated viral samples and clinical samples, and (d) multiplexing to detect a panel of common respiratory viruses. Using RT-PCR-confirmed SARS-CoV-2 positive and negative pharyngeal swab samples, we demonstrated that the antibody microarray assay exhibited a clinical sensitivity and specificity of 77.2% and 100%, respectively, which are comparable to existing FDA-authorized antigen tests. Moreover, the microarray assay is correlated with RT-PCR cycle threshold (Ct) values and is particularly effective in identifying high viral titers. The multiplexed assay can selectively detect SARS-CoV-2 and influenza virus, which can be used to discriminate these viral infections that share similar symptoms. Such protein microarray technology is amenable for scale-up and automation and can be broadly applied as a both diagnostic and research tool.

Seismic volumetric dip estimation via a supervised deep learning model by integrating realistic synthetic data sets

Accurately estimating seismic volumetric dip is a crucial task for subsequent seismic processing and interpretation. The traditional window based dip estimation methods, such as gradient structure tensor (GST) and semblance based multiple window scanning strategy, usually struggle with complicated geological structures containing multiple reflections with different dips in the analysis window. Recently, deep learning has been utilized to estimate seismic volumetric dips. However, dip labels used for current convolutional neural network (CNN) based methods are usually created using traditional dip estimation methods, which are not the ground truth, or from specific geological structure models, which only cover a part of typical geological structures. We propose a supervised deep learning model to improve the accuracy of seismic dip estimation by integrating realistic synthetic data sets. Firstly, we propose a synthetic seismic data generation workflow for seismic volumetric dip estimation, which aims to simulate geological features from real seismic data. We then create numerous unique synthetic seismic images with realistic and diverse structural features by using the proposed workflow. To ensure that generated dip labels are exactly accurate and can be regarded as the ground truth, we create synthetic seismic images by deforming horizontal reflection images according to corresponding dip labels. We finally train an end-to-end supervised deep learning model, which focuses on the parallel processing and the extraction of various feature maps simultaneously, utilizing the created realistic synthetic data. The applications on synthetic and 3D real seismic data (Netherlands F3 block) effectually demonstrate the validity and effectiveness of our proposed model.

Boosting the Liquid Chromatography Separation of Complex Bispecific Antibody Products by Using the Multi-Isocratic Elution Mode

In reversed-phase liquid chromatography (RPLC), the selectivity between major species and minor variants of protein biopharmaceutical products is always limited. Unfortunately, the stationary phase chemistry, type of mobile phase (organic modifier and salts) and temperature only have a very limited impact on selectivity. Therefore, instead of using a linear elution gradient, we evaluated a recently developed strategy, named the multi-isocratic elution mode, to improve the chromatographic resolution. In this contribution, a generic workflow involving the use of an Excel spreadsheet is provided for the rapid and successful development of multi-isocratic elution methods, without the need to use HPLC modeling software. This simple strategy was then successfully applied to very complex biopharmaceutical products; these included one reduced mAb-cytokine fusion protein and a mAb-domain-fusion (C-terminal) protein sample, containing numerous minor variants that were poorly separated from the major species. The addition of several isocratic steps during the chromatographic run provides a clear added value in terms of chromatographic selectivity for several variants, simplifying characterization of the sample with advanced MS tools. In addition to these advantages, some of the limitations of the multi-isocratic elution mode were also highlighted; these included the need to use a highly precise pumping device (preferably, a binary pumping system) and the need to prepare highly accurate mobile phases.

What’s Your Workflow? Non-Targeted Analysis of Water Samples

In recent years, there has been an effort to categorize how emerging contaminants are identified in water samples by liquid chromatography–mass spectrometry (LC–MS). This process is often referred to as a workflow, and it has been the focus of many talks and conference sessions around the world. Many scientists have looked to unify and generalize the workflow, but do we really need a generic workflow to identify a non-target in an environmental sample? Would the scientific method approach (applied to each individual problem to solve) be enough and reliable?

Clinical Performance of the RealTime High Risk HPV Assay on Self-Collected Vaginal Samples within the VALHUDES Framework.

ABSTRACTThe VALHUDES framework (NCT03064087) was established to evaluate the clinical accuracy of HPV testing on self-samples compared with HPV testing on matched clinician-taken cervical samples. Women referred to colposcopy due to previous cervical abnormalities were recruited at five Belgian colposcopy centers. A total of 486 pairs of matched cervical samples and vaginal self-samples were included in the analysis (228 collected with Evalyn Brush and 258 with Qvintip). The dry vaginal brushes were transferred into 20 mL ThinPrep PreservCyt solution. All specimens were tested with the Abbott RealTime High Risk HPV assay (Abbott RT). Testing on vaginal and cervical specimens was considered the index and comparator tests, respectively, and colposcopy and histology as the reference standard. The clinical sensitivity for CIN2+ of Abbott RT (cutoff ≤32 cycle number [CN]) on vaginal self-samples (Evalyn Brush and Qvintip combined) was 8% lower than on the cervical clinician-collected samples (ratio = 0.92 [95% CI, 0.87 to 0.98]), while the specificity was similar (ratio = 1.04 [95% CI, 0.97 to 1.12]). Sensitivity (ratio = 0.95 [95% CI, 0.89 to 1.02]) and specificity (ratio = 1.11 [95% CI, 0.995 to 1.23]) on Evalyn Brush samples was similar to cervical, while on Qvintip samples, the sensitivity was 12% lower than cervical samples (ratio = 0.88 [95% CI, 0.78 to 0.998]) with similar specificity (0.99 [95% CI, 0.90 to 1.10]). Exploratory cutoff optimization (cutoff ≤35 CN) resulted in an improvement of the relative sensitivity (self-sampling versus clinician sampling: ratio = 0.96 [95% CI, 0.91 to 1.02]) but yielded a loss in relative specificity (ratio = 0.92 [0.85 to 1.00]). The clinical accuracy of Abbott RT differed from the self-sampling device. However, after cutoff optimization, the sensitivity on self-samples taken with either of two vaginal brushes became similar to clinician-collected samples.IMPORTANCE Self-samples are becoming a crucial part of HPV-based cervical cancer screening programs to reach nonattendee women and increase screening coverage. Therefore, the VALHUDES framework was established to validate and evaluate HPV tests and devices on self-samples. Here, in the present manuscript, we evaluated the accuracy of the RealTime High Risk HPV assay (Abbott RT) on two different vaginal devices to detect cervical intraepithelial neoplasia grade two or higher (CIN2+). The study results demonstrated that the Abbott RT assay is similarly accurate on vaginal self-samples as on matched clinician-taken cervical samples after adjusting cutoff values. Moreover, we observed that some vaginal devices perform better than others in CIN2+ detection. We also underline the necessity of standardization and validation of general workflow and sample handling procedures for vaginal self-samples.

Sol-gel processing of a covalent organic framework for the generation of hierarchically porous monolithic adsorbents

Sol-gel processing of a covalent organic framework for the generation of hierarchically porous monolithic adsorbents

Decision Support Tool for the Agri-Food Sector Using Data Annotated by Ontology and Bayesian Network

The scientific literature is a valuable source of information for developing predictive models to design decision support systems. However, scientific data are heterogeneously structured expressed using different vocabularies. This study developed a generic workflow that combines ontology, databases and computer calculation tools based on the theory of belief functions and Bayesian networks. The ontology paradigm is used to help integrate data from heterogeneous sources. Bayesian network is estimated using the integrated data taking into account their reliability. The proposed method is unique in the sense that it proposes an annotation and reasoning tool dedicated to systematic analysis of the literature, which takes into account expert knowledge of the domain at several levels: ontology definition, reliability criteria and dependence relations between variables in the BN. The workflow is assessed successfully by applying it to a complex food engineering process: skimmed milk microfiltration. It represents an original contribution to the state of the art in this application domain.

Metatranscriptomic Approach to Understand the Role of the Rhizosphere Microbiome: A Review

Plant roots harbour diverse microorganisms that interact with each other and the plants. Rhizosphere is the region of soil, closely associated with plant root. The microbiome associated with them is termed as rhizobiome. Its studies give insights into the roles of the microorganisms in plant health. With the advancement of NGS techniques, the identification of the non-cultural species has turned out to be possible. Sequencing of the whole genomic D.N.A. (metagenomics) can easily identify the taxonomic and functional profile but identification of the active and inactive members of the microbiome is not fulfilled. Meta-transcriptomics helps in determining the active functional profile of a microbial community by identifying the genes expressed by the entire microbial community. The general workflow is sampling, RNA extraction, library preparation, sequencing, pre-processing of data, assembly, taxonomic/functional profiling and the differential expression analysis. There exist numerous challenges, that can be overcome through enhanced sequencing technologies and algorithms.

Assessing the Effectiveness of Correlative Ecological Niche Model Temporal Projection through Floristic Data.

Simple SummaryClimate change is the main threat for conservation in the 21st century. Reliable methodologies and tools for the evaluation of its impact are urgently needed. Correlative ecological niche models (ENMs) are effective tools for predicting the future distribution of species under climate change scenarios. Despite this, many alternative different methods have been proposed, and objective reasons for a proper selection are unclear. Therefore, a comparative study to evaluate the consistency of predictions of the main ENM algorithms was performed. To test the effectiveness of correlative ENM temporal projection, we compared predictions generated using historical data and projected to the modern climate with predictions generated using modern distribution and climate data. In total, 600 case studies were generated, by using 25 Italian endemic plant species, 12 algorithms and 2 alternative sets of environmental variables. As a result, we highlighted the similarity of eight algorithms and the poor performance of four.Correlative ecological niche modelling (ENM) is a method widely used to study the geographic distribution of species. In recent decades, it has become a leading approach for evaluating the most likely impacts of changing climate. When used to predict future distributions, ENM applications involve transferring models calibrated with modern environmental data to future conditions, usually derived from Global Climate Models (GCMs). The number of algorithms and software packages available to estimate distributions is quite high. To experimentally assess the effectiveness of correlative ENM temporal projection, we evaluated the transferability of models produced using 12 different algorithms on historical and modern data. In particular, we compared predictions generated using historical data and projected to the modern climate (simulating a “future” condition) with predictions generated using modern distribution and climate data. The models produced with the 12 ENM algorithms were evaluated in geographic (range size and coherence of predictions) and environmental space (Schoener’s D index). None of the algorithms shows an overall superior capability to correctly predict future distributions. On the contrary, a few algorithms revealed an inadequate predictive ability. Finally, we provide hints that can be used as guideline to plan further studies based on the adopted general workflow, useful for all studies involving future projections.