Robust Workflow Research Articles

Determination of the time-dependent moment tensor using time reverse imaging

We have developed an approach to determine the time-dependent moment tensor and the origin time in addition to commonly derived locations of seismic events using time reverse imaging (TRI). It is crucial to locate and characterize the occurring microseismicity without making a priori assumptions about the sources to fully understand the subsurface processes inducing seismicity. Low signal-to-noise ratios (S/Ns) often force standard methods to make assumptions about sources or only characterize selected larger magnitude events. In TRI, microearthquakes are located by back propagating the full-recorded time-reversed wavefield through a velocity model until it ideally convergences on the source location. Therefore, it is less affected by low S/Ns and potentially locates and characterizes most of the events. After distinguishing artificial convergence locations from source locations, the quality of the source location and the moment tensors are derived by recording the stress at the determined source locations during the back propagation of the time-reversed wavefield. A robust workflow is derived using synthetic test cases in a realistic scenario with velocity models that only approximate the true velocity model and/or noisy displacement traces. The influence of a rudimentary velocity model on the source-location accuracy and characterization is significant. Our workflow handles these less-than-optimal station distributions and velocity models. Finally, the derived workflow is successfully applied to field data recorded at the geothermal field of Los Humeros, Mexico. Although only a 1D velocity model is currently available, source locations and (time-dependent) moment tensors could be determined for selected events.

A Robust, Safe, and Scalable Magnetic Nanoparticle Workflow for RNA Extraction of Pathogens from Clinical and Wastewater Samples.

Molecular diagnosis and surveillance of pathogens such as SARS‐CoV‐2 depend on nucleic acid isolation. Pandemics at the scale of COVID‐19 can cause a global shortage of proprietary commercial reagents and BSL‐2 laboratories to safely perform testing. Therefore, alternative solutions are urgently needed to address these challenges. An open‐source method, magnetic‐nanoparticle‐aided viral RNA isolation from contagious samples (MAVRICS), built upon readily available reagents, and easily assembled in any basically equipped laboratory, is thus developed. The performance of MAVRICS is evaluated using validated pathogen detection assays and real‐world and contrived samples. Unlike conventional methods, MAVRICS works directly in samples inactivated in phenol‐chloroform (e.g., TRIzol), thus allowing infectious samples to be handled safely without biocontainment facilities. MAVRICS allows wastewater biomass immobilized on membranes to be directly inactivated and lysed in TRIzol followed by RNA extraction by magnetic nanoparticles, thereby greatly reducing biohazard risk and simplifying processing procedures. Using 39 COVID‐19 patient samples and two wastewater samples, it is shown that MAVRICS rivals commercial kits in detection of SARS‐CoV‐2, influenza viruses, and respiratory syncytial virus. Therefore, MAVRICS is safe, fast, and scalable. It is field‐deployable with minimal equipment requirements and could become an enabling technology for widespread testing and wastewater monitoring of diverse pathogens.

ASaiM-MT: a validated and optimized ASaiM workflow for metatranscriptomics analysis within Galaxy framework.

The Human Microbiome Project (HMP) aided in understanding the role of microbial communities and the influence of collective genetic material (the 'microbiome') in human health and disease. With the evolution of new sequencing technologies, researchers can now investigate the microbiome and map its influence on human health. Advances in bioinformatics methods for next-generation sequencing (NGS) data analysis have helped researchers to gain an in-depth knowledge about the taxonomic and genetic composition of microbial communities. Metagenomic-based methods have been the most commonly used approaches for microbiome analysis; however, it primarily extracts information about taxonomic composition and genetic potential of the microbiome under study, lacking quantification of the gene products (RNA and proteins). Conversely, metatranscriptomics, the study of a microbial community's RNA expression, can reveal the dynamic gene expression of individual microbial populations and the community as a whole, ultimately providing information about the active pathways in the microbiome. In order to address the analysis of NGS data, the ASaiM analysis framework was previously developed and made available via the Galaxy platform. Although developed for both metagenomics and metatranscriptomics, the original publication demonstrated the use of ASaiM only for metagenomics, while thorough testing for metatranscriptomics data was lacking. In the current study, we have focused on validating and optimizing the tools within ASaiM for metatranscriptomics data. As a result, we deliver a robust workflow that will enable researchers to understand dynamic functional response of the microbiome in a wide variety of metatranscriptomics studies. This improved and optimized ASaiM-metatranscriptomics (ASaiM-MT) workflow is publicly available via the ASaiM framework, documented and supported with training material so that users can interrogate and characterize metatranscriptomic data, as part of larger meta-omic studies of microbiomes.

Refraction waves full waveform inversion of deep reflection seismic profiles in the central part of Lhasa Terrane

The deep reflection seismic method is an effective technique for detecting the fine structures of the lithosphere and solving deep geological problems. At present, the methods for obtaining the underground velocity from land deep reflection seismic profile mainly rely on the travel-time information based velocity analysis and tomography, which theoretically limit the resolution of the imaging results. To achieve the goal of using deep reflection seismic profile for crustal-scale high-precision velocity imaging, we must finally overcome the problem of full waveform inversion (FWI) using full wavefield data. In this paper, we successfully conduct FWI using the early-arrival wavefields (mainly primary and multiple refraction wavefields) in the deep reflection seismic profile, which can be seen as the first important step towards the goal of crustal-scale high-precision velocity imaging. We propose a robust refraction waves FWI workflow with global normalized cross-correlation, multiple refraction matching, reverse time source estimation and wavenumber domain gradient filtering as its core algorithm. The application of the proposed FWI method to the refraction waves of the deep reflection seismic profiles in the central part of Lhasa Terrane shows that the refraction FWI images have a higher resolution to underground structures both in horizontal and vertical directions than the first-arrival tomography images. The lateral velocity change can be well-matched with the typical geological characteristics of the work area. The FWI result can better describe the lateral boundaries of the strata in different eras. At a depth of less than 3 km from the surface, the refraction FWI can correct the interface morphology of the tomography results and recover some small-scale velocity structures and high-velocity anomalies.

Detection of Mycobacterium avium ssp. paratuberculosis in Cultures From Fecal and Tissue Samples Using VOC Analysis and Machine Learning Tools.

Analysis of volatile organic compounds (VOCs) is a novel approach to accelerate bacterial culture diagnostics of Mycobacterium avium subsp. paratuberculosis (MAP). In the present study, cultures of fecal and tissue samples from MAP-infected and non-suspect dairy cattle and goats were explored to elucidate the effects of sample matrix and of animal species on VOC emissions during bacterial cultivation and to identify early markers for bacterial growth. The samples were processed following standard laboratory procedures, culture tubes were incubated for different time periods. Headspace volume of the tubes was sampled by needle trap-micro-extraction, and analyzed by gas chromatography-mass spectrometry. Analysis of MAP-specific VOC emissions considered potential characteristic VOC patterns. To address variation of the patterns, a flexible and robust machine learning workflow was set up, based on random forest classifiers, and comprising three steps: variable selection, parameter optimization, and classification. Only a few substances originated either from a certain matrix or could be assigned to one animal species. These additional emissions were not considered informative by the variable selection procedure. Classification accuracy of MAP-positive and negative cultures of bovine feces was 0.98 and of caprine feces 0.88, respectively. Six compounds indicating MAP presence were selected in all four settings (cattle vs. goat, feces vs. tissue): 2-Methyl-1-propanol, 2-methyl-1-butanol, 3-methyl-1-butanol, heptanal, isoprene, and 2-heptanone. Classification accuracies for MAP growth-scores ranged from 0.82 for goat tissue to 0.89 for cattle feces. Misclassification occurred predominantly between related scores. Seventeen compounds indicating MAP growth were selected in all four settings, including the 6 compounds indicating MAP presence. The concentration levels of 2,3,5-trimethylfuran, 2-pentylfuran, 1-propanol, and 1-hexanol were indicative for MAP cultures before visible growth was apparent. Thus, very accurate classification of the VOC samples was achieved and the potential of VOC analysis to detect bacterial growth before colonies become visible was confirmed. These results indicate that diagnosis of paratuberculosis can be optimized by monitoring VOC emissions of bacterial cultures. Further validation studies are needed to increase the robustness of indicative VOC patterns for early MAP growth as a pre-requisite for the development of VOC-based diagnostic analysis systems.

Isolation of Natural DNA Aptamers for Challenging Small-Molecule Targets, Cannabinoids.

Aptamers are nucleic acid-based affinity reagents that are isolated via an in vitro process known as systematic evolution of ligands by exponential enrichment (SELEX). Despite their great potential for a wide range of analytical applications, there are relatively few high-quality small-molecule binding aptamers, especially for "challenging" targets that have low water solubility and/or limited moieties for aptamer recognition. The use of libraries containing chemically modified bases may improve the outcome of some SELEX experiments, but this approach is costly and yields inconsistent results. Here, we demonstrate that a thoughtfully designed SELEX procedure with natural DNA libraries can isolate aptamers with high affinity and specificity for challenging small molecules, including targets for which such selections have previously failed. We first isolate a DNA aptamer with nanomolar affinity and high specificity for (-)-trans-Δ9-tetrahydrocannabinol (THC), a target previously thought to be unsuitable for SELEX with natural DNA libraries. We subsequently isolate aptamers that exhibit high affinity and cross-reactivity to two other challenging targets, synthetic cannabinoids UR-144 and XLR-11, while maintaining excellent specificity against a wide range of non-target interferents. Our findings demonstrate that natural nucleic acid libraries can yield high-quality aptamers for small-molecule targets, and we outline a robust workflow for isolating other such aptamers in future selection efforts.

A robust approach to identify roof bolts in 3D point cloud data captured from a mobile laser scanner

Roof bolts such as rock bolts and cable bolts provide structural support in underground mines. Frequent assessment of these support structures is critical to maintain roof stability and minimise safety risks in underground environments. This study proposes a robust workflow to classify roof bolts in 3D point cloud data and to generate maps of roof bolt density and spacing. The workflow was evaluated for identifying roof bolts in an underground coal mine with suboptimal lighting and global navigation satellite system (GNSS) signals not available. The approach is based on supervised classification using the multi-scale Canupo classifier coupled with a random sample consensus (RANSAC) shape detection algorithm to provide robust roof bolt identification. The issue of sparseness in point cloud data has been addressed through upsampling by using a moving least squares method. The accuracy of roof bolt identification was measured by correct identification of roof bolts (true positives), unidentified roof bolts (false negatives), and falsely identified roof bolts (false positives) using correctness, completeness, and quality metrics. The proposed workflow achieved correct identification of 89.27% of the roof bolts present in the test area. However, considering the false positives and false negatives, the overall quality metric was reduced to 78.54%.

Capillary Electrophoresis-Sodium Dodecyl Sulfate with Laser-Induced Fluorescence Detection as a Highly Sensitive and Quality Control-Friendly Method for Monitoring Adeno-Associated Virus Capsid Protein Purity.

The capsid protein purity of adeno-associated virus (AAV) is considered a critical quality attribute of AAV-based gene therapy products. However, the analytical methods currently available to monitor the viral capsid proteins, which are present in extremely low concentrations, have limited sensitivity and robustness, thus limiting their general applicability. As a result, there is an urgent need to develop robust separation methods with highly sensitive detection. In this article, we describe the first denaturation and fluorescence labeling procedure for AAV capsid proteins using the pyrylium dye Chromeo™ P503, enabling the establishment of the first capillary electrophoresis-sodium dodecyl sulfate (CE-SDS) method combined with laser-induced fluorescence (LIF) detection for AAV. Upon optimization using a quality-by-design approach, the newly developed method features a simple and robust one-step sample preparation workflow resulting in consistently labeled and denatured viral protein samples, which can subsequently be separated and quantified by CE-LIF. The method has been validated to be accurate and precise with a linear range of 50-150% of the nominal concentration of 2.0 × 1011 vector genomes per mL (vg/mL). The detection limit and quantitation limit were established to be 8.0 × 107 vg/mL (∼0.8 ng/mL) and 4.2 × 108 vg/mL (∼4 ng/mL), respectively, representing the highest sensitivity achieved for AAV capsid protein quantitation reported to date and a linear dynamic range of 8.0 × 107-3.0 × 1011 vg/mL. A comparison of the CE-SDS LIF method with existing methods, such as CE-SDS ultraviolet and sodium dodecyl sulfate-polyacrylamide gel electrophoresis with SYPRO Ruby stain, indicated that the new method has superior resolution and a significant increase in signal intensity. Capsid protein purity analysis of multiple AAV serotypes, including AAV5, scAAVrh10, AAV2, and AAV6, has been demonstrated for the first time using the same method, indicating the newly developed AAV labeling procedure and CE-LIF analysis could serve as a Quality Control-friendly platform and best-in-class analytical method for the control of AAV capsid protein purity.

TEM, SEM, and STEM-based immuno-CLEM workflows offer complementary advantages

Identifying endogenous tissue stem cells remains a key challenge in developmental and regenerative biology. To distinguish and molecularly characterise stem cell populations in large heterogeneous tissues, the combination of cytochemical cell markers with ultrastructural morphology is highly beneficial. Here, we realise this through workflows of multi-resolution immuno-correlative light and electron microscopy (iCLEM) methodologies. Taking advantage of the antigenicity preservation of the Tokuyasu technique, we have established robust protocols and workflows and provide a side-by-side comparison of iCLEM used in combination with scanning EM (SEM), scanning TEM (STEM), or transmission EM (TEM). Evaluation of the applications and advantages of each method highlights their practicality for the identification, quantification, and characterization of heterogeneous cell populations in small organisms, organs, or tissues in healthy and diseased states. The iCLEM techniques are broadly applicable and can use either genetically encoded or cytochemical markers on plant, animal and human tissues. We demonstrate how these protocols are particularly suited for investigating neural stem and progenitor cell populations of the vertebrate nervous system.

SANS quantification of bound water in water-soluble polymers across multiple concentration regimes.

Contrast-variation small-angle neutron scattering (CV-SANS) is a widely used technique for quantifying hydration water in soft matter systems, but it is predominantly applied in the dilute regime or for systems with a well-defined structure factor. Here, CV-SANS was used to quantify the number of hydration water molecules associating with three water-soluble polymers with different critical solution temperatures and types of water-solute interactions in dilute, semidilute, and concentrated solution through the exploration of novel methods of data fitting and analysis. Multiple SANS fitting workflows with varying levels of model assumptions were evaluated and compared to give insight into SANS model selection. These fitting pathways ranged from general, model-free algorithms to more standard form and structure factor fitting. In addition, Monte Carlo bootstrapping was evaluated as a method to estimate parameter uncertainty through simulation of technical replicates. The most robust fitting workflow for dilute solutions was found to be form factor fitting without CV-SANS (i.e. polymer in 100% D2O). For semidilute and concentrated solutions, while the model-free approach can be mathematically defined for CV-SANS data, the addition of a structure factor imposes physical constraints on the optimization problem, suggesting that the optimal fitting pathway should include appropriate form and structure factor models. The measured hydration numbers were consistent with the number of tightly bound water molecules associated with each monomer unit, and the concentration dependence of the hydration number was largely governed by the chemistry-specific interactions between water and polymer. Polymers with weaker water-polymer interactions (i.e. those with fewer hydration water molecules) were found to have more bound water at higher concentrations than those with stronger water-polymer interactions due to the increase in the number of forced water-polymer contacts in the concentrated system. This SANS-based method to count hydration water molecules can be applied to polymers in any concentration regime, which will lead to improved understanding of water-polymer interactions and their impact on materials design.

Missing Value Monitoring to Address Missing Values in Quantitative Proteomics.

Many classes of key functional proteins such as transcription factors or cell cycle proteins are present in the proteome at a very low concentration. These low-abundance proteins are almost entirely invisible to systematic quantitative analysis by classical data dependent proteomics methods (DDA). Moreover, DDA runs in shotgun proteomics experiments are plenty of missing values among the replicates due to the stochastic nature of the acquisition method, thus hampering the robustness of the quantitative analysis. Here, we have overcome these obstacles designing a robust workflow named missing value monitoring (MvM) in order to follow low abundance proteins dynamics.

Microscopy-based assay for semi-quantitative detection of SARS-CoV-2 specific antibodies in human sera: A semi-quantitative, high throughput, microscopy-based assay expands existing approaches to measure SARS-CoV-2 specific antibody levels in human sera.

Emergence of the novel pathogenic coronavirus SARS‐CoV‐2 and its rapid pandemic spread presents challenges that demand immediate attention. Here, we describe the development of a semi‐quantitative high‐content microscopy‐based assay for detection of three major classes (IgG, IgA, and IgM) of SARS‐CoV‐2 specific antibodies in human samples. The possibility to detect antibodies against the entire viral proteome together with a robust semi‐automated image analysis workflow resulted in specific, sensitive and unbiased assay that complements the portfolio of SARS‐CoV‐2 serological assays. Sensitive, specific and quantitative serological assays are urgently needed for a better understanding of humoral immune response against the virus as a basis for developing public health strategies to control viral spread. The procedure described here has been used for clinical studies and provides a general framework for the application of quantitative high‐throughput microscopy to rapidly develop serological assays for emerging virus infections.

On a new robust workflow for the statistical and spatial analysis of fracture data collected with scanlines (or the importance of stationarity)

Abstract. We present an innovative workflow for the statistical analysis of fracture data collected along scanlines, composed of two major stages, each one with alternative options. A prerequisite in our analysis is the assessment of stationarity of the dataset, which is motivated by statistical and geological considerations. Calculating statistics on non-stationary data can be statistically meaningless, and moreover the normalization and/or sub-setting approach that we discuss here can greatly improve our understanding of geological deformation processes. Our methodology is based on performing non-parametric statistical tests, which allow detecting important features of the spatial distribution of fractures, and on the analysis of the cumulative spacing function (CSF) and cumulative spacing derivative (CSD), which allows defining the boundaries of stationary domains in an objective way. Once stationarity has been analysed, other statistical methods already known in the literature can be applied. Here we discuss in detail methods aimed at understanding the degree of saturation of fracture systems based on the type of spacing distribution, and we evidence their limits in cases in which they are not supported by a proper spatial statistical analysis.

Inference of Hydraulically Fractured Reservoir Properties from Production Data Using the Indicator-Based Probability Perturbation Assisted History-Matching Method

Hydraulically fractured horizontal wells are widely adopted for the development of tight or shale gas reservoirs. The presence of highly heterogeneous, multi-scale, fracture systems often renders any detailed characterization of the fracture properties challenging. Discrete fracture network (DFN) model offers a viable alternative for explicit representation of multiple fractures in the domain, where the comprising fracture properties are defined in accordance to specific probability distributions. However, even with the successful modelling of a DFN, the relationship between a set of fracture parameters and the corresponding production performance is highly nonlinear, implying that a robust history-matching workflow capable of updating the pertinent DFN model parameters is required for calibrating stochastic reservoir models to both geologic and dynamic production data.This paper proposes an integrated approach for the history matching of hydraulically fractured reservoirs. First, multiple realizations of the DFN model are constructed conditioning to available geological information such as seismic data and well logs, which are useful for inferring the prior probability distributions of relevant fracture parameters. Next, the models are upscaled into equivalent continuum dual-permeability models and subjected to numerical multiphase flow simulation. The predicted production performance is compared with the actual recorded responses. Finally, the DFN-model parameters are adjusted following an indicator-based probability perturbation method. Although the probability perturbation technique has been applied to update facies distributions in the past, its application in modeling DFN distributions is limited. To account for the non-Gaussian nature of the DFN parameters, an indicator formulation is proposed. The algorithm aims at minimizing the objective function, while reducing the uncertainties in the unknown fracture parameters.The method is applied to estimate the posterior probability distributions of the transmissivity of the primary fracture (Tpf), transmissivity of the secondary induced fracture (Tsf) and global fracture intensity (Psf32G) in a multifractured shale gas well in the Horn River Basin. An initial realization of the DFN model is sampled from the prior probability distributions using the Monte Carlo simulation. These probability distributions are updated to match the production history, and multiple equi-probable realizations of the DFN models are sampled from the updated (posterior) distributions accordingly. The final sampled realizations of the DFN model are consistent with both static geological information and dynamic production history.

Semiautomated glycoproteomics data analysis workflow for maximized glycopeptide identification and reliable quantification.

Glycoproteomic data are often very complex, reflecting the high structural diversity of peptide and glycan portions. The use of glycopeptide-centered glycoproteomics by mass spectrometry is rapidly evolving in many research areas, leading to a demand in reliable data analysis tools. In recent years, several bioinformatic tools were developed to facilitate and improve both the identification and quantification of glycopeptides. Here, a selection of these tools was combined and evaluated with the aim of establishing a robust glycopeptide detection and quantification workflow targeting enriched glycoproteins. For this purpose, a tryptic digest from affinity-purified immunoglobulins G and A was analyzed on a nano-reversed-phase liquid chromatography–tandem mass spectrometry platform with a high-resolution mass analyzer and higher-energy collisional dissociation fragmentation. Initial glycopeptide identification based on MS/MS data was aided by the Byonic software. Additional MS1-based glycopeptide identification relying on accurate mass and retention time differences using GlycopeptideGraphMS considerably expanded the set of confidently annotated glycopeptides. For glycopeptide quantification, the performance of LaCyTools was compared to Skyline, and GlycopeptideGraphMS. All quantification packages resulted in comparable glycosylation profiles but featured differences in terms of robustness and data quality control. Partial cysteine oxidation was identified as an unexpectedly abundant peptide modification and impaired the automated processing of several IgA glycopeptides. Finally, this study presents a semiautomated workflow for reliable glycoproteomic data analysis by the combination of software packages for MS/MS- and MS1-based glycopeptide identification as well as the integration of analyte quality control and quantification.

A robust workflow for indirect somatic embryogenesis and cormlet production in saffron (Crocus sativus L.) and its wild allies; C. caspius and C. speciosus

Saffron (Crocus sativus L.) and its wild relatives, Crocus caspius and Crocus speciosus are of considerable significance in the pharmaceutical, nutraceutical, and ornamental bulbs industry. Towards the ultimate goal of the conservation of wild Crocus species and establishment of an efficient workflow for in vitro production of Crocuses, efficient protocols were developed for disinfection and in vitro production of cormlets in C. sativus and its wild allies C. caspius and C. speciosus. Moreover, the differential expression of the Somatic Embryogenesis Receptor-like Kinase (SERK) gene was evaluated as a potential molecular marker during embryogenesis between embryogenic and non-embryogenic calli. A highly efficient disinfection recipe and a low-cost TDZ-free protocol have been successfully developed for in vitro cormlet production in three Crocus species. MS medium containing 10.18 μM 2, 4-D + 4.44 μM BAP was most efficiently induced callus and somatic embryo formation. The highest conversion frequency and maximum cormlet weight were achieved in MS containing 5.37 μM NAA + 8.88 μM BAP. The SERK expression was significantly much higher in embryogenic calli than non-embryogenic in all Crocus species. The current low-cost and easy-to-use recipe suggests a promising in vitro propagation workflow for mass production of uniform pathogen-free cormlets of Crocus species, as well as a platform to better conservation of wild Crocus species and effective gene and genome editing using CRISPR-Cas9 in future studies.

Managing Big Data for Addressing Research Questions in a Collaborative Project on Automated Driving Impact Assessment.

While extracting meaningful information from big data is getting relevance, literature lacks information on how to handle sensitive data by different project partners in order to collectively answer research questions (RQs), especially on impact assessment of new automated driving technologies. This paper presents the application of an established reference piloting methodology and the consequent development of a coherent, robust workflow. Key challenges include ensuring methodological soundness and data validity while protecting partners’ intellectual property. The authors draw on their experiences in a 34-partner project aimed at assessing the impact of advanced automated driving functions, across 10 European countries. In the first step of the workflow, we captured the quantitative requirements of each RQ in terms of the relevant data needed from the tests. Most of the data come from vehicular sensors, but subjective data from questionnaires are processed as well. Next, we set up a data management process involving several partners (vehicle manufacturers, research institutions, suppliers and developers), with different perspectives and requirements. Finally, we deployed the system so that it is fully integrated within the project big data toolchain and usable by all the partners. Based on our experience, we highlight the importance of the reference methodology to theoretically inform and coherently manage all the steps of the project and the need for effective and efficient tools, in order to support the everyday work of all the involved research teams, from vehicle manufacturers to data analysts.

N-glycosylation analysis of mouse immunoglobulin G isolated from dried blood spots.

The association of immunoglobulin G (IgG) glycosylation changes with various human diseases and physiological conditions is well established. Since the mechanistical explanation of the regulation of IgG glycosylation and its functional role in these various states is still missing, the eyes of the biomedical community are now turned towards animal models, which enable intervention studies necessary for conclusions on causality. Mice are recognized and used as a good experimental model for human IgG glycosylation. However, smaller blood volumes, low IgG concentrations at young ages (which are most often used in mice experiments) and multiple sampling protocols during the course of longitudinal studies would profit from a robust workflow for mouse IgG glycome analysis from minute amounts of starting material, collected through a simple sampling procedure. For this purpose, we have developed a protocol for analysis of total N-glycans of IgG isolated from mouse dried blood spots (DBS), which we report here. We show that mouse DBS are a good source of material for IgG N-glycan analysis by multiplexed capillary gel electrophoresis with laser-induced fluorescence (xCGE-LIF).

Workflows in AiiDA: Engineering a high-throughput, event-based engine for robust and modular computational workflows

Over the last two decades, the field of computational science has seen a dramatic shift towards incorporating high-throughput computation and big-data analysis as fundamental pillars of the scientific discovery process. This has necessitated the development of tools and techniques to deal with the generation, storage and processing of large amounts of data. In this work we present an in-depth look at the workflow engine powering AiiDA, a widely adopted, highly flexible and database-backed informatics infrastructure with an emphasis on data reproducibility. We detail many of the design choices that were made which were informed by several important goals: the ability to scale from running on individual laptops up to high-performance supercomputers, managing jobs with runtimes spanning from fractions of a second to weeks and scaling up to thousands of jobs concurrently, and all this while maximising robustness. In short, AiiDA aims to be a Swiss army knife for high-throughput computational science. As well as the architecture, we outline important API design choices made to give workflow writers a great deal of liberty whilst guiding them towards writing robust and modular workflows, ultimately enabling them to encode their scientific knowledge to the benefit of the wider scientific community.

An Introduction to Seshat

This article introduces the Seshat: Global History Databank, its potential, and its methodology. Seshat is a databank containing vast amounts of quantitative data buttressed by qualitative nuance for a large sample of historical and archaeological polities. The sample is global in scope and covers the period from the Neolithic Revolution to the Industrial Revolution. Seshat allows scholars to capture dynamic processes and to test theories about the co-evolution (or not) of social scale and complexity, agriculture, warfare, religion, and any number of such Big Questions. Seshat is rapidly becoming a massive resource for innovative cross-cultural and cross-disciplinary research. Seshat is part of a growing trend to use comparative historical data on a large scale and contributes as such to a growing consilience between the humanities and social sciences. Seshat is underpinned by a robust and transparent workflow to ensure the ever growing dataset is of high quality.