Multiple Replicates Research Articles

A Novel Method for Measuring Lung Metabolism of Precision‐cut Lung Slices

Pulmonary diseases are associated with changes in metabolism. Cellular metabolism is dynamically regulated by signaling cascades at molecular levels and associated with the pathology at organ level. Improved understanding of cellular metabolism is necessary to investigate its role in disease and identify the potential targets for therapy. We have developed a real‐time method to adapt to Agilent XFe96 metabolic analyzer for precision‐cut lung slices (PCLS) so that compound effects on metabolism can be screened across multiple technical replicates. PCLS were cut in 3 mm maximum diameter to allow for fitting and adhering to XFe96 plate wells, which were pre‐coated with matrix gel. Once the PCLS were attached to the well bottom, the XFe96 cartridge probe can hold the tissue to measure, since the measurement volume is about 2‐6 µl, 200 micrometers (µm) above the lung tissue. This method generated reproducible oxygen consumption and extracellular acidification data in precision‐cut lung slices. We have optimized the conditions of oligomycin (OM) and Trifluoromethoxy carbonylcyanide phenylhydrazone (FCCP), rotenone and antimycin A to produce precision measurements. We have been able to utilize this technique to establish baseline metabolic rates for extracellular acidification and oxygen consumption and the dependency of those rates on mitochondrial function. In comparison with current cellular metabolic measurements, this method measures the whole tissue instead of an individual cell population. We have utilized this method to measure the effects of LPS treatment on PCLS metabolic function. Specifically, we have been able to demonstrate that LPS treatment not only leads to increased nitric oxide production in PCLS, but also reduces both glycolysis and spare respiratory capacity (SRC). This new method allows for the investigation of lung metabolism ex vivo and the screening of therapeutic targets for their interaction with lung metabolism.

Phenotypic and transcriptional changes in Escherichia coli K12 in response to simulated microgravity on the EagleStat, a new 2D microgravity analog for bacterial studies.

Understanding the impacts of microgravity on bacteria is vital for successful long duration space missions. In this environment, bacteria have been shown to become more virulent, more resistant to antibiotics and to regulate biofilm formation. Since the study of these phenomena under true microgravity is cost- and time challenging, the use of ground-based analogs might allow researchers to test hypotheses before planning and executing experiments in the space environment. We designed and developed a 2D clinostat with capabilities robust enough for bacterial studies to allow for multiple simultaneous replicates of treatment and control conditions, thus permitting the generation of growth curves, in a single run. We used computational fluid dynamics (CFD), biofilm growth measurement and differential gene expression analysis on Escherichia coli cultures grown to late exponential phase (24 h) to validate the system's ability to simulate microgravity conditions. The CFD model with a rotational speed of 8 rpm projected cells growing homogeneously distributed along the tube, while the static condition showed the accumulation of the cells at the bottom of the container. These results were empirically validated with cultures on nutrient broth. Additionally, crystal violet assays showed that higher biofilm biomass grew on the internal walls of the gravity control tubes, compared to the simulated microgravity treatment. In contrast, when cells from both treatments were grown under standard conditions, those exposed to simulated microgravity formed significantly more biofilms than their gravity counterparts. Consistent with this result, transcriptome analysis showed the upregulation of several gene families related to biofilm formation and development such as cells adhesion, aggregation and regulation of cell motility, which provides a potential transcriptional explanation for the differential phenotype observed. Our results show that when operated under parameters for simulated microgravity, our 2D clinostat creates conditions that maintain a proportion of the cells in a constant free-falling state, consistent with the effect of microgravity. Also, the high-throughput nature of our instrument facilitates, significantly, bacterial experiments that require multiple sampling timepoints and small working volumes, making this new instrument extremely efficient.

Tile-Based Pairwise Analysis of GC × GC-TOFMS Data to Facilitate Analyte Discovery and Mass Spectrum Purification.

A new tile-based pairwise analysis workflow, termed 1v1 analysis, is presented to discover and identify analytes that differentiate two chromatograms collected using comprehensive two-dimensional (2D) gas chromatography coupled with time-of-flight mass spectrometry (GC × GC-TOFMS). Tile-based 1v1 analysis easily discovered all 18 non-native analytes spiked in diesel fuel within the top 30 hits, outperforming standard pairwise chromatographic analyses. However, eight spiked analytes could not be identified with multivariate curve resolution-alternating least-squares (MCR-ALS) nor parallel factor analysis (PARAFAC) due to background contamination. Analyte identification was achieved with class comparison enabled-mass spectrum purification (CCE-MSP), which obtains a pure analyte spectrum by normalizing the spectra to an interferent mass channel (m/z) identified from 1v1 analysis and subtracting the two spectra. This report also details the development of CCE-MSP assisted MCR-ALS, which removes the identified interferent m/z from the data prior to decomposition. In total, 17 out of 18 spiked analytes had a match value (MV) > 800 with both versions of CCE-MSP. For example, MCR-ALS and PARAFAC were unable to decompose the pure spectrum of methyl decanoate (MVs < 200) due to its low 2D chromatographic resolution (∼0.34) and high interferent-to-analyte signal ratio (∼30:1). By leveraging information gained from 1v1 analysis, CCE-MSP and CCE-MSP assisted MCR-ALS obtained a pure spectrum with an average MV of 908 and 964, respectively. Furthermore, tile-based 1v1 analysis was applied to track moisture damage in cacao beans, where 86 analytes with at least a 2-fold concentration change were discovered between the unmolded and molded samples. This 1v1 analysis workflow is beneficial for studies where multiple replicates are either unavailable or undesirable to save analysis time.

Evolutionary insights into primate skeletal gene regulation using a comparative cell culture model.

The evolution of complex skeletal traits in primates was likely influenced by both genetic and environmental factors. Because skeletal tissues are notoriously challenging to study using functional genomic approaches, they remain poorly characterized even in humans, let alone across multiple species. The challenges involved in obtaining functional genomic data from the skeleton, combined with the difficulty of obtaining such tissues from nonhuman apes, motivated us to consider an alternative in vitro system with which to comparatively study gene regulation in skeletal cell types. Specifically, we differentiated six human (Homo sapiens) and six chimpanzee (Pan troglodytes) induced pluripotent stem cell lines (iPSCs) into mesenchymal stem cells (MSCs) and subsequently into osteogenic cells (bone cells). We validated differentiation using standard methods and collected single-cell RNA sequencing data from over 100,000 cells across multiple samples and replicates at each stage of differentiation. While most genes that we examined display conserved patterns of expression across species, hundreds of genes are differentially expressed (DE) between humans and chimpanzees within and across stages of osteogenic differentiation. Some of these interspecific DE genes show functional enrichments relevant in skeletal tissue trait development. Moreover, topic modeling indicates that interspecific gene programs become more pronounced as cells mature. Overall, we propose that this in vitro model can be used to identify interspecific regulatory differences that may have contributed to skeletal trait differences between species.

Bayesian calibration, process modeling and uncertainty quantification in biotechnology.

High-throughput experimentation has revolutionized data-driven experimental sciences and opened the door to the application of machine learning techniques. Nevertheless, the quality of any data analysis strongly depends on the quality of the data and specifically the degree to which random effects in the experimental data-generating process are quantified and accounted for. Accordingly calibration, i.e. the quantitative association between observed quantities and measurement responses, is a core element of many workflows in experimental sciences. Particularly in life sciences, univariate calibration, often involving non-linear saturation effects, must be performed to extract quantitative information from measured data. At the same time, the estimation of uncertainty is inseparably connected to quantitative experimentation. Adequate calibration models that describe not only the input/output relationship in a measurement system but also its inherent measurement noise are required. Due to its mathematical nature, statistically robust calibration modeling remains a challenge for many practitioners, at the same time being extremely beneficial for machine learning applications. In this work, we present a bottom-up conceptual and computational approach that solves many problems of understanding and implementing non-linear, empirical calibration modeling for quantification of analytes and process modeling. The methodology is first applied to the optical measurement of biomass concentrations in a high-throughput cultivation system, then to the quantification of glucose by an automated enzymatic assay. We implemented the conceptual framework in two Python packages, calibr8 and murefi, with which we demonstrate how to make uncertainty quantification for various calibration tasks more accessible. Our software packages enable more reproducible and automatable data analysis routines compared to commonly observed workflows in life sciences. Subsequently, we combine the previously established calibration models with a hierarchical Monod-like ordinary differential equation model of microbial growth to describe multiple replicates of Corynebacterium glutamicum batch cultures. Key process model parameters are learned by both maximum likelihood estimation and Bayesian inference, highlighting the flexibility of the statistical and computational framework.

Microbiology and Evolutionary Advances of Different Types of Pathogens in Human and Biological Conservation

Different factors involved in increasing the risk of infectious diseases caused by genetic and environmental changes. Due to high risk of infectious diseases such as chronic kidney disease, osteoarthritis, osteoporosis, Alzheimer's disease, cataracts, advances have been made in the fields of molecular sciences and engineering technology for highly effective tools and high-throughput omics technologies. Ebola virus is the most contagious as compared to the other viruses colds, influenza, or measles. Pathogenic bacteria are involved in causing of infectious diseases. Hib disease is the serious public health concern disease caused by Haemophilus influenza. Norwalk virus also causes serious abnormalities in the digestive system that ultimately leads to muscle cramp. Antimicrobial resistance have been challenging in the fields of biomedical sciences. Sequencing of bacterial genomes helpful for track the different status of fates of mutations that rise and fall through indication of multiple replicates. Genetic engineering based biomedical therapies needed to understand the target microbial cell, protein factory that secreted the variety of products.

High-Performance Data Processing Workflow Incorporating Effect-Directed Analysis for Feature Prioritization in Suspect and Nontarget Screening.

Effect-directed analysis (EDA) aims at the detection of bioactive chemicals of emerging concern (CECs) by combining toxicity testing and high-resolution mass spectrometry (HRMS). However, consolidation of toxicological and chemical analysis techniques to identify bioactive CECs remains challenging and laborious. In this study, we incorporate state-of-the-art identification approaches in EDA and propose a robust workflow for the high-throughput screening of CECs in environmental and human samples. Three different sample types were extracted and chemically analyzed using a single high-performance liquid chromatography HRMS method. Chemical features were annotated by suspect screening with several reference databases. Annotation quality was assessed using an automated scoring system. In parallel, the extracts were fractionated into 80 micro-fractions each covering a couple of seconds from the chromatogram run and tested for bioactivity in two bioassays. The EDA workflow prioritized and identified chemical features related to bioactive fractions with varying levels of confidence. Confidence levels were improved with the in silico software tools MetFrag and the retention time indices platform. The toxicological and chemical data quality was comparable between the use of single and multiple technical replicates. The proposed workflow incorporating EDA for feature prioritization in suspect and nontarget screening paves the way for the routine identification of CECs in a high-throughput manner.

A tiered approach to population-based in vitro testing for cardiotoxicity: Balancing estimates of potency and variability

Population-wide in vitro studies for characterization of cardiotoxicity hazard, risk, and population variability show that human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CM) are a powerful and high-throughput testing platform for drugs and environmental chemicals alike. However, studies in multiple donor-derived hiPSC-CMs, across large libraries of chemicals tested in concentration-response are technically complex, and study design optimization is needed to determine sufficient and fit-for-purpose population size considerations. Therefore, we tested a hypothesis that a computational down-sampling analysis based on the data from hiPSC-CM screening of 136 diverse compounds in a population of 43 non-diseased donors, including multiple replicates of the “standard” donor hiPSC-CMs, will inform optimal study designs depending on the decision context (hazard, risk and/or inter-individual variability in cardiotoxicity). Through 50 independent random subsamples of 5, 10, or 20 donors, we estimated accuracy and precision for quantifying potency, inter-individual variability, and QT prolongation risk; the results were compared to the full 43-donor cohort. We found that for potency and clinical risk of QT prolongation, a cohort of 5 randomly-selected unique donors provides accurate and precise estimates. Larger cohort sizes afforded marginal improvements, and 5 replicates of a single donor performed worse. For estimating inter-individual variability, cohorts of at least 20 donors are needed, with smaller populations on average showing bias towards underestimation in population variance. Collectively, this study shows that a variable-size hiPSC-CM-based population-wide in vitro model can be used in a number of decision scenarios for identifying cardiotoxic hazards of drugs and environmental chemicals in the population context.

Binding Affinity Measurement of Nuclear Export Signal Peptides to Their Exporter CRM1.

CRM1 recognizes hundreds to thousands of protein cargoes by binding to the eight to fifteen residue-long nuclear export signals (NESs) within their polypeptide chains. Various assays to measure the binding affinity of NESs for CRM1 have been developed. CRM1 binds to NESs with a wide range of binding affinities, with dissociation constants that span from low nanomolar to tens of micromolar. An optimized binding affinity assay with improved throughput was recently developed to measure binding affinities of NES peptides for CRM1 in the presence of excess RanGTP. The assay can measure affinities, with multiple replicates, for up to seven different NES peptides per screening plate. Here, we present a protocol for the purification of the necessary proteins and for measuring CRM1-NES binding affinities.

Design and Validation of a Device for Mitigating Fluid Microgravity Effects in Biological Research in Canister Spaceflight Hardware

The major factor influencing the behavior of microbes growing in liquids in space is microgravity. We recently measured the transcriptomic response of the Gram-positive bacterium Bacillus subtilis to the microgravity environment inside the International Space Station (ISS) in spaceflight hardware called Biological Research in Canisters-Petri Dish Fixation Units (BRIC-PDFUs). In two separate experiments in the ISS, dubbed BRIC-21 and BRIC-23, we grew multiple replicates of the same B. subtilis strain in the same hardware, growth medium, and temperature with matching ground control samples (npj Micrograv. 5:1.2019, doi: 10.1038/s41526-018-0061-0). In both experiments we observed similar responses of the transcriptome to spaceflight. However, we also noted that the liquid cultures assumed a different configuration in microgravity (a toroidal shape) compared with the ground control samples (a flat disc shape), leading us to question whether the transcriptome differences we observed were a direct result of microgravity, or a secondary result of the different liquid geometries of the samples affecting, for example, oxygen availability. To mitigate the influence of microgravity on liquid geometry in BRIC canisters, we have designed an insert to replace the standard 60-mm Petri dish in BRIC-PDFU or BRIC-LED sample compartments. In this design, liquid cultures are expected to assume a more disk-like configuration regardless of gravity or its absence. We have: (i) constructed a prototype device by 3D printing; (ii) evaluated different starting materials, treatments, and coatings for their wettability (i.e., hydrophilicity) using contact angle measurements; (iii) confirmed that the device performs as designed by drop-tower testing and; (iv) performed material biocompatibility studies using liquid cultures of Bacillus subtilis and Staphylococcus aureus bacteria. Future microgravity testing of the device in the ISS is planned.

Background rate of low-level HCV RNA in anti-HCV confirmed-positive and minipool nucleic acid test-nonreactive blood donations.

In 2019, the Food and Drug Administration revised the requirement for further testing of anti-HCV-reactive donations testing nucleic acid (NAT)-nonreactive via routine mini-pool (MP)-NAT. Individual donation (ID)-HCV NAT was required as a supplemental test prior to a second FDA-licensed anti-HCV assay; if ID-HCV-NAT is reactive, no further testing is required. This study investigated the rate of low-level RNA in anti-HCV-reactive donation samples prior to and following the implementation of supplemental ID-HCV NAT. A retrospective analysis was conducted on frozen plasma unit samples from 1161 anti-HCV confirmed-positive/HCV-NAT-nonreactive donations collected from December 2014 to January 2020. Samples were tested by multiple replicates on the Grifols Procleix Ultrio Elite (UE) assay and corresponding discriminatory HCV (dHCV) assay on the Procleix Panther System. Prospectively, the prevalence of low-level RNA in 2912 anti-HCV-reactive donations detected through routine screening from April 2020 through March 2021 was determined. In retrospective analyses, 10 (0.86%) of 1161 plasma samples were UE reactive, of which four (0.34%) were dHCV reactive (in all replicates of UE and dHCV). Of 2912 anti-HCV-reactive donation samples testing NAT-nonreactive via routine MP-NAT, three (0.1%; 95% CI: 0.04-0.30) were dHCV reactive; 37% of the remaining samples were reactive by a second anti-HCV assay and thus serologically confirmed. Retrospective and prospective analysis in comparison to earlier studies revealed that low-level HCV-RNA reactivity is decreasing over time. The very low HCV-RNA rates may be due to the widespread use of highly effective, direct-acting anti-viral treatments for those diagnosed with HCV infection.

The SEQC2 epigenomics quality control (EpiQC) study

BackgroundCytosine modifications in DNA such as 5-methylcytosine (5mC) underlie a broad range of developmental processes, maintain cellular lineage specification, and can define or stratify types of cancer and other diseases. However, the wide variety of approaches available to interrogate these modifications has created a need for harmonized materials, methods, and rigorous benchmarking to improve genome-wide methylome sequencing applications in clinical and basic research. Here, we present a multi-platform assessment and cross-validated resource for epigenetics research from the FDA’s Epigenomics Quality Control Group.ResultsEach sample is processed in multiple replicates by three whole-genome bisulfite sequencing (WGBS) protocols (TruSeq DNA methylation, Accel-NGS MethylSeq, and SPLAT), oxidative bisulfite sequencing (TrueMethyl), enzymatic deamination method (EMSeq), targeted methylation sequencing (Illumina Methyl Capture EPIC), single-molecule long-read nanopore sequencing from Oxford Nanopore Technologies, and 850k Illumina methylation arrays. After rigorous quality assessment and comparison to Illumina EPIC methylation microarrays and testing on a range of algorithms (Bismark, BitmapperBS, bwa-meth, and BitMapperBS), we find overall high concordance between assays, but also differences in efficiency of read mapping, CpG capture, coverage, and platform performance, and variable performance across 26 microarray normalization algorithms.ConclusionsThe data provided herein can guide the use of these DNA reference materials in epigenomics research, as well as provide best practices for experimental design in future studies. By leveraging seven human cell lines that are designated as publicly available reference materials, these data can be used as a baseline to advance epigenomics research.

Organ-specific genome diversity of replication-competent SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is not always confined to the respiratory system, as it impacts people on a broad clinical spectrum from asymptomatic to severe systemic manifestations resulting in death. Further, accumulation of intra-host single nucleotide variants during prolonged SARS-CoV-2 infection may lead to emergence of variants of concern (VOCs). Still, information on virus infectivity and intra-host evolution across organs is sparse. We report a detailed virological analysis of thirteen postmortem coronavirus disease 2019 (COVID-19) cases that provides proof of viremia and presence of replication-competent SARS-CoV-2 in extrapulmonary organs of immunocompromised patients, including heart, kidney, liver, and spleen (NCT04366882). In parallel, we identify organ-specific SARS-CoV-2 genome diversity and mutations of concern N501Y, T1027I, and Y453F, while the patient had died long before reported emergence of VOCs. These mutations appear in multiple organs and replicate in Vero E6 cells, highlighting their infectivity. Finally, we show two stages of fatal disease evolution based on disease duration and viral loads in lungs and plasma. Our results provide insights about the pathogenesis and intra-host evolution of SARS-CoV-2 and show that COVID-19 treatment and hygiene measures need to be tailored to specific needs of immunocompromised patients, even when respiratory symptoms cease.

Picroscope: low-cost system for simultaneous longitudinal biological imaging

Simultaneous longitudinal imaging across multiple conditions and replicates has been crucial for scientific studies aiming to understand biological processes and disease. Yet, imaging systems capable of accomplishing these tasks are economically unattainable for most academic and teaching laboratories around the world. Here, we propose the Picroscope, which is the first low-cost system for simultaneous longitudinal biological imaging made primarily using off-the-shelf and 3D-printed materials. The Picroscope is compatible with standard 24-well cell culture plates and captures 3D z-stack image data. The Picroscope can be controlled remotely, allowing for automatic imaging with minimal intervention from the investigator. Here, we use this system in a range of applications. We gathered longitudinal whole organism image data for frogs, zebrafish, and planaria worms. We also gathered image data inside an incubator to observe 2D monolayers and 3D mammalian tissue culture models. Using this tool, we can measure the behavior of entire organisms or individual cells over long-time periods.

Abstract 1122‐000073: Influence of Catheter Tip Position and Aspiration Technique on ADAPT Revascularization Success with Various Catheters

Introduction : Previous studies demonstrated that both the location of the distal access catheter tip and angle of aspiration have a significant impact on revascularization outcomes 1,2 . A direct aspiration First‐Pass technique (ADAPT) with large‐bore aspiration catheters has emerged as a fast, safe, and effective thrombectomy technique. Maximizing the catheter‐to‐vessel size has previously been shown to enhance distal flow control resulting in improved in‐vitro revascularization rates for aspiration thrombectomy 3 . However, physicians differ in their preference for aspiration catheter tip placement, typically either positioning the catheter tip at the ‘face’ of the clot or advancing the catheter tip into the clot to engage it. We hypothesize that sizing the aspiration catheter outer diameter (OD) to the inner diameter (ID) of the vessel and embedding the catheter tip in the clot may result in ‘pinning’ fragments of clot between the catheter and vessel wall, thereby negatively affecting revascularization outcomes. Withdrawal of the aspiration catheter under continuous aspiration may mitigate this effect. We investigate the influence of catheter tip position and aspiration technique on ADAPT revascularization success with various sizes of aspiration catheters. Methods : Two clot analogues phenotypes (RBC‐Rich and Fibrin/Platelet‐Rich) were created from human blood and used to form occlusions in an In‐vitro thrombectomy model as previously described 4 . Two catheter tip positions and three techniques were investigated; 1). Catheter tip proximal to the face of the clot followed by conventional aspiration, 2). Catheter tip ‘embedded’ into the clot followed by conventional aspiration, and 3). Catheter tip ‘embedded’ into the clot followed by conventional aspiration and aspiration on catheter withdrawal even if clot ingestion occurred. Two aspiration catheters were investigated; Millipede 088’’ (Perfuze Ltd) and SOFIA Plus (Terumo). Multiple replicates of each test were performed. Endpoints were First‐Pass Effect and procedural‐related distal emboli from 200–1000µm. Results : Maximizing the catheter‐to‐vessel size increases success of the ADAPT approach when the tip is located proximal to the clot face (Fig 1 A&amp;B); Millipede 088 achieves a higher First‐Pass Effect rate than SOFIA Plus. Sizing the catheter‐to‐vessel and embedding the catheter tip into the clot (Figure 1C) results in ‘pinning’ of clot fragments between the catheter and vessel wall (Figure 1D) resulting in lower rates of First‐Pass Effect. Withdrawing the catheter under continuous aspiration increases the success of the embedding method by capturing ‘pinned’ fragments. Conclusions : The position of the aspiration catheter tip and aspiration technique used both influence the success of ADAPT procedures. Sizing the catheter‐to‐vessel results in improved revascularization. However, embedding the tip into the clot when the vessel is similar in ID to the catheter OD may reduce First‐Pass Effect rates. To optimize the rates of First‐Pass Effect, aspiration catheters may be positioned at the proximal face of the clot or retracted under continuous aspiration if wedged into the clot.

Apo-H (beta-2-glycoprotein) intact N-glycan analysis by MALDI-TOF-MS using sialic acid derivatization.

Apo-H is a plasma glycoprotein. Nearly 19% of the molecular weight of this protein is composed of glycans. Up- and down-regulation and structural changes in protein glycans provide diagnostic value for disease detection. Here, an efficient, sensitive, and optimized method is developed for Apo-H N-glycans analysis by MALDI-TOF-MS in positive mode. This bioanalytical method includes sample preparation, sample purification, and detection. An Apo-H enrichment method is developed using standard proteins by anti-Apo-H beads followed by enrichment from plasma samples. SDS-PAGE confirms the Apo-H protein enrichment, which is further verified by LC-MS/MS analysis. The lower ionization efficiency of sialylated glycan hampers their analysis by MALDI-MS. For this, stabilization of sialic acids is done by selective derivatization of carboxyl groups to differentiate between α(2,3)- and α(2,6)-linked sialic acids. Glycans are further purified by HILIC-SPE and analyzed by MALDI-MS. Several branched bi- and tri-antennary glycans with fucosylation and sialylation are identified. The reproducibility of the developed method is tested by analyzing multiple replicates of human plasma, where the same glycans are consistently identified. This method could be applied for the Apo-H glycan profiling of large clinical cohorts for diagnostic purposes.

Quasar: Easy Machine Learning for Biospectroscopy.

Data volumes collected in many scientific fields have long exceeded the capacity of human comprehension. This is especially true in biomedical research where multiple replicates and techniques are required to conduct reliable studies. Ever-increasing data rates from new instruments compound our dependence on statistics to make sense of the numbers. The currently available data analysis tools lack user-friendliness, various capabilities or ease of access. Problem-specific software or scripts freely available in supplementary materials or research lab websites are often highly specialized, no longer functional, or simply too hard to use. Commercial software limits access and reproducibility, and is often unable to follow quickly changing, cutting-edge research demands. Finally, as machine learning techniques penetrate data analysis pipelines of the natural sciences, we see the growing demand for user-friendly and flexible tools to fuse machine learning with spectroscopy datasets. In our opinion, open-source software with strong community engagement is the way forward. To counter these problems, we develop Quasar, an open-source and user-friendly software, as a solution to these challenges. Here, we present case studies to highlight some Quasar features analyzing infrared spectroscopy data using various machine learning techniques.

A Bayesian approach for accurate de novo transcriptome assembly

De novo transcriptome assembly from billions of RNA-seq reads is very challenging due to alternative splicing and various levels of expression, which often leads to incorrect, mis-assembled transcripts. BayesDenovo addresses this problem by using both a read-guided strategy to accurately reconstruct splicing graphs from the RNA-seq data and a Bayesian strategy to estimate, from these graphs, the probability of transcript expression without penalizing poorly expressed transcripts. Simulation and cell line benchmark studies demonstrate that BayesDenovo is very effective in reducing false positives and achieves much higher accuracy than other assemblers, especially for alternatively spliced genes and for highly or poorly expressed transcripts. Moreover, BayesDenovo is more robust on multiple replicates by assembling a larger portion of common transcripts. When applied to breast cancer data, BayesDenovo identifies phenotype-specific transcripts associated with breast cancer recurrence.

A New Pipeline to Automatically Segment and Semi-Automatically Measure Bone Length on 3D Models Obtained by Computed Tomography.

The characterization of developmental phenotypes often relies on the accurate linear measurement of structures that are small and require laborious preparation. This is tedious and prone to errors, especially when repeated for the multiple replicates that are required for statistical analysis, or when multiple distinct structures have to be analyzed. To address this issue, we have developed a pipeline for characterization of long-bone length using X-ray microtomography (XMT) scans. The pipeline involves semi-automated algorithms for automatic thresholding and fast interactive isolation and 3D-model generation of the main limb bones, using either the open-source ImageJ plugin BoneJ or the commercial Mimics Innovation Suite package. The tests showed the appropriate combination of scanning conditions and analysis parameters yields fast and comparable length results, highly correlated with the measurements obtained via ex vivo skeletal preparations. Moreover, since XMT is not destructive, the samples can be used afterward for histology or other applications. Our new pipelines will help developmental biologists and evolutionary researchers to achieve fast, reproducible and non-destructive length measurement of bone samples from multiple animal species.

Identification of Dominant Hydrological Mechanisms Using Bayesian Inference, Multiple Statistical Hypothesis Testing, and Flexible Models

AbstractIn hydrological modeling, the identification of model mechanisms best suited for representing individual hydrological (physical) processes is of major scientific and operational interest. We present a statistical hypothesis‐testing perspective on this model identification challenge and contribute a mechanism identification framework that combines: (i) Bayesian estimation of posterior probabilities of individual mechanisms from a given ensemble of model structures; (ii) a test statistic that defines a “dominant” mechanism as a mechanism more probable than all its alternatives given observed data; and (iii) a flexible modeling framework to generate model structures using combinations of available mechanisms. The uncertainty in the test statistic is approximated using bootstrap sampling from the model ensemble. Synthetic experiments (with varying error magnitude and multiple replicates) and real data experiments are conducted using the hydrological modeling system FUSE (7 processes and 2–4 mechanisms per process yielding 624 feasible model structures) and data from the Leizarán catchment in northern Spain. The mechanism identification method is reliable: it identifies the correct mechanism as dominant in all synthetic trials where an identification is made. As data/model errors increase, statistical power (identifiability) decreases, manifesting as trials where no mechanism is identified as dominant. The real data case study results are broadly consistent with the synthetic analysis, with dominant mechanisms identified for 4 of 7 processes. Insights on which processes are most/least identifiable are also reported. The mechanism identification method is expected to contribute to broader community efforts on improving model identification and process representation in hydrology.