Computation of Steady Incompressible Flows in Unbounded Domains

In this study we revisit the problem of computing steady Navier-Stokes flows in two-dimensional unbounded domains. Precise quantitative characterization of such flows in the high-Reynolds number limit remains an open problem of theoretical fluid dynamics. Following a review of key mathematical properties of such solutions related to the slow decay of the velocity field at large distances from the obstacle, we develop and carefully validate a spectrally-accurate computational approach which ensures the correct behavior of the solution at infinity. In the proposed method the numerical solution is defined on the entire unbounded domain without the need to truncate this domain to a finite box with some artificial boundary conditions prescribed at its boundaries. Since our approach relies on the streamfunction-vorticity formulation, the main complication is the presence of a discontinuity in the streamfunction field at infinity which is related to the slow decay of this field. We demonstrate how this difficulty can be overcome by reformulating the problem using a suitable background "skeleton" field expressed in terms of the corresponding Oseen flow combined with spectral filtering. The method is thoroughly validated for Reynolds numbers spanning two orders of magnitude with the results comparing favourably against known theoretical predictions and the data available in the literature.Comment: 39 pages, 12 figures, accepted for publication in "Computers and Fluids

Addressing the pooled amplification paradox with unique molecular identifiers in single-cell RNA-seq

The incorporation of unique molecular identifiers (UMIs) in single-cell RNA-seq assays allows for the removal of amplification bias in the estimation of gene abundances. We show that UMIs can also be used to address a problem resulting from incomplete sequencing of amplified molecules in sequencing libraries that can lead to bias in gene abundance estimates. Our method, called BUTTERFLY, is based on a zero truncated negative binomial estimator and is implemented in the kallisto bustools single-cell RNA-seq workflow. We demonstrate its efficacy using a range of datasets and show that it can invert the relative abundance of certain genes in cases of a pooled amplification paradox

Atmospheric Propagation of High Energy Lasers: Thermal Blooming Simulation

High Energy Laser (HEL) propagation through turbulent atmosphere is examined via numerical simulation. The beam propagation is modeled with the paraxial equation, which in turn is written as a system of equations for a quantum fluid, via the Madelung transform. A finite volume solver is applied to the quantum fluid equations, which supports sharp gradients in beam intensity. The atmosphere is modeled via a coupled advection-diffusion equation whose initial data have Kolmogorov spectrum. In this model the combined effects of thermal blooming, beam slewing, and deep turbulence are simulated

Online Self-Supervised Thermal Water Segmentation for Aerial Vehicles

We present a new method to adapt an RGB-trained water segmentation network to target-domain aerial thermal imagery using online self-supervision by leveraging texture and motion cues as supervisory signals. This new thermal capability enables current autonomous aerial robots operating in near-shore environments to perform tasks such as visual navigation, bathymetry, and flow tracking at night. Our method overcomes the problem of scarce and difficult-to-obtain near-shore thermal data that prevents the application of conventional supervised and unsupervised methods. In this work, we curate the first aerial thermal near-shore dataset, show that our approach outperforms fully-supervised segmentation models trained on limited target-domain thermal data, and demonstrate real-time capabilities onboard an Nvidia Jetson embedded computing platform. Code and datasets used in this work will be available at: https://github.com/connorlee77/uav-thermal-water-segmentation.Comment: 8 pages, 4 figures, 3 table

Sources of variation in cell-type RNA-Seq profiles

Cell-type specific gene expression profiles are needed for many computational methods operating on bulk RNA-Seq samples, such as deconvolution of cell-type fractions and digital cytometry. However, the gene expression profile of a cell type can vary substantially due to both technical factors and biological differences in cell state and surroundings, reducing the efficacy of such methods. Here, we investigated which factors contribute most to this variation. We evaluated different normalization methods, quantified the variance explained by different factors, evaluated the effect on deconvolution of cell type fractions, and examined the differences between UMI-based single-cell RNA-Seq and bulk RNA-Seq. We investigated a collection of publicly available bulk and single-cell RNA-Seq datasets containing B and T cells, and found that the technical variation across laboratories is substantial, even for genes specifically selected for deconvolution, and this variation has a confounding effect on deconvolution. Tissue of origin is also a substantial factor, highlighting the challenge of using cell type profiles derived from blood with mixtures from other tissues. We also show that much of the differences between UMI-based single-cell and bulk RNA-Seq methods can be explained by the number of read duplicates per mRNA molecule in the single-cell sample. Our work shows the importance of either matching or correcting for technical factors when creating cell-type specific gene expression profiles that are to be used together with bulk samples

Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data

Single-cell RNA sequencing combined with genome-scale metabolic models (GEMs) has the potential to unravel the differences in metabolism across both cell types and cell states but requires new computational methods. Here, we present a method for generating cell-type-specific genome-scale models from clusters of single-cell RNA-Seq profiles. Specifically, we developed a method to estimate the minimum number of cells required to pool to obtain stable models, a bootstrapping strategy for estimating statistical inference, and a faster version of the task-driven integrative network inference for tissues\ua0algorithm for generating context-specific GEMs. In addition, we evaluated the effect of different RNA-Seq normalization methods on model topology and differences in models generated from single-cell and bulk RNA-Seq data. We applied our methods on data from mouse cortex neurons and cells from the tumor microenvironment of lung cancer and in both cases found that almost every cell subtype had a unique metabolic profile. In addition, our approach was able to detect cancer-associated metabolic differences between cancer cells and healthy cells, showcasing its utility. We also contextualized models from 202 single-cell clusters across 19 human organs using data from Human Protein Atlas and made these available in the web portal Metabolic Atlas, thereby providing a valuable resource to the scientific community. With the ever-increasing availability of single-cell RNA-Seq datasets and continuously improved GEMs, their combination holds promise to become an important approach in the study of human metabolism

DSAVE: Detection of misclassified cells in single-cell RNA-Seq data

Single-cell RNA sequencing has become a valuable tool for investigating cell types in complex tissues, where clustering of cells enables the identification and comparison of cell populations. Although many studies have sought to develop and compare different clustering approaches, a deeper investigation into the properties of the resulting populations is lacking. Specifically, the presence of misclassified cells can influence downstream analyses, highlighting the need to assess subpopulation purity and to detect such cells. We developed DSAVE (Down-SAmpling based Variation Estimation), a method to evaluate the purity of single-cell transcriptome clusters and to identify misclassified cells. The method utilizes down-sampling to eliminate differences in sampling noise and uses a log-likelihood based metric to help identify misclassified cells. In addition, DSAVE estimates the number of cells needed in a population to achieve a stable average gene expression profile within a certain gene expression range. We show that DSAVE can be used to find potentially misclassified cells that are not detectable by similar tools and reveal the cause of their divergence from the other cells, such as differing cell state or cell type. With the growing use of single-cell RNA-seq, we foresee that DSAVE will be an increasingly useful tool for comparing and purifying subpopulations in single-cell RNA-Seq datasets

Precipitation of Mn Oxides in Quaternary microbially induced sedimentary structures (MISS), Cape Vani Paleo-Hydrothermal Vent Field, Milos, Greece

Understanding microbial mediation in sediment-hosted Mn deposition has gained importance in low-temperature ore genesis research. Here we report Mn oxide ores dominated by todorokite, vernadite, hollandite, and manjiroite, which cement Quaternary microbially induced sedimentary structures (MISS) developed along bedding planes of shallow-marine to tidal-flat volcaniclastic sandstones/sandy tuffs, Cape Vani paleo-hydrothermal vent field, Milos, Greece. This work aims to decipher the link between biological Mn oxide formation, low-T hydrothermalism, and, growth and preservation of Mn-bearing MISS (MnMISS). Geobiological processes, identified by microtexture petrography, scanning and transmission electron microscopy, lipid biomarkers, bulk- and lipid-specific δ13Corganic composition, and field data, and, low-temperature hydrothermal venting of aqueous Mn2+ in sunlit shallow waters, cooperatively enabled microbially-mediated Mn (II) oxidation and biomineralization. The MnMISS biomarker content and δ13Corg signatures strongly resemble those of modern Mn-rich hydrothermal sediments, Milos coast. Biogenic and syngenetic Mn oxide precipitation established by electron paramagnetic resonance (EPR) spectroscopy and petrography, combined with hydrothermal fluid flow-induced pre-burial curing/diagenesis, may account for today’s crystalline Mn oxide resource. Our data suggests that MISS are not unique to cyanobacteria mats. Furthermore, microbial mats inhabited by aerobic methanotrophs may have contributed significantly to the formation of the MnMISS, thus widening the spectrum of environments responsible for marine Mn biometallogenesi

Modulation of extracellular matrix genes reflects the magnitude of physiological adaptation to aerobic exercise training in humans

BACKGROUND: Regular exercise reduces cardiovascular and metabolic disease partly through improved aerobic fitness. The determinants of exercise-induced gains in aerobic fitness in humans are not known. We have demonstrated that over 500 genes are activated in response to endurance-exercise training, including modulation of muscle extracellular matrix (ECM) genes. Real-time quantitative PCR, which is essential for the characterization of lower abundance genes, was used to examine 15 ECM genes potentially relevant for endurance-exercise adaptation. Twenty-four sedentary male subjects undertook six weeks of high-intensity aerobic cycle training with muscle biopsies being obtained both before and 24 h after training. Subjects were ranked based on improvement in aerobic fitness, and two cohorts were formed (n = 8 per group): the high-responder group (HRG; peak rate of oxygen consumption increased by +0.71 ± 0.1 L min(-1); p < 0.0001) while the low-responder group (LRG; peak rate of oxygen consumption did not change, +0.17 ± 0.1 L min(-1), ns). ECM genes profiled included the angiopoietin 1 and related genes (angiopoietin 2, tyrosine kinase with immunoglobulin-like and EGF-like domains 1 (TIE1) and 2 (TIE2), vascular endothelial growth factor (VEGF) and related receptors (VEGF receptor 1, VEGF receptor 2 and neuropilin-1), thrombospondin-4, α2-macroglobulin and transforming growth factor β2. RESULTS: neuropilin-1 (800%; p < 0.001) and VEGF receptor 2 (300%; p < 0.01) transcript abundance increased only in the HRG, whereas levels of VEGF receptor 1 mRNA actually declined in the LRG (p < 0.05). TIE1 and TIE2 mRNA levels were unaltered in the LRG, whereas transcription levels of both genes were increased by 2.5-fold in the HRG (p < 0.01). Levels of thrombospondin-4 (900%; p < 0.001) and α2-macroglobulin (300%, p < 0.05) mRNA increased substantially in the HRG. In contrast, the amount of transforming growth factor β2 transcript increased only in the HRG (330%; p < 0.01), whereas it remained unchanged in the LRG (-80%). CONCLUSION: We demonstrate for the first time that aerobic training activates angiopoietin 1 and TIE2 genes in human muscle, but only when aerobic capacity adapts to exercise-training. The fourfold-greater increase in aerobic fitness and markedly differing gene expression profile in the HRG indicates that these ECM genes may be critical for physiological adaptation to exercise in humans. In addition, we show that, without careful demonstration of physiological adaptation, conclusions derived from gene expression profiling of human skeletal muscle following exercise may be of limited value. We propose that future studies should (a) investigate the mechanisms that underlie the apparent link between physiological adaptation and gene expression and (b) use the genes profiled in this paper as candidates for population genetic studies