Parallel Computation of the Jacobian Matrix for Nonlinear Equation Solvers Using MATLAB

Demonstrating speedup for parallel code on a multicore shared memory PC can be challenging in MATLAB due to underlying parallel operations that are often opaque to the user. This can limit potential for improvement of serial code even for the so-called embarrassingly parallel applications. One such application is the computation of the Jacobian matrix inherent to most nonlinear equation solvers. Computation of this matrix represents the primary bottleneck in nonlinear solver speed such that commercial finite element (FE) and multi-body-dynamic (MBD) codes attempt to minimize computations. A timing study using MATLAB's Parallel Computing Toolbox was performed for numerical computation of the Jacobian. Several approaches for implementing parallel code were investigated while only the single program multiple data (spmd) method using composite objects provided positive results. Parallel code speedup is demonstrated but the goal of linear speedup through the addition of processors was not achieved due to PC architecture

Empirically-Driven Multiwavelength K-corrections At Low Redshift

K-corrections, conversions between flux in observed bands to flux in rest-frame bands, are critical for comparing galaxies at various redshifts. These corrections often rely on fits to empirical or theoretical spectral energy distribution (SED) templates of galaxies. However, the templates limit reliable K-corrections to regimes where SED models are robust. For instance, the templates are not well-constrained in some bands (e.g., WISE W4), which results in ill-determined K-corrections for these bands. We address this shortcoming by developing an empirically-driven approach to K-corrections as a means to mitigate dependence on SED templates. We perform a polynomial fit for the K-correction as a function of a galaxy's rest-frame color determined in well-constrained bands (e.g., rest-frame (g-r)) and redshift, exploiting the fact that galaxy SEDs can be described as a one parameter family at low redshift (0.01 < z < 0.09). For bands well-constrained by SED templates, our empirically-driven K-corrections are comparable to the SED fitting method of Kcorrect and SED template fitting employed in the GSWLC-M2 catalogue (the updated medium-deep GALEX-SDSS-WISE Legacy Catalogue). However, our method dramatically outperforms the available SED fitting K-corrections for WISE W4. Our method also mitigates incorrect template assumptions and enforces the K-correction to be 0 at z = 0. Our K-corrected photometry and code are publicly available.Comment: 15 pages, 9 figures, submitted to MNRA

A Summary of Revisions Applied to a Turbulence Response Analysis Method for Flexible Aircraft Configurations

A software program and associated methodology to study gust loading on aircraft exists for a classification of geometrically simplified flexible configurations. This program consists of a simple aircraft response model with two rigid and three flexible symmetric degrees-of - freedom and allows for the calculation of various airplane responses due to a discrete one-minus- cosine gust as well as continuous turbulence. Simplifications, assumptions, and opportunities for potential improvements pertaining to the existing software program are first identified, then a revised version of the original software tool is developed with improved methodology to include more complex geometries, additional excitation cases, and additional output data so as to provide a more useful and precise tool for gust load analysis. In order to improve the original software program to enhance usefulness, a wing control surface and horizontal tail control surface is added, an extended application of the discrete one-minus-cosine gust input is employed, a supplemental continuous turbulence spectrum is implemented, and a capability to animate the total vehicle deformation response to gust inputs is included. These revisions and enhancements are implemented and an analysis of the results is used to validate the modifications

Feedback Control of Flight Speed to Reduce Unmanned Aerial System Noise

The aim of this initial study is to incorporate an acoustic metric into the flight control system of an unmanned aerial vehicle. This could be used to mitigate the noise impact of unmanned aerial systems operating near residential communities. To incorporate an acoustic metric into a pre-existing flight control system, two things are required: a source noise model, and an acoustic controller. An acoustic model was developed based on Gutin's work to estimate propeller noise. The flight control system was augmented with a controller to reduce propeller noise using feedback control of the commanded flight speed until an acoustic target was met. This control approach focuses on modifying flight speed only, with no perturbation to the trajectory. Multiple flight simulations were performed and the results showed that integrating an acoustic metric into the flight control system of an unmanned aerial system is possible

Conditionally Calibrated Predictive Distributions by Probability-Probability Map: Application to Galaxy Redshift Estimation and Probabilistic Forecasting

Uncertainty quantification is crucial for assessing the predictive ability of AI algorithms. Much research has been devoted to describing the predictive distribution (PD) $F(y|\mathbf{x})$ of a target variable $y \in \mathbb{R}$ given complex input features $\mathbf{x} \in \mathcal{X}$. However, off-the-shelf PDs (from, e.g., normalizing flows and Bayesian neural networks) often lack conditional calibration with the probability of occurrence of an event given input $\mathbf{x}$ being significantly different from the predicted probability. Current calibration methods do not fully assess and enforce conditionally calibrated PDs. Here we propose \texttt{Cal-PIT}, a method that addresses both PD diagnostics and recalibration by learning a single probability-probability map from calibration data. The key idea is to regress probability integral transform scores against $\mathbf{x}$. The estimated regression provides interpretable diagnostics of conditional coverage across the feature space. The same regression function morphs the misspecified PD to a re-calibrated PD for all $\mathbf{x}$. We benchmark our corrected prediction bands (a by-product of corrected PDs) against oracle bands and state-of-the-art predictive inference algorithms for synthetic data. We also provide results for two applications: (i) probabilistic nowcasting given sequences of satellite images, and (ii) conditional density estimation of galaxy distances given imaging data (so-called photometric redshift estimation). Our code is available as a Python package https://github.com/lee-group-cmu/Cal-PIT .Comment: 21 pages, 11 figures. Under review. Code available as a Python package https://github.com/lee-group-cmu/Cal-PI

SutA is a bacterial transcription factor expressed during slow growth in Pseudomonas aeruginosa

Microbial quiescence and slow growth are ubiquitous physiological states, but their study is complicated by low levels of metabolic activity. To address this issue, we used a time-selective proteome-labeling method [bioorthogonal noncanonical amino acid tagging (BONCAT)] to identify proteins synthesized preferentially, but at extremely low rates, under anaerobic survival conditions by the opportunistic pathogen Pseudomonas aeruginosa. One of these proteins is a transcriptional regulator that has no homology to any characterized protein domains and is posttranscriptionally up-regulated during survival and slow growth. This small, acidic protein associates with RNA polymerase, and chromatin immunoprecipitation (ChIP) followed by high-throughput sequencing suggests that the protein associates with genomic DNA through this interaction. ChIP signal is found both in promoter regions and throughout the coding sequences of many genes and is particularly enriched at ribosomal protein genes and in the promoter regions of rRNA genes. Deletion of the gene encoding this protein affects expression of these and many other genes and impacts biofilm formation, secondary metabolite production, and fitness in fluctuating conditions. On the basis of these observations, we have designated the protein SutA (survival under transitions A)

Revolutionising Fish Ageing: Using Near Infrared Spectroscopy to Age Fish

The project aimed to evaluate the innovative application of NIRS as a reliable, repeatable, and cost-effective method of ageing fish, using otoliths of Barramundi and Snapper as study species. Specific research questions included assessing how geographic and seasonal variation in otoliths affects NIRS predictive models of fish age, as well as how the NIR spectra of otoliths change in the short-term (i.e., <12 months) and long-term (i.e., historical otolith collections) and what effect this has on the predictive ability of NIRS models. The cost-effectiveness of using NIRS to supplement standard fish ageing methods was also evaluated using a hypothetical case study of Barramundi

Selective Proteomic Analysis of Antibiotic-Tolerant Cellular Subpopulations in Pseudomonas aeruginosa Biofilms

Biofilm infections exhibit high tolerance against antibiotic treatment. The study of biofilms is complicated by phenotypic heterogeneity; biofilm subpopulations differ in their metabolic activities and their responses to antibiotics. Here, we describe the use of the bio-orthogonal noncanonical amino acid tagging (BONCAT) method to enable selective proteomic analysis of a Pseudomonas aeruginosa biofilm subpopulation. Through controlled expression of a mutant methionyl-tRNA synthetase, we targeted BONCAT labeling to cells in the regions of biofilm microcolonies that showed increased tolerance to antibiotics. We enriched and identified proteins synthesized by cells in these regions. Compared to the entire biofilm proteome, the labeled subpopulation was characterized by a lower abundance of ribosomal proteins and was enriched in proteins of unknown function. We performed a pulse-labeling experiment to determine the dynamic proteomic response of the tolerant subpopulation to supra-MIC treatment with the fluoroquinolone antibiotic ciprofloxacin. The adaptive response included the upregulation of proteins required for sensing and repairing DNA damage and substantial changes in the expression of enzymes involved in central carbon metabolism. We differentiated the immediate proteomic response, characterized by an increase in flagellar motility, from the long-term adaptive strategy, which included the upregulation of purine synthesis. This targeted, selective analysis of a bacterial subpopulation demonstrates how the study of proteome dynamics can enhance our understanding of biofilm heterogeneity and antibiotic tolerance.IMPORTANCE Bacterial growth is frequently characterized by behavioral heterogeneity at the single-cell level. Heterogeneity is especially evident in the physiology of biofilms, in which distinct cellular subpopulations can respond differently to stresses, including subpopulations of pathogenic biofilms that are more tolerant to antibiotics. Global proteomic analysis affords insights into cellular physiology but cannot identify proteins expressed in a particular subpopulation of interest. Here, we report a chemical biology method to selectively label, enrich, and identify proteins expressed by cells within distinct regions of biofilm microcolonies. We used this approach to study changes in protein synthesis by the subpopulation of antibiotic-tolerant cells throughout a course of treatment. We found substantial differences between the initial response and the long-term adaptive strategy that biofilm cells use to cope with antibiotic stress. The method we describe is readily applicable to investigations of bacterial heterogeneity in diverse contexts

Selective proteomic analysis of antibiotic-tolerant cellular subpopulations in pseudomonas aeruginosa biofilms

Biofilm infections exhibit high tolerance against antibiotic treatment. The study of biofilms is complicated by phenotypic heterogeneity; biofilm subpopulations differ in their metabolic activities and their responses to antibiotics. Here, we describe the use of the bio-orthogonal noncanonical amino acid tagging (BONCAT) method to enable selective proteomic analysis of a Pseudomonas aeruginosa biofilm subpopulation. Through controlled expression of a mutant methionyl-tRNA synthetase, we targeted BONCAT labeling to cells in the regions of biofilm microcolonies that showed increased tolerance to antibiotics. We enriched and identified proteins synthesized by cells in these regions. Compared to the entire biofilm proteome, the labeled subpopulation was characterized by a lower abundance of ribosomal proteins and was enriched in proteins of unknown function. We performed a pulse-labeling experiment to determine the dynamic proteomic response of the tolerant subpopulation to supra-MIC treatment with the fluoroquinolone antibiotic ciprofloxacin. The adaptive response included the upregulation of proteins required for sensing and repairing DNA damage and substantial changes in the expression of enzymes involved in central carbon metabolism. We differentiated the immediate proteomic response, characterized by an increase in flagellar motility, from the long-term adaptive strategy, which included the upregulation of purine synthesis. This targeted, selective analysis of a bacterial subpopulation demonstrates how the study of proteome dynamics can enhance our understanding of biofilm heterogeneity and antibiotic tolerance

How well do local relations predict gas-phase metallicity gradients? : results from SDSS-IV MaNGA

Gas-phase metallicity gradients in galaxies provide important clues to those galaxies’ formation histories. Using SDSS-IV MaNGA data, we previously demonstrated that gas metallicity gradients vary systematically and significantly across the galaxy mass–size plane: at stellar masses beyond approximately 1010 M , more extended galaxies display steeper gradients (in units of dex/Re) at a given stellar mass. Here, we set out to develop a physical interpretation of these findings by examining the ability of local ∼kpc-scale relations to predict the gradient behaviour along the mass–size plane. We find that local stellar mass surface density, when combined with total stellar mass, is sufficient to reproduce the overall mass–size trend in a qualitative sense. We further find that we can improve the predictions by correcting for residual trends relating to the recent star formation histories of star-forming regions. However, we find as well that the most extended galaxies display steeper average gradients than predicted, even after correcting for residual metallicity trends with other local parameters. From these results, we argue that gas-phase metallicity gradients can largely be understood in terms of known local relations, but we also discuss some possible physical causes of discrepant gradients