A model local interpretation routine for deep learning based radio galaxy classification

Radio galaxy morphological classification is one of the critical steps when producing source catalogues for large-scale radio continuum surveys. While many recent studies attempted to classify source radio morphology from survey image data using deep learning algorithms (i.e., Convolutional Neural Networks), they concentrated on model robustness most time. It is unclear whether a model similarly makes predictions as radio astronomers did. In this work, we used Local Interpretable Model-agnostic Explanation (LIME), an state-of-the-art eXplainable Artificial Intelligence (XAI) technique to explain model prediction behaviour and thus examine the hypothesis in a proof-of-concept manner. In what follows, we describe how \textbf{LIME} generally works and early results about how it helped explain predictions of a radio galaxy classification model using this technique.Comment: 4 pages, 1 figure, accepted summary paper for URSI GASS 2023 J0

Infants exposed in utero to Hurricane Maria have gut microbiomes with reduced diversity and altered metabolic capacity

The gut microbiome is a potentially important mechanism that links prenatal disaster exposures with increased disease risks. However, whether prenatal disaster exposures are associated with alterations in the infant\u27s gut microbiome remains unknown. We established a birth cohort study named Hurricane as the Origin of Later Alterations in Microbiome (HOLA) after Hurricane Maria struck Puerto Rico in 2017. We enrolled vaginally born Latino term infants aged 2 to 6 months, includin

Galaxy Light profile neural Networks (GaLNets). II. Bulge-Disc decomposition in optical space-based observations

Bulge-disk (B-D) decomposition is an effective diagnostic to characterize the galaxy morphology and understand its evolution across time. So far, high-quality data have allowed detailed B-D decomposition to redshift below 0.5, with limited excursions over small volumes at higher redshifts. Next-generation large sky space surveys in optical, e.g. from the China Space Station Telescope (CSST), and near-infrared, e.g. from the space EUCLID mission, will produce a gigantic leap in these studies as they will provide deep, high-quality photometric images over more than 15000 deg2 of the sky, including billions of galaxies. Here, we extend the use of the Galaxy Light profile neural Network (GaLNet) to predict 2-S\'ersic model parameters, specifically from CSST data. We simulate point-spread function (PSF) convolved galaxies, with realistic B-D parameter distributions, on CSST mock observations to train the new GaLNet and predict the structural parameters (e.g. magnitude, effective radius, Sersic index, axis ratio, etc.) of both bulge and disk components. We find that the GaLNet can achieve very good accuracy for most of the B-D parameters down to an $r$-band magnitude of 23.5 and redshift $\sim$1. The best accuracy is obtained for magnitudes, implying accurate bulge-to-total (B/T) estimates. To further forecast the CSST performances, we also discuss the results of the 1-S\'ersic GaLNet and show that CSST half-depth data will allow us to derive accurate 1-component models up to $r\sim$24 and redshift z$\sim$1.7

Genome-Wide Gene by Environment Interaction Analysis Identifies Common SNPs at 17q21.2 that Are Associated with Increased Body Mass Index Only among Asthmatics.

Asthmatics have an increased risk of being overweight/obese. Although the underlying mechanisms of this are unclear, genetic factors are believed to play an essential role. To identify common genetic variants that are associated with asthma-related BMI increase, we performed a genome-wide gene by environment (asthma) interaction analysis for the outcome of BMI in the Multi-Ethnic Study of Atherosclerosis (MESA) study (N = 2474 Caucasians, 257 asthmatics), and replicated findings in the Framingham Heart Study (FHS) offspring cohort (N = 1408 Caucasians, 382 asthmatics). The replicable tagging SNP, rs2107212, was further examined in stratified analyses. Seven SNPs clustered in 17q21.2 were identified to be associated with higher BMI among asthmatics (interaction p < 5×10-7 in MESA and p < 0.05 in FHS). In both MESA and FHS asthmatics, subjects carrying the A allele on rs2107212 had significantly higher odds of obesity than non-carriers, which was not the case for non-asthmatics. We further examined BMI change subsequent to asthma diagnosis over a period of 26 years in FHS and demonstrated greater BMI increase among asthmatics compared to non-asthmatics. Asthmatics carrying the A allele at rs2107212 had significantly greater net BMI increase over the 26-year period compared to non-asthmatics. In this study, we found that common genetic variants on 17q21.2 are associated with post-asthma BMI increase among Caucasians. This finding will help elucidate pathways involved in the comorbidity of asthma and obesity

Association of rs2107212 genotypes and BMI change by asthma status in FHS.

<p>A: For a subset of FHS subjects who had BMI and asthma information at both exam 2 (1979) and exam 8 (2005) (n = 983), the means of BMI at both time points are shown according to the rs2107212 genotypes AA/AG and GG, stratified by asthma status. B: Net BMI change (BMI value in 2005—BMI value in 1979) with rs2107212 genotypes AA/AG and GG stratified by asthma status is shown. The numbers of subjects are shown in each bar. Data are represented as the mean ± SEM. *p<0.05 under Welch’s t-test.</p

Forest plot showing the association of rs2107212 with obesity in MESA, FHS and meta-analysis.

<p>Asthma status-specific odds ratios (OR) of being obese for rs2107212 were calculated using logistic regression models adjusted for age, gender and the first principal component in MESA and FHS respectively. The minor allele A is the “risk” allele, and an additive genetic model was used. Meta-analysis was performed under a fixed effect model since the heterogeneity tests were not significant. Boxes indicate group-specific OR point estimates, and lines indicate the respective 95% confidence interval (CI). Diamonds indicate meta-analysis OR and 95% CI.</p

Additional file 18: Supplemental Methods. of Multiethnic genome-wide association study identifies ethnic-specific associations with body mass index in Hispanics and African Americans

Details regarding ethnic-specific linear regression model covariates and checks for normality of the response variable. (DOCX 184 kb

Association of rs2107212 genotypes and BMI, stratified by asthma status in MESA (A) and FHS (B), respectively.

<p>Body mass index (BMI, kg/m²) was compared across subjects carrying the AA, AG or GG genotypes for rs2107212 in asthmatics and non-asthmatics. Numbers of subjects are shown in each bar. Data are represented as the mean ± SEM. Significance was tested using Welch’s t-test (*p<0.05, **p<0.01 and ***p<0.001).</p

Manhattan plot of interaction p-values derived from the MESA genome-wide interaction analysis.

<p>Linear regression models, including main effects for SNP and asthma, and an interaction term for SNP × asthma, were used. Models were also adjusted for age, sex, and the first principal component. The red (upper) line represents the genome-wide significant p-value (0.05/721,893 = 6.93×10⁻⁸). The blue (lower) line represents the genome-wide suggestive p-value of 5×10⁻⁷.</p