1,577 research outputs found
Photo-zSNthesis: Converting Type Ia Supernova Lightcurves to Redshift Estimates via Deep Learning
Upcoming photometric surveys will discover tens of thousands of Type Ia
supernovae (SNe Ia), vastly outpacing the capacity of our spectroscopic
resources. In order to maximize the science return of these observations in the
absence of spectroscopic information, we must accurately extract key
parameters, such as SN redshifts, with photometric information alone. We
present Photo-zSNthesis, a convolutional neural network-based method for
predicting full redshift probability distributions from multi-band supernova
lightcurves, tested on both simulated Sloan Digital Sky Survey (SDSS) and Vera
C. Rubin Legacy Survey of Space and Time (LSST) data as well as observed SDSS
SNe. We show major improvements over predictions from existing methods on both
simulations and real observations as well as minimal redshift-dependent bias,
which is a challenge due to selection effects, e.g. Malmquist bias. The PDFs
produced by this method are well-constrained and will maximize the cosmological
constraining power of photometric SNe Ia samples.Comment: submitted to Ap
Transformers for scientific data: a pedagogical review for astronomers
The deep learning architecture associated with ChatGPT and related generative
AI products is known as transformers. Initially applied to Natural Language
Processing, transformers and the self-attention mechanism they exploit have
gained widespread interest across the natural sciences. The goal of this
pedagogical and informal review is to introduce transformers to scientists. The
review includes the mathematics underlying the attention mechanism, a
description of the original transformer architecture, and a section on
applications to time series and imaging data in astronomy. We include a
Frequently Asked Questions section for readers who are curious about generative
AI or interested in getting started with transformers for their research
problem.Comment: 17 pages, 5 figure
A Convolutional Neural Network Approach to Supernova Time-Series Classification
One of the brightest objects in the universe, supernovae (SNe) are powerful
explosions marking the end of a star's lifetime. Supernova (SN) type is defined
by spectroscopic emission lines, but obtaining spectroscopy is often
logistically unfeasible. Thus, the ability to identify SNe by type using
time-series image data alone is crucial, especially in light of the increasing
breadth and depth of upcoming telescopes. We present a convolutional neural
network method for fast supernova time-series classification, with observed
brightness data smoothed in both the wavelength and time directions with
Gaussian process regression. We apply this method to full duration and
truncated SN time-series, to simulate retrospective as well as real-time
classification performance. Retrospective classification is used to
differentiate cosmologically useful Type Ia SNe from other SN types, and this
method achieves >99% accuracy on this task. We are also able to differentiate
between 6 SN types with 60% accuracy given only two nights of data and 98%
accuracy retrospectively.Comment: Accepted at the ICML 2022 Workshop on Machine Learning for
Astrophysic
Sum-of-Parts Models: Faithful Attributions for Groups of Features
An explanation of a machine learning model is considered "faithful" if it
accurately reflects the model's decision-making process. However, explanations
such as feature attributions for deep learning are not guaranteed to be
faithful, and can produce potentially misleading interpretations. In this work,
we develop Sum-of-Parts (SOP), a class of models whose predictions come with
grouped feature attributions that are faithful-by-construction. This model
decomposes a prediction into an interpretable sum of scores, each of which is
directly attributable to a sparse group of features. We evaluate SOP on
benchmarks with standard interpretability metrics, and in a case study, we use
the faithful explanations from SOP to help astrophysicists discover new
knowledge about galaxy formation
Management of hospitalised adults with coronavirus disease 2019 (COVID-19):A European Respiratory Society living guideline
INTRODUCTION
Hospitalised patients with coronavirus disease 19 (COVID-19) as a result of SARS-CoV-2 infection have a high mortality rate and frequently require non-invasive respiratory support or invasive ventilation. Optimising and standardising management through evidence-based guidelines may improve quality of care and therefore patient outcomes.
METHODS
A task force from the European Respiratory Society and endorsed by the Chinese Thoracic Society identified priority interventions (pharmacological and non-pharmacological) for the initial version of this "living guideline" using the PICO (population, intervention, comparator, outcome) format. The GRADE approach was used for assessing the quality of evidence and strength of recommendations. Systematic literature reviews were performed, and data pooled by meta-analysis where possible. Evidence tables were presented and evidence to decision frameworks were used to formulate recommendations.
RESULTS
Based on the available evidence at the time of guideline development (February 20th, 2021) the panel makes a strong recommendation in favour of the use of systemic corticosteroids in patients requiring supplementary oxygen or ventilatory support, and for the use of anticoagulation in hospitalised patients. The panel makes a conditional recommendation for IL-6 receptor antagonist monoclonal antibody treatment and high flow nasal oxygen or continuous positive airway pressure in patients with hypoxaemic respiratory failure. The panel make strong recommendations against the use of hydroxychloroquine and lopinavir-ritonavir. Conditional recommendations are made against the use of azithromycin, hydroxychloroquine and azithromycin, colchicine, and remdesivir, in the latter case specifically in patients requiring invasive mechanical ventilation. No recommendation was made for remdesivir in patients requiring supplemental oxygen. Further recommendations for research are made.
CONCLUSION
The evidence base for management of COVID-19 now supports strong recommendations in favour and against specific interventions. These guidelines will be regularly updated as further evidence becomes available
Risk factors and prediction of hypoglycaemia using the Hypo-RESOLVE cohort:a secondary analysis of pooled data from insulin clinical trials
AIMS/HYPOTHESIS: The objective of the Hypoglycaemia REdefining SOLutions for better liVES (Hypo-RESOLVE) project is to use a dataset of pooled clinical trials across pharmaceutical and device companies in people with type 1 or type 2 diabetes to examine factors associated with incident hypoglycaemia events and to quantify the prediction of these events.METHODS: Data from 90 trials with 46,254 participants were pooled. Analyses were done for type 1 and type 2 diabetes separately. Poisson mixed models, adjusted for age, sex, diabetes duration and trial identifier were fitted to assess the association of clinical variables with hypoglycaemia event counts. Tree-based gradient-boosting algorithms (XGBoost) were fitted using training data and their predictive performance in terms of area under the receiver operating characteristic curve (AUC) evaluated on test data. Baseline models including age, sex and diabetes duration were compared with models that further included a score of hypoglycaemia in the first 6 weeks from study entry, and full models that included further clinical variables. The relative predictive importance of each covariate was assessed using XGBoost's importance procedure. Prediction across the entire trial duration for each trial (mean of 34.8 weeks for type 1 diabetes and 25.3 weeks for type 2 diabetes) was assessed.RESULTS: For both type 1 and type 2 diabetes, variables associated with more frequent hypoglycaemia included female sex, white ethnicity, longer diabetes duration, treatment with human as opposed to analogue-only insulin, higher glucose variability, higher score for hypoglycaemia across the 6 week baseline period, lower BP, lower lipid levels and treatment with psychoactive drugs. Prediction of any hypoglycaemia event of any severity was greater than prediction of hypoglycaemia requiring assistance (level 3 hypoglycaemia), for which events were sparser. For prediction of level 1 or worse hypoglycaemia during the whole follow-up period, the AUC was 0.835 (95% CI 0.826, 0.844) in type 1 diabetes and 0.840 (95% CI 0.831, 0.848) in type 2 diabetes. For level 3 hypoglycaemia, the AUC was lower at 0.689 (95% CI 0.667, 0.712) for type 1 diabetes and 0.705 (95% CI 0.662, 0.748) for type 2 diabetes. Compared with the baseline models, almost all the improvement in prediction could be captured by the individual's hypoglycaemia history, glucose variability and blood glucose over a 6 week baseline period.CONCLUSIONS/INTERPRETATION: Although hypoglycaemia rates show large variation according to sociodemographic and clinical characteristics and treatment history, looking at a 6 week period of hypoglycaemia events and glucose measurements predicts future hypoglycaemia risk.</p
Risk factors and prediction of hypoglycaemia using the Hypo-RESOLVE cohort:a secondary analysis of pooled data from insulin clinical trials
AIMS/HYPOTHESIS: The objective of the Hypoglycaemia REdefining SOLutions for better liVES (Hypo-RESOLVE) project is to use a dataset of pooled clinical trials across pharmaceutical and device companies in people with type 1 or type 2 diabetes to examine factors associated with incident hypoglycaemia events and to quantify the prediction of these events.METHODS: Data from 90 trials with 46,254 participants were pooled. Analyses were done for type 1 and type 2 diabetes separately. Poisson mixed models, adjusted for age, sex, diabetes duration and trial identifier were fitted to assess the association of clinical variables with hypoglycaemia event counts. Tree-based gradient-boosting algorithms (XGBoost) were fitted using training data and their predictive performance in terms of area under the receiver operating characteristic curve (AUC) evaluated on test data. Baseline models including age, sex and diabetes duration were compared with models that further included a score of hypoglycaemia in the first 6 weeks from study entry, and full models that included further clinical variables. The relative predictive importance of each covariate was assessed using XGBoost's importance procedure. Prediction across the entire trial duration for each trial (mean of 34.8 weeks for type 1 diabetes and 25.3 weeks for type 2 diabetes) was assessed.RESULTS: For both type 1 and type 2 diabetes, variables associated with more frequent hypoglycaemia included female sex, white ethnicity, longer diabetes duration, treatment with human as opposed to analogue-only insulin, higher glucose variability, higher score for hypoglycaemia across the 6 week baseline period, lower BP, lower lipid levels and treatment with psychoactive drugs. Prediction of any hypoglycaemia event of any severity was greater than prediction of hypoglycaemia requiring assistance (level 3 hypoglycaemia), for which events were sparser. For prediction of level 1 or worse hypoglycaemia during the whole follow-up period, the AUC was 0.835 (95% CI 0.826, 0.844) in type 1 diabetes and 0.840 (95% CI 0.831, 0.848) in type 2 diabetes. For level 3 hypoglycaemia, the AUC was lower at 0.689 (95% CI 0.667, 0.712) for type 1 diabetes and 0.705 (95% CI 0.662, 0.748) for type 2 diabetes. Compared with the baseline models, almost all the improvement in prediction could be captured by the individual's hypoglycaemia history, glucose variability and blood glucose over a 6 week baseline period.CONCLUSIONS/INTERPRETATION: Although hypoglycaemia rates show large variation according to sociodemographic and clinical characteristics and treatment history, looking at a 6 week period of hypoglycaemia events and glucose measurements predicts future hypoglycaemia risk.</p
Update March 2022:management of hospitalised adults with coronavirus disease-19 (COVID-19): a european respiratory society living guideline
The ERS COVID-19 guidelines make recommendations for corticosteroids, anti-IL-6 monoclonal antibodies, baricitinib, anticoagulation and non-invasive respiratory support for hospitalised patients with COVID-19 https://bit.ly/3QgHH7
Gene content evolution in the arthropods
Arthropods comprise the largest and most diverse phylum on Earth and play vital roles in nearly every ecosystem. Their diversity stems in part from variations on a conserved body plan, resulting from and recorded in adaptive changes in the genome. Dissection of the genomic record of sequence change enables broad questions regarding genome evolution to be addressed, even across hyper-diverse taxa within arthropods. Using 76 whole genome sequences representing 21 orders spanning more than 500 million years of arthropod evolution, we document changes in gene and protein domain content and provide temporal and phylogenetic context for interpreting these innovations. We identify many novel gene families that arose early in the evolution of arthropods and during the diversification of insects into modern orders. We reveal unexpected variation in patterns of DNA methylation across arthropods and examples of gene family and protein domain evolution coincident with the appearance of notable phenotypic and physiological adaptations such as flight, metamorphosis, sociality, and chemoperception. These analyses demonstrate how large-scale comparative genomics can provide broad new insights into the genotype to phenotype map and generate testable hypotheses about the evolution of animal diversity
A large scale hearing loss screen reveals an extensive unexplored genetic landscape for auditory dysfunction
The developmental and physiological complexity of the auditory system is likely reflected in the underlying set of genes involved in auditory function. In humans, over 150 non-syndromic loci have been identified, and there are more than 400 human genetic syndromes with a hearing loss component. Over 100 non-syndromic hearing loss genes have been identified in mouse and human, but we remain ignorant of the full extent of the genetic landscape involved in auditory dysfunction. As part of the International Mouse Phenotyping Consortium, we undertook a hearing loss screen in a cohort of 3006 mouse knockout strains. In total, we identify 67 candidate hearing loss genes. We detect known hearing loss genes, but the vast majority, 52, of the candidate genes were novel. Our analysis reveals a large and unexplored genetic landscape involved with auditory function
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