Deep learning denoising by dimension reduction: Application to the ORION-B line cubes

Context. The availability of large bandwidth receivers for millimeter radio telescopes allows the acquisition of position-position-frequency data cubes over a wide field of view and a broad frequency coverage. These cubes contain much information on the physical, chemical, and kinematical properties of the emitting gas. However, their large size coupled with inhomogenous signal-to-noise ratio (SNR) are major challenges for consistent analysis and interpretation.Aims. We search for a denoising method of the low SNR regions of the studied data cubes that would allow to recover the low SNR emission without distorting the signals with high SNR.Methods. We perform an in-depth data analysis of the 13 CO and C 17 O (1 -- 0) data cubes obtained as part of the ORION-B large program performed at the IRAM 30m telescope. We analyse the statistical properties of the noise and the evolution of the correlation of the signal in a given frequency channel with that of the adjacent channels. This allows us to propose significant improvements of typical autoassociative neural networks, often used to denoise hyperspectral Earth remote sensing data. Applying this method to the 13 CO (1 -- 0) cube, we compare the denoised data with those derived with the multiple Gaussian fitting algorithm ROHSA, considered as the state of the art procedure for data line cubes.Results. The nature of astronomical spectral data cubes is distinct from that of the hyperspectral data usually studied in the Earth remote sensing literature because the observed intensities become statistically independent beyond a short channel separation. This lack of redundancy in data has led us to adapt the method, notably by taking into account the sparsity of the signal along the spectral axis. The application of the proposed algorithm leads to an increase of the SNR in voxels with weak signal, while preserving the spectral shape of the data in high SNR voxels.Conclusions. The proposed algorithm that combines a detailed analysis of the noise statistics with an innovative autoencoder architecture is a promising path to denoise radio-astronomy line data cubes. In the future, exploring whether a better use of the spatial correlations of the noise may further improve the denoising performances seems a promising avenue. In addition

Cerebral venous sinus thrombosis due to vaccine-induced immune thrombotic thrombocytopenia in middle-income countries

Background: Adenovirus-based COVID-19 vaccines are extensively used in low- and middle-income countries (LMICs). Remarkably, cases of cerebral venous sinus thrombosis due to vaccine-induced immune thrombotic thrombocytopenia (CVST-VITT) have rarely been reported from LMICs. Aims: We studied the frequency, manifestations, treatment, and outcomes of CVST-VITT in LMICs. Methods: We report data from an international registry on CVST after COVID-19 vaccination. VITT was classified according to the Pavord criteria. We compared CVST-VITT cases from LMICs to cases from high-income countries (HICs). Results: Until August 2022, 228 CVST cases were reported, of which 63 were from LMICs (all middle-income countries [MICs]: Brazil, China, India, Iran, Mexico, Pakistan, Turkey). Of these 63, 32 (51%) met the VITT criteria, compared to 103 of 165 (62%) from HICs. Only 5 of the 32 (16%) CVST-VITT cases from MICs had definite VITT, mostly because anti-platelet factor 4 antibodies were often not tested. The median age was 26 (interquartile range [IQR] 20–37) versus 47 (IQR 32–58) years, and the proportion of women was 25 of 32 (78%) versus 77 of 103 (75%) in MICs versus HICs, respectively. Patients from MICs were diagnosed later than patients from HICs (1/32 [3%] vs. 65/103 [63%] diagnosed before May 2021). Clinical manifestations, including intracranial hemorrhage, were largely similar as was intravenous immunoglobulin use. In-hospital mortality was lower in MICs (7/31 [23%, 95% confidence interval (CI) 11–40]) than in HICs (44/102 [43%, 95% CI 34–53], p = 0.039). Conclusions: The number of CVST-VITT cases reported from LMICs was small despite the widespread use of adenoviral vaccines. Clinical manifestations and treatment of CVST-VITT cases were largely similar in MICs and HICs, while mortality was lower in patients from MICs.</p

Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study

Background: The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on postoperative recovery needs to be understood to inform clinical decision making during and after the COVID-19 pandemic. This study reports 30-day mortality and pulmonary complication rates in patients with perioperative SARS-CoV-2 infection. Methods: This international, multicentre, cohort study at 235 hospitals in 24 countries included all patients undergoing surgery who had SARS-CoV-2 infection confirmed within 7 days before or 30 days after surgery. The primary outcome measure was 30-day postoperative mortality and was assessed in all enrolled patients. The main secondary outcome measure was pulmonary complications, defined as pneumonia, acute respiratory distress syndrome, or unexpected postoperative ventilation. Findings: This analysis includes 1128 patients who had surgery between Jan 1 and March 31, 2020, of whom 835 (74·0%) had emergency surgery and 280 (24·8%) had elective surgery. SARS-CoV-2 infection was confirmed preoperatively in 294 (26·1%) patients. 30-day mortality was 23·8% (268 of 1128). Pulmonary complications occurred in 577 (51·2%) of 1128 patients; 30-day mortality in these patients was 38·0% (219 of 577), accounting for 81·7% (219 of 268) of all deaths. In adjusted analyses, 30-day mortality was associated with male sex (odds ratio 1·75 [95% CI 1·28–2·40], p\textless0·0001), age 70 years or older versus younger than 70 years (2·30 [1·65–3·22], p\textless0·0001), American Society of Anesthesiologists grades 3–5 versus grades 1–2 (2·35 [1·57–3·53], p\textless0·0001), malignant versus benign or obstetric diagnosis (1·55 [1·01–2·39], p=0·046), emergency versus elective surgery (1·67 [1·06–2·63], p=0·026), and major versus minor surgery (1·52 [1·01–2·31], p=0·047). Interpretation: Postoperative pulmonary complications occur in half of patients with perioperative SARS-CoV-2 infection and are associated with high mortality. Thresholds for surgery during the COVID-19 pandemic should be higher than during normal practice, particularly in men aged 70 years and older. Consideration should be given for postponing non-urgent procedures and promoting non-operative treatment to delay or avoid the need for surgery. Funding: National Institute for Health Research (NIHR), Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, NIHR Academy, Sarcoma UK, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research

Mixture of noises and sampling of non-log-concave posterior distributions

International audienceThis work considers a challenging radio-astronomyinverse problem of physical parameter inference from multispec-tral observations. The forward model underlying this problem isa computationally expensive numerical simulation. In addition,the observation model mixes different sources of noise yieldinga non-concave log-likelihood function. To overcome these issues,we introduce a likelihood approximation with controlled error.Given the absence of ground truth, parameter inference isconducted with a Markov chain Monte Carlo (MCMC) algorithmto provide credibility intervals along with point estimates. To thisaim, we propose a new sampler that addresses the numericalchallenges induced by the observation model, in particular thenon-log-concavity of the posterior distribution. The efficiency ofthe proposed method is demonstrated on synthetic yet realisticastrophysical data. We believe that the proposed approach isvery general and can be adapted to many similar difficult inverseproblem

Mixture of noises and sampling of non-log-concave posterior distributions

International audienceThis work considers a challenging radio-astronomyinverse problem of physical parameter inference from multispec-tral observations. The forward model underlying this problem isa computationally expensive numerical simulation. In addition,the observation model mixes different sources of noise yieldinga non-concave log-likelihood function. To overcome these issues,we introduce a likelihood approximation with controlled error.Given the absence of ground truth, parameter inference isconducted with a Markov chain Monte Carlo (MCMC) algorithmto provide credibility intervals along with point estimates. To thisaim, we propose a new sampler that addresses the numericalchallenges induced by the observation model, in particular thenon-log-concavity of the posterior distribution. The efficiency ofthe proposed method is demonstrated on synthetic yet realisticastrophysical data. We believe that the proposed approach isvery general and can be adapted to many similar difficult inverseproblem

Mixture of noises and sampling of non-log-concave posterior distributions

International audienceThis work considers a challenging radio-astronomyinverse problem of physical parameter inference from multispec-tral observations. The forward model underlying this problem isa computationally expensive numerical simulation. In addition,the observation model mixes different sources of noise yieldinga non-concave log-likelihood function. To overcome these issues,we introduce a likelihood approximation with controlled error.Given the absence of ground truth, parameter inference isconducted with a Markov chain Monte Carlo (MCMC) algorithmto provide credibility intervals along with point estimates. To thisaim, we propose a new sampler that addresses the numericalchallenges induced by the observation model, in particular thenon-log-concavity of the posterior distribution. The efficiency ofthe proposed method is demonstrated on synthetic yet realisticastrophysical data. We believe that the proposed approach isvery general and can be adapted to many similar difficult inverseproblem

Mélange de bruits et échantillonnage de posterior non log-concave

International audienceThis work considers a radio-astronomy inverse problem of physical parameters inference from multispectral images. The forward model is a numerical simulation, and the observation model mixes different sources of noise. This results in a non-explicit non-log-concave likelihood function. We introduce a likelihood approximation with controlled error that allow the conception of a Monte Carlo Markov Chain (MCMC) method. The obtained sampler provides credibility intervals along with point estimates. We believe that the proposed approach is sufficiently generic to be applied to similar inverse problems.Ce travail considère un problème inverse en astrophysique, qui consiste à estimer un ensemble de paramètres physiques à partir d'images multispectrales. Le modèle direct sous-jacent est une simulation numérique et le modèle d'observation mélange différentes sources de bruit. Ces caractéristiques donnent lieu à une fonction de log-vraisemblance non explicite et non concave. Nous en définissons une approximation qui permet de concevoir une méthode de Monte Carlo par chaîne de Markov (MCMC). L'échantillonneur obtenu fournit estimations ponctuelles et intervalles de crédibilité associés. L'approche proposée est suffisamment générale pour être appliquée à des problèmes inverses similaires

Mélange de bruits et échantillonnage de posterior non log-concave

International audienceThis work considers a radio-astronomy inverse problem of physical parameters inference from multispectral images. The forward model is a numerical simulation, and the observation model mixes different sources of noise. This results in a non-explicit non-log-concave likelihood function. We introduce a likelihood approximation with controlled error that allow the conception of a Monte Carlo Markov Chain (MCMC) method. The obtained sampler provides credibility intervals along with point estimates. We believe that the proposed approach is sufficiently generic to be applied to similar inverse problems.Ce travail considère un problème inverse en astrophysique, qui consiste à estimer un ensemble de paramètres physiques à partir d'images multispectrales. Le modèle direct sous-jacent est une simulation numérique et le modèle d'observation mélange différentes sources de bruit. Ces caractéristiques donnent lieu à une fonction de log-vraisemblance non explicite et non concave. Nous en définissons une approximation qui permet de concevoir une méthode de Monte Carlo par chaîne de Markov (MCMC). L'échantillonneur obtenu fournit estimations ponctuelles et intervalles de crédibilité associés. L'approche proposée est suffisamment générale pour être appliquée à des problèmes inverses similaires