Cosmic Evolution of Black Holes and Spheroids. IV. The BH Mass - Spheroid Luminosity Relation

From high-resolution images of 23 Seyfert-1 galaxies at z=0.36 and z=0.57 obtained with the Near Infrared Camera and Multi-Object Spectrometer on board the Hubble Space Telescope (HST), we determine host-galaxy morphology, nuclear luminosity, total host-galaxy luminosity and spheroid luminosity. Keck spectroscopy is used to estimate black hole mass (M_BH). We study the cosmic evolution of the M_BH-spheroid luminosity (L_sph) relation. In combination with our previous work, totaling 40 Seyfert-1 galaxies, the covered range in BH mass is substantially increased, allowing us to determine for the first time intrinsic scatter and correct evolutionary trends for selection effects. We re-analyze archival HST images of 19 local reverberation-mapped active galaxies to match the procedure adopted at intermediate redshift. Correcting spheroid luminosity for passive luminosity evolution and taking into account selection effects, we determine that at fixed present-day V-band spheroid luminosity, M_BH/L_sph \propto (1+z)^(2.8+/-1.2). When including a sample of 44 quasars out to z=4.5 taken from the literature, with luminosity and BH mass corrected to a self-consistent calibration, we extend the BH mass range to over two orders of magnitude, resulting in M_BH/L_sph \propto (1+z)^(1.4+/-0.2). The intrinsic scatter of the relation, assumed constant with redshift, is 0.3+/-0.1 dex (<0.6 dex at 95% CL). The evolutionary trend suggests that BH growth precedes spheroid assembly. Interestingly, the M_BH-total host-galaxy luminosity relation is apparently non-evolving. It hints at either a more fundamental relation or that the spheroid grows by a redistribution of stars. However, the high-z sample does not follow this relation, indicating that major mergers may play the dominant role in growing spheroids above z~1.Comment: 39 pages, 11 figures. Accepted for publication in the Astrophysical Journa

Haematologica

Not available

Vaccine breakthrough hypoxemic COVID-19 pneumonia in patients with auto-Abs neutralizing type I IFNs

Life-threatening `breakthrough' cases of critical COVID-19 are attributed to poor or waning antibody response to the SARS- CoV-2 vaccine in individuals already at risk. Pre-existing autoantibodies (auto-Abs) neutralizing type I IFNs underlie at least 15% of critical COVID-19 pneumonia cases in unvaccinated individuals; however, their contribution to hypoxemic breakthrough cases in vaccinated people remains unknown. Here, we studied a cohort of 48 individuals ( age 20-86 years) who received 2 doses of an mRNA vaccine and developed a breakthrough infection with hypoxemic COVID-19 pneumonia 2 weeks to 4 months later. Antibody levels to the vaccine, neutralization of the virus, and auto- Abs to type I IFNs were measured in the plasma. Forty-two individuals had no known deficiency of B cell immunity and a normal antibody response to the vaccine. Among them, ten (24%) had auto-Abs neutralizing type I IFNs (aged 43-86 years). Eight of these ten patients had auto-Abs neutralizing both IFN-a2 and IFN-., while two neutralized IFN-omega only. No patient neutralized IFN-ss. Seven neutralized 10 ng/mL of type I IFNs, and three 100 pg/mL only. Seven patients neutralized SARS-CoV-2 D614G and the Delta variant (B.1.617.2) efficiently, while one patient neutralized Delta slightly less efficiently. Two of the three patients neutralizing only 100 pg/mL of type I IFNs neutralized both D61G and Delta less efficiently. Despite two mRNA vaccine inoculations and the presence of circulating antibodies capable of neutralizing SARS-CoV-2, auto-Abs neutralizing type I IFNs may underlie a significant proportion of hypoxemic COVID-19 pneumonia cases, highlighting the importance of this particularly vulnerable population

COVID-19 symptoms at hospital admission vary with age and sex: results from the ISARIC prospective multinational observational study

Background: The ISARIC prospective multinational observational study is the largest cohort of hospitalized patients with COVID-19. We present relationships of age, sex, and nationality to presenting symptoms. Methods: International, prospective observational study of 60 109 hospitalized symptomatic patients with laboratory-confirmed COVID-19 recruited from 43 countries between 30 January and 3 August 2020. Logistic regression was performed to evaluate relationships of age and sex to published COVID-19 case definitions and the most commonly reported symptoms. Results: ‘Typical’ symptoms of fever (69%), cough (68%) and shortness of breath (66%) were the most commonly reported. 92% of patients experienced at least one of these. Prevalence of typical symptoms was greatest in 30- to 60-year-olds (respectively 80, 79, 69%; at least one 95%). They were reported less frequently in children (≤ 18 years: 69, 48, 23; 85%), older adults (≥ 70 years: 61, 62, 65; 90%), and women (66, 66, 64; 90%; vs. men 71, 70, 67; 93%, each P &lt; 0.001). The most common atypical presentations under 60 years of age were nausea and vomiting and abdominal pain, and over 60 years was confusion. Regression models showed significant differences in symptoms with sex, age and country. Interpretation: This international collaboration has allowed us to report reliable symptom data from the largest cohort of patients admitted to hospital with COVID-19. Adults over 60 and children admitted to hospital with COVID-19 are less likely to present with typical symptoms. Nausea and vomiting are common atypical presentations under 30 years. Confusion is a frequent atypical presentation of COVID-19 in adults over 60 years. Women are less likely to experience typical symptoms than men

Principales maladies des espèces piscicoles élevées en Méditerranée (étude de la dermatose chronique ulcérative chez le sar à museau pointu, le pagre et le pageot rose)

Une revue des principales maladies infectieuses, parasitaires, nutritionnelles et environnementales des espèces marines élevées en méditerranées a été réalisée. Il ressort que la vibriose, la pasteurellose, l'encéphalopathie et rétinopathie virale, l'entéromyxose à Myxidium leei, l'isopodose à Ceratothoa oestroides et les anomalies de développement ont des conséquences économiques catastrophiques. D'autres maladies moins fréquentes ou émergentes ont une incidence moins important ou qui n'est pas connue.BANYULS/MER-Observ.Océanol. (660162201) / SudocNANTES-Ecole Nat.Vétérinaire (441092302) / SudocSudocFranceF

Améliorer la parcellisation fonctionnelle individuelle via l'apprentissage par transfert

International audienceResting-state functional magnetic resonance imaging enables the exploration of the functional brain organization and its representation via large-scale networks. Summary measures, such as functional connectivity profiles or networks' spatial topography, present inter-individual differences with applications in detecting cognitive disorders or predicting behavioral traits. However, the accuracy of these measures can be significantly disrupted due to the limited number of subjects in clinical data.Transfer learning coupled with Bayesian modeling can be leveraged to overcome this issue. We simultaneously estimate the posteriors for the individual cortical topography and functional connectivity profiles by applying a variational inference technique over probabilistic graphical models. After extracting these features, inference performance is evaluated through the regression of behavioral scores. The Bayesian formalism allows information transfer by sharing parameters pre-trained on a large dataset, especially the posteriors inferred at a population level. This way, on the HCP dataset, we show that the knowledge captured on a large sub-population ($\sim$750 subjects and 4 scans per subject) helps improve the model trained on a much smaller sample (50 to 250 subjects with 1 scan per subject), even if this sample stems from another dataset, here CamCAN. By doing it with 50 subjects, we achieve comparable performance in behavioral prediction than a three times larger dataset

PAVI: Plate-Amortized Variational Inference

International audienceGiven observed data and a probabilistic generative model, Bayesian inference searches for the distribution of the model's parameters that could have yielded the data. Inference is challenging for large population studies where millions of measurements are performed over a cohort of hundreds of subjects, resulting in a massive parameter space. This large cardinality renders off-the-shelf Variational Inference (VI) computationally impractical.In this work, we design structured VI families that efficiently tackle large population studies. Our main idea is to share the parameterization and learning across the different i.i.d. variables in a generative model -symbolized by the model's .We name this concept . Contrary to off-the-shelf stochastic VI --which slows down inference-- plate amortization results in orders of magnitude faster to train variational distributions. Applied to large-scale hierarchical problems, PAVI yields expressive, parsimoniously parameterized VI with an affordable training time --effectively unlocking inference in those regimes.We illustrate the practical utility of PAVI through a challenging Neuroimaging example featuring 400 million latent parameters, demonstrating a significant step towards scalable and expressive Variational Inference

PAVI: Plate-Amortized Variational Inference

International audienceGiven observed data and a probabilistic generative model, Bayesian inference searches for the distribution of the model's parameters that could have yielded the data. Inference is challenging for large population studies where millions of measurements are performed over a cohort of hundreds of subjects, resulting in a massive parameter space. This large cardinality renders off-the-shelf Variational Inference (VI) computationally impractical.In this work, we design structured VI families that efficiently tackle large population studies. Our main idea is to share the parameterization and learning across the different i.i.d. variables in a generative model -symbolized by the model's .We name this concept . Contrary to off-the-shelf stochastic VI --which slows down inference-- plate amortization results in orders of magnitude faster to train variational distributions. Applied to large-scale hierarchical problems, PAVI yields expressive, parsimoniously parameterized VI with an affordable training time --effectively unlocking inference in those regimes.We illustrate the practical utility of PAVI through a challenging Neuroimaging example featuring 400 million latent parameters, demonstrating a significant step towards scalable and expressive Variational Inference

Forest stand segmentation using airborne Lidar data and very high resolution multispectral imagery

International audienc