The i-process and CEMP-r/s stars

© Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike Licence. We investigate whether the anomalous elemental abundance patterns in some of the C-enhanced metal-poor-r/s (CEMP-r/s) stars are consistent with predictions of nucleosynthesis yields from the i-process, a neutron-capture regime at neutron densities intermediate between those typical for the slow (s) and rapid (r) processes. Conditions necessary for the i-process are expected to be met at multiple stellar sites, such as the He-core and He-shell flashes in low-metallicity low-mass stars, super-AGB and post-AGB stars, as well as low-metallicity massive stars. We have found that single-exposure one-zone simulations of the i-process reproduce the abundance patterns in some of the CEMP-r/s stars much better than the model that assumes a superposition of yields from s and r-process sources. Our previous study of nuclear data uncertainties relevant to the i-process revealed that they could have a significant impact on the i-process yields obtained in our idealized one-zone calculations, leading, for example, to ∼ 0:7dex uncertainty in our predicted [Ba/La] ratio. Recent 3D hydrodynamic simulations of convection driven by a He-shell flash in post-AGB Sakurai's object have discovered a new mode of non-radial instabilities: the Global Oscillation of Shell H-ingestion. This has demonstrated that spherically symmetric stellar evolution simulations cannot be used to accurately model physical conditions for the i-process

The i-process and CEMP-r/s stars

© Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike Licence. We investigate whether the anomalous elemental abundance patterns in some of the C-enhanced metal-poor-r/s (CEMP-r/s) stars are consistent with predictions of nucleosynthesis yields from the i-process, a neutron-capture regime at neutron densities intermediate between those typical for the slow (s) and rapid (r) processes. Conditions necessary for the i-process are expected to be met at multiple stellar sites, such as the He-core and He-shell flashes in low-metallicity low-mass stars, super-AGB and post-AGB stars, as well as low-metallicity massive stars. We have found that single-exposure one-zone simulations of the i-process reproduce the abundance patterns in some of the CEMP-r/s stars much better than the model that assumes a superposition of yields from s and r-process sources. Our previous study of nuclear data uncertainties relevant to the i-process revealed that they could have a significant impact on the i-process yields obtained in our idealized one-zone calculations, leading, for example, to ∼ 0:7dex uncertainty in our predicted [Ba/La] ratio. Recent 3D hydrodynamic simulations of convection driven by a He-shell flash in post-AGB Sakurai's object have discovered a new mode of non-radial instabilities: the Global Oscillation of Shell H-ingestion. This has demonstrated that spherically symmetric stellar evolution simulations cannot be used to accurately model physical conditions for the i-process

Prediction of mechanistic subtypes of Parkinson’s using patient-derived stem cell models

Parkinson’s disease is a common, incurable neurodegenerative disorder that is clinically heterogeneous: it is likely that different cellular mechanisms drive the pathology in different individuals. So far it has not been possible to define the cellular mechanism underlying the neurodegenerative disease in life. We generated a machine learning-based model that can simultaneously predict the presence of disease and its primary mechanistic subtype in human neurons. We used stem cell technology to derive control or patient-derived neurons, and generated different disease subtypes through chemical induction or the presence of mutation. Multidimensional fluorescent labelling of organelles was performed in healthy control neurons and in four different disease subtypes, and both the quantitative single-cell fluorescence features and the images were used to independently train a series of classifiers to build deep neural networks. Quantitative cellular profile-based classifiers achieve an accuracy of 82%, whereas image-based deep neural networks predict control and four distinct disease subtypes with an accuracy of 95%. The machine learning-trained classifiers achieve their accuracy across all subtypes, using the organellar features of the mitochondria with the additional contribution of the lysosomes, confirming the biological importance of these pathways in Parkinson’s. Altogether, we show that machine learning approaches applied to patient-derived cells are highly accurate at predicting disease subtypes, providing proof of concept that this approach may enable mechanistic stratification and precision medicine approaches in the future

A coarsened multinomial regression model for perinatal mother to child transmission of HIV

Background: In trials designed to estimate rates of perinatal mother to child transmission of HIV, HIV assays are scheduled at multiple points in time. Still, infection status for some infants at some time points may be unknown, particularly when interim analyses are conducted. Methods: Logistic regression models are commonly used to estimate covariate-adjusted transmission rates, but their methods for handling missing data may be inadequate. Here we propose using coarsened multinomial regression models to estimate cumulative and conditional rates of HIV transmission. Through simulation, we compare the proposed models to standard logistic models in terms of bias, mean squared error, coverage probability, and power. We consider a range of treatment effect and visit process scenarios, while including imperfect sensitivity of the assay and contamination of the endpoint due to early breastfeeding transmission. We illustrate the approach through analysis of data from a clinical trial designed to prevent perinatal transmission. Results: The proposed cumulative and conditional models performed well when compared to their logistic counterparts. Performance of the proposed cumulative model was particularly strong under scenarios where treatment was assumed to increase the risk of in utero transmission but decrease the risk of intrapartum and overall perinatal transmission and under scenarios designed to represent interim analyses. Power to estimate intrapartum and perinatal transmission was consistently higher for the proposed models. Conclusion: Coarsened multinomial regression models are preferred to standard logistic models for estimation of perinatal mother to child transmission of HIV, particularly when assays are missing or occur off-schedule for some infants.U.S. National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), and Dept. of Health and Human Services (DHHS)

Toward ab initio density functional theory for nuclei

We survey approaches to nonrelativistic density functional theory (DFT) for nuclei using progress toward ab initio DFT for Coulomb systems as a guide. Ab initio DFT starts with a microscopic Hamiltonian and is naturally formulated using orbital-based functionals, which generalize the conventional local-density-plus-gradients form. The orbitals satisfy single-particle equations with multiplicative (local) potentials. The DFT functionals can be developed starting from internucleon forces using wave-function based methods or by Legendre transform via effective actions. We describe known and unresolved issues for applying these formulations to the nuclear many-body problem and discuss how ab initio approaches can help improve empirical energy density functionals.Comment: 69 pages, 16 figures, many revisions based on feedback. To appear in Progress in Particle and Nuclear Physic

Notulae to the Italian native vascular flora: 3.

In this contribution new data concerning the distribution of native vascular flora in Italy are presented. It includes new records, exclusions, and confirmations to the Italian administrative regions for taxa in the genera Asplenium, Bolboschoenus, Botrychium, Chamaerops, Crocus, Galeopsis, Grafia, Helosciadium, Hieracium, Juniperus, Leucanthemum, Lolium, Medicago, Phalaris, Piptatherum, Potamogeton, Salicornia, Salvia, Seseli, Silene, Spiraea, Torilis and Vicia. Rhaponticoides calabrica is proposed as synonym novum of R. centaurium. Furthermore, new combinations in the genera Galatella and Lactuca are proposed

Notulae to the Italian alien vascular flora: 1

In this contribution, new data concerning the Italian distribution of alien vascular flora are presented. It includes new records, exclusions, and confirmations for Italy or for Italian administrative regions for taxa in the genera Agave, Arctotheca, Berberis, Bidens, Cardamine, Catalpa, Cordyline, Cotoneaster, Dichondra, Elaeagnus, Eragrostis, Impatiens, Iris, Koelreuteria, Lamiastrum, Lantana, Ligustrum, Limnophila, Lonicera, Lycianthes, Maclura, Mazus, Paspalum, Pelargonium, Phyllanthus, Pyracantha, Ruellia, Sorghum, Symphyotrichum, Triticum, Tulbaghia and Youngia

Using imaging to combat a pandemic:rationale for developing the UK National COVID-19 Chest Imaging Database

No abstract available

Towards nationally curated data archives for clinical radiology image analysis at scale: Learnings from national data collection in response to a pandemic

The prevalence of the coronavirus SARS-CoV-2 disease has resulted in the unprecedented collection of health data to support research. Historically, coordinating the collation of such datasets on a national scale has been challenging to execute for several reasons, including issues with data privacy, the lack of data reporting standards, interoperable technologies, and distribution methods. The coronavirus SARS-CoV-2 disease pandemic has highlighted the importance of collaboration between government bodies, healthcare institutions, academic researchers and commercial companies in overcoming these issues during times of urgency. The National COVID-19 Chest Imaging Database, led by NHSX, British Society of Thoracic Imaging, Royal Surrey NHS Foundation Trust and Faculty, is an example of such a national initiative. Here, we summarise the experiences and challenges of setting up the National COVID-19 Chest Imaging Database, and the implications for future ambitions of national data curation in medical imaging to advance the safe adoption of artificial intelligence in healthcare