Scaling laws for the photo-ionisation cross section of two-electron atoms

The cross sections for single-electron photo-ionisation in two-electron atoms show fluctuations which decrease in amplitude when approaching the double-ionisation threshold. Based on semiclassical closed orbit theory, we show that the algebraic decay of the fluctuations can be characterised in terms of a threshold law $\sigma \propto |E|^{\mu}$ as $E \to 0_-$ with exponent $\mu$ obtained as a combination of stability exponents of the triple-collision singularity. It differs from Wannier's exponent dominating double ionisation processes. The details of the fluctuations are linked to a set of infinitely unstable classical orbits starting and ending in the non-regularisable triple collision. The findings are compared with quantum calculations for a model system, namely collinear helium.Comment: 4 pages, 1 figur

Contrast Adaptive Tissue Classification by Alternating Segmentation and Synthesis

Deep learning approaches to the segmentation of magnetic resonance images have shown significant promise in automating the quantitative analysis of brain images. However, a continuing challenge has been its sensitivity to the variability of acquisition protocols. Attempting to segment images that have different contrast properties from those within the training data generally leads to significantly reduced performance. Furthermore, heterogeneous data sets cannot be easily evaluated because the quantitative variation due to acquisition differences often dwarfs the variation due to the biological differences that one seeks to measure. In this work, we describe an approach using alternating segmentation and synthesis steps that adapts the contrast properties of the training data to the input image. This allows input images that do not resemble the training data to be more consistently segmented. A notable advantage of this approach is that only a single example of the acquisition protocol is required to adapt to its contrast properties. We demonstrate the efficacy of our approaching using brain images from a set of human subjects scanned with two different T1-weighted volumetric protocols.Comment: 10 pages. MICCAI SASHIMI Workshop 202

Pandemic Boredom: Little Evidence That Lockdown-Related Boredom Affects Risky Public Health Behaviors Across 116 Countries

Some public officials have expressed concern that policies mandating collective public health behaviors (e.g., national/regional "lockdown ") may result in behavioral fatigue that ultimately renders such policies ineffective. Boredom, specifically, has been singled out as one potential risk factor for noncompliance. We examined whether there was empirical evidence to support this concern during the COVID-19 pandemic in a large cross-national sample of 63,336 community respondents from 116 countries. Although boredom was higher in countries with more COVID-19 cases and in countries that instituted more stringent lockdowns, such boredom did not predict longitudinal within-person decreases in social distancing behavior (or vice versa; n = 8,031) in early spring and summer of 2020. Overall, we found little evidence that changes in boredom predict individual public health behaviors (handwashing, staying home, self-quarantining, and avoiding crowds) over time, or that such behaviors had any reliable longitudinal effects on boredom itself. In summary, contrary to concerns, we found little evidence that boredom posed a public health risk during lockdown and quarantine

Advancing brain barriers RNA sequencing: guidelines from experimental design to publication

Background: RNA sequencing (RNA-Seq) in its varied forms has become an indispensable tool for analyzing differential gene expression and thus characterization of specific tissues. Aiming to understand the brain barriers genetic signature, RNA seq has also been introduced in brain barriers research. This has led to availability of both, bulk and single-cell RNA-Seq datasets over the last few years. If appropriately performed, the RNA-Seq studies provide powerful datasets that allow for significant deepening of knowledge on the molecular mechanisms that establish the brain barriers. However, RNA-Seq studies comprise complex workflows that require to consider many options and variables before, during and after the proper sequencing process.Main body: In the current manuscript, we build on the interdisciplinary experience of the European PhD Training Network BtRAIN (https://www.btrain-2020.eu/) where bioinformaticians and brain barriers researchers collaborated to analyze and establish RNA-Seq datasets on vertebrate brain barriers. The obstacles BtRAIN has identified in this process have been integrated into the present manuscript. It provides guidelines along the entire workflow of brain barriers RNA-Seq studies starting from the overall experimental design to interpretation of results. Focusing on the vertebrate endothelial blood–brain barrier (BBB) and epithelial blood-cerebrospinal-fluid barrier (BCSFB) of the choroid plexus, we provide a step-by-step description of the workflow, highlighting the decisions to be made at each step of the workflow and explaining the strengths and weaknesses of individual choices made. Finally, we propose recommendations for accurate data interpretation and on the information to be included into a publication to ensure appropriate accessibility of the data and reproducibility of the observations by the scientific community.Conclusion: Next generation transcriptomic profiling of the brain barriers provides a novel resource for understanding the development, function and pathology of these barrier cells, which is essential for understanding CNS homeostasis and disease. Continuous advancement and sophistication of RNA-Seq will require interdisciplinary approaches between brain barrier researchers and bioinformaticians as successfully performed in BtRAIN. The present guidelines are built on the BtRAIN interdisciplinary experience and aim to facilitate collaboration of brain barriers researchers with bioinformaticians to advance RNA-Seq study design in the brain barriers community

Incorporating radiomics into clinical trials: expert consensus on considerations for data-driven compared to biologically-driven quantitative biomarkers

Existing Quantitative Imaging Biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials

Antifungal Activity of Fused Mannich Ketones Triggers an Oxidative Stress Response and Is Cap1-Dependent in Candida albicans

International audienceWe investigated the antifungal activity of fused Mannich ketone (FMK) congeners and two of their aminoalcohol derivatives. In particular, FMKs with five-membered saturated rings were shown to have minimum inhibitory concentration (MIC90s) ranging from 0.8 to 6 µg/mL toward C. albicans and the closely related C. parapsilosis and C. krusei while having reduced efficacy toward C. glabrata and almost no efficacy against Aspergillus sp. Transcript profiling of C. albicans cells exposed for 30 or 60 min to 2-(morpholinomethyl)-1-indanone, a representative FMK with a five-membered saturated ring, revealed a transcriptional response typical of oxidative stress and similar to that of a C. albicans Cap1 transcriptional activator. Consistently, C. albicans lacking the CAP1 gene was hypersensitive to this FMK, while C. albicans strains overexpressing CAP1 had decreased sensitivity to 2-(morpholinomethyl)-1-indanone. Quantitative structure-activity relationship studies revealed a correlation of antifungal potency and the energy of the lowest unoccupied molecular orbital of FMKs and unsaturated Mannich ketones thereby implicating redox cycling-mediated oxidative stress as a mechanism of action. This conclusion was further supported by the loss of antifungal activity upon conversion of representative FMKs to aminoalcohols that were unable to participate in redox cycles