Kinetic equations for Stark line shapes

The BBGKY formalism is revisited in the framework of plasma spectroscopy. We address the issue of Stark line shape modeling by using kinetic transport equations. In the most simplified treatment of these equations, triple correlations between an emitter and the perturbing charged particles are neglected and a collisional description of Stark effect is obtained. Here we relax this assumption and retain triple correlations using a generalization of the Kirkwood truncature hypothesis to quantum operator. An application to hydrogen lines is done in the context of plasma diagnostic, and indicates that the neglect of triple correlations can lead to a significant overestimate of the line width.Comment: 13 pages, 1 figur

Kerr effect as a tool for the investigation of dynamic heterogeneities

We propose a dynamic Kerr effect experiment for the distinction between dynamic heterogeneous and homogeneous relaxation in glassy systems. The possibility of this distinction is due to the inherent nonlinearity of the Kerr effect signal. We model the slow reorientational molecular motion in supercooled liquids in terms of non-inertial rotational diffusion. The Kerr effect response, consisting of two terms, is calculated for heterogeneous and for homogeneous variants of the stochastic model. It turns out that the experiment is able to distinguish between the two scenarios. We furthermore show that exchange between relatively 'slow' and 'fast' environments does not affect the possibility of frequency-selective modifications. It is demonstrated how information about changes in the width of the relaxation time distribution can be obtained from experimental results.Comment: 23 pages incl. 6 figures accepted for publication in The Journal of Chemical Physic

IMAGE: A New Tool for the Prediction of Transcription Factor Binding Sites

IMAGE is an application tool, based on the vector quantization method, aiding the discovery of nucleotidic sequences corresponding to Transcription Factor binding sites. Starting from the knowledge of regulation regions of a number of co-expressed genes, the software is able to predict the occurrence of specific motifs of different lengths (starting from 6 base pairs) with a defined number of punctual mutations

Prevalence and Risk Factors of Psychiatric Symptoms Among Older People in England During the COVID‑19 Pandemic: a Latent Class Analysis

The COVID-19 pandemic has affected mental health and social connections. Older people may be disproportionately affected, placing them at increased risk for complex mental ill-health outcomes and quality of life undermined by anxiety and depression. Understanding gender differences in the determinants of anxiety and depression symptoms is crucial to policy and practice. This study aims to examine gender-specific symptom subtypes (and subthreshold symptoms) in an older English population sampled during the COVID period, in relation to their socio-demographic, social, and health circumstances. The sample comprises all individuals aged 50 years or older and included in the English Longitudinal Study of Ageing COVID-19 sub-study conducted during June–July 2020. Latent class analysis (LCA) defined indicative sample subgroups of clinically relevant anxiety and depression. Multinomial logistic regression assessed associations between socio-demographic characteristics, health and social care indicators, loneliness, and pre-pandemic mental ill-health. LCA derived three classes of self-reported depression and anxiety: for females (1) comorbid depression and anxiety (19.9% of the sample), (2) depression and subthreshold anxiety (31.6%), and (3) no or low symptoms of depression and anxiety (48.5%), and for males (1) comorbid depression and anxiety (12.8%), (2) subthreshold anxiety and depression (29.6%), and (3) no or low depression and anxiety (57.6%). Multinomial logistic regression analyses indicate that compared to those with low/no mental health symptoms, severity of pandemic-era mental ill-health was positively associated with pre-pandemic mental health levels, worry over finances, having access to essentials, loneliness, and access to health and social care services. Findings support the persistence of comorbidity of both depression and anxiety in the pandemic period. Results may inform government health strategy on interventions to prevent social isolation and mitigate the effects of the pandemic on deteriorating mental health in older people who may be more susceptible