Contribution à l’étude de dose due à la radioactivité gamma du sol sur la rive de la baie des Français, Antsiranana, Madagascar

Le but de ce travail est d’étudier la radioactivité gamma des sols sur la rive de la baie des français et de déterminer l’impact dosimétrique sur la population locale dans les localités d’Ankorikihely, de betahitry et de Lazaret-Nord. Des échantillons du sol ont été prélevés et analysés pour identifier les activités spécifiques du potassium-40, des familles de l’Uranium-238 et du Thorium-232. Ces échantillons ont été analysés sur une chaine de spectrométrie gamma, muni d’un détecteur NaI(Tl) et du logiciel Scinti-vision. Le traitement des spectres a été réalisé en utilisant la méthode de Rybach. Après mesures, les résultats suivants ont été obtenus : les activités spécifiques du potassium-40 varient de 112 ± 34 Bq.kg-1 à 565 ± 50Bq.kg-1, avec une moyenne de 313 Bq.kg-1. Cette dernière est inferieure à la valeur moyenne mondiale, qui est 400 Bq.kg-1.Les activités spécifiques de la famille de l’uranium-238 varient de 87 ± 32 Bq.kg-1 à 190 ± 46Bq.kg-1, avec une moyenne de 139 Bq.kg-1. Cette valeur moyenne est quatre fois supérieure à la valeur moyenne mondiale, qui est de 35 Bq.kg-1. Les activités spécifiques de la famille du thorium-232 varient de 81 ± 20 Bq.kg-1 à 161 ± 23 Bq.kg-1, avec une moyenne de 126 Bq.kg-1. Cette valeur moyenne est quatre fois plus que la valeur moyenne mondiale, qui est de 30 Bq.kg-1. Les débits de dose absorbée à 1m au-dessus de la surface de sol varient entre 89 ± 14 nGy.h-1 et 209± 29 nGy.h-1 avec une moyenne de 153 nGy.h-1. Les doses efficaces annuelles sont comprises entre 0,14 ± 0,02 mSv.a-1 et 0,25 ± 0,03 mSv.a-1. Ces valeurs annuelles sont bien en-dessous de la valeur limite acceptable pour le public qui est de 1 mSv.a-1.Mots-clés: radioactivite, spectrométrie gamma, dose absorbée, dose efficace annuelle. Study of the dosimetric impact due to the gamma radioactivity of the soil in the border of the “baie des français”, Antsiranana, MadagascarThe aim of this work is to study gamma radioactivity of the soil in the border of the “baie des français” and then to determine the dosimetric impact to the local population of Ankorikihely, betahitry and Lazaret-Nord. Samples have been collected to identify the specific activities of the potassium-40, Uranium-238 series and Thorium-232.series. These samples have been analyzed by gamma spectrometry system, with NaI(Tl) detector using ScintiVision software. The spectrum processing has been done by using the method developed by Rybach. The results show that:The potassium-40 activity ranges are from 112 ± 34 Bq.kg-1 to 565 ± 50 Bq.kg-1, with an average of 313Bq.kg-1. This latter is below the work average value of 400 Bq.kg-1. The uranium-238 activity varies between 87 ± 32 Bq.kg-1 and 190 ± 46Bq.kg-1, with an average of 139 Bq.kg-1. This average value is 4 times higher than the world average value of 35 Bq.kg-1. The thorium-232 activity varies from 81 ± 20 Bq.kg-1 to (161 ± 23) Bq.kg-1, with an average of 126 Bq.kg-1. This average value is 4 times higher than the world average value of 30 Bq.kg-1. The absorbed dose rate at 1m above soil surface varies between 89 ± 14nGy.h-1and 209± 29 nGy.h-1, with an average of 153nGy.h-1. The annual effective dose ranges from 0.14 ± 0.02mSv.y-1to 0.25 ± 0.03mSv.y-1. The estimated annual effective doses are below 1mSv.y-1, the limit value for the public.Keywords: radioactivity, gamma spectrometry, absorbed dose rate, annual effective dose

Green tea extract only affects markers of oxidative status postprandially: lasting antioxidant effect of flavonoid-free diet

Epidemiological studies suggest that foods rich in flavonoids might reduce the risk of cardiovascular disease and cancer. The objective of the present study was to investigate the effect of green tea extract (GTE) used as a food antioxidant on markers of oxidative status after dietary depletion of flavonoids and catechins. The study was designed as a 2×3 weeks blinded human cross-over intervention study (eight smokers, eight non-smokers) with GTE corresponding to a daily intake of 18·6 mg catechins/d. The GTE was incorporated into meat patties and consumed with a strictly controlled diet otherwise low in flavonoids. GTE intervention increased plasma antioxidant capacity from 1·35 to 1·56 (P<0·02) in postprandially collected plasma, most prominently in smokers. The intervention did not significantly affect markers in fasting blood samples, including plasma or haemoglobin protein oxidation, plasma oxidation lagtime, or activities of the erythrocyte superoxide dismutase, glutathione peroxidase, glutathione reductase and catalase. Neither were fasting plasma triacylglycerol, cholesterol, α-tocopherol, retinol, β-carotene, or ascorbic acid affected by intervention. Urinary 8-oxo-deoxyguanosine excretion was also unaffected. Catechins from the extract were excreted into urine with a half-life of less than 2 h in accordance with the short-term effects on plasma antioxidant capacity. Since no long-term effects of GTE were observed, the study essentially served as a fruit and vegetables depletion study. The overall effect of the 10-week period without dietary fruits and vegetables was a decrease in oxidative damage to DNA, blood proteins, and plasma lipids, concomitantly with marked changes in antioxidative defenc

Endogenous Risks and Learning in Climate Change Decision Analysis

We analyze the effects of risks and learning on climate change decisions. A two-stage, dynamic, climate change stabilization problem is formulated. The explicit incorporation of ex-post learning induces risk aversion among ex-ante decisions, which is characterized in linear models by VaR- and CVaR-type risk measures. Combined with explicit introduction of "safety" constraints, it creates a "hit-or-miss" type decision-making situation and shows that, even in linear models, learning may lead to either less-or more restrictive ex-ante emission reductions. We analyze stylized elements of the model in order to identify the key factors driving outcomes, in particular, the critical role of quantiles of probability distributions characterizing key uncertainties

MemBrain: Improving the Accuracy of Predicting Transmembrane Helices

Prediction of transmembrane helices (TMH) in α helical membrane proteins provides valuable information about the protein topology when the high resolution structures are not available. Many predictors have been developed based on either amino acid hydrophobicity scale or pure statistical approaches. While these predictors perform reasonably well in identifying the number of TMHs in a protein, they are generally inaccurate in predicting the ends of TMHs, or TMHs of unusual length. To improve the accuracy of TMH detection, we developed a machine-learning based predictor, MemBrain, which integrates a number of modern bioinformatics approaches including sequence representation by multiple sequence alignment matrix, the optimized evidence-theoretic K-nearest neighbor prediction algorithm, fusion of multiple prediction window sizes, and classification by dynamic threshold. MemBrain demonstrates an overall improvement of about 20% in prediction accuracy, particularly, in predicting the ends of TMHs and TMHs that are shorter than 15 residues. It also has the capability to detect N-terminal signal peptides. The MemBrain predictor is a useful sequence-based analysis tool for functional and structural characterization of helical membrane proteins; it is freely available at http://chou.med.harvard.edu/bioinf/MemBrain/

SARS-CoV-2 infection rates of antibody-positive compared with antibody-negative health-care workers in England: a large, multicentre, prospective cohort study (SIREN).

BACKGROUND: Increased understanding of whether individuals who have recovered from COVID-19 are protected from future SARS-CoV-2 infection is an urgent requirement. We aimed to investigate whether antibodies against SARS-CoV-2 were associated with a decreased risk of symptomatic and asymptomatic reinfection. METHODS: A large, multicentre, prospective cohort study was done, with participants recruited from publicly funded hospitals in all regions of England. All health-care workers, support staff, and administrative staff working at hospitals who could remain engaged in follow-up for 12 months were eligible to join The SARS-CoV-2 Immunity and Reinfection Evaluation study. Participants were excluded if they had no PCR tests after enrolment, enrolled after Dec 31, 2020, or had insufficient PCR and antibody data for cohort assignment. Participants attended regular SARS-CoV-2 PCR and antibody testing (every 2-4 weeks) and completed questionnaires every 2 weeks on symptoms and exposures. At enrolment, participants were assigned to either the positive cohort (antibody positive, or previous positive PCR or antibody test) or negative cohort (antibody negative, no previous positive PCR or antibody test). The primary outcome was a reinfection in the positive cohort or a primary infection in the negative cohort, determined by PCR tests. Potential reinfections were clinically reviewed and classified according to case definitions (confirmed, probable, or possible) and symptom-status, depending on the hierarchy of evidence. Primary infections in the negative cohort were defined as a first positive PCR test and seroconversions were excluded when not associated with a positive PCR test. A proportional hazards frailty model using a Poisson distribution was used to estimate incidence rate ratios (IRR) to compare infection rates in the two cohorts. FINDINGS: From June 18, 2020, to Dec 31, 2020, 30 625 participants were enrolled into the study. 51 participants withdrew from the study, 4913 were excluded, and 25 661 participants (with linked data on antibody and PCR testing) were included in the analysis. Data were extracted from all sources on Feb 5, 2021, and include data up to and including Jan 11, 2021. 155 infections were detected in the baseline positive cohort of 8278 participants, collectively contributing 2 047 113 person-days of follow-up. This compares with 1704 new PCR positive infections in the negative cohort of 17 383 participants, contributing 2 971 436 person-days of follow-up. The incidence density was 7·6 reinfections per 100 000 person-days in the positive cohort, compared with 57·3 primary infections per 100 000 person-days in the negative cohort, between June, 2020, and January, 2021. The adjusted IRR was 0·159 for all reinfections (95% CI 0·13-0·19) compared with PCR-confirmed primary infections. The median interval between primary infection and reinfection was more than 200 days. INTERPRETATION: A previous history of SARS-CoV-2 infection was associated with an 84% lower risk of infection, with median protective effect observed 7 months following primary infection. This time period is the minimum probable effect because seroconversions were not included. This study shows that previous infection with SARS-CoV-2 induces effective immunity to future infections in most individuals. FUNDING: Department of Health and Social Care of the UK Government, Public Health England, The National Institute for Health Research, with contributions from the Scottish, Welsh and Northern Irish governments

Interplay of structural and electronic phase separation in single crystalline La(2)CuO(4.05) studied by neutron and Raman scattering

We report a neutron and Raman scattering study of a single-crystal of La(2)CuO(4.05) prepared by high temperature electrochemical oxidation. Elastic neutron scattering measurements show the presence of two phases, corresponding to the two edges of the first miscibility gap, all the way up to 300 K. An additional oxygen redistribution, driven by electronic energies, is identified at 250 K in Raman scattering (RS) experiments by the simultaneous onset of two-phonon and two-magnon scattering, which are fingerprints of the insulating phase. Elastic neutron scattering measurements show directly an antiferromagnetic ordering below a N\'eel temperature of T_N =210K. The opening of the superconducting gap manifests itself as a redistribution of electronic Raman scattering below the superconducting transition temperature, T_c = 24K. A pronounced temperature-dependent suppression of the intensity of the (100) magnetic Bragg peak has been detected below T_c. We ascribe this phenomenon to a change of relative volume fraction of superconducting and antiferromagnetic phases with decreasing temperature caused by a form of a superconducting proximity effect.Comment: 9 pages, including 9 eps figures, submitted to PR

A Combination of Compositional Index and Genetic Algorithm for Predicting Transmembrane Helical Segments

Transmembrane helix (TMH) topology prediction is becoming a focal problem in bioinformatics because the structure of TM proteins is difficult to determine using experimental methods. Therefore, methods that can computationally predict the topology of helical membrane proteins are highly desirable. In this paper we introduce TMHindex, a method for detecting TMH segments using only the amino acid sequence information. Each amino acid in a protein sequence is represented by a Compositional Index, which is deduced from a combination of the difference in amino acid occurrences in TMH and non-TMH segments in training protein sequences and the amino acid composition information. Furthermore, a genetic algorithm was employed to find the optimal threshold value for the separation of TMH segments from non-TMH segments. The method successfully predicted 376 out of the 378 TMH segments in a dataset consisting of 70 test protein sequences. The sensitivity and specificity for classifying each amino acid in every protein sequence in the dataset was 0.901 and 0.865, respectively. To assess the generality of TMHindex, we also tested the approach on another standard 73-protein 3D helix dataset. TMHindex correctly predicted 91.8% of proteins based on TM segments. The level of the accuracy achieved using TMHindex in comparison to other recent approaches for predicting the topology of TM proteins is a strong argument in favor of our proposed method. Availability: The datasets, software together with supplementary materials are available at: http://faculty.uaeu.ac.ae/nzaki/TMHindex.htm

Proteomic Profile of Reversible Protein Oxidation Using PROP, Purification of Reversibly Oxidized Proteins

Signal transduction pathways that are modulated by thiol oxidation events are beginning to be uncovered, but these discoveries are limited by the availability of relatively few analytical methods to examine protein oxidation compared to other signaling events such as protein phosphorylation. We report here the coupling of PROP, a method to purify reversibly oxidized proteins, with the proteomic identification of the purified mixture using mass spectrometry. A gene ontology (GO), KEGG enrichment and Wikipathways analysis of the identified proteins indicated a significant enrichment in proteins associated with both translation and mRNA splicing. This methodology also enabled the identification of some of the specific cysteine residue targets within identified proteins that are reversibly oxidized by hydrogen peroxide treatment of intact cells. From these identifications, we determined a potential consensus sequence motif associated with oxidized cysteine residues. Furthermore, because we identified proteins and specific sites of oxidation from both abundant proteins and from far less abundant signaling proteins (e.g. hepatoma derived growth factor, prostaglandin E synthase 3), the results suggest that the PROP procedure was efficient. Thus, this PROP-proteomics methodology offers a sensitive means to identify biologically relevant redox signaling events that occur within intact cells

Towards Machine Wald

The past century has seen a steady increase in the need of estimating and predicting complex systems and making (possibly critical) decisions with limited information. Although computers have made possible the numerical evaluation of sophisticated statistical models, these models are still designed \emph{by humans} because there is currently no known recipe or algorithm for dividing the design of a statistical model into a sequence of arithmetic operations. Indeed enabling computers to \emph{think} as \emph{humans} have the ability to do when faced with uncertainty is challenging in several major ways: (1) Finding optimal statistical models remains to be formulated as a well posed problem when information on the system of interest is incomplete and comes in the form of a complex combination of sample data, partial knowledge of constitutive relations and a limited description of the distribution of input random variables. (2) The space of admissible scenarios along with the space of relevant information, assumptions, and/or beliefs, tend to be infinite dimensional, whereas calculus on a computer is necessarily discrete and finite. With this purpose, this paper explores the foundations of a rigorous framework for the scientific computation of optimal statistical estimators/models and reviews their connections with Decision Theory, Machine Learning, Bayesian Inference, Stochastic Optimization, Robust Optimization, Optimal Uncertainty Quantification and Information Based Complexity.Comment: 37 page

Cystinosin, MPDU1, SWEETs and KDELR Belong to a Well-Defined Protein Family with Putative Function of Cargo Receptors Involved in Vesicle Trafficking

Classification of proteins into families based on remote homology often helps prediction of their biological function. Here we describe prediction of protein cargo receptors involved in vesicle formation and protein trafficking. Hidden Markov model profile-to-profile searches in protein databases using endoplasmic reticulum lumen protein retaining receptors (KDEL, Erd2) as query reveal a large and diverse family of proteins with seven transmembrane helices and common topology and, most likely, similar function. Their coding genes exist in all eukaryota and in several prokaryota. Some are responsible for metabolic diseases (cystinosis, congenital disorder of glycosylation), others are candidate genes for genetic disorders (cleft lip and palate, certain forms of cancer) or solute uptake and efflux (SWEETs) and many have not yet been assigned a function. Comparison with the properties of KDEL receptors suggests that the family members could be involved in protein trafficking and serve as cargo receptors. This prediction sheds new light on a range of biologically, medically and agronomically important proteins and could open the way to discovering the function of many genes not yet annotated. Experimental testing is suggested