Recently, a new family of lithium-rich antiperovskites, Li 3 OA (A = halogen), which presents superionic conductivity, emerged as a promising both safe and commercially applicable solid electrolyte for lithium ion batteries. In this paper we employed classical atomistic quasistatic calculations to obtain the concentration of lithium vacancies and interstitials for stoichiometric samples of Li 3 OCl. The obtained concentrations as well as vacancy and interstitial migration energies reinforced the assumption that vacancies are the charge carriers in both stoichiometric and divalent metal doped samples, but raise the possibility that the high ionic conductivity in LiCl-deficient samples are in fact driven by interstitials, in opposition to what has been assumed so far. The Li 3 OCl stability at higher temperatures was investigated based on Gibbs energies of decomposition from 0 K up to 550 K. They are negative in the whole temperature range, which suggests that there exists a high Gibbs energy barrier between Li 3 OCl and starter materials preventing decomposition.

OBJECTIVE To estimate the 2020 all-cause and COVID-19 excess mortality according to sex, age, race/color, and state, and to compare mortality rates by selected causes with that of the five previous years in Brazil. METHODS Data from the Mortality Information System were used. Expected deaths for 2020 were estimated from 2015 to 2019 data using a negative binomial log-linear model. RESULTS Excess deaths in Brazil in 2020 amounted to 13.7%, and the ratio of excess deaths to COVID-19 deaths was 0.90. Reductions in deaths from cardiovascular diseases (CVD), respiratory diseases, and external causes, and an increase in ill-defined causes were all noted. Excess deaths were also found to be heterogeneous, being higher in the Northern, Center-Western, and Northeastern states. In some states, the number of COVID-19 deaths was lower than that of excess deaths, whereas the opposite occurred in others. Moreover, excess deaths were higher in men aged 20 to 59, and in black, yellow, or indigenous individuals. Meanwhile, excess mortality was lower in women, in individuals aged 80 years or older, and in whites. Additionally, deaths among those aged 0 to 19 were 7.2% lower than expected, with reduction in mortality from respiratory diseases and external causes. There was also a drop in mortality due to external causes in men and in those aged 20 to 39 years. Moreover, reductions in deaths from CVD and neoplasms were noted in some states and groups. CONCLUSION There is evidence of underreporting of COVID-19 deaths and of the possible impact of restrictive measures in the reduction of deaths from external causes and respiratory diseases. The impacts of COVID-19 on mortality were heterogeneous among the states and groups, revealing that regional, demographic, socioeconomic, and racial differences expose individuals in distinct ways to the risk of death from both COVID-19 and other causes.

Here, we report that in lithium-rich anti-perovskites (LiRAPs) with lithium halide deficiency, Li+ interstitials outnumber vacancies by 2–3 orders of magnitude, so that interstitials might be the actual charge carriers responsible for superionic conduction, as opposed to what has been assumed so far.

Breast cancer occurs with high frequency among the world's population and its effects impact the patients' perception of their own sexuality and their very personal image. This work presents a computational methodology that helps specialists detect breast masses in mammogram images. The first stage of the methodology aims to improve the mammogram image. This stage consists in removing objects outside the breast, reducing noise and highlighting the internal structures of the breast. Next, cellular neural networks are used to segment the regions that might contain masses. These regions have their shapes analyzed through shape descriptors (eccentricity, circularity, density, circular disproportion and circular density) and their textures analyzed through geostatistic functions (Ripley's K function and Moran's and Geary's indexes). Support vector machines are used to classify the candidate regions as masses or non-masses, with sensitivity of 80%, rates of 0.84 false positives per image and 0.2 false negatives per image, and an area under the ROC curve of 0.87.

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