Fast, parallel and secure cryptography algorithm using Lorenz's attractor

A novel cryptography method based on the Lorenz's attractor chaotic system is presented. The proposed algorithm is secure and fast, making it practical for general use. We introduce the chaotic operation mode, which provides an interaction among the password, message and a chaotic system. It ensures that the algorithm yields a secure codification, even if the nature of the chaotic system is known. The algorithm has been implemented in two versions: one sequential and slow and the other, parallel and fast. Our algorithm assures the integrity of the ciphertext (we know if it has been altered, which is not assured by traditional algorithms) and consequently its authenticity. Numerical experiments are presented, discussed and show the behavior of the method in terms of security and performance. The fast version of the algorithm has a performance comparable to AES, a popular cryptography program used commercially nowadays, but it is more secure, which makes it immediately suitable for general purpose cryptography applications. An internet page has been set up, which enables the readers to test the algorithm and also to try to break into the cipher in

Epidemiological study of burns at the Hospital of Clinics, Federal University of the Triângulo Mineiro

Introduction: Burns are a serious public health problem. Knowledge of the main epidemiological data of affected patients is paramount for preventing and establishing the best clinical treatment for these individuals. The objective was to survey the epidemiological profile of burns, the main mechanisms involved, treatments, and outcomes of these cases at the Hospital de Clínicas da UFTM. Methods: This is a retrospective and cross-sectional study of hospitalizations for burns at HC-UFTM from January 2015 to December 2019. Gender, age, depth of burns, location, etiology, use of opioids, hospitalization duration, outcome (hospital discharge or death), and extent of burned areas (EBA%). Results: This study was composed of 138 patients, divided into three groups: group 1 (G1), 39 patients; group 2 (G2), 89 participants, of which 5 died; and group 3 (G3), 10 participants, 4 of whom died. As for the trauma mechanism, the most common was scald (17.39%), followed by thermal burn (13.76%), alcohol (8.69%), and electric burns (5.79%). Conclusion: There was a higher prevalence of second-degree burns in male patients. The most prevalent mechanism of trauma was scald, and the EBA had an average of 23.9%. The face and neck were the most affected segments, and 61.59% of the patients required opioids during hospitalization. Regarding the outcome, 91.30% of patients were discharged from the hospital, and 6.52% died, with most cases in G2

Segmentação automática de áreas urbanas em imagens de sensoriamento remoto

A adoção de ferramentas e métodos de visão computacional aplicados as imagens de sensoriamento remoto pode trazer um grande benefício para os estudos de urbanismo. Neste trabalho é apresentado um arcabouço computacional para auxiliar a aquisição e análise de imagens de sensoriamento remoto, provindas do Google Earth®. O sistema proposto se conecta ao Google Earth e compõe uma imagem especificada pelas coordenadas de latitude e longitude informadas pelo usuário. O sistema também segmenta automaticamente a imagem em áreas edificadas e não-edificadas. O processo de segmentação é realizado por meio de métricas do histograma cromático. É apresentado um experimento com imagens de duas cidades do interior paulista, São Carlos e Rio Claro, a fim de validar o sistema e determinar quais as melhores métricas de histogramas devem ser utilizadas para a tarefa.Computer vision methods applied in remote sensing images can bring important benefits to the field of urbanism. In this work, a computer framework to aid acquiring and analyzing remote sensing images from Google Earth® is presented. The proposed system connects to the Google Earth and extracts an image specified by the latitude and longitude coordinates. The image is automatically segmented into edified and non-edified regions. The segmentation process is carried out by the analysis of the image chromatic histogram. An experiment showing results from the segmentation of two São Paulo State’s towns are\ud presented. The comparison of the histogram metrics and the color channels and its segmentation performance are discussed.CNPq (303746/2007-1; 504476/2007-6; 504476/2007-6

Combinatorial CRISPR screen identifies fitness effects of gene paralogues.

Genetic redundancy has evolved as a way for human cells to survive the loss of genes that are single copy and essential in other organisms, but also allows tumours to survive despite having highly rearranged genomes. In this study we CRISPR screen 1191 gene pairs, including paralogues and known and predicted synthetic lethal interactions to identify 105 gene combinations whose co-disruption results in a loss of cellular fitness. 27 pairs influence fitness across multiple cell lines including the paralogues FAM50A/FAM50B, two genes of unknown function. Silencing of FAM50B occurs across a range of tumour types and in this context disruption of FAM50A reduces cellular fitness whilst promoting micronucleus formation and extensive perturbation of transcriptional programmes. Our studies reveal the fitness effects of FAM50A/FAM50B in cancer cells

The GLEaMviz computational tool, a publicly available software to explore realistic epidemic spreading scenarios at the global scale

<p>Abstract</p> <p>Background</p> <p>Computational models play an increasingly important role in the assessment and control of public health crises, as demonstrated during the 2009 H1N1 influenza pandemic. Much research has been done in recent years in the development of sophisticated data-driven models for realistic computer-based simulations of infectious disease spreading. However, only a few computational tools are presently available for assessing scenarios, predicting epidemic evolutions, and managing health emergencies that can benefit a broad audience of users including policy makers and health institutions.</p> <p>Results</p> <p>We present "GLEaMviz", a publicly available software system that simulates the spread of emerging human-to-human infectious diseases across the world. The GLEaMviz tool comprises three components: the client application, the proxy middleware, and the simulation engine. The latter two components constitute the GLEaMviz server. The simulation engine leverages on the Global Epidemic and Mobility (GLEaM) framework, a stochastic computational scheme that integrates worldwide high-resolution demographic and mobility data to simulate disease spread on the global scale. The GLEaMviz design aims at maximizing flexibility in defining the disease compartmental model and configuring the simulation scenario; it allows the user to set a variety of parameters including: compartment-specific features, transition values, and environmental effects. The output is a dynamic map and a corresponding set of charts that quantitatively describe the geo-temporal evolution of the disease. The software is designed as a client-server system. The multi-platform client, which can be installed on the user's local machine, is used to set up simulations that will be executed on the server, thus avoiding specific requirements for large computational capabilities on the user side.</p> <p>Conclusions</p> <p>The user-friendly graphical interface of the GLEaMviz tool, along with its high level of detail and the realism of its embedded modeling approach, opens up the platform to simulate realistic epidemic scenarios. These features make the GLEaMviz computational tool a convenient teaching/training tool as well as a first step toward the development of a computational tool aimed at facilitating the use and exploitation of computational models for the policy making and scenario analysis of infectious disease outbreaks.</p

Comparing large-scale computational approaches to epidemic modeling: Agent-based versus structured metapopulation models

<p>Abstract</p> <p>Background</p> <p>In recent years large-scale computational models for the realistic simulation of epidemic outbreaks have been used with increased frequency. Methodologies adapt to the scale of interest and range from very detailed agent-based models to spatially-structured metapopulation models. One major issue thus concerns to what extent the geotemporal spreading pattern found by different modeling approaches may differ and depend on the different approximations and assumptions used.</p> <p>Methods</p> <p>We provide for the first time a side-by-side comparison of the results obtained with a stochastic agent-based model and a structured metapopulation stochastic model for the progression of a baseline pandemic event in Italy, a large and geographically heterogeneous European country. The agent-based model is based on the explicit representation of the Italian population through highly detailed data on the socio-demographic structure. The metapopulation simulations use the GLobal Epidemic and Mobility (GLEaM) model, based on high-resolution census data worldwide, and integrating airline travel flow data with short-range human mobility patterns at the global scale. The model also considers age structure data for Italy. GLEaM and the agent-based models are synchronized in their initial conditions by using the same disease parameterization, and by defining the same importation of infected cases from international travels.</p> <p>Results</p> <p>The results obtained show that both models provide epidemic patterns that are in very good agreement at the granularity levels accessible by both approaches, with differences in peak timing on the order of a few days. The relative difference of the epidemic size depends on the basic reproductive ratio, <it>R</it>₀, and on the fact that the metapopulation model consistently yields a larger incidence than the agent-based model, as expected due to the differences in the structure in the intra-population contact pattern of the approaches. The age breakdown analysis shows that similar attack rates are obtained for the younger age classes.</p> <p>Conclusions</p> <p>The good agreement between the two modeling approaches is very important for defining the tradeoff between data availability and the information provided by the models. The results we present define the possibility of hybrid models combining the agent-based and the metapopulation approaches according to the available data and computational resources.</p

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