Convolutional Neural Network Applied to SARS-CoV-2 Sequence Classification

COVID-19, the illness caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus belonging to the Coronaviridade family, a single-strand positive-sense RNA genome, has been spreading around the world and has been declared a pandemic by the World Health Organization. On 17 January 2022, there were more than 329 million cases, with more than 5.5 million deaths. Although COVID-19 has a low mortality rate, its high capacities for contamination, spread, and mutation worry the authorities, especially after the emergence of the Omicron variant, which has a high transmission capacity and can more easily contaminate even vaccinated people. Such outbreaks require elucidation of the taxonomic classification and origin of the virus (SARS-CoV-2) from the genomic sequence for strategic planning, containment, and treatment of the disease. Thus, this work proposes a high-accuracy technique to classify viruses and other organisms from a genome sequence using a deep learning convolutional neural network (CNN). Unlike the other literature, the proposed approach does not limit the length of the genome sequence. The results show that the novel proposal accurately distinguishes SARS-CoV-2 from the sequences of other viruses. The results were obtained from 1557 instances of SARS-CoV-2 from the National Center for Biotechnology Information (NCBI) and 14,684 different viruses from the Virus-Host DB. As a CNN has several changeable parameters, the tests were performed with forty-eight different architectures; the best of these had an accuracy of 91.94 +/- 2.62% in classifying viruses into their realms correctly, in addition to 100% accuracy in classifying SARS-CoV-2 into its respective realm, Riboviria. For the subsequent classifications (family, genera, and subgenus), this accuracy increased, which shows that the proposed architecture may be viable in the classification of the virus that causes COVID-19.Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES)High-Performance Computing Center at UFRN(NPAD/UFRN

Alternativas para o aproveitamento industrial da pimenta-do-reino (Piper nigrum L.).

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Biocomposite for Prolonged Release of Water-Soluble Drugs

This study aimed to develop a prolonged-release system based on palygorskite and chitosan, which are natural ingredients widely available, affordable, and accessible. The chosen model drug was ethambutol (ETB), a tuberculostatic drug with high aqueous solubility and hygroscopicity, which is incompatible with other drugs used in tuberculosis therapy. The composites loaded with ETB were obtained using different proportions of palygorskite and chitosan through the spray drying technique. The main physicochemical properties of the microparticles were determined using XRD, FTIR, thermal analysis, and SEM. Additionally, the release profile and biocompatibility of the microparticles were evaluated. As a result, the chitosan–palygorskite composites loaded with the model drug appeared as spherical microparticles. The drug underwent amorphization within the microparticles, with an encapsulation efficiency greater than 84%. Furthermore, the microparticles exhibited prolonged release, particularly after the addition of palygorskite. They demonstrated biocompatibility in an in vitro model, and their release profile was influenced by the proportion of inputs in the formulation. Therefore, incorporating ETB into this system offers improved stability for the administered product in the initial tuberculosis pharmacotherapy dose, minimizing its contact with other tuberculostatic agents in the treatment, as well as reducing its hygroscopicityCoordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brazil (CAPES)—Process n 88887.131333/2016-00

Avaliação sócio-ambiental do kit Embrapa de ordenha manual na produção de leite de cabra.

O presente trabalho teve como objetivo fazer uma avaliação dos impactos sócio-ambientais, exante e ex-post, resultante do kit de ordenha manual em produtores da agricultura familiar. O estudo foi realizado em 14 propriedades do município de Monteiro/PB. A avaliação de desempenho sócio-ambiental ocorreu através da aplicação do Sistema de Avaliação de Impacto Ambiental de Inovações Tecnológicas Agropecuárias (Ambitec-Agro), o qual foram aplicados dois módulos: Ambitec-Produção Animal e Ambitec-Social. A adoção do kit gerou impactos sociais e ambientais positivos em todas as propriedades

Obtenção de açaí desidratado.

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Secure Biometric Authentication With Improved Accuracy

We propose a new hybrid protocol for cryptographically secure biometric authentication. The main advantages of the proposed protocol over previous solutions can be summarised as follows: (1) potential for much better accuracy using different types of biometric signals, including behavioural ones; and (2) improved user privacy, since user identities are not transmitted at any point in the protocol execution. The new protocol takes advantage of state-of-the-art identification classifiers, which provide not only better accuracy, but also the possibility to perform authentication without knowing who the user claims to be. Cryptographic security is based on the Paillier public key encryption scheme

Implementação de um método de detecção molecular de Xylella fastidiosa em plantas de citros e insetos vetores.

A Clorose Variegada dos Citros (CVC) ou amarelinho é uma das principais doenças que afeta os pomares de citros no Brasil. O agente causal da doença é a bactéria Xylella fastidiosa, limitada ao xilema das plantas e de crescimento lento

From Compact Discs to Streaming: A Comparison of Eras within the Brazilian Market

The music industry has undergone many changes in the last few decades, notably since vinyl, cassettes and compact discs faded away as streaming platforms took the world by storm. This Digital evolution has made huge volumes of data about music consumption available. Based on such data, we perform cross-era comparisons between Physical and Digital media within the music market in Brazil. First, we build artists' success time series to detect and characterize hot streak periods, defined as high-impact bursts that occur in sequence, in both eras. Then, we identify groups of artists with distinct success levels by applying a cluster analysis based on hot streaks' features. We find the same clusters for both Physical and Digital eras: Spike Hit Artists, Big Hit Artists, and Top Hit Artists. Our results reveal significant changes in the music industry dynamics over the years by identifying the core of each era