Wavelet-based multifractal analysis and fractal Signature of SARS-Cov-2 Coronavirus Variants genomes.

In this paper, the SARS-CoV-2 coronavirus variants of concern and of interest genomes are analyzed using the wavelet transform modulus maxima lines (WTMM) method. The goal is to track the monofractal behavior of the virus genomes and to investigate the Long-Range-Correlation (LRC) character through the estimation of the Hurst exponent. The obtained results demonstrate the multifractal and the anti-correlated characters in the variants of concern for the Knucleotidic and GC DNA coding. The fractal signatures of SARS-CoV-2 coronavirus variants are investigated through the indicator matrix maps of the genomes, they exhibit the same patterns for the variants (Alpha, Delta) and (Eta, Lota, Kappa) with moving positions, while the variants Beta, Gamma and Epsilon have different indicator matrixes. The fractal dimensions of SARS-CoV-2 variants are oscillating aroundI, except the Epsilon variant from USA, where the fractal dimension is 1.70.

Discrete Multiple Kernel k-means

The multiple kernel k-means (MKKM) and its variants utilize complementary information from different kernels, achieving better performance than kernel k-means (KKM). However, the optimization procedures of previous works all comprise two stages, learning the continuous relaxed label matrix and obtaining the discrete one by extra discretization procedures. Such a two-stage strategy gives rise to a mismatched problem and severe information loss. To address this problem, we elaborate a novel Discrete Multiple Kernel k-means (DMKKM) model solved by an optimization algorithm that directly obtains the cluster indicator matrix without subsequent discretization procedures. Moreover, DMKKM can strictly measure the correlations among kernels, which is capable of enhancing kernel fusion by reducing redundancy and improving diversity. What’s more, DMKKM is parameter-free avoiding intractable hyperparameter tuning, which makes it feasible in practical applications. Extensive experiments illustrated the effectiveness and superiority of the proposed model.

Indicadores de desempenho: uma proposta para pecuária familiar sob a ótica conceitual do balanced scoredcard sustentável

Objective: Build a matrix of indicators, according to the conceptual perspective of the BSC, adapting to the dimensions of sustainability related to family ranchingMethodology: A qualitative approach was used, carrying out a bibliographic and documentary study, in addition to interviews with rural producers and specialists on the subject.Originality: Historically, there is a lack of tools and management methodologies for the work context, especially in view of the new environmental and sustainability requirements. Thus, the work contributes to mitigate this gap in the context.Main results: The work generated a proposal / matrix with sustainability indicators for family livestock. Matrix composed of 28 indicators distributed in the four dimensions (environmental, economic, productive and social) of sustainability.Theoretical/methodological contributions: The main methodological contribution was to describe the process of how to propose a matrix of indicators, using planning tools such as the SWOT matrix and the BSC. Producers and specialists contributed to the construction of the SWOT matrix, later also contributed to the construction of the indicator matrix, in order to tend to the SWOT matrix.Social contributions/to management: As a social contribution we can mention the issue of the matrix of indicators being supported by the four dimensions of sustainability. For management, the matrix itself, in addition to the specifications of each indicator, which can enable better management of family ranching properties.

Indicator Regularized Non-Negative Matrix Factorization Method-Based Drug Repurposing for COVID-19

A novel coronavirus, named COVID-19, has become one of the most prevalent and severe infectious diseases in human history. Currently, there are only very few vaccines and therapeutic drugs against COVID-19, and their efficacies are yet to be tested. Drug repurposing aims to explore new applications of approved drugs, which can significantly reduce time and cost compared with de novo drug discovery. In this study, we built a virus-drug dataset, which included 34 viruses, 210 drugs, and 437 confirmed related virus-drug pairs from existing literature. Besides, we developed an Indicator Regularized non-negative Matrix Factorization (IRNMF) method, which introduced the indicator matrix and Karush-Kuhn-Tucker condition into the non-negative matrix factorization algorithm. According to the 5-fold cross-validation on the virus-drug dataset, the performance of IRNMF was better than other methods, and its Area Under receiver operating characteristic Curve (AUC) value was 0.8127. Additionally, we analyzed the case on COVID-19 infection, and our results suggested that the IRNMF algorithm could prioritize unknown virus-drug associations.

Fractal signatures of SARS-CoV2 coronavirus, the indicator matrix, the fractal dimension and the 2D directional wavelet transform: A comparative study with SARS-CoV, MERS-CoV and SARS-like coronavirus

SummaryThe main goal of this paper is to show the 2D fractal signatures of SARS-CoV2 coronavirus, indicator matrixes maps showing the concentration of nucleotide acids are built form the RNA sequences, and then the fractal dimension and 2D Directional Wavelet Transform (DCWT) are calculated. Analysis of 21 RNA sequences downloaded from NCBI database shows that indicator matrixes and 2D DCWT exhibit the same patterns with different positions, while the fractal dimensions are oscillating around 1.60. A comparison with SARS-CoV, MERS-CoV and SARS-like Coronavirus shows slightly different fractal dimensions, however the indicator matrix and 2D DCWT exhibit the same patterns for the couple (SARS-CoV2, SARS-CoV) and (MERS-CoV, SARS-like) Coronavirus. Obtained results show that SARS-CoV2 is probably a result of SARS-CoV mutation process.

Multi-View Clustering in Latent Embedding Space

Previous multi-view clustering algorithms mostly partition the multi-view data in their original feature space, the efficacy of which heavily and implicitly relies on the quality of the original feature presentation. In light of this, this paper proposes a novel approach termed Multi-view Clustering in Latent Embedding Space (MCLES), which is able to cluster the multi-view data in a learned latent embedding space while simultaneously learning the global structure and the cluster indicator matrix in a unified optimization framework. Specifically, in our framework, a latent embedding representation is firstly discovered which can effectively exploit the complementary information from different views. The global structure learning is then performed based on the learned latent embedding representation. Further, the cluster indicator matrix can be acquired directly with the learned global structure. An alternating optimization scheme is introduced to solve the optimization problem. Extensive experiments conducted on several real-world multi-view datasets have demonstrated the superiority of our approach.

Systemizing Professional Development for Teaching Through English in Higher Education

This chapter suggests that successful and sustainable implementation of EMI in higher education can benefit from a systems approach. Drawing on general system theory, this approach enables a systemic development process for having academic programmes taught through English so that the roles of all key components of a university as an ecosystem are harmonized. The established and emergent components of the process are identified through a key development indicator matrix. The matrix enables holistic coordination so as to maximize the potential for long-term quality impact of teaching through English. The matrix is described here with respect to a 2015-2019 higher education languages strategy implemented at a major public university in Mexico, which has led to development and launch processes for English-medium education.

Validation of an indicator matrix for the assessment of the Neonatal Hearing Screening Program

ABSTRACT Purpose: to validate an indicator matrix to assess the Neonatal Hearing Screening Program (NHSP). Methods: methodology development research. A total of 13 speech-language-hearing therapists with a specialization in audiology and/or at least three-year experience in neonatal hearing screening participated in the validation process. Quantitative and qualitative data were collected to develop the indicator matrix, which was then submitted to the validation process. The results of the specialists’ evaluation, in this stage, were quantitatively analyzed with the item content validation index (I-CVI) and scale content validation index (S-CVI). Results: regarding the indicators classified as quite or fully adequate, the mean I-CVI was the same as the mean S-CVI (0.95), evidencing excellence in their content validity. Concerning the scores classified as quite or fully adequate, the I-CVI mean was also identical to that of S-CVI (0.83), thus, reaching a consensus. Conclusion: this matrix with 33 indicators that had their content validated with consensus, will consistently contribute to assessing NHS services in Brazil.

Outcome Indicator Matrix (OIM): a Tool to Assess National Action Plans

: The Outcome Indicator Matrix (OIM) (MIR in Spanish) is a tool that has been adapted by the Universidad de El Salvador (UES) to monitor commitments in national action plans (NAPs). It has been used to assess all commitments presented to the Open Government Partnership (OGP) by the Government of El Salvador that are in its NAP. The OIM-MIR is a result of integrating the logical framework method, used by many governments and public institutions and the focus of the Regulatory Impact Analysis (RIA) (OECD, 2018), which international organizations such as the OECD use to organize actions and procedures to produce a specific outcome.