Leveraging Robot Programming to Foster Computational Thinking

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06N-P63\u3b1 and TA-P63\u3b1 exhibit intrinsic differences in transactivation specificities that depend on distinct features of DNA target sites.

TP63 is a member of the TP53 gene family that encodes for up to ten different TA and 06N isoforms through alternative promoter usage and alternative splicing. Besides being a master regulator of gene expression for squamous epithelial proliferation, differentiation and maintenance, P63, through differential expression of its isoforms, plays important roles in tumorigenesis. All P63 isoforms share an immunoglobulin-like folded DNA binding domain responsible for binding to sequence-specific response elements (REs), whose overall consensus sequence is similar to that of the canonical p53 RE. Using a defined assay in yeast, where P63 isoforms and RE sequences are the only variables, and gene expression assays in human cell lines, we demonstrated that human TA- and 06N-P63\u3b1 proteins exhibited differences in transactivation specificity not observed with the corresponding P73 or P53 protein isoforms. These differences 1) were dependent on specific features of the RE sequence, 2) could be related to intrinsic differences in their oligomeric state and cooperative DNA binding, and 3) appeared to be conserved in evolution. Since genotoxic stress can change relative ratio of TA- and 06N-P63\u3b1 protein levels, the different transactivation specificity of each P63 isoform could potentially influence cellular responses to specific stresses

Incremental dimension reduction of tensors with random index

We present an incremental, scalable and efficient dimension reduction technique for tensors that is based on sparse random linear coding. Data is stored in a compactified representation with fixed size, which makes memory requirements low and predictable. Component encoding and decoding are performed on-line without computationally expensive re-analysis of the data set. The range of tensor indices can be extended dynamically without modifying the component representation. This idea originates from a mathematical model of semantic memory and a method known as random indexing in natural language processing. We generalize the random-indexing algorithm to tensors and present signal-to-noise-ratio simulations for representations of vectors and matrices. We present also a mathematical analysis of the approximate orthogonality of high-dimensional ternary vectors, which is a property that underpins this and other similar random-coding approaches to dimension reduction. To further demonstrate the properties of random indexing we present results of a synonym identification task. The method presented here has some similarities with random projection and Tucker decomposition, but it performs well at high dimensionality only (n>10^3). Random indexing is useful for a range of complex practical problems, e.g., in natural language processing, data mining, pattern recognition, event detection, graph searching and search engines. Prototype software is provided. It supports encoding and decoding of tensors of order >= 1 in a unified framework, i.e., vectors, matrices and higher order tensors.Comment: 36 pages, 9 figure

Cultivar de soja BRS 8381: indicação para os estados de Goiás, Mato Grosso, Minas Gerais e Distrito Federal.

Trabalho publicado também nos Resumos do CONGRESSO BRASILEIRO DE SOJA, 6., 2012, Cuiabá

HIV- 1 lentivirus tethering to the genome is associated with transcription factor binding sites found in genes that favour virus survival

Data availability: The databases generated during an/or analysed during the current study are available from the corresponding author on reasonable request.Copyright © The Author(s) 2022. Lentiviral vectors (LV) are attractive for permanent and effective gene therapy. However, integration into the host genome can cause insertional mutagenesis highlighting the importance of understanding of LV integration. Insertion site (IS) tethering is believed to involve cellular proteins such as PSIP1/LEDGF/p75, which binds to the virus pre-integration complexes (PICs) helping to target the virus genome. Transcription factors (TF) that bind both the vector LTR and host genome are also suspected influential to this. To determine the role of TF in the tethering process, we mapped predicted transcription factor binding sites (pTFBS) near to IS chosen by HIV-1 LV using a narrow 20 bp window in infected human induced pluripotent stem cells (iPSCs) and their hepatocyte-like cell (HLC) derivatives. We then aligned the pTFBS with these sequences found in the LTRs of native and self-inactivated LTRs. We found significant enrichment of these sequences for pTFBS essential to HIV-1 life cycle and virus survival. These same sites also appear in HIV-1 patient IS and in mice infected with HIV-1 based LV. This in silco data analysis suggests pTFBS present in the virus LTR and IS sites selected by HIV-1 LV are important to virus survival and propagation.NC3Rs CRACK IT Challenge 21: InMutagene award, sponsored by GSK and Novartis

Antibody response elicited by the SARS-CoV-2 vaccine booster in patients with multiple sclerosis: Who gains from it?

Background and purpose: Although two doses of COVID-19 vaccine elicited a protective humoral response in most persons with multiple sclerosis (pwMS), a significant group of them treated with immunosuppressive disease-modifying therapies (DMTs) showed less efficient responses. Methods: This prospective multicenter observational study evaluates differences in immune response after a third vaccine dose in pwMS. Results: Four hundred seventy-three pwMS were analyzed. Compared to untreated patients, there was a 50-fold decrease (95% confidence interval [CI] = 14.3–100.0, p < 0.001) in serum SARS-CoV-2 antibody levels in those on rituximab, a 20-fold decrease (95% CI = 8.3–50.0, p < 0.001) in those on ocrelizumab, and a 2.3-fold decrease (95% CI = 1.2–4.6, p = 0.015) in those on fingolimod. As compared to the antibody levels after the second vaccine dose, patients on the anti-CD20 drugs rituximab and ocrelizumab showed a 2.3-fold lower gain (95% CI = 1.4–3.8, p = 0.001), whereas those on fingolimod showed a 1.7-fold higher gain (95% CI = 1.1–2.7, p = 0.012), compared to patients treated with other DMTs. Conclusions: All pwMS increased their serum SARS-CoV-2 antibody levels after the third vaccine dose. The mean antibody values of patients treated with ocrelizumab/rituximab remained well below the empirical "protective threshold" for risk of infection identified in the CovaXiMS study (>659 binding antibody units/mL), whereas for patients treated with fingolimod this value was significantly closer to the cutoff

p53 Transactivation and the Impact of Mutations, Cofactors and Small Molecules Using a Simplified Yeast-Based Screening System

The p53 tumor suppressor, which is altered in most cancers, is a sequence-specific transcription factor that is able to modulate the expression of many target genes and influence a variety of cellular pathways. Inactivation of the p53 pathway in cancer frequently occurs through the expression of mutant p53 protein. In tumors that retain wild type p53, the pathway can be altered by upstream modulators, particularly the p53 negative regulators MDM2 and MDM4. promoter, ii) single copy, chromosomally located p53-responsive and control luminescence reporters, iii) enhanced chemical uptake using modified ABC-transporters, iv) small-volume formats for treatment and dual-luciferase assays, and v) opportunities to co-express p53 with other cofactor proteins. This robust system can distinguish different levels of expression of WT and mutant p53 as well as interactions with MDM2 or 53BP1.We found that the small molecules Nutlin and RITA could both relieve the MDM2-dependent inhibition of WT p53 transactivation function, while only RITA could impact p53/53BP1 functional interactions. PRIMA-1 was ineffective in modifying the transactivation capacity of WT p53 and missense p53 mutations. This dual-luciferase assay can, therefore, provide a high-throughput assessment tool for investigating a matrix of factors that can influence the p53 network, including the effectiveness of newly developed small molecules, on WT and tumor-associated p53 mutants as well as interacting proteins