Over time astrocytes have been thought to function in an auxiliary manner, providing neurons with metabolic and structural support. However, recent research suggests they may play a fundamental role in the generation and propagation of focal epileptic seizures by causing synchronized electrical bursts in neurons. It would be helpful to have a simple mathematical model that represents this dynamic and incorporates these updated experimental results. We have created a two-compartment model of a typical neuron found in the hippocampal CA1 region, an area often thought to be the origin of these seizures. The focus is on properly modeling the astrocytic input to examine the pathological excitation of these neurons and subsequent transmission of the signals. In particular, we consider the intracellular astrocytic calcium fluctuations which are associated with slow inward currents in neighbouring neurons. Using our model, a variety of experimental results are reproduced, and comments are made about the potential differences between graded and “all-or-none” astrocytes.

<p dir="ltr">There is a growing trend towards the development of parsimonious surrogate models for studying physical phenomena. While they typically offer less accuracy, these models bypass the computational costs of numerical methods, usually by multiple orders of magnitude, allowing statistical applications such as sensitivity analysis, stochastic treatments, parametric problems, and uncertainty quantification. Researchers have explored generalized surrogate frameworks leveraging Gaussian processes, various basis function expansions, support vector machines, and neural networks. Dynamical fields, represented through time-dependent partial differential equation, pose a particular hardship for existing frameworks due to their high dimensional representation, and possibly multi-scale solutions.</p><p dir="ltr">In this work, we present a novel architecture for solving time-dependent partial differential equations using co-ordinate neural networks and time-marching updates through hyper-networks. We show that it provides a temporally meshed and spatially mesh-free solution which are causally coherent as justified through a theoretical treatment of Lie groups. We showcase results on some benchmark problems in computational physics while discussing their performance against similar physics-informed approaches like physics-informed DeepOnets and Physics informed neural networks.</p>

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / This work presents two educational proposals, directed to the high school, of matrix product aplication. The first proposal is made on geometric transformations (reflection, rotation, scale, shearing) with images of letters of the alphabet. It is divided into two parts: in the first, students need to make the product of matrices and the construction of the images, while in the second GeoGebra software is used to assist in the calculation and construction of the figures. The second proposal is about the projection of population growth through Leslie Model, using the Octave software as calculation tool. Before each proposal is provided the theory that supports geometric transformations as well the Leslie Model and several examples to clarify these definitions. / Este trabalho apresenta duas propostas didáticas, direcionadas ao Ensino Médio, de aplicação do produto matricial. A primeira proposta é feita sobre transformações geométricas (reflexão, rotação, escala, cisalhamento) com a imagem de letras do nosso alfabeto. Ela está dividida em duas partes: na primeira, os alunos devem fazer o produto de matrizes e a construção das imagens à mão, enquanto na segunda é utilizado o programa GeoGebra para auxiliar nos cálculos e na construção das figuras. A segunda proposta é feita sobre projeção de crescimento populacional através do Modelo de Leslie, sendo utilizado o programa Octave como ferramenta de cálculo. Antes de cada proposta, é oferecida a teoria que fundamenta tanto as transformações geométricas quanto o Modelo de Leslie, e vários exemplos são usados para esclarecer essas definições.

miRNAs are non-coding RNAs of approx. 22 nucleotides in length that inhibit gene expression at the post-transcriptional level. By virtue of this gene regulation mechanism, miRNAs play a critical role in several biological processes and patho-physiological conditions, including cancers. miRNA behavior is a result of a multi-level complex interaction network involving miRNA-mRNA, TF-miRNA-gene, and miRNA-chemical interactions; hence the precise patterns through which a miRNA regulates a certain disease(s) are still elusive. Herein, I have developed an integrative genomics methods/pipeline to (i) build a miRNA regulomics and data analytics repository, (ii) create/model these interactions into networks and use optimization techniques, motif based analyses, network inference strategies and influence diffusion concepts to predict miRNA regulations and its role in diseases, especially related to cancers. By these methods, we are able to determine the regulatory behavior of miRNAs and potential causal miRNAs in specific diseases and potential biomarkers/targets for drug and medicinal therapeutics.