Vodcast as ideating medium in STEM lesson plan in teaching heat transfer

This study attempts to link the clay soil in the locality to the topic of heat transfer as a contextualization point. It attempted to slightly modify the seven-step STEM lesson by having the part of the prototyping be first tried by the teacher, thereby having a change-of-hat to anticipate the ‘what if’ questions of the students. The teacher-researchers experimentation provided critical information to scaffold the students in the prototyping part.  The evaluation of experts shows that the modified STEM lesson can be an excellent tool and the vodcast has been found to be a very satisfactory component of the STEM lesson. It is described as a very useful material in teaching heat transfer and related thermodynamics concepts and is highly recommended for use in both distance learning and face-to-face modality. Further, the clay oven exploration has come up with a refined clay oven production process, wherein the clay oven prototype has the capacity for the contextualization of heat transfer. It is recommended that a formal implementation be conducted to refine and standardize the lesson delivery

Desenvolvimento ponderal de caprinos mesticos (Gurgueia x Pardo alema), no municipio de Teresina.

bitstream/item/97300/1/PAND460001.pd

Interação de frações proteicas de Chromobacterium violaceum com esporos de Colletotrichum sp. isolado de guaranazeiro.

O objetivo deste trabalho foi testar extratos proteicos de C. violaceum fracionados por diferenças de solubilidade dos seus componentes quanto à atividade inibitória da protrusão de hifas de Colletotrichum sp. isolado de folhas de guaranazeiro (Paullinia cupana var. sorbilis) com sintomas de antracnose, principal doença dos guaranazais no Amazonas

Frações protéicas de Chromobacterium violaceum em interação com esporos de Colletotrichum sp. isolado de guaranazeiro.

O objetivo deste trabalho foi testar frações de extratos protéicos de C. violaceum, distintas quanto à solubilidade, com relação à atividade inibitória da protrusão de hifas dos esporos de Colletotrichum sp. isolado de folhas de guaranazeiro (Paullinia cupana var. sorbilis) com sintomas de antracnose.bitstream/item/63833/1/ComTec-69-2009.pd

Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks

Recurrent neural networks (RNNs) are widely used in computational neuroscience and machine learning applications. In an RNN, each neuron computes its output as a nonlinear function of its integrated input. While the importance of RNNs, especially as models of brain processing, is undisputed, it is also widely acknowledged that the computations in standard RNN models may be an over-simplification of what real neuronal networks compute. Here, we suggest that the RNN approach may be made both neurobiologically more plausible and computationally more powerful by its fusion with Bayesian inference techniques for nonlinear dynamical systems. In this scheme, we use an RNN as a generative model of dynamic input caused by the environment, e.g. of speech or kinematics. Given this generative RNN model, we derive Bayesian update equations that can decode its output. Critically, these updates define a 'recognizing RNN' (rRNN), in which neurons compute and exchange prediction and prediction error messages. The rRNN has several desirable features that a conventional RNN does not have, for example, fast decoding of dynamic stimuli and robustness to initial conditions and noise. Furthermore, it implements a predictive coding scheme for dynamic inputs. We suggest that the Bayesian inversion of recurrent neural networks may be useful both as a model of brain function and as a machine learning tool. We illustrate the use of the rRNN by an application to the online decoding (i.e. recognition) of human kinematics

Bayesian Comparison of Neurovascular Coupling Models Using EEG-fMRI

Functional magnetic resonance imaging (fMRI), with blood oxygenation level-dependent (BOLD) contrast, is a widely used technique for studying the human brain. However, it is an indirect measure of underlying neuronal activity and the processes that link this activity to BOLD signals are still a topic of much debate. In order to relate findings from fMRI research to other measures of neuronal activity it is vital to understand the underlying neurovascular coupling mechanism. Currently, there is no consensus on the relative roles of synaptic and spiking activity in the generation of the BOLD response. Here we designed a modelling framework to investigate different neurovascular coupling mechanisms. We use Electroencephalographic (EEG) and fMRI data from a visual stimulation task together with biophysically informed mathematical models describing how neuronal activity generates the BOLD signals. These models allow us to non-invasively infer the degree of local synaptic and spiking activity in the healthy human brain. In addition, we use Bayesian model comparison to decide between neurovascular coupling mechanisms. We show that the BOLD signal is dependent upon both the synaptic and spiking activity but that the relative contributions of these two inputs are dependent upon the underlying neuronal firing rate. When the underlying neuronal firing is low then the BOLD response is best explained by synaptic activity. However, when the neuronal firing rate is high then both synaptic and spiking activity are required to explain the BOLD signal

Dynamic causal modelling for EEG and MEG

Dynamic Causal Modelling (DCM) is an approach first introduced for the analysis of functional magnetic resonance imaging (fMRI) to quantify effective connectivity between brain areas. Recently, this framework has been extended and established in the magneto/encephalography (M/EEG) domain. DCM for M/EEG entails the inversion a full spatiotemporal model of evoked responses, over multiple conditions. This model rests on a biophysical and neurobiological generative model for electrophysiological data. A generative model is a prescription of how data are generated. The inversion of a DCM provides conditional densities on the model parameters and, indeed on the model itself. These densities enable one to answer key questions about the underlying system. A DCM comprises two parts; one part describes the dynamics within and among neuronal sources, and the second describes how source dynamics generate data in the sensors, using the lead-field. The parameters of this spatiotemporal model are estimated using a single (iterative) Bayesian procedure. In this paper, we will motivate and describe the current DCM framework. Two examples show how the approach can be applied to M/EEG experiments