In Vivo Neural Tract Tracing Procedures: Unravelling Neural Hodology Using Fluorescent and Non-fluorescent Neural Tract Tracers, Optogenetic Approach, and Diffusion Tensor Neurotractography Protocols.

Fluorescent and non-fluorescent neural tract tracers enable the investigation of neural pathways in both peripheral and central nervous systems in laboratory animals demonstrating images with high resolution and great anatomic precision. Anterograde and retrograde viral tracers are important cutting-edge tools for neuroanatomical mapping. The optogenetic consists of an advanced alternative for in vivo neural tract tracing procedures, fundamentally considering the possibility to dissect and modulate pathways either exciting or inhibiting neural circuits in laboratory animals. The neurotractography by diffusion tensor imaging in vivo procedures enables the study of neural pathways in humans with reasonable accuracy. Here we describe the procedure of classical anatomic neural tract tracing and modern optogenetic technique performed in anima vili in addition to different diffusion tensor neurotractography performed in anima nobili.

National Holidays and Social Mobility Behaviors: Alternatives for Forecasting COVID-19 Deaths in Brazil.

In this paper, we investigate the influence of holidays and community mobility on the transmission rate and death count of COVID-19 in Brazil. We identify national holidays and hallmark holidays to assess their effect on disease reports of confirmed cases and deaths. First, we use a one-variate model with the number of infected people as input data to forecast the number of deaths. This simple model is compared with a more robust deep learning multi-variate model that uses mobility and transmission rates (R0, Re) from a SEIRD model as input data. A principal components model of community mobility, generated by the principal component analysis (PCA) method, is added to improve the input features for the multi-variate model. The deep learning model architecture is an LSTM stacked layer combined with a dense layer to regress daily deaths caused by COVID-19. The multi-variate model incremented with engineered input features can enhance the forecast performance by up to 18.99% compared to the standard one-variate data-driven model.