WHOSE MODEL IS IT!: BRIDGING THE GAP BETWEEN ENGINEERING AND STATISTICS

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/71421/1/j.1747-1567.1999.tb01539.x.pd

Usage of the MATLAB environment for neural networks

Tato bakalářská práce se věnuje základům teorie neuronových sítí a jejich modelováním v prostředí MATLAB. Práce může být rozdělena do čtyř celků. Po úvodu k práci je v první kapitole vysvětleno teoretické pozadí neuronových sítí. Kapitola uvádí stručnou historii, biologické pozadí neuronových sítí a pojednává o základních síťových architekturách a procesu trénování těchto sítí. Další část práce se zabývá samotnou implementací neuronových sítí v prostředí MATLAB, přípravou dat, vytvářením síti, jejich simulací a testováním. Poslední část dokumentu obsahuje návrh dvou úloh pro seznámení studentů s modelováním neuronových sítí v MATLABe.This bachelor thesis discusses the basic theory and modelling of neural networks in the software environment of MATLAB. The thesis can be divided into four parts. After an introduction into the thesis, the theoretical background of the neural netwoks is explained in the first chapter. This chapter features a brief history and a biological background of neural networks and deals with the basic network architectures and the training processes. The next part is the description of how to implement networks in a general way using the MATLAB enviroment, so it deals with preparation of data, creation, simulation and training of a neural network. The last part of the paper covers a design of two excersises created in order to introduce modelling of the neural networks in the MATLAB enviroment to the students.

Combined Experimental and System-Level Analyses Reveal the Complex Regulatory Network of miR-124 during Human Neurogenesis

Non-coding RNAs regulate many biological processes including neurogenesis. The brain-enriched miR-124 has been assigned as a key player of neuronal differentiation via its complex but little understood regulation of thousands of annotated targets. To systematically chart its regulatory functions, we used CRISPR/Cas9 gene editing to disrupt all six miR-124 alleles in human induced pluripotent stem cells. Upon neuronal induction, miR-124-deleted cells underwent neurogenesis and became functional neurons, albeit with altered morphology and neurotransmitter specification. Using RNA-induced-silencing-complex precipitation, we identified 98 high-confidence miR-124 targets, of which some directly led to decreased viability. By performing advanced transcription-factor-network analysis, we identified indirect miR-124 effects on apoptosis, neuronal subtype differentiation, and the regulation of previously uncharacterized zinc finger transcription factors. Our data emphasize the need for combined experimental- and system-level analyses to comprehensively disentangle and reveal miRNA functions, including their involvement in the neurogenesis of diverse neuronal cell types found in the human brain

C9 ORF 72 expansion in a family with bipolar disorder

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98405/1/bdi12063.pd

bsamGP: An R Package for Bayesian Spectral Analysis Models Using Gaussian Process Priors

The Bayesian spectral analysis model (BSAM) is a powerful tool to deal with semiparametric methods in regression and density estimation based on the spectral representation of Gaussian process priors. The bsamGP package for R provides a comprehensive set of programs for the implementation of fully Bayesian semiparametric methods based on BSAM. Currently, bsamGP includes semiparametric additive models for regression, generalized models and density estimation. In particular, bsamGP deals with constrained regression models with monotone, convex/concave, S-shaped and U-shaped functions by modeling derivatives of regression functions as squared Gaussian processes. bsamGP also contains Bayesian model selection procedures for testing the adequacy of a parametric model relative to a non-specific semiparametric alternative and the existence of the shape restriction. To maximize computational efficiency, we carry out posterior sampling algorithms of all models using compiled Fortran code. The package is illustrated through Bayesian semiparametric analyses of synthetic data and benchmark data

A fine romance? Developing a transformational school-university partnership

This paper investigates the complexities involved in a school-university partnership between a secondary school, Highview College and Federation University, both located in Australia. The authors argue that Federation University and Highview College have worked together to develop a transformational partnership in a Community of Practice (CoP) that has benefits for both parties. The authors report the findings through the analogy of a relationship unfolding. Using a qualitative methodology, it was found that through the development of a transformational partnership, a number of benefits had even-tuated. These benefits include authentic learning experiences and the raising of university aspirations for school students. © 2021 James Nicholas Publishers