Human Papilloma Virus Analysis of HPV FISH patterns in low and high grade Cervical Intraepithelial Neoplasia

Human Papilloma Virus is the most common sexually transmitted infection, with anestimated 80% of sexually active men and women acquiring an infection at some pointin their lifetime. 10-20% of infected individuals can not clear this infection effectivelyand consequentially are at risk for progression of Cervical Intraepithelial Neoplasia(CIN) to cancer. Presence of HPV can be determined using PCR and/or (Fluorescence) InSitu Hybridization. The aim of the performed experiments was to determine the generalFISH patterns that are specifically linked to low grade and high grade CIN lesions and toinvestigate whether or not these patterns could be used to grade these lesions.12 formalin fixed and paraffin embedded sections from one patient and 30 formalin fixedand paraffin embedded sections from different patients where used to perform a FISHprocedure and to analyze the general FISH hybridization pattern for CIN 1,2 and 3. For theanalysis 3 distinct patterns for the physical status of the virus were determined: episomal,integrated and mixed pattern. Also the presence of replication, load and the ratio betweenbasal load and superficial load was analyzed to determine the general pattern.Results show that load and physical status of the virus are not associated with the severityof the lesions. High loads are present in both high and low grade lesions. Also physicalstatus of the virus is not different for the sections, episomal and mixed patterns are foundin low and high grades. Only integrated pattern is a marker for severity, as this is only foundin CIN 3. Presence of replication is most common in CIN 1, this might contribute to correctgrading. The ratio between the load in the Basal layer and the load in the superficial layeris the most informative discriminant of severity: <0.5 for CIN 1, 0.5<load<1 for CIN 2 and 1for CIN 3. Based on these results it is possible to classify the severity of the lesio

Simulation and Theory of Large-Scale Cortical Networks

Cerebral cortex is composed of intricate networks of neurons. These neuronal networks are strongly interconnected: every neuron receives, on average, input from thousands or more presynaptic neurons. In fact, to support such a number of connections, a majority of the volume in the cortical gray matter is filled by axons and dendrites. Besides the networks, neurons themselves are also highly complex. They possess an elaborate spatial structure and support various types of active processes and nonlinearities. In the face of such complexity, it seems necessary to abstract away some of the details and to investigate simplified models. In this thesis, such simplified models of neuronal networks are examined on varying levels of abstraction. Neurons are modeled as point neurons, both rate-based and spike-based, and networks are modeled as block-structured random networks. Crucially, on this level of abstraction, the models are still amenable to analytical treatment using the framework of dynamical mean-field theory. The main focus of this thesis is to leverage the analytical tractability of random networks of point neurons in order to relate the network structure, and the neuron parameters, to the dynamics of the neurons—in physics parlance, to bridge across the scales from neurons to networks. More concretely, four different models are investigated: 1) fully connected feedforward networks and vanilla recurrent networks of rate neurons; 2) block-structured networks of rate neurons in continuous time; 3) block-structured networks of spiking neurons; and 4) a multi-scale, data-based network of spiking neurons. We consider the first class of models in the light of Bayesian supervised learning and compute their kernel in the infinite-size limit. In the second class of models, we connect dynamical mean-field theory with large-deviation theory, calculate beyond mean-field fluctuations, and perform parameter inference. For the third class of models, we develop a theory for the autocorrelation time of the neurons. Lastly, we consolidate data across multiple modalities into a layer- and population-resolved model of human cortex and compare its activity with cortical recordings. In two detours from the investigation of these four network models, we examine the distribution of neuron densities in cerebral cortex and present a software toolbox for mean-field analyses of spiking networks

How serious do we need to be? Improving Information Literacy skills through gaming and interactive elements

Nowadays technology makes information accessible for everyone everywhere. The art of selecting the best information in a short period of time and use it correctly is called information literacy. Information literacy training provides students with the tools necessary to efficiently find and correctly use the information needed for learning purposes. Acquiring these skills is a process that takes time: during the whole academic period, learners need different information types that require other ways of information seeking and processing. The Millennials, the new generation of students that now populate the universities have a new way of processing information. They have a very short attention span and they are more critical about the what, when and how they learn [Oblinger, 2005]. Classical learning methods, where teachers tell students what they need to do are not attractive. Students get bored quickly, not paying attention to the lesson. How can libraries offer those lessons in such a manner that students get motivated to learn and use it every time they search for information? The challenge for academic libraries is to motivate students to acquire information skills, so that they use these skills in their academic study and keep looking for new tips and tricks on information retrieval. The approach to how to get students to this point is a concern for academic libraries. Learning methods can be used to improve educational materials on information literacy. Many libraries are making efforts to develop more effective ways of teaching information literacy. This paper aims to contribute to this issue, describing a research project on the learning effects of students using a game and a web-based tutorial on Information Literacy developed by the Vrije University Amsterdam

Linking Network and Neuron-level Correlations by Renormalized Field Theory

It is frequently hypothesized that cortical networks operate close to a critical point. Advantages of criticality include rich dynamics well-suited for computation and critical slowing down, which may offer a mechanism for dynamic memory. However, mean-field approximations, while versatile and popular, inherently neglect the fluctuations responsible for such critical dynamics. Thus, a renormalized theory is necessary. We consider the Sompolinsky-Crisanti-Sommers model which displays a well studied chaotic as well as a magnetic transition. Based on the analogue of a quantum effective action, we derive self-consistency equations for the first two renormalized Greens functions. Their self-consistent solution reveals a coupling between the population level activity and single neuron heterogeneity. The quantitative theory explains the population autocorrelation function, the single-unit autocorrelation function with its multiple temporal scales, and cross correlations

Large Deviations Approach to Random Recurrent Neuronal Networks: Parameter Inference and Fluctuation-Induced Transitions

We here unify the field theoretical approach to neuronal networks with large deviations theory. For a prototypical random recurrent network model with continuous-valued units, we show that the effective action is identical to the rate function and derive the latter using field theory. This rate function takes the form of a Kullback-Leibler divergence which enables data-driven inference of model parameters and calculation of fluctuations beyond mean-field theory. Lastly, we expose a regime with fluctuation-induced transitions between mean-field solutions.Comment: Extension to multiple population

Usage and Scaling of an Open-Source Spiking Multi-Area Model of Monkey Cortex

We are entering an age of `big' computational neuroscience, in which neural network models are increasing in size and in numbers of underlying data sets. Consolidating the zoo of models into large-scale models simultaneously consistent with a wide range of data is only possible through the effort of large teams, which can be spread across multiple research institutions. To ensure that computational neuroscientists can build on each other's work, it is important to make models publicly available as well-documented code. This chapter describes such an open-source model, which relates the connectivity structure of all vision-related cortical areas of the macaque monkey with their resting-state dynamics. We give a brief overview of how to use the executable model specification, which employs NEST as simulation engine, and show its runtime scaling. The solutions found serve as an example for organizing the workflow of future models from the raw experimental data to the visualization of the results, expose the challenges, and give guidance for the construction of ICT infrastructure for neuroscience

Public Relations Work to Increase Attainability Relating to EMail-Communication

The mere allotment of an e-mail address does in no way ensure answering quotas, which could be called sufficient to dependably hand out important information via roundmail. A loss of accessibility through e-mail addresses already provided by the university can be due to various technical problems and personal deficiencies of motivation. Students in particular often use external providers and are often also not on familiar terms with the use of consistent personal e-mail archives, POP-accounts, Web-Mail-Portals and the use of forewarding functions. Special email-courses are being offered within regular further training for staff and students