Neurofilament proteins in the postnatal rat hippocampus. Developmental expression and changes in experimental epilepsy

Neurofilament proteins (NFs) are the major components of the intermediate filaments of the neuronal cytoskeleton. The three different NF proteins; the low (NF-L), medium (NF-M),and dendrites.NF proteins play an important role in neuronal development, and plasticity,and seem to contribute to the pathophysiology of several diseases. However, the detailed expression patterns of NF proteins in the course of postnatal aturation, and in response to seizures in the rat have remained unknown. In this work, I have studied the developmental expression and cellular distribution of the three NF proteins in the rat hippocampus during the postnatal development. The reactivity of NF proteins in response to kainic acid (KA)-induced status epilepticus (SE)was studied in the hippocampus of 9-day-old rats, and using in vitro organotypic hippocampal slices cultures prepared from P6-7 rats. The results showed that NF-L and NF-M proteins are expressed already at the postnatal day 1, while the expression of NF-H mainly occurred during the second postnatal week. The immunoreactivity of NF proteins varied depending on the cell type and sub-cellular location in the hippocampus. In adult rats, KA-induced SE typically results in severe and permanent NF degradation. However, in our P9 rats KA-induced SE resulted in a transient increase in the expression of NF proteins during the first few hours but not degradation. No neuronal death or mossy fiber sprouting was observed at any time after SE. The in vitro studies with OHCs, which mimick the in vivo developing models where a local injection of KA is applied(e.g. intrahippocampal), indicated that NF proteins were rapidly degraded in response to KA treatment, this effect being effectively inhibited by the treatment with the AMPA receptor antagonist CNQX, and calpain inhibitor MDL-28170. These compounds also significantly ameliorated the KA-induced region-specific neuronal damage. The NMDA receptor antagonist and the L-type Ca2+ channel blocker did not have any significant effect. In conclusion, the results indicate that the developmental expression of NF in the rat hippocampus is differentially regulated and targeted in the different hippocampal cell types during the postnatal development. Furthermore, despite SE, the mechanisms leading to NF degradation and neuronal death are not activated in P9 rats unlike in adults. The reason for this remains unknown. The results in organotypic hippocampal cultures confirm the validity of this in vitro model to study development processes, and to perform pharmacological studies. The results also suggest that calpain proteases as interesting pharmacological targets to reduce neuronal damage after acute excitotoxic insults.

Local Binary Patterns in Focal-Plane Processing. Analysis and Applications

Feature extraction is the part of pattern recognition, where the sensor data is transformed into a more suitable form for the machine to interpret. The purpose of this step is also to reduce the amount of information passed to the next stages of the system, and to preserve the essential information in the view of discriminating the data into different classes. For instance, in the case of image analysis the actual image intensities are vulnerable to various environmental effects, such as lighting changes and the feature extraction can be used as means for detecting features, which are invariant to certain types of illumination changes. Finally, classification tries to make decisions based on the previously transformed data. The main focus of this thesis is on developing new methods for the embedded feature extraction based on local non-parametric image descriptors. Also, feature analysis is carried out for the selected image features. Low-level Local Binary Pattern (LBP) based features are in a main role in the analysis. In the embedded domain, the pattern recognition system must usually meet strict performance constraints, such as high speed, compact size and low power consumption. The characteristics of the final system can be seen as a trade-off between these metrics, which is largely affected by the decisions made during the implementation phase. The implementation alternatives of the LBP based feature extraction are explored in the embedded domain in the context of focal-plane vision processors. In particular, the thesis demonstrates the LBP extraction with MIPA4k massively parallel focal-plane processor IC. Also higher level processing is incorporated to this framework, by means of a framework for implementing a single chip face recognition system. Furthermore, a new method for determining optical flow based on LBPs, designed in particular to the embedded domain is presented. Inspired by some of the principles observed through the feature analysis of the Local Binary Patterns, an extension to the well known non-parametric rank transform is proposed, and its performance is evaluated in face recognition experiments with a standard dataset. Finally, an a priori model where the LBPs are seen as combinations of n-tuples is also presented

The effect of focal point parameters in fiber laser welding of structural steel

In this thesis the effect of focal point parameters in fiber laser welding of structural steel is studied. The goal is to establish relations between laser power, focal point diameter and focal point position with the resulting quality, weld-bead geometry and hardness of the welds. In the laboratory experiments, AB AH36 shipbuilding steel was welded in an I-butt joint configuration using IPG YLS-10000 continuous wave fiber laser. The quality of the welds produced were evaluated based on standard SFS-EN ISO 13919-1. The weld-bead geometry was defined from the weld cross-sections and Vickers hardness test was used to measure hardness's from the middle of the cross-sections. It was shown that all the studied focal point parameters have an effect on the quality, weld-bead geometry and hardness of the welds produced.

Detection of Epilepsy with a Commercial EEG Headband

Epilepsy is a chronic brain disorder, characterized by reoccurring seizures. Automatic sei-zure detector, incorporated into a mobile closed-loop system, can improve the quality of life for the people with epilepsy. Commercial EEG headbands, such as Emotiv Epoc, have a potential to be used as the data acquisition devices for such a system. In order to estimate that potential, epileptic EEG signals from the commercial devices were emulated in this work based on the EEG data from a clinical dataset. The emulated characteristics include the referencing scheme, the set of electrodes used, the sampling rate, the sample resolution and the noise level. Performance of the existing algorithm for detection of epileptic seizures, developed in the context of clinical data, has been evaluated on the emulated commercial data. The results show, that after the transformation of the data towards the characteristics of Emotiv Epoc, the detection capabilities of the algorithm are mostly preserved. The ranges of acceptable changes in the signal parameters are also estimated.