Factors predicting mortality in type 2 diabetes. With special reference to physical exercise, blood pressure, proteinuria, inflammation and P wave duration

Background: Type 2 diabetes patients have a 2-4 fold risk of cardiovascular disease (CVD) compared to the general population. In type 2 diabetes, several CVD risk factors have been identified, including obesity, hypertension, hyperglycemia, proteinuria, sedentary lifestyle and dyslipidemia. Although much of the excess CVD risk can be attributed to these risk factors, a significant proportion is still unknown. Aims: To assess in middle-aged type 2 diabetic subjects the joint relations of several conventional and non-conventional CVD risk factors with respect to cardiovascular and total mortality. Subjects and methods: This thesis is part of a large prospective, population based East-West type 2 diabetes study that was launched in 1982-1984. It includes 1,059 middle-aged (45-64 years old) participants. At baseline, a thorough clinical examination and laboratory measurements were performed and an ECG was recorded. The latest follow-up study was performed 18 years later in January 2001 (when the subjects were 63-81 years old). The study endpoints were total mortality and mortality due to CVD, coronary heart disease (CHD) and stroke. Results: Physically more active patients had significantly reduced total, CVD and CHD mortality independent of high-sensitivity C-reactive protein (hs-CRP) levels unless proteinuria was present. Among physically active patients with a hs-CRP level >3 mg/L, the prognosis of CVD mortality was similar to patients with hs-CRP levels ≤3 mg/L. The worst prognosis was among physically inactive patients with hs-CRP levels >3 mg/L. Physically active patients with proteinuria had significantly increased total and CVD mortality by multivariate analyses. After adjustment for confounding factors, patients with proteinuria and a systolic BP <130 mmHg had a significant increase in total and CVD mortality compared to those with a systolic BP between 130 and 160 mmHg. The prognosis was similar in patients with a systolic BP <130 mmHg and ≥160 mmHg. Among patients without proteinuria, a systolic BP <130 mmHg was associated with a non-significant reduction in mortality. A P wave duration ≥114 ms was associated with a 2.5-fold increase in stroke mortality among patients with prevalent CHD or claudication. This finding persisted in multivariable analyses. Among patients with no comorbidities, there was no relationship between P wave duration and stroke mortality. Conclusions: Physical activity reduces total and CVD mortality in patients with type 2 diabetes without proteinuria or with elevated levels of hs-CRP, suggesting that the anti-inflammatory effect of physical activity can counteract increased CVD morbidity and mortality associated with a high CRP level. In patients with proteinuria the protective effect was not, however, present. Among patients with proteinuria, systolic BP <130 mmHg may increase mortality due to CVD. These results demonstrate the importance of early intervention to prevent CVD and to control all-cause mortality among patients with type 2 diabetes. The presence of proteinuria should be taken into account when defining the target systolic BP level for prevention of CVD deaths. A prolongation of the duration of the P wave was associated with increased stroke mortality among high-risk patients with type 2 diabetes. P wave duration is easy to measure and merits further examination to evaluate its importance for estimation of the risk of stroke among patients with type 2 diabetes.

Simulation of new p-type pixel strip detector with enhanced multiplication effect

This Master’s Thesis is dedicated to the simulation of new p-type pixel strip detector with enhanced multiplication effect. It is done for high-energy physics experiments upgrade such as Super Large Hadron Collider especially for Compact Muon Solenoid particle track silicon detectors. These detectors are used in very harsh radiation environment and should have good radiation hardness. The device engineering technology for developing more radiation hard particle detectors is used for minimizing the radiation degradation. New detector structure with enhanced multiplication effect is proposed in this work. There are studies of electric field and electric charge distribution of conventional and new p-type detector under reverse voltage bias and irradiation. Finally, the dependence of the anode current from the applied cathode reverse voltage bias under irradiation is obtained in this Thesis. For simulation Silvaco Technology Computer Aided Design software was used. Athena was used for creation of doping profiles and device structures and Atlas was used for getting electrical characteristics of the studied devices. The program codes for this software are represented in Appendixes.

Modeling Front End Electronics for a Gas Electron Multiplier Detector

The Large Hadron Collider (LHC) in The European Organization for Nuclear Research (CERN) will have a Long Shutdown sometime during 2017 or 2018. During this time there will be maintenance and a possibility to install new detectors. After the shutdown the LHC will have a higher luminosity. A promising new type of detector for this high luminosity phase is a Triple-GEM detector. During the shutdown these detectors will be installed at the Compact Muon Solenoid (CMS) experiment. The Triple-GEM detectors are now being developed at CERN and alongside also a readout ASIC chip for the detector. In this thesis a simulation model was developed for the ASICs analog front end. The model will help to carry out more extensive simulations and also simulate the whole chip before the whole design is finished. The proper functioning of the model was tested with simulations, which are also presented in the thesis.

Readout architectures for hybrid pixel detector readout chips

The original contribution of this thesis to knowledge are novel digital readout architectures for hybrid pixel readout chips. The thesis presents asynchronous bus-based architecture, a data-node based column architecture and a network-based pixel matrix architecture for data transportation. It is shown that the data-node architecture achieves readout efficiency 99% with half the output rate as a bus-based system. The network-based solution avoids “broken” columns due to some manufacturing errors, and it distributes internal data traffic more evenly across the pixel matrix than column-based architectures. An improvement of > 10% to the efficiency is achieved with uniform and non-uniform hit occupancies. Architectural design has been done using transaction level modeling (TLM) and sequential high-level design techniques for reducing the design and simulation time. It has been possible to simulate tens of column and full chip architectures using the high-level techniques. A decrease of > 10 in run-time is observed using these techniques compared to register transfer level (RTL) design technique. Reduction of 50% for lines-of-code (LoC) for the high-level models compared to the RTL description has been achieved. Two architectures are then demonstrated in two hybrid pixel readout chips. The first chip, Timepix3 has been designed for the Medipix3 collaboration. According to the measurements, it consumes < 1 W/cm^2. It also delivers up to 40 Mhits/s/cm^2 with 10-bit time-over-threshold (ToT) and 18-bit time-of-arrival (ToA) of 1.5625 ns. The chip uses a token-arbitrated, asynchronous two-phase handshake column bus for internal data transfer. It has also been successfully used in a multi-chip particle tracking telescope. The second chip, VeloPix, is a readout chip being designed for the upgrade of Vertex Locator (VELO) of the LHCb experiment at CERN. Based on the simulations, it consumes < 1.5 W/cm^2 while delivering up to 320 Mpackets/s/cm^2, each packet containing up to 8 pixels. VeloPix uses a node-based data fabric for achieving throughput of 13.3 Mpackets/s from the column to the EoC. By combining Monte Carlo physics data with high-level simulations, it has been demonstrated that the architecture meets requirements of the VELO (260 Mpackets/s/cm^2 with efficiency of 99%).

Generative Part-Based Gabor Object Detector

Object detection is a fundamental task of computer vision that is utilized as a core part in a number of industrial and scientific applications, for example, in robotics, where objects need to be correctly detected and localized prior to being grasped and manipulated. Existing object detectors vary in (i) the amount of supervision they need for training, (ii) the type of a learning method adopted (generative or discriminative) and (iii) the amount of spatial information used in the object model (model-free, using no spatial information in the object model, or model-based, with the explicit spatial model of an object). Although some existing methods report good performance in the detection of certain objects, the results tend to be application specific and no universal method has been found that clearly outperforms all others in all areas. This work proposes a novel generative part-based object detector. The generative learning procedure of the developed method allows learning from positive examples only. The detector is based on finding semantically meaningful parts of the object (i.e. a part detector) that can provide additional information to object location, for example, pose. The object class model, i.e. the appearance of the object parts and their spatial variance, constellation, is explicitly modelled in a fully probabilistic manner. The appearance is based on bio-inspired complex-valued Gabor features that are transformed to part probabilities by an unsupervised Gaussian Mixture Model (GMM). The proposed novel randomized GMM enables learning from only a few training examples. The probabilistic spatial model of the part configurations is constructed with a mixture of 2D Gaussians. The appearance of the parts of the object is learned in an object canonical space that removes geometric variations from the part appearance model. Robustness to pose variations is achieved by object pose quantization, which is more efficient than previously used scale and orientation shifts in the Gabor feature space. Performance of the resulting generative object detector is characterized by high recall with low precision, i.e. the generative detector produces large number of false positive detections. Thus a discriminative classifier is used to prune false positive candidate detections produced by the generative detector improving its precision while keeping high recall. Using only a small number of positive examples, the developed object detector performs comparably to state-of-the-art discriminative methods.