No abstract

In previous experiments, we observed signs of cardiac failure in mice overexpressing lipoprotein lipase (LPL) under the control of a muscle specific promotor and in peroxisome proliferators activated receptor alpha (PPARalpha) knockout mice overexpressing LPL under the control of the same promotor. In our current investigations, we focussed on morphological consequences and changes in mRNA and protein expression in hearts from these animals. mRNA expression was analysed by differential display analysis and Northern blot as well as by cDNA microarray analysis followed by pathway analysis. Protein expression was examined using immunoblot and immunohistochemistry. Fibrosis was determined by chromotrope aniline blue staining for collagen. A distinct increase in the expression of alpha-tubulin mRNA was noted in hearts of all mutant mouse strains compared with the control. This result was paralleled by increased alpha-tubulin protein expression. Using cDNA microarray analysis, we detected an activation of apoptosis, in particular an increase of caspase-3 expression in hearts of mice overexpressing LPL but not in PPARalpha knockout mice overexpressing LPL. This finding was confirmed immunohistochemically. In addition, we identified a distinct interstitial increase in collagen and an increase around blood vessels. In our mouse model, we detect mRNA and protein changes typical for cardiomyopathy even before overt clinical signs of heart failure. In addition, a small but distinct increase in the rate of apoptosis of cardiomyocytes and fibrotic changes contributes to cardiac failure in mice overexpressing LPL, whereas additional deficiency in PPARalpha seems to protect hearts from these effects.

Our findings demonstrate that for patients with carotid and coronary artery disease, both hybrid and staged revascularization by CAS and OPCAB are feasible and safe therapeutic strategies with good early and long-term outcomes. However, our results have to be substantiated by larger scale studies and randomized trials.

Low-cost Global Navigation Satellite System (GNSS) receivers and monocular cameras are widely used in daily activities. The complementary nature of these two devices is ideal for outdoor navigation. In this paper, we investigate the integration of GNSS and monocular camera measurements in a simultaneous localization and mapping system. The proposed system first aligns the coordinates between two sensors. Subsequently, the measurements are fused by an optimization-based scheme. Our system can function in real-time and obtain the absolute position, scale, and attitude of the vehicle. It achieves a high accuracy without a preset map and also has the capability to work with a preset map. The system can easily be extended to create other forms of maps or for other types of cameras. Experimental results on a popular public dataset are presented to validate the performance of the proposed system.

Machine learning has attracted widespread attention and evolved into an enabling technology for a wide range of highly successful applications, such as intelligent computer vision, speech recognition, medical diagnosis, and more. Yet a special need has arisen where, due to privacy, usability, and/or the right to be forgotten , information about some specific samples needs to be removed from a model, called machine unlearning. This emerging technology has drawn significant interest from both academics and industry due to its innovation and practicality. At the same time, this ambitious problem has led to numerous research efforts aimed at confronting its challenges. To the best of our knowledge, no study has analyzed this complex topic or compared the feasibility of existing unlearning solutions in different kinds of scenarios. Accordingly, with this survey, we aim to capture the key concepts of unlearning techniques. The existing solutions are classified and summarized based on their characteristics within an up-to-date and comprehensive review of each category’s advantages and limitations. The survey concludes by highlighting some of the outstanding issues with unlearning techniques, along with some feasible directions for new research opportunities.