Unequal allelic expression of wild-type and mutated β-myosin in familial hypertrophic cardiomyopathy

Familial hypertrophic cardiomyopathy (FHC) is an autosomal dominant disease, which in about 30% of the patients is caused by missense mutations in one allele of the β-myosin heavy chain (β-MHC) gene (MYH7). To address potential molecular mechanisms underlying the family-specific prognosis, we determined the relative expression of mutant versus wild-type MYH7-mRNA. We found a hitherto unknown mutation-dependent unequal expression of mutant to wild-type MYH7-mRNA, which is paralleled by similar unequal expression of β-MHC at the protein level. Relative abundance of mutated versus wild-type MYH7-mRNA was determined by a specific restriction digest approach and by real-time PCR (RT-qPCR). Fourteen samples from M. soleus and myocardium of 12 genotyped and clinically well-characterized FHC patients were analyzed. The fraction of mutated MYH7-mRNA in five patients with mutation R723G averaged to 66 and 68% of total MYH7-mRNA in soleus and myocardium, respectively. For mutations I736T, R719W and V606M, fractions of mutated MYH7-mRNA in M. soleus were 39, 57 and 29%, respectively. For all mutations, unequal abundance was similar at the protein level. Importantly, fractions of mutated transcripts were comparable among siblings, in younger relatives and unrelated carriers of the same mutation. Hence, the extent of unequal expression of mutated versus wild-type transcript and protein is characteristic for each mutation, implying cis-acting regulatory mechanisms. Bioinformatics suggest mRNA stability or splicing effectors to be affected by certain mutations. Intriguingly, we observed a correlation between disease expression and fraction of mutated mRNA and protein. This strongly suggests that mutation-specific allelic imbalance represents a new pathogenic factor for FHC

Low Altitude Pathfinding Service

Pathfinding algorithms find a path from one node to another in a data structure known as a graph. They are heavily used in GPS devices. For GPS devices in cars, a graph is created from the world’s road networks, which can then be used to find a path from the user’s current location to their destination. To build an autonomous flying drone, it has to find it’s way around the world. Because they do not have to follow a road network, it may be possible to take a direct path. However, this ignores the terrain, and it may naively fly over a mountain, spending a lot of energy climbing to the required altitude. Airborne LIDAR scanning can create very high resolution surface maps. By converting these maps into a graph, we can use a pathfinding algorithm to find the path of least resistance. This allows the drone to fly around buildings and hills rather than above them. However, the problem with this is that these graphs become very large. For roads, they can be heavily simplified, but this is not the case for terrain. This means that pathfinding algorithms become extremely slow. To be as efficient as possible, the paths should be rounded out, which requires post-processing. The assignment given to us by Kongsberg Defence & Aerospace is to investigate whether a machine-learning based approach could work in this case. The hope is that a machine-learned approach can be run in a fraction of the time, and not require post-processing. To make investigation easier, we had to build a web service which allows us to run and compare the different implementations we came up with. In this report, we show how the service is built, and what the final result was. We explain the different machine-learning approaches we have experimented with. The chosen process model, using the principles of Kanban, is explained in detail. In the end, we show that the idea definitely has potential, and that further research in this field is warranted. We were unable to draw any meaningful conclusions as to the viability of this approach

Erythrocytes Are Oxygen-Sensing Regulators of the Cerebral Microcirculation

Energy production in the brain depends almost exclusively on oxidative metabolism. Neurons have small energy reserves and require a continuous supply of oxygen (O2). It is therefore not surprising that one of the hallmarks of normal brain function is the tight coupling between cerebral blood flow and neuronal activity. Since capillaries are embedded in the O2-consuming neuropil, we have here examined whether activity-dependent dips in O2 tension drive capillary hyperemia. In vivo analyses showed that transient dips in tissue O2 tension elicit capillary hyperemia. Ex vivo experiments revealed that red blood cells (RBCs) themselves act as O2 sensors that autonomously regulate their own deformability and thereby flow velocity through capillaries in response to physiological decreases in O2 tension. This observation has broad implications for understanding how local changes in blood flow are coupled to synaptic transmission