Scientific discoveries this week: 3/4/13

Munich, Germany
According to a new study from the Ludwig Maximilian University of Munich (LMU), a protein called TDP-43 causes muscle wasting and stunted nerve cells leading to dementia. The finding supports the idea that this protein plays a big role in amyotrophic lateral sclerosis (ALS). ALS is an incurable neurological disease which manifests first by muscle wasting. Both limbs and respiratory muscles are affected leading to impaired mobility and breathing problems. Patients usually die with a few years after symptoms emerge but in rare cases such as Stephen Hawking, patients can live with the disease for a long time. Increasing evidence that ALS and frontaltemporal dementia (FTD) may have similar causes. The symptoms overlap and common factors were found at the microscopic level. The disease affects humans with particles accumulating and forming clumps in nerve cells, especially the TDP-43 protein. The protein is normally located in the cell nucleus and involved in processing genetic information, but with the disease TDP-43, accumulates outside the nucleus forming aggregates. The protein’s normal function is disrupted and it no longer reaches the nucleus. The study was done on zebrafish. Their genetic code was modified so that they did not produce TDP-43 protein. This led to the young fish demonstrating muscle wasting and dying a few days after hatching. According to LMU, the study revealed that loss of function of TDP-43 does seem to play a critical role in both ALS and FTD.

MIT, Massachusettes
Researchers at MIT presented a new algorithm for solving graph Laplacians that is faster and simpler than its predecessors. The researchers believe that the simplicity of their algorithm should make it much faster and easier to implement in software, and could generalize graph Laplacians to other contexts. Applications using graph Laplacians involve scheduling, image processing, network analysis and more. A graph Laplacian is a matrix that describes a graph. A graph is an collection of nodes, often depicted as circles, and edges as lines that connect the nodes. The nodes might represent tasks to be performed. In many graphs, the numbers have different numbers associated with them that could represent cost or other data about moving from one step to another in a operation. The Laplacian graph describes the weights between all the edges, but it can be a series of linear equations. Solving those equations is crucial for analyzing the graphs. Earlier approaches considered a series of approximations of the graph of interest. Unfortunately the rules for constructing the sequence of graphs could be complex, proving that solving the equations to find a good approximation required mathematical ingenuity. The researchers’ approach is much simpler. They find a spanning tree which is a tree or graph with no closed loops that touches all the graphs’ nodes, but dispenses with the edges that create loops. Using the spanning they add back one of the missing edges, creating a loop. A loop means that two nodes are connected by two different paths. The researchers showed that this simple repetitive process of adding edges and rebalancing will converge on the solution of the graph Laplacian.

Researchers from the University of Southern California projected pixels on to a headset to help the visually impaired in everyday tasks such as navigation, and object finding. Developed using a video camera and algorithm the researchers hope they can enhance the vision of patients already fitted with retinal implants. Blind people with retinal implants can detect motion and large objects, but they are still lack resolution. The researchers believe their algorithm will enhance retinal implants providing the user with better resolution when they are looking for a specific item. 19 subjects were involved in the study. They each trained first to get used to the pixelated vision and during the study they took part in three different experiments. The subject walked an obstacle course, found objects on empty tables, and searched for a particular target in a cluttered environment. A camera collected real-world information in the view of the subject and the information was conveyed to the subject. The subjects learned to adapt to pixelated vision in all of the tasks, suggesting that image processing algorithms can be used to provide greater confidence to patients when performing tasks, especially in a new environment.

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