Optimization and assessment of Omega-3 fatty acids from high-dimensional spectroscopic data in the Atlantic salmon breeding programs

Atlantic salmon is well known as a rich source of Omega-3 fatty acids (in particular, ALA, DHA, and EPA), and these fatty acids are heritable, thus, the selection of parents will influence their levels in the offspring. The gold standard method of recording Omega-3 fatty acids in Atlantic salmon is costly, time-consuming, and destructive to the sample. For selective breeding purposes, a more affordable method is needed to measure Omega-3 fatty acids in thousands of related salmon. In many breeding programs, vibrational spectroscopy is primarily used with the Partial Least Squares Regression (PLSR) model to measure and predict phenotypes such as Omega-3 fatty acids. However, there is a knowledge gap in estimating heritability using vibrational spectroscopy and finding the effect of sample selection and variable selection methods in the data analysis process perspective. Hence, we optimized and assessed the predicted Omega-3 fatty acids from the high-dimensional spectroscopy data (NIR and Raman spectroscopy data) in the breeding program according to the multiple scenarios combined with sample selection (Kennard-Stone and Random Sampling) and variable selection (with or without Markov Blanket). We found that the PLSR model accuracy and the resulting heritability estimates generally increase with adopting the variable selection method. We also found that NIR spectroscopy has a good affinity with Kennard-Stone sampling, while Raman spectroscopy showed stable performance regardless of sample selection. It is possible to achieve improved PLSR model accuracy and heritability estimates by utilizing the Markov Blanket approach

Beonjim

Sextett. Komposition im Rahmen des Kompositionsstudiums am Institut für Neue Musik der Hochschule für Musik Freibur

Mit gelben Birnen hänget

Quartett. Komposition im Rahmen des Kompositionsstudiums am Institut für Neue Musik der Hochschule für Musik Freibur

Nachbild

Komposition im Rahmen des Kompositionsstudiums am Institut für Neue Musik der Hochschule für Musik Freibur

Diagnostic Color Strip Reader for World Health Partners Clinics

Despite the advancement of medical technology, many people in developing countries like India and Kenya still suffer from treatable diseases. In many of the health clinics in these areas, color strips are used for checkups and diagnosis of diseases. However, a big problem with these color strips is that the diagnosis of color strips take a long time because they have to be manually checked. Currently, World Health Partners (WHP) works with doctors and hospitals in India and Kenya to provide more accessible healthcare through telehealth networks to get consultations from rural clinics to specialists at hospitals. We are working with WHP to streamline the process of color strip diagnosis, by creating an application that goes through the process of reading a color strip in a single step. Our application analyzes an image of a color strip and returns the concentration of the different factors being tested on the color strip. By doing so, we provide a precise analysis of color strips, instead of having to wait for a specialist

Wortkettenspiel

Komposition im Rahmen des Kompositionsstudiums am Institut für Neue Musik der Hochschule für Musik Freibur

Superpixel-based Semantic Segmentation Trained by Statistical Process Control

Semantic segmentation, like other fields of computer vision, has seen a remarkable performance advance by the use of deep convolution neural networks. However, considering that neighboring pixels are heavily dependent on each other, both learning and testing of these methods have a lot of redundant operations. To resolve this problem, the proposed network is trained and tested with only 0.37% of total pixels by superpixel-based sampling and largely reduced the complexity of upsampling calculation. The hypercolumn feature maps are constructed by pyramid module in combination with the convolution layers of the base network. Since the proposed method uses a very small number of sampled pixels, the end-to-end learning of the entire network is difficult with a common learning rate for all the layers. In order to resolve this problem, the learning rate after sampling is controlled by statistical process control (SPC) of gradients in each layer. The proposed method performs better than or equal to the conventional methods that use much more samples on Pascal Context, SUN-RGBD dataset.Comment: Accepted in British Machine Vision Conference (BMVC), 201

Going the distance for protein function prediction: a new distance metric for protein interaction networks

Due to an error introduced in the production process, the x-axes in the first panels of Figure 1 and Figure 7 are not formatted correctly. The correct Figure 1 can be viewed here: http://dx.doi.org/10.1371/annotation/343bf260-f6ff-48a2-93b2-3cc79af518a9In protein-protein interaction (PPI) networks, functional similarity is often inferred based on the function of directly interacting proteins, or more generally, some notion of interaction network proximity among proteins in a local neighborhood. Prior methods typically measure proximity as the shortest-path distance in the network, but this has only a limited ability to capture fine-grained neighborhood distinctions, because most proteins are close to each other, and there are many ties in proximity. We introduce diffusion state distance (DSD), a new metric based on a graph diffusion property, designed to capture finer-grained distinctions in proximity for transfer of functional annotation in PPI networks. We present a tool that, when input a PPI network, will output the DSD distances between every pair of proteins. We show that replacing the shortest-path metric by DSD improves the performance of classical function prediction methods across the board.MC, HZ, NMD and LJC were supported in part by National Institutes of Health (NIH) R01 grant GM080330. JP was supported in part by NIH grant R01 HD058880. This material is based upon work supported by the National Science Foundation under grant numbers CNS-0905565, CNS-1018266, CNS-1012910, and CNS-1117039, and supported by the Army Research Office under grant W911NF-11-1-0227 (to MEC). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript