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MSI Users Bulletin – June 2018

The Users Bulletin provides a summary of new policies, procedures, and events of interest to MSI users. It is published quarterly. To request technical assistance with your MSI account, please contact help@msi.umn.edu . 1. Leadership Changes at MSI: Director of Research Computing Claudia Neuhauser...

Wildlife in the Serengeti

Camera traps, which are motion- or heat-activated automatic cameras, are revolutionizing how researchers can study ecosystems, since they are noninvasive, relatively inexpensive, and are capable of monitoring large areas and diverse species. A few years ago, members of the research group of MSI...

HIV and HTLV Molecular and Cell Biology

Abstract: 
<h3 class="red">HIV and HTLV Molecular and Cell Biology</h3><p>These researchers are using MSI resources for two projects.</p><ul><li>HIV Reverse Transcriptase-Mediated Mutagenesis: HIV-1 has a high mutation rate, which contributes to its ability to evade the host immune system, limits the efficacy of antiretroviral drugs, and drives the emergence of drug resistance. Drug resistance conferring mutations as well as other viral mutations are primarily attributed to the error-prone nature of reverse transcriptase (RT). An intentional increase in RT-mediated mutations decreases virus infectivity by increasing the mutation rate to a level that is not able to maintain survival of the virus population. The potency by which HIV-1 infectivity can be decreased by increasing RT-mediated errors has led to an initiative to discover small molecules that may increase the HIV-1 mutation rate. An interdisciplinary collaborative team has been assembled to conduct discovery studies to identify new small molecules that increase RT-mediated errors, use molecular analyses to identify the mechanism(s) by which small molecules increase the HIV mutation rate and result in virus extinction, and to assess the mechanism of RT-mediated mutation using biochemical methods. Through preliminary studies, the researchers have identified four small molecules that increase RT-mediated mutations. In order to elucidate the structure-activity relationship driving this increase and to optimize this activity, they are first pursuing discovery studies to identify small molecules that can increase RT-mediated errors. The antiviral and mutagenic activities of these molecules will be assessed in cell culture. Second, they will examine the mechanism by which small molecules induce mutations and cause virus extinction in HIV-1 using cell culture methodologies. Here they will examine small molecules that they have already discovered as well as any lead molecules that they identify. Third, they will investigate the mechanism of action using biochemical methods to elucidate the mechanistic basis for increased RT-mediated mutation. Successful completion of these studies will provide deeper insight into the mechanisms of RT-mediated viral mutagenesis and its impact on viral replication and extinction.</li><li>HTLV-1 Particle Analysis and Gag Interactions: Human T-cell leukemia virus (HTLV-1) infects about 20 million individuals worldwide and is the etiological agent of an adult T-cell leukemia/lymphoma (ATLL). It can also result in an inflammatory disease syndrome called HTLV-1-associated myelopathy (HAM)/tropical spastic paraparesis (TSP). Prevalence rates for HTLV-1 infection in the general population are greater than 1% in the Caribbean Basin, Central Africa, and South Japan. HTLV-1 is notorious for being difficult to study in cell culture, which has prohibited a rigorous analysis of how these viruses replicate in cells, including the steps involved in retrovirus assembly. The details for how retrovirus particle assembly occurs are poorly understood even for other more tractable retroviral systems. Using a tractable model system, state-of-the-art biophysical approaches, and an interdisciplinary research team, these researchers have made novel observations that form the basis for this project. The researchers are beginning to investigate questions related to HTLV-1 particle size, Gag stoichiometry in particles, and HTLV-1 Gag interactions in living cells using multiple experimental approaches. In particular, they will apply cryo-electron microscopy/tomography (cryo-EM/ET), total internal reflection fluorescence (TIRF) microscopy, and the novel single-molecule technology of fluorescence fluctuation spectroscopy (FFS) to investigate questions related to particle size and Gag stoichiometry, Gag targeting to membrane, and HTLV-1 particle biogenesis. The results from these studies should provide further insight into fundamental aspects of HTLV-1 and retrovirus particle assembly, which may aid in developing therapeutics.</li></ul><p>Return to this PI&#39;s <a href="https://www.msi.umn.edu/pi/0d33d301363019526805d587cb23ef3a/20756">main page</a>.</p>
Group name: 
manskylm

Pathway Targeted Deep Brain Stimulation for Parkinson's Disease

Abstract: 
<h3 class="red"><span>Pathway Targeted Deep Brain Stimulation for Parkinson&#39;s Disease</span></h3><p><span>Deep brain stimulation (DBS) is a surgical procedure used to treat various neurological and neuropsychiatric disorders, including Parkinson&rsquo;s disease, essential tremor, dystonia, and obsessive-compulsive disorder. To date, over 100,000 patients worldwide have undergone DBS surgery, and this number is expected to increase significantly. DBS involves the surgical implantation of an electrode into a specific brain target, for delivery of electrical stimulation, alleviating disease symptoms. However, one of the ambiguities of this procedure is that the clinical outcomes can vary greatly across patients. One plausible explanation for the differences may lie in unoptimized DBS lead placement and the stimulation of undesired anatomical structures and white-matter pathways. Thus, the success of this surgical technique is critically dependent on the precise placement of the DBS electrode. At present, DBS surgery relies on a two-step procedure: initial target localization is based on stereotactic imaging combined with cadaveric atlas-derived consensus coordinates. However, current clinical imaging methods do not allow for a clear visualization of DBS target structures, which can result in electrode placement errors. Consequently, this step is followed by an invasive microelectrode recording procedure that is used for target validation, but carries risk.</span></p><p>This project aims to improve the imaging-based target localization and visualizations for DBS surgery. Capitalizing on the advantages of high-field (7 Tesla) MRI, combined with several image post-processing and visualization techniques, these researchers will develop a patient-specific 3D volumetric model of the DBS target, the surrounding white matter tracks, and the neighboring structures. These unique imaging and visualization capabilities will provide unparalleled anatomical and connectivity characterization of each patient. This work is innovative in that it will bring state of the art imaging techniques into a clinical setting &ndash; a clear example of translation and implementation of cutting edge basic science methods into the clinical realm. By merging the information obtained via each imaging approach, a comprehensive, patient-specific, 3D model of each patient&rsquo;s target area will be generated. Each anatomical model will include the DBS target structure of interest, adjacent white matter bundles, as well as along the intended trajectory of the DBS electrode. In addition to the pre-surgery patient-specific anatomical model, a postoperative CT image will be obtained and co-registered to preoperative MR images, including the final electrode location into the model.</p><p>Return to this <a href="https://www.msi.umn.edu/pi/7ca34b273ded784c9a150e82c52cb8cb/42843">PI&#39;s main page</a>.</p>
Group name: 
hareln

Tracking Pollution Sources in Urban Water Systems

Maintaining the health of urban waterways is an ongoing battle. Urban areas, due to their high population densities, have a number of challenges in maintaining clean water. While sewage systems have been managed so that pollutants are largely controlled, the contribution of pollutants from other...

Computational Chemistry

Abstract: 
<div class="node-column-original" style="font-size: 14px; background-color: rgb(255, 255, 255); color: rgb(51, 51, 51);"><div class="view view-access-requests view-id-access_requests view-display-id-by_group_name view-dom-id-1b61323b188b3c0fe150f2683573cccc" style="font-size: 14px;"><div class="view-content" style="font-size: 14px;"><div class="views-row views-row-1" style="font-size: 14px;"><h4 class="views-field views-field-abstract" style="font-size: 14px;">Computational Chemistry</h4><p class="views-field views-field-abstract" style="font-size: 14px;"><span class="field-content" style="font-size: 14px;">This group is using MSI resources to model chemical systems of interest in their research group. These may be conformational studies of molecules, electrochemical, or transition metal species. The researchers seek to gain insights into mechanism and physical properties.</span></p><p class="views-field views-field-abstract" style="font-size: 14px;"><span style="font-size: 14px; line-height: 1.5;">A bibliography of this group&rsquo;s publications is attached.</span></p><p class="views-field views-field-abstract" style="font-size: 14px;">Return to this PI&#39;s <a href="https://www.msi.umn.edu/pi/8341ed7c9e80776ff4842de5b50475eb/17273">main page</a>.</p><p class="views-field views-field-abstract" style="font-size: 14px;">&nbsp;</p></div></div></div></div><p>&nbsp;</p>
Group name: 
douglasc

Computational Biology and Machine Learning

Abstract: 
<h3 class="red">Computational Biology and Machine Learning</h3><p>The Kuang lab is interested in developing general machine learning approaches for integrative analysis of large-scale genomic data to understand the molecular characteristics of biological functions and phenotypes. They design theoretically principled methods in the categories of kernel methods, graph-based learning algorithms, sequence alignment methods and various statistical models for a unified analysis of the biological data in a data-driven perspective. Current projects center around the following topics:</p><ul><li>Cancer genomics: Development of graph-based learning algorithms, sequence alignment algorithms and association rule-mining algorithms for building predictive models and mining biomarkers of cancer phenotypes from microarray gene expressions, ArrayCGH DNA copy number variations, SNPs and protein-protein interactions.</li><li>Phenome-genome association analysis:&nbsp;Development of graph-based learning algorithms for analyzing disease and gene associations in a network context.</li><li>Protein remote homology detection:&nbsp;Development of kernel algorithms and label propagation algorithms to infer the correlation between protein-protein interactions, protein structures and functions.&nbsp;</li><li>Semi-supervised learning algorithms:&nbsp;Graph-based learning, transfer learning, sparse group learning and kernel learning methods.</li></ul><p>Return to this PI&#39;s <a href="https://www.msi.umn.edu/pi/3ab98edd8ff00602cba6be6b4786e55b/13757">main page</a>.</p>
Group name: 
kuangr

Optimization Algorithms for Computer Vision Tasks and POMDP Planning for Environmental Monitoring Projects

Abstract: 
<h3 class="red">Optimization Algorithms for Computer Vision Tasks and POMDP Planning for Environmental Monitoring Projects</h3><p>These researchers are using MSI for two research areas:</p><ul><li>Problems in related environmental monitoring applications such as invasive carp monitoring in Minnesota lakes.&nbsp;In particular, the researchers study the problem of designing search strategies for finding a target that is moving according to a random walk motion model in a square region. They formulate the problem as a Partially Observable Markov Decision Process (POMDP), and use a state-of-the-art toolbox (APPL: Approximate POMDP Planning toolkit developed by D. Hsu et al.) that helps reduce the state space. However, even for relatively small size environments the state space is large, requiring the use of MSI.</li><li>Coordinating an Unmanned Aerial Vehicle (UAV) and an Unmanned Ground Vehicle (UGV) for a precision agriculture application in Minnesota apple orchards.&nbsp;The researchers study the problem of reconstructing all apple trees along each row in orchard using cameras mounted on a UGV. The corresponding algorithm for bundle adjustment needs to be tested on Matlab. However, even for a single tree, the total number of images is large. MSI supercomputers are used to get the first result about tree reconstruction in an orchard for further research.</li></ul><p>Return to this PI&#39;s <a href="https://www.msi.umn.edu/pi/8e1f8c80372f55721edf650190dc5e0c/10519">main page</a>.</p>
Group name: 
isleri

4/9/13: MSI PI Cramer Named CSE Associate Dean

Professor Chris Cramer ( Fellow , Chemistry ) has been named the College of Science and Engineering’s new associate dean for academic affairs, the college announced on April 9, 2013. The appointment will become effective on July 1, 2013. You can read the announcement on the chemistry department’s...

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