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  • richardmitnick 11:23 am on December 24, 2015 Permalink | Reply
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    From U Colorado: “CU-Boulder study reveals evolutionary arms race between Ebola virus, bats” 

    U Colorado

    University of Colorado Boulder.

    December 22, 2015
    Sara Sawyer, 303-735-0531
    ssawyer@colorado.edu

    Trent Knoss, CU-Boulder media relations, 303-735-0528
    trent.knoss@colorado.edu

    Temp 1
    The Ebola virus, isolated in November 2014 from patient blood samples obtained in Mali. The virus was isolated on Vero cells in a BSL-4 suite at Rocky Mountain Laboratories. Credit: NIAID

    The Ebola virus and fruit bats have been waging a molecular battle for survival that may have started at least 25 million years ago, according to a study led by researchers at the University of Colorado Boulder, Albert Einstein College of Medicine and the U.S. Army Medical Research Institute of Infectious Diseases (USAMRIID).

    The findings, published today in the journaleLife [no link], shed new light on the biological factors that determine which bat species may harbor the virus in between outbreaks in humans and how bats may transmit the virus to people.

    The researchers showed that a single amino acid change in the Ebola virus could overcome the resistance of the African straw-colored fruit bat cells to infection. These findings hint at one way in which Ebola and other highly infectious filoviruses can evolve to better infect a host.

    “There seems to be a low barrier for Ebola virus to establish itself in this type of bat,” said co-lead author Sara Sawyer, an associate professor in CU-Boulder’s Molecular, Cellular, and Developmental Biology and the BioFrontiers Institute. “One has to wonder why that has not happened yet.”

    To learn more, the researchers exposed cells from four types of African bats (two of them previously linked to Ebola) to several filoviruses, including Ebola. Cells from only one type of bat proved resistant to Ebola virus infection: the African straw-colored fruit bat, which is commonly hunted for bushmeat in West Africa and migrates long distances.

    Outbreaks of Ebola virus disease among humans are thought to begin when a person comes into contact with a wild animal carrying Ebola virus.

    “We knew from our previous research that Ebola virus infects host cells by attaching its surface glycoprotein to a host cell receptor called NPC1,” said Kartik Chandran, an associate professor of microbiology and immunology at Albert Einstein College of Medicine in New York and a co-lead author of the study. “Here, we show how bats have evolved to resist Ebola infection and how, in turn, the virus could have evolved to overcome that resistance.”

    “Identifying potential animal reservoir hosts for Ebola virus will provide a crucial guide for public health prevention and response programs going forward,” said Maryska Kaczmarek, a graduate researcher in Sawyer’s lab at CU-Boulder and a co-author of the study.

    There are currently no FDA-approved treatments or vaccines for the Ebola virus. The 2014 Ebola outbreak in West Africa was the world’s deadliest to date, infecting an estimated 28,000 people and killing more than 11,000, according to the Centers for Disease Control and Prevention.

    The study was co-authored by Melinda Ng, Esther Ndungo, Rohit Jangra and Rohan Biswas, all at Albert Einstein; John Hawkins and Ann Demogines, all at University of Texas at Austin; Andrew Herbert, Ana Kuehne and Rebekah James, all at USAMRIID; Tabea Binger and Marcel Müller at University of Bonn Medical Center; Robert Gifford at University of Glasgow; Meng Yu and Lin-Fa Wang at Duke-NUS Graduate Medical School; Thijn Brummelkamp at Netherlands Cancer Institute; Christian Drosten at the German Centre for Infectious Diseases Research; and Jens Kuhn at the National Institutes of Health’s Integrated Research Facility at Fort Detrick.

    This research was supported by grants from National Institutes of Health, the Defense Threat Reduction Agency, European Union FP-7 Antigone, the EBOKON Project, and the National Research Foundation Singapore.

    See the full article here .

    If you want to help in the fight against Ebola, join World Community Grid [WCG]and attach to the Outsmart Ebola Together project running at the Scripps Institute. WCG runs on BOINC software from UC Berkeley.

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    U Colorado Campus

    As the flagship university of the state of Colorado, CU-Boulder is a dynamic community of scholars and learners situated on one of the most spectacular college campuses in the country. As one of 34 U.S. public institutions belonging to the prestigious Association of American Universities (AAU) – and the only member in the Rocky Mountain region – we have a proud tradition of academic excellence, with five Nobel laureates and more than 50 members of prestigious academic academies.

    CU-Boulder has blossomed in size and quality since we opened our doors in 1877 – attracting superb faculty, staff, and students and building strong programs in the sciences, engineering, business, law, arts, humanities, education, music, and many other disciplines.

    Today, with our sights set on becoming the standard for the great comprehensive public research universities of the new century, we strive to serve the people of Colorado and to engage with the world through excellence in our teaching, research, creative work, and service.

     
  • richardmitnick 9:22 pm on December 16, 2015 Permalink | Reply
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    From Mapping Cancer Markers at WCG: “Working to detect lung and ovarian cancers before they start” 

    New WCG Logo

    15 Dec 2015
    The Mapping Cancer Markers research team

    Summary
    Recent stages of the Mapping Cancer Markers (MCM) project have illuminated the protein-protein interactions and biological pathways involved in lung cancer, and have also suggested surprising results about its biomarkers. Once this current stage is complete, MCM will transition to analyzing ovarian cancer. Thanks to your help, we are making discoveries and helping the international research community. Dr. Jurisica, in particular, is one of the most frequently cited researchers worldwide.

    Third stage of lung cancer analysis underway

    In our previous update, we announced a second, targeted stage of lung cancer signature discovery. We have since moved to a new, third stage in lung cancer analysis: targeting high-scoring, uncorrelated biomarkers. These different stages are all part of an overall effort to understand lung cancer signatures. The first stage surveyed possible lung cancer signatures drawn from the complete set of biomarkers in our lung cancer dataset. The statistics gathered in this first stage were used to narrow the list of biomarkers to explore in subsequent stages. The second and third stages explore lung cancer signatures drawn from small sets of high-performing signatures, chosen by two different methods. In the second stage, we focused on a 1% subset of biomarkers, selected by the frequency with which each appeared in high-scoring signatures from the initial stage. In the third stage, we selected a different subset of biomarkers that are both high-scoring and largely uncorrelated to one another.

    Correlation is a measure of information shared between two data sources. Two biomarkers are correlated if they exhibit similar patterns in the cancer dataset. For example, two correlated genes might show high activity in one set of tumour samples, low activity in a second set, and average activity in a third. Including two highly-correlated biomarkers in the same signature can reduce the quality of the signature, because they would be contributing redundant information to the signature. For a fixed-size signature, a redundant biomarker would potentially displace another biomarker that has different information content.

    As an analogy, consider the information contained in a small library of textbooks. Say there are three books, A, B, and C. If A and B are two copies of the same textbook, one of them is redundant. Removing B from the library would not change the information contained in the library, and replacing B with a different textbook (D), would increase the information in the library. If A and B were similar, but not identical books (e.g., two books on introduction to molecular biology written by different authors), there would still be some overlap in the texts, and a possible advantage to replacing B with D.

    Signature performance

    Because the target biomarkers in this third stage were selected to be minimally inter-correlated, every signature should be free of redundant information. We therefore hypothesized that signatures in the third stage would perform better on average than those in the second stage. Figure 1 shows the surprising results: second stage signatures (potentially containing correlated biomarkers) outperformed those from the third stage. We are analysing these results further, to determine the main reasons for the performance difference.

    2
    Figure 1. Distribution of signature scores for second (black) and third stage (blue) signatures. As expected, larger signatures generally outperform smaller. Surprisingly, second stage signatures outperform third stage on average.

    Size effects on biomarker rank in top signatures

    Larger signatures (i.e., signatures containing more biomarkers) incorporate more information and can potentially offer better accuracy, but are more complex and expensive to implement in the clinic. All three stages of MCM thus far have explored lung cancer signatures of multiple sizes. For each signature size we considered, the target biomarker subsets for the second stage were chosen separately, based on statistics from the first stage. The set of biomarkers selected for the third is fixed across all signature sizes. This fixed set allows us to compare the effects of signature size on each biomarker’s frequency in high-scoring signatures. Figure 2 shows the frequency change when moving from 10 biomarkers per signature to 20. Each dot in the graph represents a biomarker. The X axis represents the frequency with which biomarkers appear in size_10 signatures. The Y axis indicates frequency in size_20 signatures. Note that the biomarkers change in rank but are generally correlated. Size_10 signatures show greater biomarker frequency spread: some have relatively high frequency, and many are low-frequency. The biomarker frequencies in larger (size_20) signatures are more even.

    Biomarker pairs as protein interactions?

    We applied and extended the analysis of biomarker pairs described in the August 2015 update to early results from third stage data, looking specifically for pairs of biomarkers in both the second and third stages that appear surprisingly frequently in the highest-scoring lung cancer signatures. When two genes or proteins appear in signatures together with greater frequency than expected randomly, we predict a stronger cancer-related connection (interaction).

    We searched for any known connections (interactions) in The Integrated Interactions Database (IID), a database of known and predicted protein-protein interactions created by our lab [1]. We found several interactions in IID that mirror these cancer interactions, but the overlap was not statistically significant.

    2
    Figure 2. Biomarker frequencies in size_10 vs. size_20 signatures. Points to the left of the diagonal line represent biomarkers occurring more frequently in size_20 signatures. Note the overall correlation in ranks between sizes, but greater variation in frequencies for shorter signatures.

    Pathway enrichment in second and third stage targets

    We also took the genes selected for the second and third stages, and searched for them in a database of biological pathways. See Figure 3. We discovered our lists of genes were enriched (present in statistically significant numbers; p ≤ 0.01) in several pathways. See Table 1.

    Although our analysis is ongoing, we can see that two of the identified pathways are components of Mevalonate metabolism. Mevalonate pathways are already targets for many drugs such as statins and have been implicated as targets for treatment in lung cancer [2, 3]. Some of the downstream analysis will focus on how the signatures discovered by World Community Grid processing will ultimately connect to pathways and other research. We have used Mevalonate as an example, but there are many more that can be examined to assess the viability of our best signatures.

    Table 1. List of biological pathways enriched with MCM’s “discovered-pair” genes. P-values < 0.01 indicate statistical significance.
    Pathway Name p-value
    Mevalonate from acetyl CoA step 2 3 0.003236
    Biotinidase Deficiency metabolite pathway 0.004845
    Biotin Metabolism 0.004845
    Biotinidase Deficiency 0.004845
    Multiple carboxylase deficiency neonatal or early onset form
    0.004845
    Mevalonate biosynthesis 0.004845
    Synthesis of Ketone Bodies 0.006449
    Ketone Body Metabolism 0.008048
    Succinyl CoA 3 ketoacid CoA transferase deficiency
    0.008048
    Synthesis and Degradation of Ketone Bodies 0.01
    Fatty acid triacylglycerol and ketone body metabolism
    0.008892
    Vitamin H biotin metabolism 0.009643
    Dermatan sulfate degradation metazoa 0.009643

    4
    Figure 3. Biological pathways enriched by biomarker targets in the second (sizes 10 and 20) and third (all sizes) stages. Some pathways are common to all three.

    The third stage is nearly complete, and will be the final piece of MCM lung cancer analysis on World Community Grid before we switch to ovarian cancer.

    Ovarian cancer is a gynecologic malignancy that ranks 8th for incidence and 5th for death rate among all women’s cancers. The American National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) program estimated 22,240 new cases and 14,030 deaths from ovarian cancer in 2013. Patients are usually diagnosed at an advanced stage (61% present metastasized cancer) and have poor prognosis (27.3 months for metastasized stage (SEER)).

    Ovarian cancer was chosen as our next dataset because of long experience with this disease in our own lab, and in those of collaborators. We look forward to using MCM to glean new insights into ovarian cancer.

    We expect the transition to ovarian cancer research to begin in early 2016, and do not anticipate any interruption in the flow of work units.

    Thank you to World Community Grid members

    We wish to thank World Community Grid members for their continued support and interest for this and other projects. Without you, this work would not be possible.

    References

    1. Kotlyar M, Pastrello C, Sheahan N, Jurisica I. Integrated interactions database: tissue-specific view of the human and model organism interactomes. Nucleic Acids Res. 2015 Oct 29

    2. Hwa Young Lee, In Kyoung Kim, Hye In Lee, Hye Sun Kang, Chan Kwon Park, Jick Hwan Ha, Seung Joon Kim, Sang Haak Lee. Mevalonate pathway inhibitors as chemopreventive agents on lung cancer cell lines: p53 might be a potent regulator. [abstract]. In: Proceedings of the Eleventh Annual AACR International Conference on Frontiers in Cancer Prevention Research; 2012 Oct 16-19; Anaheim, CA. Philadelphia (PA): AACR; Cancer Prev Res 2012;5(11 Suppl):Abstract nr A48.

    3. Yano K. Lipid metabolic pathways as lung cancer therapeutic targets: a computational study. Int J Mol Med. 2012 Apr;29(4):519-29. doi: 10.3892/ijmm.2011.876. Epub 2011 Dec 30.

    Some additional relevant presentations and publications
    In several papers we have used strategies described above and protein interaction networks to identify better prognostic markers and new treatment options:

    Singh M, Garg N, Venugopal C, Hallett RM, Tokar T, McFarlane N, Arpin C, Page B, Haftchenary S, Todic A, Rosa DA, Lai P, Gómez-Biagi R, Ali AM, Lewis A, Geletu M, Mahendram S, Bakhshinyan D, Manoranjan B, Vora P, Qazi M, Murty NK, Hassell JA, Jurisica I, Gunning P, Singh SK. STAT3 pathway regulates lung-derived brain metastasis initiating cell capacity through miR-21 activation. Oncotarget (accepted June 30, 2015, ONC-2014-02546)
    Navab R, Strumpf D, To C, Pasko E, Kim KS, Park CJ, Hai J, Liu J, Jonkman J, Barczyk M, Bandarchi B, Wang YH, Venkat K, Ibrahimov E, Pham NA, Ng C, Radulovich N, Zhu CQ, Pintilie M, Wang D, Lu A, Jurisica I, Walker GC, Gullberg D, Tsao MS. Integrin a11b1 regulates cancer stromal stiffness and promotes tumorigenecity in non-small cell lung cancer, Oncogene, 2015. In press.
    Agostini M, Zangrando A, Pastrello C, D’Angelo E, Romano G, Giovannoni R, Giordan M, Maretto I, Bedin C, Zanon C, Digito M, Esposito G, Mescoli C, Lavitrano M, Rizzolio F, Jurisica I, Giordano A, Pucciarelli S, Nitti D. A functional biological network centered on XRCC3: a new possible marker of chemoradiotherapy resistance in rectal cancer patients, Cancer Biol Ther, 16(8):1160-71, 2015.
    Agostini M, Janssen KP, Kim LJ, D’Angelo E, Pizzini S, Zangrando A, Zanon C, Pastrello C, Maretto I, Digito M, Bedin C, Jurisica I, Rizzolio F, Giordano A, Bortoluzzi S, Nitti D, Pucciarelli S. An integrative approach for the identification of prognostic and predictive biomarkers in rectal cancer. Oncotarget. 2015. Sep 2.
    Stewart, E.L., Mascaux, C., Pham, N-A, Sakashita, S., Sykes, J., Kim, L., Yanagawa, N., Allo, G., Ishizawa, K., Wang, D., Zhu, C.Q., Li, M., Ng, C., Liu, N., Pintilie, M., Martin, P., John, T., Jurisica, I., Leighl, N.B., Neel, B.G., Waddell, T.K., Shepherd, F.A., Liu, G., Tsao, M-S. Clinical Utility of Patient Derived Xenografts to Determine Biomarkers of Prognosis and Map Resistance Pathways in EGFR-Mutant Lung Adenocarcinoma, J Clin Oncol, 33(22):2472-80, 2015.
    Camargo, J. F., Resende, M., Zamel, R., Klement, W., Bhimji, A., Huibner, S., Kumar, D., Humar, A., Jurisica, I., Keshavjee, S., Kaul, R., Husain, S. Potential role of CC chemokine receptor 6 (CCR6) in prediction of late-onset CMV infection following solid organ transplant. Clinical Transplantation, 2015. In press. doi: 10.1111/ctr.12531
    Fortney, K., Griesman, G., Kotlyar, M., Pastrello, C., Angeli, M., Tsao, M.S., Jurisica, I. Prioritizing therapeutics for lung cancer: An integrative meta-analysis of cancer gene signatures and chemogenomic data, PLoS Comp Biol, 11(3): e1004068, 2015.

    Integrative analyses also help provide better explanations of experimental results and more accurate models:

    Benleulmi-Chaachoua, A., Chen, L., Sokolina, K., Wong, V., Jurisica, I., Emerit, M.B., Darmon, M., Espin, A., Stagljar, I., Tafelmeyer, P., Zamponi, G.W., Delagrange, P., Maurice, P., Jockers, R. Protein interactome mining defines melatonin MT1 receptors as integral component of presynaptic protein complexes of neurons, Journal of Pineal Research, In press

    Some of this work was presented at multiple meetings and institutions: including keynotes at The 14th International Conference on Machine Learning and Applications and The American Society for Blood and Marrow Transplantation, Corporate Council Meeting; and invited highlight talks at Intelligent Systems for Molecular Biology Conference and Basel Computational Biology Conference.

    Media Coverage

    Also, for the second year in a row, Dr. Jurisica has been included in Thomson Reuters highly cited researcher list; Out of 108 in computer science and 3,125 world-wide in 21 fields of science.

    See the full article here.

    Please help promote STEM in your local schools.
    STEM Icon

    Stem Education Coalition

    World Community Grid (WCG) brings people together from across the globe to create the largest non-profit computing grid benefiting humanity. It does this by pooling surplus computer processing power. We believe that innovation combined with visionary scientific research and large-scale volunteerism can help make the planet smarter. Our success depends on like-minded individuals – like you.”

    WCG projects run on BOINC software from UC Berkeley.

    BOINC is a leader in the field(s) of Distributed Computing, Grid Computing and Citizen Cyberscience.BOINC is more properly the Berkeley Open Infrastructure for Network Computing.

    BOINC WallPaper

    CAN ONE PERSON MAKE A DIFFERENCE? YOU BET!

    “Download and install secure, free software that captures your computer’s spare power when it is on, but idle. You will then be a World Community Grid volunteer. It’s that simple!” You can download the software at either WCG or BOINC.

    Please visit the project pages-
    Outsmart Ebola together

    Outsmart Ebola Together

    Mapping Cancer Markers
    mappingcancermarkers2

    Uncovering Genome Mysteries
    Uncovering Genome Mysteries

    Say No to Schistosoma

    GO Fight Against Malaria

    Drug Search for Leishmaniasis

    Computing for Clean Water

    The Clean Energy Project

    Discovering Dengue Drugs – Together

    Help Cure Muscular Dystrophy

    Help Fight Childhood Cancer

    Help Conquer Cancer

    Human Proteome Folding

    FightAIDS@Home

    World Community Grid is a social initiative of IBM Corporation
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  • richardmitnick 11:34 am on November 25, 2015 Permalink | Reply
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    From AAAS: “Progress, but still much to do, AIDS report finds” 

    AAAS

    AAAS

    24 November 2015
    Jon Cohen

    1
    A cell infected with HIV. NIAID/Flickr (CC BY 2.0)

    Of the estimated 36.9 million HIV-infected people in the world, 70% live in sub-Saharan Africa. Of these, 49% do not know their HIV status and about 57% are not receiving antiretroviral drugs, according to a report released today by the Joint United Nations Programme on HIV/AIDS.

    The report, which arrives in the run-up to World AIDS Day on 1 December, celebrates the progress that has been made in getting antiretrovirals to 15.8 million people by June of 2015. But it also notes how far many countries are from meeting World Health Organization guidelines issued in September, which call for every infected person to receive treatment.

    The huge, ongoing push to start all infected people on antiretrovirals emerged from recent evidence that early initiation of treatment benefits the health of infected people, and also makes it extremely unlikely that they will transmit the virus (if they fully suppress their own infection). But in sub-Saharan Africa, the report notes, an estimated 68% of infected people have not suppressed their HIV levels.

    Some other statistical highlights from the report:

    About 36.9 million people globally were living with HIV at the end of 2014. (That is the midpoint within an estimated range of 34.3 million–41.4 million people).
    2 million (1.9 million–2.2 million) people became newly infected with HIV by the end of 2014. New HIV infections have fallen by 35% since 2000.
    1.2 million (980,000–1.6 million) people died from AIDS-related illnesses in 2014.
    As of June 2015, 15.8 million people living with HIV were accessing antiretroviral therapy, up from 13.6 million in June 2014.

    See the full article here .

    The American Association for the Advancement of Science is an international non-profit organization dedicated to advancing science for the benefit of all people.

    You can help in the fight against AIDS. Join World Community Grid (WCG) and attach to the Fight AIDS@home project. You will be donating unused computer capacity to process data in this compute intensive project. WCG runs on BOINC software from UC Berkeley.

    FightAids@home

    WCG Logo New

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  • richardmitnick 6:00 pm on September 30, 2015 Permalink | Reply
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    From WCG’s FightAIDS@Home 

    New WCG Logo

    As part of Office of Science and Technology Policy ‪#‎WHCitSci‬ forum, we are very happy to announce the second phase of our FightAIDS@Home project.

    10 years of virtual simulations for FightAIDS@Home is considered the biggest drug docking experiment ever conducted. Now the challenge is to find the better drugs, fast.

    That’s where Phase 2 and you come in. Join FightAIDS@Home to help support pioneering HIV research.

    1
    Model of a complete HIV Virion with all of the component molecules

    Summary
    The team behind FightAIDS@Home is launching Phase 2 of the project, putting to use a more accurate simulation tool to help them determine which of the Phase 1 results merit further investigation. Phase 2 will also be applying this analysis technique at an unprecedented scale, which if proven successful, can benefit medical research not only for HIV but many other diseases as well.

    There have been some amazing advances in the fight against the human immunodeficiency virus (HIV), including treatments that have improved and extended millions of lives. But the fight continues – HIV is continually mutating, and as it does it evolves resistance to existing treatments. With tens of millions of people currently living with HIV, and millions more infected every year, the search for more effective HIV treatments is as critical as ever. Our team is therefore launching a new phase of HIV research to build on the success of the first phase and more accurately analyze the most promising drug candidates we’ve identified so far.

    For almost a decade, FightAIDS@Home has contributed to this fight by exploring different ways of disabling the virus. World Community Grid members have provided my team an unprecedented amount of computing power, enabling us to investigate a huge number of potential cures. To date, volunteers have performed over 20 billion comparisons between candidate chemicals and different binding sites on the virus. Along the way, our team has improved the tools used in the fight, by developing – and validating – software tools to simulate chemical binding, and discovering new potential binding sites for drugs to attack. These tools have even supported other medical research efforts, both on World Community Grid and elsewhere.

    The massive success of FightAIDS@Home has also generated a new challenge: thousands of potential ‘hits’ (chemicals that might form the basis of effective drugs) – a handful of which we’re synthesizing for additional testing. But because there are so many, it is prohibitively expensive and time consuming to synthesize and lab test all of those chemicals. The project now needs a new computational method to double-check the promising Phase 1 results, and ensure that only the most thoroughly vetted and probable candidate compounds proceed for further investigation. Phase 2 of FightAIDS@Home will address both of these goals: refining the Phase 1 results and validating the technology needed to make more accurate simulations.

    Specifically, Phase 2 uses a new analysis technique called BEDAM (Binding Energy Distribution Analysis Method), which is implemented using software called Academic IMPACT developed by our collaborators at Temple University. BEDAM has proven effective at carrying out more accurate simulations in computational contests, but thanks to World Community Grid volunteers, we now have an opportunity to apply it to analyze molecules at an unprecedented scale. This is important because if successful, these techniques can be applied to other drug discovery searches beyond HIV.

    3
    Collaborating labs for the HIVE Center. Prof. Art Olson directs the Center and collaborators include Prof. Ron Levy, who is partnering with the FightAIDS@Home project.

    Phase 2 is more radical than its name suggests – World Community Grid volunteers have the opportunity to help us validate a new promising research paradigm that can help the search for treatments for many diseases, not just HIV. It’s only because of the commitment shown by volunteers that FightAIDS@Home has been able to accomplish so much thus far. We hope we can count on your continued support as we continue this important journey.

    To contribute to FightAIDS@Home – Phase 2, join World Community Grid, or if you are already a volunteer, make sure the project is selected on your My Projects page.

    See the full article here.

    Please help promote STEM in your local schools.
    STEM Icon

    Stem Education Coalition

    World Community Grid (WCG) brings people together from across the globe to create the largest non-profit computing grid benefiting humanity. It does this by pooling surplus computer processing power. We believe that innovation combined with visionary scientific research and large-scale volunteerism can help make the planet smarter. Our success depends on like-minded individuals – like you.”

    WCG projects run on BOINC software from UC Berkeley.

    BOINC is a leader in the field(s) of Distributed Computing, Grid Computing and Citizen Cyberscience.BOINC is more properly the Berkeley Open Infrastructure for Network Computing.

    BOINC WallPaper

    CAN ONE PERSON MAKE A DIFFERENCE? YOU BETCHA!!

    “Download and install secure, free software that captures your computer’s spare power when it is on, but idle. You will then be a World Community Grid volunteer. It’s that simple!” You can download the software at either WCG or BOINC.

    Please visit the project pages-
    Outsmart Ebola together

    Outsmart Ebola Together

    Mapping Cancer Markers
    mappingcancermarkers2

    Uncovering Genome Mysteries
    Uncovering Genome Mysteries

    Say No to Schistosoma

    GO Fight Against Malaria

    Drug Search for Leishmaniasis

    Computing for Clean Water

    The Clean Energy Project

    Discovering Dengue Drugs – Together

    Help Cure Muscular Dystrophy

    Help Fight Childhood Cancer

    Help Conquer Cancer

    Human Proteome Folding

    FightAIDS@Home

    World Community Grid is a social initiative of IBM Corporation
    IBM Corporation
    ibm

    IBM – Smarter Planet
    sp

     
  • richardmitnick 2:42 pm on September 2, 2015 Permalink | Reply
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    From Clean Energy at WCG: “Summer is a great time to focus on solar energy” 

    WCG
    World Community Grid

    Clean Energy

    Clean Energy Project

    2 Sep 2015

    By: The Clean Energy Project team
    Harvard University

    Summary
    A busy summer has led to several advances in the Clean Energy Project: new team members, new database search functionality, new publications and (hopefully) new funding!

    1
    Front: Wendy Woodin, Dr. Ed Pyzer-Knapp, Dipti Jasrasaria Back: Dr. Steven Lopez

    The Clean Energy Project (CEP) team has been working very hard this summer, and have had a number of successes to show for it.

    We are very happy to introduce the latest addition to our team – Dr. Steven Lopez has joined us from UCLA, where he worked for Ken Houk on computational organic chemistry. Steven’s knowledge of chemical reactivity, and reaction mechanisms will be invaluable to the team as we strive to deliver libraries of molecules which are synthetically accessible.

    We have been lucky enough to get funding for two undergraduates – Wendy and Dipti – to study with the team over the summer. Dipti has continued her work on machine learning on crystals, and Wendy has worked on hashing functions. These hashing functions will be deployed in our new database to enable users to perform searches for molecules similar to their search term; we believe this search option will further enhance the utility of the database for the discovery of new organic photovoltaic materials.

    We are also very happy to say that Ed and Kewei have had a manuscript accepted into the journal Advanced Functional Materials. Advanced Functional Materials is one of the most prestigious journals for this area of study so we are very excited to have been accepted! We will share the details of the manuscript once it gets published.

    Finally, we have just submitted a couple of grant proposals for continuing to fund the CEP in the years to come. Grant proposals are incredibly important for keeping our project running, and so we will keep our fingers crossed for a successful response!

    As ever, we are very appreciative for the computing time you donate since without it, we would be unable to perform the research which goes on in the CEP. So, thank you again…and keep crunching!

    See the full article here.

    Please help promote STEM in your local schools.

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    The Harvard Clean Energy Project Database contains data and analyses on 2.3 million candidate compounds for organic photovoltaics. It is an open resource designed to give researchers in the field of organic electronics access to promising leads for new material developments.

    Would you like to help find new compounds for organic solar cells? By participating in the Harvard Clean Energy Project you can donate idle computer time on your PC for the discovery and design of new materials. Visit WorldCommunityGrid to get the BOINC software on which the project runs.

    CleanEnergyProjectPartners

    CEP runs on software from BOINC, Berkeley Open Infrastructure for Network computing.

    BOINCLarge

     
  • richardmitnick 3:57 pm on July 14, 2015 Permalink | Reply
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    From SETI@home: Interview with Dave Anderson 

    SETI@home
    SETI@home

    David Anderson talks BOINC, Citizen Science, and Why We Still Need You

    David Anderson is co-creator of SETI@home, and the Director of BOINC, the Berkeley Open Infrastructure for Network Computing. David is a computer scientist by trade, and mathematician by training who’s had a decades long interest in distributed computing and volunteer science.

    Download BOINC and join the science!
    http://boinc.berkeley.edu/download.php

    Learn more about David
    https://seti.berkeley.edu/user/46

    Follow us on Twitter:
    https://twitter.com/setiathome
    Facebook:
    https://www.facebook.com/BerkeleySETI

    Watch, enjoy, learn, maybe think about joining up.

    See the full article here.

    Please help promote STEM in your local schools.

    STEM Icon

    Stem Education Coalition

    The science of SETI@home
    SETI (Search for Extraterrestrial Intelligence) is a scientific area whose goal is to detect intelligent life outside Earth. One approach, known as radio SETI, uses radio telescopes to listen for narrow-bandwidth radio signals from space. Such signals are not known to occur naturally, so a detection would provide evidence of extraterrestrial technology.

    Radio telescope signals consist primarily of noise (from celestial sources and the receiver’s electronics) and man-made signals such as TV stations, radar, and satellites. Modern radio SETI projects analyze the data digitally. More computing power enables searches to cover greater frequency ranges with more sensitivity. Radio SETI, therefore, has an insatiable appetite for computing power.

    Previous radio SETI projects have used special-purpose supercomputers, located at the telescope, to do the bulk of the data analysis. In 1995, David Gedye proposed doing radio SETI using a virtual supercomputer composed of large numbers of Internet-connected computers, and he organized the SETI@home project to explore this idea. SETI@home was originally launched in May 1999.

    SETI@home is not a part of the SETI Institute

    The SET@home screensaver image
    SETI@home screensaver

    To participate in this project, download and install the BOINC software on which it runs. Then attach to the project. While you are at BOINC, look at some of the other projects which you might find of interest.

    BOINC

     
  • richardmitnick 4:46 pm on March 10, 2015 Permalink | Reply
    Tags: , BOINC,   

    From WCG: “Top distributed computing projects still hard at work fighting the world’s worst health issues” in IT World 

    New WCG Logo

    1

    March 9, 2015
    Andy Patrizio

    This past fall saw the worst Ebola outbreak ever ravage western Africa, and while medical researchers are trying to find a drug to treat or prevent the disease, the process is long and complicated. That’s because you don’t just snap your fingers and produce a drug with a virus like Ebola. What’s needed is a massive amount of trial and error to find chemical compounds that can bind with the proteins in the virus and inhibit replication. In labs, it can take years or decades.

    Thanks to thousands of strangers, Ebola researchers are getting the help and computing power they need to shave off the time needed to find new drugs by a few years.

    Distributed computing is not a new concept, but as it is constituted today, it’s an idea born of the Internet. Contributors download a small app that runs in the background and uses spare PC compute cycles to perform a certain process.

    When you are running a PC and using it for Word, Outlook and browsing, you are using a pittance of the compute power in a modern CPU, maybe 5% total, and that’s only in bursts. Distributed computing programs use the other 95%, or less if you specify, and if you need more compute power for work, the computing clients dial back their work and let you have the CPU power you need. If you leave the PC on when not using it, the application goes full out.

    There is a wide variety of programs, and one of them is aimed at finding drugs to help stop Ebola. It’s part of the World Community Grid (WCG), run by IBM and using software developed at the University of California at Berkeley.

    The WCG has almost 700,000 members with three million devices signed up to crunch away on those projects, according to Dr. Viktors Berstis, architect and chief scientist for the WCG at IBM. All told, WCG is running nearly 30 drug projects.

    WCG uses software developed at Berkley called BOINC, or the Berkeley Open Infrastructure for Network Computing. Past distributed projects, and even current ones such as Folding@Home, use their own client to do the work. BOINC is a National Science Foundation-funded project to create one distributed computing client that any project can use, thus sparing researchers the effort of having to reinvent the wheel and just focus on their project and not the client.

    The program simulates how a potential compound reacts vs. the target, such as a protein in a virus that is needed for the disease to survive. With distributed computing, WCG can go through millions of compounds for any given target and cut down dramatically on the research time it would take to do it in a lab.

    “It’s applicable to anything that takes a lot of CPU time and can be split up into millions of independent-running jobs. The only difference between World Community Grid and a supercomputer is the supercomputer processors can talk to each other,” said Dr. Berstis.

    The Ebola hunt

    In the case of Ebola, WCG has partnered with The Scripps Institute, a biomedical research group in La Jolla, Calif., to launch Outsmart Ebola Together [see below]. The project will target multiple hemorrhagic viruses in the Ebola family, according to Dr. Erica Saphire, the researcher heading the program at Scripps.

    The project will target one specific protein that is used to attach the virus to healthy cells in a human to then replicate. This protein is being targeted because unlike other proteins in the viruses, it can’t mutate. “The site they are targeting is the way the virus finds its way into the cell. So if it changes too much in any way, it’s not viable. It’s one of the few places [in the virus] that can’t change. It has to keep that the same. So that makes an ideal drug target,” said Dr. Saphire.

    Dr. Saphire said the FightAIDS@Home group [see below]at Scripps often gets done in a few months what would have taken 10 years otherwise and wants to put the three million devices of WCG to work. “With this massive computational power, we’re asking what can we understand that we’ve never understood before,” she said. “It’s the most fundamentally important thing my lab has ever done. It’s also the biggest.”

    Scripps is a large, well-funded institute and could easily afford supercomputers, but Dr. Saphire said WCG is a better option. “It turns out that having hundreds of thousands of computers in parallel accelerates things more than having a supercomputer here,” she said.

    Success stories

    Dr. Art Olson, professor in a department of integrative computational and structural biology at Scripps, has used WCG for the FightAIDS@Home project since 2005, and before that with a now-defunct company called United Devices back in 2000 in one of the first distributed biomedical computing project.

    The first papers published by Dr. Olson’s group talked about the mutation of the HIV protease, the part of the HIV virus that handles replication, and how they can both target the protease to stop replication and how the protease develops drug resistance. “That gave us a set of targets to try and find drugs that could be effective against that spanning set of mutants,” said Dr. Olson.

    Like Dr. Saphire, he prefers the massive number of CPUs available via WCG over an in-house supercomputer. “We have very good computing resources here, but we’re not the only people who use the computing resources at Scripps. We can only get 300 CPUs at any given time, whereas on the World Community Grid we can get tens of thousands of CPUs to use at any given time. So it’s a major boost. We would never even try to do the scope of the kinds of dockings we do using just our local institutional resources,” he said.

    There are other WCG successes besides Scripps. Dr. Berstis said one success story was simulations of carbon nanotubes [see below]. Water flows through the tubes 10,000 times more efficiently than thought, so there are now experiments to find less expensive methods of filtering or desalinating water than using the very expensive reverse osmosis filters.

    A recently disclosed project from the Help Fight Childhood Cancer group at WCG [see below]found compounds to cure childhood neuroblastoma, a cancer of the nervous system. Done in conjunction with a group in Japan, they found 7 drug candidates with a 95% likelihood of curing the cancer.

    Finally, there was a cancer project that looked at images of biopsies with machine optical scanning. Eventually an algorithm was developed that helped analyze those images to determine if cancer cells are present. “They are as good as humans now so it will help identify if there is cancer present or not much faster,” said Dr. Berstis.

    DIY distributed computing

    The concept of using idle CPU cycles instead of investing millions in supercomputers is not lost on IT departments or companies with big processing tasks. Anecdotal stories of firms setting up their own internal distributed computing networks have been around for several years, although most firms will not discuss them out of concern for giving away a competitive advantage.

    CDx Diagnostics, which develops equipment to detect cancer in its earliest stage, was willing to discuss its efforts. It has built a data center of computers just to do processing, plus it utilizes idle CPU cycles on employee computers, to build its own internal grid computing environment to analyze digitized microscopic slide data for detection of cellular changes that would indicate cancerous and pre-cancerous cells.

    CDx needed a cheap system that can process 590GB of image data generated per pathology slide, and patients can have multiple slides, in less than four minutes. On a single PC, such analysis would normally take four hours. And it’s still no replacement for human eyes. Slides are still processed by humans, but the grid system can pick up on anomalies, or note there are none.

    Employees leave their computers on when they go home at night. The client PCs tell the servers their computing capabilities and the servers decide which computers get what kinds of workloads. Faster computers get the higher priority in doing the next task, said Robert Tjon, vice president of engineering and developer of the grid.

    Tjon said the best performance for price is from commodity hardware, which is robust, highly reconfigurable and scalable as long as there is a centralized system that can manage the external resources efficiently so the computers are constantly fed data.

    “One hundred percent utilization of the computer resources will keep the cost of the overall grid down in terms of space, heat, power, and manpower to keep the system up. We also like the fact that Intel invests billions to make the computer cheaper and faster and we only have to pay the price of a regular, popular consumer item,” he said.

    So it could be that your idle PC may one day save your life.

    See the full article here.

    Please help promote STEM in your local schools.
    STEM Icon

    Stem Education Coalition

    World Community Grid (WCG) brings people together from across the globe to create the largest non-profit computing grid benefiting humanity. It does this by pooling surplus computer processing power. We believe that innovation combined with visionary scientific research and large-scale volunteerism can help make the planet smarter. Our success depends on like-minded individuals – like you.”

    WCG projects run on BOINC software from UC Berkeley.

    BOINC is a leader in the field(s) of Distributed Computing, Grid Computing and Citizen Cyberscience.BOINC is more properly the Berkeley Open Infrastructure for Network Computing.

    CAN ONE PERSON MAKE A DIFFERENCE? YOU BETCHA!!

    “Download and install secure, free software that captures your computer’s spare power when it is on, but idle. You will then be a World Community Grid volunteer. It’s that simple!” You can download the software at either WCG or BOINC.

    Please visit the project pages-
    Outsmart Ebola together

    Outsmart Ebola Together

    Mapping Cancer Markers
    mappingcancermarkers2

    Uncovering Genome Mysteries
    Uncovering Genome Mysteries

    Say No to Schistosoma

    GO Fight Against Malaria

    Drug Search for Leishmaniasis

    Computing for Clean Water

    The Clean Energy Project

    Discovering Dengue Drugs – Together

    Help Cure Muscular Dystrophy

    Help Fight Childhood Cancer

    Help Conquer Cancer

    Human Proteome Folding

    FightAIDS@Home

    World Community Grid is a social initiative of IBM Corporation
    IBM Corporation
    ibm

    IBM – Smarter Planet
    sp

     
  • richardmitnick 3:01 pm on February 12, 2015 Permalink | Reply
    Tags: , BOINC, ,   

    From Mapping Cancer Markers at WCG: “Using one cancer to help defeat many: Mapping Cancer Markers makes progress” 

    WCG new
    World Community Grid

    Mapping Cancer Markers

    Mapping Cancer Markers Banner

    Mapping Cancer Markers

    By: The Mapping Cancer Markers research team
    12 Feb 2015

    Summary
    Results from the first stage of the Mapping Cancer Markers project are helping the researchers identify the markers for lung cancer, as well as improve their research methodology as they move on to analyze other cancers.

    2

    Once again, the Mapping Cancer Markers (MCM) team would like to extend a huge thank you to the World Community Grid members. Although we publish this thank you each update, we are truly grateful for your contribution to this project.

    The MCM project has continued to process lung cancer data, exploring fixed-length random gene signatures. This long stage of the project is nearly over, and we are preparing to transition our focus to a narrower set of genes of interest. Target genes will be chosen by a process combining statistics from the initial results, with pathway and biological-network analysis.

    Analytics

    In our previous update, we reported the adoption of a new package, the IBM® InfoSphere® Streams real-time analytics platform, to process our World Community Grid data. The majority of our work since the last update has concentrated on continued development and expansion of our Streams system in order to handle the incoming data more robustly and efficiently.

    There are two main reasons why stream-processing design is better for processing MCM results than a batch-computing approach. One reason relates to the nature of World Community Grid: a huge computing resource that continuously consumes work units and produces compute results. Data is best processed as it arrives, to avoid backlogs or storage limitations.

    Importantly, as we transition to the new focus, this enables us to make the process of designing new work units based on partial results more effective. MCM will soon focus on genes of interest revealed by our broad survey of gene-signature space in the first stage. To narrow the focus, we will take an iterative approach, where we design small batches of work units (e.g., 100,000 units), submit them to World Community Grid, analyze the results, and then incorporate the new analysis into designing the next batch. In this way, we will slowly converge towards the answers we are seeking. Because of the continuous nature of the MCM project, and the volume of data we receive on a daily basis, it is imperative that our analysis system processes results quickly enough to generate the next set of work units.

    New stage in lung cancer signature discovery

    The MCM project has continued to process lung cancer data, exploring random fixed-length signatures of between 5 and 25 biomarkers. This computational component of the “landscape” stage is winding down, and we are preparing to transition our focus to a narrower set of genes of interest. Target genes will be selected by integrating results from several methods, carefully combining statistics from the initial results with pathway and biological-network analysis.

    Network analysis/integration of pathway knowledge

    One of the most exciting (and crucial) parts of this project is the integration of other research to help understand the results we are collecting. We already know that in most cancers no single biomarker is sufficient, we can find thousands of clinically-relevant signatures, and, most importantly, many seemingly weak markers when combined with others provide highly useful information. Therefore, we have been trying to find these “best supporting actors” and then the best signatures through “integrative network analysis”.

    3

    Figure 1: An iterative strategy for biomarker discovery. Work units are processed on World Community Grid. The results are analyzed via a Streams pipeline. This generates a list of high-scoring genes, which combined with biological network information (NAViGaTOR
    ) are used to design new MCM work units targeting areas of interest in signature space.

    We know that disease is more accurately described in terms of altered signaling cascades (pathways): higher-level patterns composed of multiple genes in a biological network. A pathway can be defined as a series of reactions (“steps”) that result in a certain biochemical process. For example, one could consider the electrical and mechanical systems in a car as a set of interrelated pathways. These systems are important for the overall function of the car; however, some are clearly more important than others. In the same way, a particular cancer occurrence could have a single catastrophic cause (a missing engine block) or smaller, multiple causes affecting the same system (e.g., the bolts holding the exhaust system together).

    Around the world, researchers are continually finding, publishing and curating biological pathways and their building blocks (protein interactions). We are taking this information and applying it to high-scoring genes and gene signatures identified from Mapping Cancer Marker results. For example, if the first part of our landscape study identified a certain gene as a potential target, we can see via our network analysis (NAViGaTOR) as well as other external sources if that same gene is involved in known pathways. We can then gather information about those pathways and refine our findings by resubmitting work units to World Community Grid. In essence, we are identifying genes of interest by combining top-scoring genes with pathway and network context. Those investigations will continue to refine our search space and converge on better and better solutions. Below, we list some examples of this work, but especially Kotlyar et al., Nature Methods, 2015 work provides comprehensive in silico prediction of these signaling cascades. Wong et al., Proteomics, 2015 introduces systematic approach to derive important information about cancer-related structures in these networks. Fortney et al., PLoS Computational Biology uses results of this work to identify potential new treatment options for lung cancer.

    Transition to the targeted stage

    We expect a gradual and seamless transition to the new stage of MCM, with no interruption in the supply of work units, and no changes to the visualization or code. Both stages will overlap for a period as the last statistics from the first stage are gathered, and the initial, targeted work units are sent out. Average work unit run-time should remain the same. The consistency of run-times should remain the same or improve.

    See the full article here.

    Please help promote STEM in your local schools.

    STEM Icon

    Stem Education Coalition

    Cancers, one of the leading causes of death worldwide, come in many different types and forms in which uncontrolled cell growth can spread to other parts of the body. Unchecked and untreated, cancer can spread from an initial site to other parts of the body and ultimately lead to death. The disease is caused by genetic or environmental changes that interfere with biological mechanisms that control cell growth. These changes, as well as normal cell activities, can be detected in tissue samples through the presence of their unique chemical indicators, such as DNA and proteins, which together are known as “markers.” Specific combinations of these markers may be associated with a given type of cancer.

    The pattern of markers can determine whether an individual is susceptible to developing a specific form of cancer, and may also predict the progression of the disease, helping to suggest the best treatment for a given individual. For example, two patients with the same form of cancer may have different outcomes and react differently to the same treatment due to a different genetic profile. While several markers are already known to be associated with certain cancers, there are many more to be discovered, as cancer is highly heterogeneous.

    Mapping Cancer Markers on World Community Grid aims to identify the markers associated with various types of cancer. The project is analyzing millions of data points collected from thousands of healthy and cancerous patient tissue samples. These include tissues with lung, ovarian, prostate, pancreatic and breast cancers. By comparing these different data points, researchers aim to identify patterns of markers for different cancers and correlate them with different outcomes, including responsiveness to various treatment options.

    Some related published work

    Hoeng J, Peitsch MC, Meyer, P. and Jurisica, I. Where are we at regarding Species Translation? A review of the sbv IMPROVER Challenge, Bioinformatics, 2015. In press.

    Fortney, K., Griesman, G., Kotlyar, M., Pastrello, C., Angeli, M., Tsao, M.S., Jurisica, I. Prioritizing therapeutics for lung cancer: An integrative meta-analysis of cancer gene signatures and chemogenomic data, PLoS Comp Biol, 2015, In Press.

    Kotlyar M., Pastrello C., Pivetta, F., Lo Sardo A., Cumbaa, C., Li, H., Naranian, T., Niu Y., Ding Z., Vafaee F., Broackes-Carter F., Petschnigg, J., Mills, G.B., Jurisicova, A., Stagljar, I., Maestro, R., & Jurisica, I. In silico prediction of physical protein interactions and characterization of interactome orphans, Nat Methods, 12(1):79-84, 2015.

    Vucic, E. A., Thu, K. T., Pikor, L. A., Enfield, K. S. S., Yee, J., English, J. C., MacAulay, C. E., Lam, S., Jurisica, I., Lam, W. L. Smoking status impacts microRNA mediated prognosis and lung adenocarcinoma biology, BMC Cancer, 14: 778, 2014. E-pub 2014/10/25

    Lalonde, E., Ishkanian, A. S., Sykes, J., Fraser, M., Ross-Adam, H., Erho, N., Dunning, M., Lamb, A.D., Moon, N.C., Zafarana, G., Warren, A.Y., Meng, A., Thoms, J., Grzadkowski, M.R., Berlin, A., Halim, S., Have, C.L., Ramnarine, V.R., Yao, C.Q., Malloff, C.A., Lam, L. L., Xie, H., Harding, N.J., Mak, D.Y.F., Chu1, K. C., Chong, L.C., Sendorek, D.H., P’ng, C., Collins, C.C., Squire, J.A., Jurisica, I., Cooper, C., Eeles, R., Pintilie, M., Pra, A.D., Davicioni, E., Lam, W. L., Milosevic, M., Neal, D.E., van der Kwast, T., Boutros, P.C., Bristow, R.G., Tumour genomic and microenvironmental heterogeneity for integrated prediction of 5-year biochemical recurrence of prostate cancer: a retrospective cohort study. Lancet Oncology. 15(13):1521-32, 2014.

    Dingar, D., Kalkat, M., Chan, M. P-K, Bailey, S.D., Srikumar, T., Tu, W.B., Ponzielli, R., Kotlyar, M., Jurisica, I., Huang, A., Lupien, M., Penn, L.Z., Raught, B. BioID identifies novel c-MYC interacting partners in cultured cells and xenograft tumors, Proteomics, pii: S1874-3919(14)00462-X, 2014. doi: 10.1016/j.jprot.2014.09.029

    Wong, S. W. H., Cercone, N., Jurisica, I. Comparative network analysis via differential graphlet communities, Special Issue of Proteomics dedicated to Signal Transduction, Proteomics, 15(2-3):608-17, 2015. E-pub 2014/10/07. doi: 10.1002/pmic.201400233

    Berlin, A., Lalonde, E., Sykes, J., Zafarana, G., Chu, K.C., Ramnarine, V.R., Ishkanian, A., Sendorek, D.H.S., Pasic, I., Lam, W.L., Jurisica, I., van der Kwast, T., Milosevic, M., Boutros, P.C., Bristow, R.G.. NBN Gain Is Predictive for Adverse Outcome Following Image-Guided Radiotherapy for Localized Prostate Cancer, Oncotarget, 3:e133, 2014.

    Lapin, V., Shirdel, E., Wei, X., Mason, J., Jurisica, I., Mak, T.W., Kinome-wide screening of HER2+ breast cancer cells for molecules that mediate cell proliferation or sensitize cells to trastuzumab therapy, Oncogenesis, 3, e133; doi:10.1038/oncsis.2014.45, 2014.

    Tu WB, Helander S, Pilstål R, Hickman KA, Lourenco C, Jurisica I, Raught B, Wallner B, Sunnerhagen M, Penn LZ. Myc and its interactors take shape. Biochim Biophys Acta. pii: S1874-9399(14)00154-0.

    This project runs on BOINC software. Visit BOINC or WCG, download and install the software and attach to the project. While you are at BOINC and WCG, look over the other projects for some that you might find of interest.

    WCG

    BOINC

     
  • richardmitnick 5:14 pm on January 28, 2015 Permalink | Reply
    Tags: , BOINC, , , ,   

    From WCG: “Using grid computing to understand an underwater world” 

    New WCG Logo

    SustainableWater screensaver

    28 Jan 2015
    By: Gerard P. Learmonth Sr., M.B.A., M.S., Ph.D.
    University of Virginia

    The Computing for Sustainable Water (CFSW) project focused on the Chesapeake Bay watershed in the United States. This is the largest watershed in the US and covers all or part of six states (Virginia, West Virginia, Maryland, Delaware, Pennsylvania, and New York) and Washington, D.C., the nation’s capital. The Bay has been under environmental pressure for many years. Previous efforts to address the problem have been unsuccessful. As a result, the size of the Bay’s anoxic region (dead zone) continues to affect the native blue crab (callinectes sapidus) population.

    2
    Callinectes sapidus – the blue crab

    he problem is largely a result of nutrient flow (nitrogen and phosphorous) into the Bay that occurs due to agricultural, industrial, and land development activities. Federal, state, and local agencies attempt to control nutrient flow through a set of incentives known as Best Management Practices (BMPs). Entities adopting BMPs typically receive payments. Each BMP is believed to be helpful in some way for controlling nutrient flow. However, the effectiveness of the various BMPs has not been studied on an appropriately large scale. Indeed, there is no clear scientific evidence for the effectiveness of some BMPs that have already been widely adopted.

    The Computing for Sustainable Water project conducted a set of large-scale simulation experiments of the impact of BMPs on nutrient flow into the Chesapeake Bay and the resulting environmental health of the Bay. Table 1 lists the 23 BMPs tested in this project. Initially, a simulation run with no BMPs was produced as a baseline case. Then each individual BMP was run separately and compared with the baseline. Table 2 shows the results of these statistical comparisons.

    Table 1. Best Management Practices employed in the Chesapeake Bay watershed
    3

    Table 2. Statistical results comparing each BMP to a baseline (no-BMPs) simulation experiment.
    4

    Student’s t-tests of individual BMPs compared to base case of no BMPs * = significant at α = 0.10; ** = significant at α = 0.05; *** = significant at α = 0.01
    For more information about t-statistic, click here. For more information about p-value, click here.

    These results identify several BMPs that are effective in reducing the corresponding nitrogen and phosphorous loads entering the Chesapeake Bay. In particular, BMPs 4, 7, and 23 are highly effective. These results are very informative for policymakers not only in the Chesapeake Bay watershed but globally as well, because many regions of the world experience similar problems and employ similar BMPs.

    In all, World Community Grid members facilitated over 19.1 million experiments. These include various combinations of BMPs to discover the possible effectiveness of combinations of BMPs. The analysis of these experiments continues for combinations of BMPs.

    We would like to once again express our gratitude to the World Community Grid community. A project of this size and scope simply would not have been possible without your help.

    See the full article here.

    Please help promote STEM in your local schools.
    STEM Icon

    Stem Education Coalition

    World Community Grid (WCG) brings people together from across the globe to create the largest non-profit computing grid benefiting humanity. It does this by pooling surplus computer processing power. We believe that innovation combined with visionary scientific research and large-scale volunteerism can help make the planet smarter. Our success depends on like-minded individuals – like you.”

    WCG projects run on BOINC software from UC Berkeley.

    BOINC is a leader in the field(s) of Distributed Computing, Grid Computing and Citizen Cyberscience.BOINC is more properly the Berkeley Open Infrastructure for Network Computing.

    CAN ONE PERSON MAKE A DIFFERENCE? YOU BETCHA!!

    “Download and install secure, free software that captures your computer’s spare power when it is on, but idle. You will then be a World Community Grid volunteer. It’s that simple!” You can download the software at either WCG or BOINC.

    Please visit the project pages-
    Outsmart Ebola together

    Outsmart Ebola Together

    Mapping Cancer Markers
    mappingcancermarkers2

    Uncovering Genome Mysteries
    Uncovering Genome Mysteries

    Say No to Schistosoma

    GO Fight Against Malaria

    Drug Search for Leishmaniasis

    Computing for Clean Water

    The Clean Energy Project

    Discovering Dengue Drugs – Together

    Help Cure Muscular Dystrophy

    Help Fight Childhood Cancer

    Help Conquer Cancer

    Human Proteome Folding

    FightAIDS@Home

    Computing for Sustainable Water

     
  • richardmitnick 3:37 pm on January 27, 2015 Permalink | Reply
    Tags: , , BOINC, , , ,   

    From JPL: “Citizen Scientists Lead Astronomers to Mystery Objects in Space” 

    JPL

    January 27, 2015
    Whitney Clavin
    Jet Propulsion Laboratory, Pasadena, California
    818-354-4673
    whitney.clavin@jpl.nasa.gov

    1
    Volunteers using the web-based Milky Way Project brought star-forming features nicknamed “yellowballs” to the attention of researchers, who later showed that they are a phase of massive star formation. The yellow balls — which are several hundred to thousands times the size of our solar system — are pictured here in the center of this image taken by NASA’s Spitzer Space Telescope. Infrared light has been assigned different colors; yellow occurs where green and red overlap. The yellow balls represent an intermediary stage of massive star formation that takes place before massive stars carve out cavities in the surrounding gas and dust (seen as green-rimmed bubbles with red interiors in this image).

    Infrared light of 3.6 microns is blue; 8-micron light is green; and 24-micron light is red.

    2
    This series of images show three evolutionary phases of massive star formation, as pictured in infrared images from NASA’s Spitzer Space Telescope. The stars start out in thick cocoon of dust (left), evolve into hotter features dubbed “yellowballs” (center); and finally, blow out cavities in the surrounding dust and gas, resulting in green-rimmed bubbles with red centers (right). The process shown here takes roughly a million years. Even the oldest phase shown here is fairly young, as massive stars live a few million years. Eventually, the stars will migrate away from their birth clouds.

    In this image, infrared light of 3.6 microns is blue; 8-micron light is green; and 24-micron light is red.

    NASA’s Jet Propulsion Laboratory, Pasadena, California, manages the Spitzer Space Telescope mission for NASA’s Science Mission Directorate, Washington. Science operations are conducted at the Spitzer Science Center at the California Institute of Technology in Pasadena. Spacecraft operations are based at Lockheed Martin Space Systems Company, Littleton, Colorado. Data are archived at the Infrared Science Archive housed at the Infrared Processing and Analysis Center at Caltech. Caltech manages JPL for NASA.

    NASA Spitzer Telescope
    Spitzer

    Milkyway@home
    MilkyWay@home

    Milkyway@Home uses the BOINC platform to harness volunteered computing resources, creating a highly accurate three dimensional model of the Milky Way galaxy using data gathered by the Sloan Digital Sky Survey (SDSS). This project enables research in both astroinformatics and computer science.

    SDSS Telescope
    SDSS Telescope

    BOINC

    In computer science, the project is investigating different optimization methods which are resilient to the fault-prone, heterogeneous and asynchronous nature of Internet computing; such as evolutionary and genetic algorithms, as well as asynchronous newton methods. While in astroinformatics, Milkyway@Home is generating highly accurate three dimensional models of the Sagittarius stream, which provides knowledge about how the Milky Way galaxy was formed and how tidal tails are created when galaxies merge.

    Milkyway@Home is a joint effort between Rensselaer Polytechnic Institute‘s departments of Computer Science and Physics, Applied Physics and Astronomy. Feel free to contact us via our forums, or email astro@cs.lists.rpi.edu.

    See the full article here.

    Please help promote STEM in your local schools.

    STEM Icon

    Stem Education Coalition

    NASA JPL Campus

    Jet Propulsion Laboratory (JPL) is a federally funded research and development center and NASA field center located in the San Gabriel Valley area of Los Angeles County, California, United States. Although the facility has a Pasadena postal address, it is actually headquartered in the city of La Cañada Flintridge [1], on the northwest border of Pasadena. JPL is managed by the nearby California Institute of Technology (Caltech) for the National Aeronautics and Space Administration. The Laboratory’s primary function is the construction and operation of robotic planetary spacecraft, though it also conducts Earth-orbit and astronomy missions. It is also responsible for operating NASA’s Deep Space Network.

    Caltech Logo
    jpl

     
    • academix2015 4:22 pm on January 27, 2015 Permalink | Reply

      Web based Milky Way project would open up new opportunities for amateur astronomers. Thank you.

      Like

    • academix2015 4:22 pm on January 27, 2015 Permalink | Reply

      Reblogged this on Academic Avenue and commented:
      How about studying the intricacies of the astronomical processes and phenomena in the Milky Way?

      Like

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