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if ($a.is_logged_in() && $viewedUser.is_current_user()) { $('body').addClass('profile-viewed-by-owner'); } $socialProfiles = []</script><div id="js-react-on-rails-context" style="display:none" data-rails-context="{"inMailer":false,"i18nLocale":"en","i18nDefaultLocale":"en","href":"https://bu.academia.edu/EugeneStanley","location":"/EugeneStanley","scheme":"https","host":"bu.academia.edu","port":null,"pathname":"/EugeneStanley","search":null,"httpAcceptLanguage":null,"serverSide":false}"></div> <div class="js-react-on-rails-component" style="display:none" data-component-name="ProfileCheckPaperUpdate" data-props="{}" data-trace="false" data-dom-id="ProfileCheckPaperUpdate-react-component-37c75b7f-57b5-4847-9986-a93d3d69a7c3"></div> <div id="ProfileCheckPaperUpdate-react-component-37c75b7f-57b5-4847-9986-a93d3d69a7c3"></div> <div class="DesignSystem"><div class="onsite-ping" id="onsite-ping"></div></div><div class="profile-user-info DesignSystem"><div class="social-profile-container"><div class="left-panel-container"><div class="user-info-component-wrapper"><div class="user-summary-cta-container"><div class="user-summary-container"><div class="social-profile-avatar-container"><img class="profile-avatar u-positionAbsolute" alt="Eugene Stanley" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/7630487/2749271/3203685/s200_eugene.stanley.jpg" /></div><div class="title-container"><h1 class="ds2-5-heading-sans-serif-sm">Eugene Stanley</h1><div class="affiliations-container fake-truncate js-profile-affiliations"><div><a class="u-tcGrayDarker" href="https://bu.academia.edu/">Boston University</a>, <a class="u-tcGrayDarker" href="https://bu.academia.edu/Departments/Physics/Documents">Physics</a>, <span class="u-tcGrayDarker">Faculty Member</span></div></div></div></div><div class="sidebar-cta-container"><button class="ds2-5-button hidden profile-cta-button grow js-profile-follow-button" data-broccoli-component="user-info.follow-button" data-click-track="profile-user-info-follow-button" data-follow-user-fname="Eugene" data-follow-user-id="7630487" data-follow-user-source="profile_button" data-has-google="false"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">add</span>Follow</button><button class="ds2-5-button hidden profile-cta-button grow js-profile-unfollow-button" data-broccoli-component="user-info.unfollow-button" data-click-track="profile-user-info-unfollow-button" data-unfollow-user-id="7630487"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">done</span>Following</button></div></div><div class="user-stats-container"><a><div class="stat-container js-profile-followers"><p class="label">Followers</p><p class="data">10,772</p></div></a><a><div class="stat-container js-profile-followees" data-broccoli-component="user-info.followees-count" data-click-track="profile-expand-user-info-following"><p class="label">Following</p><p class="data">5</p></div></a><a><div class="stat-container js-profile-coauthors" data-broccoli-component="user-info.coauthors-count" data-click-track="profile-expand-user-info-coauthors"><p class="label">Co-authors</p><p class="data">5</p></div></a><span><div class="stat-container"><p class="label"><span class="js-profile-total-view-text">Public Views</span></p><p class="data"><span class="js-profile-view-count"></span></p></div></span></div><div class="user-bio-container"><div class="profile-bio fake-truncate js-profile-about" style="margin: 0px;">Gene Stanley was born in Oklahoma City and obtained his B.A. in physics at Wesleyan University in 1962. He performed biological physics research with Max Delbrueck in 1963 (funded by a Fulbright in Germany) and was awarded the Ph.D. in physics at Harvard in 1967 after completing a thesis on critical phenomena in magnetic systems under the guidance of T. A. Kaplan and J. H. Van Vleck. Stanley was a Miller Fellow at Berkeley with C. Kittel, where he wrote an Oxford monograph, INTRODUCTION TO PHASE TRANSITIONS AND CRITICAL PHENOMENA, which won the Choice Award for Outstanding Academic Book of 1971. He was appointed Assistant Professor of Physics at MIT in 1969 and was promoted to Associate Professor in 1971. He was appointed Herman von Helmholtz Associate Professor in 1973 in recognition of his interdepartmental teaching and research with the Harvard-MIT Program in Health Sciences and Technology. In 1976 Stanley joined Boston University as Professor of Physics and Associate Professor of Physiology (in the School of Medicine). In 1978 and 1979, he was promoted to Professor of Physiology and University Professor, respectively. In 2007 he was offered joint appointments with the Chemistry and Biomedical Engineering Departments. He is currently honorary professor at the Institute for Advanced Studies, University of Pavia, and at Eotvos Lorand University, Budapest. Stanley works in collaboration with students and colleagues attempting to understand puzzles of interdisciplinary science. His main current focus is understanding the anomalous behavior of liquid water in bulk, nanoconfined, and biological environments. He has also worked on a range of other topics in complex systems, such as quantifying correlations among the constituents of the Alzheimer brain, and quantifying fluctuations in noncoding and coding DNA sequences, interbeat intervals of the healthy and diseased heart. His publications have received 38,537 citations [31,968 to articles and 6569 to books] his "Hirsch index is 90":http://argento.bu.edu/hes/articles/highlycited.html. Two of his papers were reproduced in THE PHYSICAL REVIEW, THE FIRST HUNDRED YEARS: A SELECTION OF SEMINAL PAPERS AND COMMENTARIES. Stanley has been elected to the US National Academy of Sciences, the Brazilian Academy of Sciences, and is an Honorary Member of the Hungarian Physical Society and an Honorary Professor at Eotvos Lorand University (Budapest). Stanley received the Boltzmann Medal, given by the International Union of Pure and Applied Physics (IUPAP), and the Teresiana Medal in Complex Systems Research given by the University of Pavia. He also received the "Distinguished Teaching Scholar" Director's Award from the National Science Foundation, the APS Nicholson Medal for Human Outreach, a Guggenheim Memorial Fellowship, the David Turnbull Prize, a BP Venture Research Award, the Floyd K. Richtmyer Memorial Lectureship Award, the Memory Ride Award for Alzheimer Research, and the Massachusetts Professor of the Year awarded by the Council for Advancement and Support of Education. He has received five Doctorates Honoris Causa, from Bar-Ilan University, Eotvos Lorand University (Budapest), The University of Liege, The University of Dortmund, and The University of Wroclaw. Stanley has served as thesis advisor to 96 Ph.D. candidates at MIT and Boston University, and has worked with 106 research associates. With Nicole Ostrowsky, Stanley co-founded a series of NATO Advanced Study Institutes in interdisciplinary physics in Cargese (in 1985, 1988, and 1990), he co-directed the 1996, 2003, and 2009 Enrico Fermi Schools of Physics on Complex Systems, and he chaired the 1998 Gordon Conference on Water and the 1986 IUPAP International Conference on Statistical Mechanics, Statphys16. Stanley serves on the International Jury for the 500,000 euro "Women in Science" L'Oreal-UNESCO Prize. He is chair of the NAS/Keck Futures Initiative on Complexity, and is an active member of the NAS Committee Forefronts of Science at the Interface of Physical and Life Sciences, charged with finding ways for fostering useful collaborations between physicists and life scientists, He also serves on two NAS committees concerned with threat networks and threatened networks.<br /><div class="js-profile-less-about u-linkUnstyled u-tcGrayDarker u-textDecorationUnderline u-displayNone">less</div></div></div><div class="ri-section"><div class="ri-section-header"><span>Interests</span><a class="ri-more-link js-profile-ri-list-card" data-click-track="profile-user-info-primary-research-interest" data-has-card-for-ri-list="7630487">View All (6)</a></div><div class="ri-tags-container"><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="7630487" href="https://www.academia.edu/Documents/in/Physics"><div id="js-react-on-rails-context" style="display:none" 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data-dom-id="Pill-react-component-feeed4fa-3d14-4ff7-840b-43190aef0759"></div> <div id="Pill-react-component-feeed4fa-3d14-4ff7-840b-43190aef0759"></div> </a><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="7630487" href="https://www.academia.edu/Documents/in/Statistical_Physics"><div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Statistical Physics"]}" data-trace="false" data-dom-id="Pill-react-component-d7a2d21f-ab42-49d4-ae2f-f5d67d096495"></div> <div id="Pill-react-component-d7a2d21f-ab42-49d4-ae2f-f5d67d096495"></div> </a><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="7630487" href="https://www.academia.edu/Documents/in/Mathematical_Physics"><div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Mathematical Physics"]}" data-trace="false" data-dom-id="Pill-react-component-fb0ca87c-72a0-496e-81f3-052d85d32840"></div> <div id="Pill-react-component-fb0ca87c-72a0-496e-81f3-052d85d32840"></div> </a><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="7630487" href="https://www.academia.edu/Documents/in/Chemical_Physics"><div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Chemical Physics"]}" data-trace="false" data-dom-id="Pill-react-component-4c4c7922-9142-47cd-8a83-60704183ed36"></div> <div id="Pill-react-component-4c4c7922-9142-47cd-8a83-60704183ed36"></div> </a></div></div></div></div><div class="right-panel-container"><div class="user-content-wrapper"><div class="uploads-container" id="social-redesign-work-container"><div class="upload-header"><h2 class="ds2-5-heading-sans-serif-xs">Uploads</h2></div><div class="documents-container backbone-social-profile-documents" style="width: 100%;"><div class="u-taCenter"></div><div class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Eugene Stanley</h3></div><div class="js-work-strip profile--work_container" data-work-id="6474533"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/6474533/Capital_death_in_the_world_market"><img alt="Research paper thumbnail of Capital death in the world market" class="work-thumbnail" src="https://attachments.academia-assets.com/33261083/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/6474533/Capital_death_in_the_world_market">Capital death in the world market</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We study the gross domestic product (GDP) per capita together with the market capitalization (MCA...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We study the gross domestic product (GDP) per capita together with the market capitalization (MCAP) per capita as two indicators of the effect of globalization. We ﬁnd that g, the GDP per capita, as a function of m, the MCAP per capita, follows a power law with average exponent close to 1/3. In addition, the Zipf ranking approach conﬁrms that the m for countries with initially lower values of m tends to grow more rapidly than for countries with initially larger values of m. If the trends over the past 20 years continue to hold in the future, then the Zipf ranking approach leads to the prediction that in about 50 years, all countries participating in globalization will have comparable values of their MCAP per capita. We call this economic state “capital death,” in analogy to the physics state of “heat death” predicted by thermodynamic arguments.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8a471545fbbcf63d1bf4f6cfc6b5220f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":33261083,"asset_id":6474533,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/33261083/download_file?st=MTczMjM5NTk2Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="6474533"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="6474533"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 6474533; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=6474533]").text(description); $(".js-view-count[data-work-id=6474533]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 6474533; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='6474533']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 6474533, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "8a471545fbbcf63d1bf4f6cfc6b5220f" } } $('.js-work-strip[data-work-id=6474533]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":6474533,"title":"Capital death in the world market","translated_title":"","metadata":{"abstract":"We study the gross domestic product (GDP) per capita together with the market capitalization (MCAP) per capita as two indicators of the effect of globalization. We ﬁnd that g, the GDP per capita, as a function of m, the MCAP per capita, follows a power law with average exponent close to 1/3. In addition, the Zipf ranking approach conﬁrms that the m for countries with initially lower values of m tends to grow more rapidly than for countries with initially larger values of m. If the trends over the past 20 years continue to hold in the future, then the Zipf ranking approach leads to the prediction that in about 50 years, all countries participating in globalization will have comparable values of their MCAP per capita. We call this economic state “capital death,” in analogy to the physics state of “heat death” predicted by thermodynamic arguments."},"translated_abstract":"We study the gross domestic product (GDP) per capita together with the market capitalization (MCAP) per capita as two indicators of the effect of globalization. We ﬁnd that g, the GDP per capita, as a function of m, the MCAP per capita, follows a power law with average exponent close to 1/3. In addition, the Zipf ranking approach conﬁrms that the m for countries with initially lower values of m tends to grow more rapidly than for countries with initially larger values of m. If the trends over the past 20 years continue to hold in the future, then the Zipf ranking approach leads to the prediction that in about 50 years, all countries participating in globalization will have comparable values of their MCAP per capita. We call this economic state “capital death,” in analogy to the physics state of “heat death” predicted by thermodynamic arguments.","internal_url":"https://www.academia.edu/6474533/Capital_death_in_the_world_market","translated_internal_url":"","created_at":"2014-03-19T09:33:18.224-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":7630487,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":33261083,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/33261083/thumbnails/1.jpg","file_name":"Capital_death_in_the_world_market.pdf","download_url":"https://www.academia.edu/attachments/33261083/download_file?st=MTczMjM5NTk2Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Capital_death_in_the_world_market.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/33261083/Capital_death_in_the_world_market-libre.pdf?1395247090=\u0026response-content-disposition=attachment%3B+filename%3DCapital_death_in_the_world_market.pdf\u0026Expires=1731454089\u0026Signature=STXjCjbXYSjdJewtffBo~Gt-herPHiTyqBA2j-0tKD-jA2yiZ8Q64dVY6bkfL9dbxUTwEWfcsJ~JBTaLkhFBVIE6PX5w~A56VYpkPGiRhfNz5aU1wymkifnf3e7v3gUHwl40jrdlkHP17tADw1Z0xp6D94eNP-bB6miqrDRQqBH0bWCkeMf~0cI~XDpVL9zvw7Yx8pTk3BQFFgD4qcg7nDyFXJnmRpWkQUFdelQBOH54XwrfKUv7dzmINSjYRy7RXEvXCmZTm0BOFzp~fgKi8ISs57lDx~zxe8MM17UzVGHJpT8IzG14URTJpfGRlI~38n9e-6v11POTJFx1Akq0hw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Capital_death_in_the_world_market","translated_slug":"","page_count":6,"language":"en","content_type":"Work","owner":{"id":7630487,"first_name":"Eugene","middle_initials":null,"last_name":"Stanley","page_name":"EugeneStanley","domain_name":"bu","created_at":"2013-12-16T05:51:48.537-08:00","display_name":"Eugene Stanley","url":"https://bu.academia.edu/EugeneStanley"},"attachments":[{"id":33261083,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/33261083/thumbnails/1.jpg","file_name":"Capital_death_in_the_world_market.pdf","download_url":"https://www.academia.edu/attachments/33261083/download_file?st=MTczMjM5NTk2Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Capital_death_in_the_world_market.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/33261083/Capital_death_in_the_world_market-libre.pdf?1395247090=\u0026response-content-disposition=attachment%3B+filename%3DCapital_death_in_the_world_market.pdf\u0026Expires=1731454089\u0026Signature=STXjCjbXYSjdJewtffBo~Gt-herPHiTyqBA2j-0tKD-jA2yiZ8Q64dVY6bkfL9dbxUTwEWfcsJ~JBTaLkhFBVIE6PX5w~A56VYpkPGiRhfNz5aU1wymkifnf3e7v3gUHwl40jrdlkHP17tADw1Z0xp6D94eNP-bB6miqrDRQqBH0bWCkeMf~0cI~XDpVL9zvw7Yx8pTk3BQFFgD4qcg7nDyFXJnmRpWkQUFdelQBOH54XwrfKUv7dzmINSjYRy7RXEvXCmZTm0BOFzp~fgKi8ISs57lDx~zxe8MM17UzVGHJpT8IzG14URTJpfGRlI~38n9e-6v11POTJFx1Akq0hw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":27708,"name":"Econophysics","url":"https://www.academia.edu/Documents/in/Econophysics"}],"urls":[]}, dispatcherData: dispatcherData }); 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Our results illustrate the potential that combining extensive behavioral data sets offers for a better understanding of collective human behavior.","grobid_abstract_attachment_id":33209712},"translated_abstract":null,"internal_url":"https://www.academia.edu/6400953/Quantifying_Trading_Behavior_in_Financial_Markets_Using_Google_Trends","translated_internal_url":"","created_at":"2014-03-13T10:00:48.481-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":7630487,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":33209712,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/33209712/thumbnails/1.jpg","file_name":"Quantifying_Trading_Behavior_in_Financial_Markets_Using_Google_Trends_Nature_Scientific_Reports_3__1684_(2013).__Described_in_The_Digita.pdf","download_url":"https://www.academia.edu/attachments/33209712/download_file?st=MTczMjM5NTk2Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Quantifying_Trading_Behavior_in_Financia.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/33209712/Quantifying_Trading_Behavior_in_Financial_Markets_Using_Google_Trends_Nature_Scientific_Reports_3__1684_%282013%29.__Described_in_The_Digita-libre.pdf?1394729930=\u0026response-content-disposition=attachment%3B+filename%3DQuantifying_Trading_Behavior_in_Financia.pdf\u0026Expires=1731944598\u0026Signature=PIgq3jC9Yu0VVecDAH4jvLe4VqwUbN91NDot7ppkeFhWtA6ijDNfZ2sQYEe63bFuPrBP7UT-XApnbBD3lgTmcyDtJMUkHk7zXjSQittzjNiMzz3k120XsdUxeUyscP7yefyHipdXRadl478PNZVNmZ4ZpYS8AamQShBU9ytCLP-ntlfAyYQR63pZVEA9tAs0O8pIBvvoXf4i2xQugiX-4~DkLrczT4mdhrwMRZNIP3Gk44jrA4HFXmJ-0IKeOkSn-AsHYuHXiPPV1I6mDBAK8PtuxB2TnxgCZWIAZIc6Zw8E2tgw1UMz1IfXOdl4OXA6WuOMz7KJRmBslQFKBHqZCA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Quantifying_Trading_Behavior_in_Financial_Markets_Using_Google_Trends","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":7630487,"first_name":"Eugene","middle_initials":null,"last_name":"Stanley","page_name":"EugeneStanley","domain_name":"bu","created_at":"2013-12-16T05:51:48.537-08:00","display_name":"Eugene Stanley","url":"https://bu.academia.edu/EugeneStanley"},"attachments":[{"id":33209712,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/33209712/thumbnails/1.jpg","file_name":"Quantifying_Trading_Behavior_in_Financial_Markets_Using_Google_Trends_Nature_Scientific_Reports_3__1684_(2013).__Described_in_The_Digita.pdf","download_url":"https://www.academia.edu/attachments/33209712/download_file?st=MTczMjM5NTk2Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Quantifying_Trading_Behavior_in_Financia.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/33209712/Quantifying_Trading_Behavior_in_Financial_Markets_Using_Google_Trends_Nature_Scientific_Reports_3__1684_%282013%29.__Described_in_The_Digita-libre.pdf?1394729930=\u0026response-content-disposition=attachment%3B+filename%3DQuantifying_Trading_Behavior_in_Financia.pdf\u0026Expires=1731944598\u0026Signature=PIgq3jC9Yu0VVecDAH4jvLe4VqwUbN91NDot7ppkeFhWtA6ijDNfZ2sQYEe63bFuPrBP7UT-XApnbBD3lgTmcyDtJMUkHk7zXjSQittzjNiMzz3k120XsdUxeUyscP7yefyHipdXRadl478PNZVNmZ4ZpYS8AamQShBU9ytCLP-ntlfAyYQR63pZVEA9tAs0O8pIBvvoXf4i2xQugiX-4~DkLrczT4mdhrwMRZNIP3Gk44jrA4HFXmJ-0IKeOkSn-AsHYuHXiPPV1I6mDBAK8PtuxB2TnxgCZWIAZIc6Zw8E2tgw1UMz1IfXOdl4OXA6WuOMz7KJRmBslQFKBHqZCA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="6146974"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/6146974/Percolation_of_a_general_network_of_networks"><img alt="Research paper thumbnail of Percolation of a general network of networks" class="work-thumbnail" src="https://attachments.academia-assets.com/33040751/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/6146974/Percolation_of_a_general_network_of_networks">Percolation of a general network of networks</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Percolation theory is an approach to study vulnerability of a system. We develop analytical frame...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Percolation theory is an approach to study vulnerability of a system. We develop analytical framework and analyze percolation properties of a network composed of interdependent networks (NetONet). Typically, percolation of a single network shows that the damage in the network due to a failure is a continuous function of the fraction of failed nodes. In sharp contrast, in NetONet, due to the cascading failures, the percolation transition may be discontinuous and even a single node failure may lead to abrupt collapse of the system. We demonstrate our general framework for a NetONet composed of n classic ErdH{o}s-R'{e}nyi (ER) networks, where each network depends on the same number m of other networks, i.e., a random regular network of interdependent ER networks. In contrast to a mph{treelike} NetONet in which the size of the largest connected cluster (mutual component) depends on n, the loops in the RR NetONet cause the largest connected cluster to depend only on m. We also analyzed the extremely vulnerable feedback condition of coupling. In the case of ER networks, the NetONet only exhibits two phases, a second order phase transition and collapse, and there is no first phase transition regime unlike the no feedback condition. In the case of NetONet composed of RR networks, there exists a first order phase transition when q is large and second order phase transition when q is small. Our results can help in designing robust interdependent systems.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ac947c9de39b271a09221dd31739e6ec" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":33040751,"asset_id":6146974,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/33040751/download_file?st=MTczMjM5NTk2Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="6146974"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="6146974"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 6146974; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=6146974]").text(description); $(".js-view-count[data-work-id=6146974]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 6146974; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='6146974']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 6146974, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "ac947c9de39b271a09221dd31739e6ec" } } $('.js-work-strip[data-work-id=6146974]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":6146974,"title":"Percolation of a general network of networks","translated_title":"","metadata":{"abstract":"Percolation theory is an approach to study vulnerability of a system. We develop analytical framework and analyze percolation properties of a network composed of interdependent networks (NetONet). Typically, percolation of a single network shows that the damage in the network due to a failure is a continuous function of the fraction of failed nodes. In sharp contrast, in NetONet, due to the cascading failures, the percolation transition may be discontinuous and even a single node failure may lead to abrupt collapse of the system. We demonstrate our general framework for a NetONet composed of n classic ErdH{o}s-R'{e}nyi (ER) networks, where each network depends on the same number m of other networks, i.e., a random regular network of interdependent ER networks. In contrast to a \u001bmph{treelike} NetONet in which the size of the largest connected cluster (mutual component) depends on n, the loops in the RR NetONet cause the largest connected cluster to depend only on m. We also analyzed the extremely vulnerable feedback condition of coupling. In the case of ER networks, the NetONet only exhibits two phases, a second order phase transition and collapse, and there is no first phase transition regime unlike the no feedback condition. In the case of NetONet composed of RR networks, there exists a first order phase transition when q is large and second order phase transition when q is small. Our results can help in designing robust interdependent systems."},"translated_abstract":"Percolation theory is an approach to study vulnerability of a system. We develop analytical framework and analyze percolation properties of a network composed of interdependent networks (NetONet). Typically, percolation of a single network shows that the damage in the network due to a failure is a continuous function of the fraction of failed nodes. In sharp contrast, in NetONet, due to the cascading failures, the percolation transition may be discontinuous and even a single node failure may lead to abrupt collapse of the system. We demonstrate our general framework for a NetONet composed of n classic ErdH{o}s-R'{e}nyi (ER) networks, where each network depends on the same number m of other networks, i.e., a random regular network of interdependent ER networks. In contrast to a \u001bmph{treelike} NetONet in which the size of the largest connected cluster (mutual component) depends on n, the loops in the RR NetONet cause the largest connected cluster to depend only on m. We also analyzed the extremely vulnerable feedback condition of coupling. In the case of ER networks, the NetONet only exhibits two phases, a second order phase transition and collapse, and there is no first phase transition regime unlike the no feedback condition. In the case of NetONet composed of RR networks, there exists a first order phase transition when q is large and second order phase transition when q is small. Our results can help in designing robust interdependent systems.","internal_url":"https://www.academia.edu/6146974/Percolation_of_a_general_network_of_networks","translated_internal_url":"","created_at":"2014-02-20T04:39:49.656-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":7630487,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":33040751,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/33040751/thumbnails/1.jpg","file_name":"EugeneStanley.pdf","download_url":"https://www.academia.edu/attachments/33040751/download_file?st=MTczMjM5NTk2Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Percolation_of_a_general_network_of_netw.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/33040751/EugeneStanley-libre.pdf?1393945996=\u0026response-content-disposition=attachment%3B+filename%3DPercolation_of_a_general_network_of_netw.pdf\u0026Expires=1731454089\u0026Signature=T39-D4rvlLzNkRtR75WSKnxGn13A6Sz8uKXF1d6~Vwb6W0eGyQva5~L-7zf5-gMyuBpK0vXgWdVoe62-yEruWhSGMkQxN3KLjTBucdB59JTMXpQ3J1qe6zZjfuTtuhHPfbvLU84Iy3JiLhm1aJQawhDJkONJIV98waicf9zH8MBrVX4tAJjlYooZx88FCFO68Bf204u-PtbyBAo4nuvy4VtHyToJX4J5Ruy-AbzT9~sYir8AKrdluKB5ykFQ~HNP4lN7Z-36MAOHe2P7-RFayJyxoY6BUe9QoRP3YdYZn2~Vho6bBT2Sagc8QCkmoVn-PPYed6TyfafULaW2VbsrQg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Percolation_of_a_general_network_of_networks","translated_slug":"","page_count":31,"language":"en","content_type":"Work","owner":{"id":7630487,"first_name":"Eugene","middle_initials":null,"last_name":"Stanley","page_name":"EugeneStanley","domain_name":"bu","created_at":"2013-12-16T05:51:48.537-08:00","display_name":"Eugene Stanley","url":"https://bu.academia.edu/EugeneStanley"},"attachments":[{"id":33040751,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/33040751/thumbnails/1.jpg","file_name":"EugeneStanley.pdf","download_url":"https://www.academia.edu/attachments/33040751/download_file?st=MTczMjM5NTk2Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Percolation_of_a_general_network_of_netw.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/33040751/EugeneStanley-libre.pdf?1393945996=\u0026response-content-disposition=attachment%3B+filename%3DPercolation_of_a_general_network_of_netw.pdf\u0026Expires=1731454089\u0026Signature=T39-D4rvlLzNkRtR75WSKnxGn13A6Sz8uKXF1d6~Vwb6W0eGyQva5~L-7zf5-gMyuBpK0vXgWdVoe62-yEruWhSGMkQxN3KLjTBucdB59JTMXpQ3J1qe6zZjfuTtuhHPfbvLU84Iy3JiLhm1aJQawhDJkONJIV98waicf9zH8MBrVX4tAJjlYooZx88FCFO68Bf204u-PtbyBAo4nuvy4VtHyToJX4J5Ruy-AbzT9~sYir8AKrdluKB5ykFQ~HNP4lN7Z-36MAOHe2P7-RFayJyxoY6BUe9QoRP3YdYZn2~Vho6bBT2Sagc8QCkmoVn-PPYed6TyfafULaW2VbsrQg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="5738240"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/5738240/Spontaneous_recovery_in_dynamical_networks"><img alt="Research paper thumbnail of Spontaneous recovery in dynamical networks" class="work-thumbnail" src="https://attachments.academia-assets.com/32772320/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/5738240/Spontaneous_recovery_in_dynamical_networks">Spontaneous recovery in dynamical networks</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Much research has been carried out to explore the structural properties and vulnerability of comp...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Much research has been carried out to explore the structural properties and vulnerability of complex networks. Of particular interest are abrupt dynamic events that cause networks to irreversibly fail. However, in many real-world phenomena, such as brain seizures in neuroscience or sudden market crashes in finance, after an inactive period of time a significant part of the damaged network is capable of spontaneously becoming active again. The process often occurs repeatedly. To model this marked network recovery, we examine the effect of local node recoveries and stochastic contiguous spreading, and find that they can lead to the spontaneous emergence of macroscopic ‘phase-flipping’ phenomena. As the network is of finite size and is stochastic, the fraction of active nodes z switches back and forth between the two network collective modes characterized by high network activity and low network activity. Furthermore, the system exhibits a strong hysteresis behaviour analogous to phase transitions near a critical point. We present real-world network data exhibiting phase switching behaviour in accord with the predictions of the model.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="548500c25f41be1a1922c3bc5ae41f18" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":32772320,"asset_id":5738240,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/32772320/download_file?st=MTczMjM5NTk2Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="5738240"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="5738240"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 5738240; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=5738240]").text(description); $(".js-view-count[data-work-id=5738240]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 5738240; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='5738240']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 5738240, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "548500c25f41be1a1922c3bc5ae41f18" } } $('.js-work-strip[data-work-id=5738240]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":5738240,"title":"Spontaneous recovery in dynamical networks","translated_title":"","metadata":{"abstract":"Much research has been carried out to explore the structural properties and vulnerability of complex networks. Of particular interest are abrupt dynamic events that cause networks to irreversibly fail. However, in many real-world phenomena, such as brain seizures in neuroscience or sudden market crashes in finance, after an inactive period of time a significant part of the damaged network is capable of spontaneously becoming active again. The process often occurs repeatedly. To model this marked network recovery, we examine the effect of local node recoveries and stochastic contiguous spreading, and find that they can lead to the spontaneous emergence of macroscopic ‘phase-flipping’ phenomena. As the network is of finite size and is stochastic, the fraction of active nodes z switches back and forth between the two network collective modes characterized by high network activity and low network activity. Furthermore, the system exhibits a strong hysteresis behaviour analogous to phase transitions near a critical point. We present real-world network data exhibiting phase switching behaviour in accord with the predictions of the model."},"translated_abstract":"Much research has been carried out to explore the structural properties and vulnerability of complex networks. Of particular interest are abrupt dynamic events that cause networks to irreversibly fail. However, in many real-world phenomena, such as brain seizures in neuroscience or sudden market crashes in finance, after an inactive period of time a significant part of the damaged network is capable of spontaneously becoming active again. The process often occurs repeatedly. To model this marked network recovery, we examine the effect of local node recoveries and stochastic contiguous spreading, and find that they can lead to the spontaneous emergence of macroscopic ‘phase-flipping’ phenomena. As the network is of finite size and is stochastic, the fraction of active nodes z switches back and forth between the two network collective modes characterized by high network activity and low network activity. Furthermore, the system exhibits a strong hysteresis behaviour analogous to phase transitions near a critical point. We present real-world network data exhibiting phase switching behaviour in accord with the predictions of the model.","internal_url":"https://www.academia.edu/5738240/Spontaneous_recovery_in_dynamical_networks","translated_internal_url":"","created_at":"2014-01-16T04:48:08.708-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":7630487,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":32772320,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/32772320/thumbnails/1.jpg","file_name":"Spontaneous_recovery_in_dynamical_networks.pdf","download_url":"https://www.academia.edu/attachments/32772320/download_file?st=MTczMjM5NTk2Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Spontaneous_recovery_in_dynamical_networ.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/32772320/Spontaneous_recovery_in_dynamical_networks-libre.pdf?1391225572=\u0026response-content-disposition=attachment%3B+filename%3DSpontaneous_recovery_in_dynamical_networ.pdf\u0026Expires=1731454089\u0026Signature=eKmuSRz3gQiMRoIPVmK4Tqjr-y-JyVq3o3kPSco~Vo5WsjTOwSPDtUoDjZ1LLXvi6T76huingKui~nmg-S1fFgjOKWcJtJ-iwmk8cKz8RK9sh5PdFqe07CK96rgvxbWW8qcKKlRldpgrayXbx0achz6r~moyZMuK~SYgGZc3skufsVzKxjhLDFJXLWiat1d8FWG9o-jTmqIH-awMnpvvmU49oUiqmXvuz3yAE0uXU7edKiqqa-BECOtf0-2uf33zuYyQQE0vo-yvbBvW3sirZ20XUtlN5rBtcsJFprzTvCw3c0WQt3qDR~rttcv23tQ4qQ6P7fZbSmDf~gTOBmKLUQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Spontaneous_recovery_in_dynamical_networks","translated_slug":"","page_count":5,"language":"en","content_type":"Work","owner":{"id":7630487,"first_name":"Eugene","middle_initials":null,"last_name":"Stanley","page_name":"EugeneStanley","domain_name":"bu","created_at":"2013-12-16T05:51:48.537-08:00","display_name":"Eugene Stanley","url":"https://bu.academia.edu/EugeneStanley"},"attachments":[{"id":32772320,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/32772320/thumbnails/1.jpg","file_name":"Spontaneous_recovery_in_dynamical_networks.pdf","download_url":"https://www.academia.edu/attachments/32772320/download_file?st=MTczMjM5NTk2Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Spontaneous_recovery_in_dynamical_networ.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/32772320/Spontaneous_recovery_in_dynamical_networks-libre.pdf?1391225572=\u0026response-content-disposition=attachment%3B+filename%3DSpontaneous_recovery_in_dynamical_networ.pdf\u0026Expires=1731454089\u0026Signature=eKmuSRz3gQiMRoIPVmK4Tqjr-y-JyVq3o3kPSco~Vo5WsjTOwSPDtUoDjZ1LLXvi6T76huingKui~nmg-S1fFgjOKWcJtJ-iwmk8cKz8RK9sh5PdFqe07CK96rgvxbWW8qcKKlRldpgrayXbx0achz6r~moyZMuK~SYgGZc3skufsVzKxjhLDFJXLWiat1d8FWG9o-jTmqIH-awMnpvvmU49oUiqmXvuz3yAE0uXU7edKiqqa-BECOtf0-2uf33zuYyQQE0vo-yvbBvW3sirZ20XUtlN5rBtcsJFprzTvCw3c0WQt3qDR~rttcv23tQ4qQ6P7fZbSmDf~gTOBmKLUQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); 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The toxicity of Ab oligomers depends on their structure, which is governed by assembly dynamics. Due to limitations of current experimental techniques, a detailed knowledge of oligomer structure at the atomic level is missing. We introduce a molecular dynamics approach to study Ab dimer formation. 1), We use discrete molecular dynamics simulations of a coarse-grained model to identify a variety of dimer conformations; and 2), we employ allatom molecular mechanics simulations to estimate thermodynamic stability of all dimer conformations. Our simulations of a coarse-grained Ab peptide model predicts 10 different planar b-strand dimer conformations. We then estimate the free energies of all dimer conformations in all-atom molecular mechanics simulations with explicit water. We compare the free energies of Ab(1-42) and Ab(1-40) dimers. 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "dd92683378a196d47fde5badd3a62375" } } $('.js-work-strip[data-work-id=5458720]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":5458720,"title":"Anisotropic percolation and the d-dimensional surface roughening problem","translated_title":"","metadata":{"grobid_abstract":"We review recent numerical simulations of several models of interface growth in ddimensional media with quenched disorder. These models belong to the universality class of anisotropic diode-resistor percolation networks. The values of the roughness exponent (Y = 0.63 ? 0.01 (d = 1 + 1) and (Y = 0.48 2 0.02 (d = 2 + 1) are in good agreement with our recent experiments. The values of (Y in higher dimensions (a = 0.38 5 0.03 in d = 4 and [Y = 0.27 ? 0.05 in d = 5) do not support a recent theoretical conjecture.","grobid_abstract_attachment_id":32578345},"translated_abstract":null,"internal_url":"https://www.academia.edu/5458720/Anisotropic_percolation_and_the_d_dimensional_surface_roughening_problem","translated_internal_url":"","created_at":"2013-12-17T06:13:30.706-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":7630487,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":32578345,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/32578345/thumbnails/1.jpg","file_name":"Anisotropic_percolation_and_the_d-dimensional_surface_roughening_problem.pdf","download_url":"https://www.academia.edu/attachments/32578345/download_file?st=MTczMjM5NTk2Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Anisotropic_percolation_and_the_d_dimens.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/32578345/Anisotropic_percolation_and_the_d-dimensional_surface_roughening_problem-libre.pdf?1391118249=\u0026response-content-disposition=attachment%3B+filename%3DAnisotropic_percolation_and_the_d_dimens.pdf\u0026Expires=1731830880\u0026Signature=I02pn51c6Dm5r2I6H1pMafa3m41uezU6i~IOopKLBhTqxnFXXY1xKE4X4ekIgwQykcxvOm4PST9yg9x~~ZyHTcFROv85RSAwOsGphhx8idaa3RXMI3DV9E7jnFQQDlXECde8dfPsM0cv3g25xj30dxhtByqr1EFfQjvEO4zbl--YPU3Rnf6DWniWG72N8FviU8cxuwyni5jZK9Hq399e7vB2XsWJ-B24z3-4bKr0iN91PpOoEK~zOF3q8TA5a--Q0tIGbpvP9WChGnMjAyCiU4nsHRldwK99kOoe5aeMnjOXbr0qRqiORq9GwF6jaWO9oynGZohMZP6lMAbJ2wJSug__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Anisotropic_percolation_and_the_d_dimensional_surface_roughening_problem","translated_slug":"","page_count":12,"language":"en","content_type":"Work","owner":{"id":7630487,"first_name":"Eugene","middle_initials":null,"last_name":"Stanley","page_name":"EugeneStanley","domain_name":"bu","created_at":"2013-12-16T05:51:48.537-08:00","display_name":"Eugene Stanley","url":"https://bu.academia.edu/EugeneStanley"},"attachments":[{"id":32578345,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/32578345/thumbnails/1.jpg","file_name":"Anisotropic_percolation_and_the_d-dimensional_surface_roughening_problem.pdf","download_url":"https://www.academia.edu/attachments/32578345/download_file?st=MTczMjM5NTk2Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Anisotropic_percolation_and_the_d_dimens.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/32578345/Anisotropic_percolation_and_the_d-dimensional_surface_roughening_problem-libre.pdf?1391118249=\u0026response-content-disposition=attachment%3B+filename%3DAnisotropic_percolation_and_the_d_dimens.pdf\u0026Expires=1731830880\u0026Signature=I02pn51c6Dm5r2I6H1pMafa3m41uezU6i~IOopKLBhTqxnFXXY1xKE4X4ekIgwQykcxvOm4PST9yg9x~~ZyHTcFROv85RSAwOsGphhx8idaa3RXMI3DV9E7jnFQQDlXECde8dfPsM0cv3g25xj30dxhtByqr1EFfQjvEO4zbl--YPU3Rnf6DWniWG72N8FviU8cxuwyni5jZK9Hq399e7vB2XsWJ-B24z3-4bKr0iN91PpOoEK~zOF3q8TA5a--Q0tIGbpvP9WChGnMjAyCiU4nsHRldwK99kOoe5aeMnjOXbr0qRqiORq9GwF6jaWO9oynGZohMZP6lMAbJ2wJSug__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); 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Inhibiting the pathologic folding and oligomerization of A␤ could be effective in the prevention and treatment of Alzheimer's disease. Here, using all-atom molecular dynamics simulations in explicit solvent, we probe the initial stages of folding of a decapeptide segment of A␤, A␤21-30, shown experimentally to nucleate the folding process. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="5458718"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/5458718/Ab_initio_Discrete_Molecular_Dynamics_Approach_to_Protein_Folding_and_Aggregation"><img alt="Research paper thumbnail of Ab initio Discrete Molecular Dynamics Approach to Protein Folding and Aggregation" class="work-thumbnail" src="https://attachments.academia-assets.com/32578339/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/5458718/Ab_initio_Discrete_Molecular_Dynamics_Approach_to_Protein_Folding_and_Aggregation">Ab initio Discrete Molecular Dynamics Approach to Protein Folding and Aggregation</a></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ea6c800cef8dd518cf0f35756556612d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":32578339,"asset_id":5458718,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/32578339/download_file?st=MTczMjM5NTk2Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="5458718"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="5458718"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 5458718; 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Insights into amyloid structural formation and assembly through computational approaches.","grobid_abstract_attachment_id":32578339},"translated_abstract":null,"internal_url":"https://www.academia.edu/5458718/Ab_initio_Discrete_Molecular_Dynamics_Approach_to_Protein_Folding_and_Aggregation","translated_internal_url":"","created_at":"2013-12-17T06:13:30.126-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":7630487,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":32578339,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/32578339/thumbnails/1.jpg","file_name":"Ab_initio_Discrete_Molecular_Dynamics_Approach_to_Protein_Folding_and_Aggregation.pdf","download_url":"https://www.academia.edu/attachments/32578339/download_file?st=MTczMjM5NTk2Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Ab_initio_Discrete_Molecular_Dynamics_Ap.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/32578339/Ab_initio_Discrete_Molecular_Dynamics_Approach_to_Protein_Folding_and_Aggregation-libre.pdf?1391117869=\u0026response-content-disposition=attachment%3B+filename%3DAb_initio_Discrete_Molecular_Dynamics_Ap.pdf\u0026Expires=1731830881\u0026Signature=UBAl4ugkAy8tUpI5k9qMcADUOV2-e6gqKykGOdDEV8vbsXd-WMQP9KkUXqawA2dc7ZonL2sJK3umYdSZpcULvdgDRK2C2RbXrdrdakhZLBWa7boYXaRixEm7krhGX6XVQGDuVsn7xpUUumREXUWqHyv0JTBq~tOfmCvSvwuMP2zELGdDv55A19n7VbRT8XPDRlx8L94fBG6yAc1qfgLzMQGdCV2h2sCg7F0LU2J1fPOprFzYAk67kPoHwnSyyTa2ARj~eSrqbjpBaGF26Qdcg2yJWXyyy1kIheJCbNOLoi6-2sLe067ZWiY9I3IvGwuQh3aVKKe82M-MOCD9nYc3HQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Ab_initio_Discrete_Molecular_Dynamics_Approach_to_Protein_Folding_and_Aggregation","translated_slug":"","page_count":25,"language":"en","content_type":"Work","owner":{"id":7630487,"first_name":"Eugene","middle_initials":null,"last_name":"Stanley","page_name":"EugeneStanley","domain_name":"bu","created_at":"2013-12-16T05:51:48.537-08:00","display_name":"Eugene Stanley","url":"https://bu.academia.edu/EugeneStanley"},"attachments":[{"id":32578339,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/32578339/thumbnails/1.jpg","file_name":"Ab_initio_Discrete_Molecular_Dynamics_Approach_to_Protein_Folding_and_Aggregation.pdf","download_url":"https://www.academia.edu/attachments/32578339/download_file?st=MTczMjM5NTk2Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Ab_initio_Discrete_Molecular_Dynamics_Ap.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/32578339/Ab_initio_Discrete_Molecular_Dynamics_Approach_to_Protein_Folding_and_Aggregation-libre.pdf?1391117869=\u0026response-content-disposition=attachment%3B+filename%3DAb_initio_Discrete_Molecular_Dynamics_Ap.pdf\u0026Expires=1731830881\u0026Signature=UBAl4ugkAy8tUpI5k9qMcADUOV2-e6gqKykGOdDEV8vbsXd-WMQP9KkUXqawA2dc7ZonL2sJK3umYdSZpcULvdgDRK2C2RbXrdrdakhZLBWa7boYXaRixEm7krhGX6XVQGDuVsn7xpUUumREXUWqHyv0JTBq~tOfmCvSvwuMP2zELGdDv55A19n7VbRT8XPDRlx8L94fBG6yAc1qfgLzMQGdCV2h2sCg7F0LU2J1fPOprFzYAk67kPoHwnSyyTa2ARj~eSrqbjpBaGF26Qdcg2yJWXyyy1kIheJCbNOLoi6-2sLe067ZWiY9I3IvGwuQh3aVKKe82M-MOCD9nYc3HQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); 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Substantial clinical and experimental evidence supports the hypothesis that amyloid β-protein (Aβ) aggregation produces assemblies with potent neurotoxic properties that cause AD. For this reason, it is important to elucidate the structural dynamics of Aβ aggregation at atomic level. We apply the discrete molecular dynamics method coupled with a four-bead protein model to study the aggregation of Aβ16-22, a peptide that contains the Aβ central hydrophobic cluster, Leu 17 -Ala 21 , found to be crucial in mediating Aβ assembly. Backbone hydrogen bond interactions are incorporated into the model. Effective hydrophobic and electrostatic interactions between side-chains are parameterized using amino acid-specific hydropathies and net charges. The aggregation of up to 16 Aβ16-22 peptides is studied. 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Peng","title":"Discrete Molecular Dynamics Study of Aβ16-22 Folding and Aggregation"}],"downloadable_attachments":[{"id":32578335,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/32578335/thumbnails/1.jpg","file_name":"Discrete_Molecular_Dynamics_Study_of_Aβ16-22_Folding_and_Aggregation.pdf","download_url":"https://www.academia.edu/attachments/32578335/download_file?st=MTczMjM5NTk2Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Discrete_Molecular_Dynamics_Study_of_A1.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/32578335/Discrete_Molecular_Dynamics_Study_of_A%CE%B216-22_Folding_and_Aggregation-libre.pdf?1391123710=\u0026response-content-disposition=attachment%3B+filename%3DDiscrete_Molecular_Dynamics_Study_of_A1.pdf\u0026Expires=1731830881\u0026Signature=FkwyYl6gJJ3uq4et2SwEXkMGIhOPCum9cTvkyLJRdOLrPWe6S77CEiyLTqS3Ii6w2M3kDuNkYk16Qv9V9e3P2LGRGmk-KoAm-wifIaH~5rGobYEtRznupfl7-GFgg4roX~4MBW85YJB9~yayUGCEnApYCrsgO-qqs-hO1FC~A0Xbxk7Lu3QBGWY-bO6iVQaZb0znT36CqYjOTtQM3kd~nQ-1Bp-kc~7cm-fNlSOJNONoPi9gGVYhhX9V4wV~m~bfFcQLmKbhd9GNAFpe9UKBW9LRHF5uH4-CNTJZUCFEUhmcqlUYGtip6NQcxu9hKr1XhW0GPwDCMp7AMXF2xsq7Uw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Discrete_Molecular_Dynamics_Study_of_Aβ16_22_Folding_and_Aggregation","translated_slug":"","page_count":48,"language":"en","content_type":"Work","owner":{"id":7630487,"first_name":"Eugene","middle_initials":null,"last_name":"Stanley","page_name":"EugeneStanley","domain_name":"bu","created_at":"2013-12-16T05:51:48.537-08:00","display_name":"Eugene Stanley","url":"https://bu.academia.edu/EugeneStanley"},"attachments":[{"id":32578335,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/32578335/thumbnails/1.jpg","file_name":"Discrete_Molecular_Dynamics_Study_of_Aβ16-22_Folding_and_Aggregation.pdf","download_url":"https://www.academia.edu/attachments/32578335/download_file?st=MTczMjM5NTk2Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Discrete_Molecular_Dynamics_Study_of_A1.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/32578335/Discrete_Molecular_Dynamics_Study_of_A%CE%B216-22_Folding_and_Aggregation-libre.pdf?1391123710=\u0026response-content-disposition=attachment%3B+filename%3DDiscrete_Molecular_Dynamics_Study_of_A1.pdf\u0026Expires=1731830881\u0026Signature=FkwyYl6gJJ3uq4et2SwEXkMGIhOPCum9cTvkyLJRdOLrPWe6S77CEiyLTqS3Ii6w2M3kDuNkYk16Qv9V9e3P2LGRGmk-KoAm-wifIaH~5rGobYEtRznupfl7-GFgg4roX~4MBW85YJB9~yayUGCEnApYCrsgO-qqs-hO1FC~A0Xbxk7Lu3QBGWY-bO6iVQaZb0znT36CqYjOTtQM3kd~nQ-1Bp-kc~7cm-fNlSOJNONoPi9gGVYhhX9V4wV~m~bfFcQLmKbhd9GNAFpe9UKBW9LRHF5uH4-CNTJZUCFEUhmcqlUYGtip6NQcxu9hKr1XhW0GPwDCMp7AMXF2xsq7Uw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":145,"name":"Biochemistry","url":"https://www.academia.edu/Documents/in/Biochemistry"},{"id":146,"name":"Bioinformatics","url":"https://www.academia.edu/Documents/in/Bioinformatics"},{"id":502,"name":"Biophysics","url":"https://www.academia.edu/Documents/in/Biophysics"},{"id":4233,"name":"Computational Biology","url":"https://www.academia.edu/Documents/in/Computational_Biology"}],"urls":[]}, dispatcherData: dispatcherData }); 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Here we test the hypothesis that the scaling properties of the dynamics of healthy physiological systems are more stable than those of pathological systems by studying beat-to-beat fluctuations in the human heart rate. We develop techniques based on the Fano factor and Allan factor functions, as well as on detrended fluctuation analysis, for quantifying deviations from uniform power-law scaling in nonstationary time series. By analyzing extremely long data sets of up to Nϭ10 5 beats for 11 healthy subjects, we find that the fluctuations in the heart rate scale approximately uniformly over several temporal orders of magnitude. By contrast, we find that in data sets of comparable length for 14 subjects with heart disease, the fluctuations grow erratically, indicating a loss of scaling stability.","grobid_abstract_attachment_id":32578330},"translated_abstract":null,"internal_url":"https://www.academia.edu/5458712/Deviations_from_uniform_power_law_scaling_in_nonstationary_time_series","translated_internal_url":"","created_at":"2013-12-17T06:13:28.216-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":7630487,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":32578330,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/32578330/thumbnails/1.jpg","file_name":"Deviations_from_uniform_power_law_scaling_in_nonstationary_time_series.pdf","download_url":"https://www.academia.edu/attachments/32578330/download_file?st=MTczMjM5NTk2Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Deviations_from_uniform_power_law_scalin.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/32578330/Deviations_from_uniform_power_law_scaling_in_nonstationary_time_series-libre.pdf?1391189431=\u0026response-content-disposition=attachment%3B+filename%3DDeviations_from_uniform_power_law_scalin.pdf\u0026Expires=1731830881\u0026Signature=aEUPvCDCykygUOPRt-GYGSZ8F1QgLLsMUv~u30H-qNODqZL-eOUE8~lyGdc31zVbbXAWfZXo4jPhuqAF-zNTkzhlXS2L0GCM9kBwC0M4krz94NwX~zR3VmxvPkqvCR8vez073U57QNf1G7nVo5Ila8tb7WbyMGYOjmmyTpsph5fT50kNxgD2S3VWYNe2-KRyVVkvn9ZWCJZQta1SH6sEpW9T7uTXMhZeNg50RO3jqoKjjkFlBBJlJgohIsdzvLQ2A5NXY9hH3SahyQCsTRHYDqZdWb52lKP6FjeFvM9w9pQ35gt84qU3Ds5jFKt5PQdTVtjwLXlTocTDHra69lDOfQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Deviations_from_uniform_power_law_scaling_in_nonstationary_time_series","translated_slug":"","page_count":5,"language":"en","content_type":"Work","owner":{"id":7630487,"first_name":"Eugene","middle_initials":null,"last_name":"Stanley","page_name":"EugeneStanley","domain_name":"bu","created_at":"2013-12-16T05:51:48.537-08:00","display_name":"Eugene Stanley","url":"https://bu.academia.edu/EugeneStanley"},"attachments":[{"id":32578330,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/32578330/thumbnails/1.jpg","file_name":"Deviations_from_uniform_power_law_scaling_in_nonstationary_time_series.pdf","download_url":"https://www.academia.edu/attachments/32578330/download_file?st=MTczMjM5NTk2Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Deviations_from_uniform_power_law_scalin.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/32578330/Deviations_from_uniform_power_law_scaling_in_nonstationary_time_series-libre.pdf?1391189431=\u0026response-content-disposition=attachment%3B+filename%3DDeviations_from_uniform_power_law_scalin.pdf\u0026Expires=1731830881\u0026Signature=aEUPvCDCykygUOPRt-GYGSZ8F1QgLLsMUv~u30H-qNODqZL-eOUE8~lyGdc31zVbbXAWfZXo4jPhuqAF-zNTkzhlXS2L0GCM9kBwC0M4krz94NwX~zR3VmxvPkqvCR8vez073U57QNf1G7nVo5Ila8tb7WbyMGYOjmmyTpsph5fT50kNxgD2S3VWYNe2-KRyVVkvn9ZWCJZQta1SH6sEpW9T7uTXMhZeNg50RO3jqoKjjkFlBBJlJgohIsdzvLQ2A5NXY9hH3SahyQCsTRHYDqZdWb52lKP6FjeFvM9w9pQ35gt84qU3Ds5jFKt5PQdTVtjwLXlTocTDHra69lDOfQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); 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Development of oligomer-specific therapeutic agents requires a mechanistic understanding of the oligomerization process. This is a daunting task because amyloidogenic protein oligomers often are metastable and comprise structurally heterogeneous populations in equilibrium with monomers and fibrils. A single methodological approach cannot elucidate the entire protein assembly process. An integrated multidisciplinary program is required. We discuss here the synergistic application of in hydro, in vacuo, and in silico methods to the study of the amyloid -protein, the key pathogenetic agent in Alzheimer's disease.","grobid_abstract_attachment_id":32578333},"translated_abstract":null,"internal_url":"https://www.academia.edu/5458713/Elucidating_Amyloid_Protein_Folding_and_Assembly_A_Multidisciplinary_Approach","translated_internal_url":"","created_at":"2013-12-17T06:13:28.223-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":7630487,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":32578333,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/32578333/thumbnails/1.jpg","file_name":"Elucidating_Amyloid_o-Protein_Folding_and_Assembly_A_Multidisciplinary_Approach.pdf","download_url":"https://www.academia.edu/attachments/32578333/download_file?st=MTczMjM5NTk2Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Elucidating_Amyloid_Protein_Folding_and.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/32578333/Elucidating_Amyloid_o-Protein_Folding_and_Assembly_A_Multidisciplinary_Approach-libre.pdf?1391116959=\u0026response-content-disposition=attachment%3B+filename%3DElucidating_Amyloid_Protein_Folding_and.pdf\u0026Expires=1731830881\u0026Signature=D1u5JfrQyZHSvNNmmKNk6voS81214-1FGhYFEiv3fQeWBSVGlzzQeDZsqjvVitJXRq7fy0ZO7REUwaL2Pnz~1FBO6Mli5J8neVKvp1HWljjGlBVjO0fcWfFWCXGtTfJ2q9xOa8BsNnfb3UsSh5rBd3HDfKLwTkV5RGoAXOSHnk5S~jDTqIhdOxoxuf8srPXmkfUSbXlgx1iWKr-Bw8NypfIvP7yofk7DkaHWhi2HiNy75cPbdUNdQE3DwI2qr2NJp0~MUkVo7B8TpmwK6XBF5kYXdbP-cuc2XXjvAA1CJOjDYBIMC3mqfZ891-UntbHXKemb1tBEIpUwPNGZA6IFFA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Elucidating_Amyloid_Protein_Folding_and_Assembly_A_Multidisciplinary_Approach","translated_slug":"","page_count":11,"language":"en","content_type":"Work","owner":{"id":7630487,"first_name":"Eugene","middle_initials":null,"last_name":"Stanley","page_name":"EugeneStanley","domain_name":"bu","created_at":"2013-12-16T05:51:48.537-08:00","display_name":"Eugene Stanley","url":"https://bu.academia.edu/EugeneStanley"},"attachments":[{"id":32578333,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/32578333/thumbnails/1.jpg","file_name":"Elucidating_Amyloid_o-Protein_Folding_and_Assembly_A_Multidisciplinary_Approach.pdf","download_url":"https://www.academia.edu/attachments/32578333/download_file?st=MTczMjM5NTk2Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Elucidating_Amyloid_Protein_Folding_and.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/32578333/Elucidating_Amyloid_o-Protein_Folding_and_Assembly_A_Multidisciplinary_Approach-libre.pdf?1391116959=\u0026response-content-disposition=attachment%3B+filename%3DElucidating_Amyloid_Protein_Folding_and.pdf\u0026Expires=1731830881\u0026Signature=D1u5JfrQyZHSvNNmmKNk6voS81214-1FGhYFEiv3fQeWBSVGlzzQeDZsqjvVitJXRq7fy0ZO7REUwaL2Pnz~1FBO6Mli5J8neVKvp1HWljjGlBVjO0fcWfFWCXGtTfJ2q9xOa8BsNnfb3UsSh5rBd3HDfKLwTkV5RGoAXOSHnk5S~jDTqIhdOxoxuf8srPXmkfUSbXlgx1iWKr-Bw8NypfIvP7yofk7DkaHWhi2HiNy75cPbdUNdQE3DwI2qr2NJp0~MUkVo7B8TpmwK6XBF5kYXdbP-cuc2XXjvAA1CJOjDYBIMC3mqfZ891-UntbHXKemb1tBEIpUwPNGZA6IFFA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); 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We use this approach to calculate exactly the first low-density expansion of LBB@) for arbitrary system dimensionality d, where L B B @ ) is the mean number of backbone bonds and p is the bond occupation probability. Standard series extrapolation methods provide estimates of the fractal dimension of the backbone for all d ; these disagree with the Sierpinski gasket model of the backbone. We also calculate the first low-density expansions of Lmin@) and L e d @ ) which are, respectively, the mean number of bonds in the minimum path between i and","grobid_abstract_attachment_id":32578334},"translated_abstract":null,"internal_url":"https://www.academia.edu/5458711/Exact_enumeration_approach_to_fractal_properties_of_the_percolation_backbone_and_1_%CF%83_expansion","translated_internal_url":"","created_at":"2013-12-17T06:13:28.201-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":7630487,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":32578334,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/32578334/thumbnails/1.jpg","file_name":"Exact_enumeration_approach_to_fractal_properties_of_the_percolation_backbone_and_1_σ_expansion.pdf","download_url":"https://www.academia.edu/attachments/32578334/download_file?st=MTczMjM5NTk2Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Exact_enumeration_approach_to_fractal_pr.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/32578334/Exact_enumeration_approach_to_fractal_properties_of_the_percolation_backbone_and_1_%CF%83_expansion-libre.pdf?1391129956=\u0026response-content-disposition=attachment%3B+filename%3DExact_enumeration_approach_to_fractal_pr.pdf\u0026Expires=1731830881\u0026Signature=OpcNDLoxq0iTNqKRhz3rMyz5H4V4tR8lc7YUhfpFLQXr~C9s0AXPNqsOU9NZMa88~KLEGtjzgR3K0o0CpL~2MwcjUAK88vJ0XUvuGh5uzcA0F2Dwo4D-BWSr4jQadabdK8LxJlSakd0bAbKqnCAH7iqoPy~4wzvSlgEN44KnJtwIkcHchDGPvEZdTASIf2uMPee2k2Im-9BruXJvGabsiPsF2pjaxnFP5O1vf-WmLD4U1vzeyJNLGIneQpxsTh1oumbCGwV2cYAQurQz~mo8BT8WCKVEW92j5pNbtsuKdKyRy871RlReDVg95gcFR9WoodPHdNF2Nn0XyrAOegWWBg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Exact_enumeration_approach_to_fractal_properties_of_the_percolation_backbone_and_1_σ_expansion","translated_slug":"","page_count":8,"language":"en","content_type":"Work","owner":{"id":7630487,"first_name":"Eugene","middle_initials":null,"last_name":"Stanley","page_name":"EugeneStanley","domain_name":"bu","created_at":"2013-12-16T05:51:48.537-08:00","display_name":"Eugene Stanley","url":"https://bu.academia.edu/EugeneStanley"},"attachments":[{"id":32578334,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/32578334/thumbnails/1.jpg","file_name":"Exact_enumeration_approach_to_fractal_properties_of_the_percolation_backbone_and_1_σ_expansion.pdf","download_url":"https://www.academia.edu/attachments/32578334/download_file?st=MTczMjM5NTk2Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Exact_enumeration_approach_to_fractal_pr.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/32578334/Exact_enumeration_approach_to_fractal_properties_of_the_percolation_backbone_and_1_%CF%83_expansion-libre.pdf?1391129956=\u0026response-content-disposition=attachment%3B+filename%3DExact_enumeration_approach_to_fractal_pr.pdf\u0026Expires=1731830881\u0026Signature=OpcNDLoxq0iTNqKRhz3rMyz5H4V4tR8lc7YUhfpFLQXr~C9s0AXPNqsOU9NZMa88~KLEGtjzgR3K0o0CpL~2MwcjUAK88vJ0XUvuGh5uzcA0F2Dwo4D-BWSr4jQadabdK8LxJlSakd0bAbKqnCAH7iqoPy~4wzvSlgEN44KnJtwIkcHchDGPvEZdTASIf2uMPee2k2Im-9BruXJvGabsiPsF2pjaxnFP5O1vf-WmLD4U1vzeyJNLGIneQpxsTh1oumbCGwV2cYAQurQz~mo8BT8WCKVEW92j5pNbtsuKdKyRy871RlReDVg95gcFR9WoodPHdNF2Nn0XyrAOegWWBg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":511,"name":"Materials Science","url":"https://www.academia.edu/Documents/in/Materials_Science"}],"urls":[]}, dispatcherData: dispatcherData }); 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Experimental approaches to study A self-assembly are limited, because most relevant aggregates are quasi-stable and inhomogeneous. In contrast, simulations can provide significant insights into the problem, including specific sites in the molecule that would be attractive for drug targeting and details of the assembly pathways leading to the production of toxic assemblies. Here we review computer simulation approaches to understanding the structural biology of A . We discuss the ways in which these simulations help guide experimental work, and in turn, how experimental results guide the development of theoretical and simulation approaches that may be of general utility in understanding pathologic protein folding and assembly.","grobid_abstract_attachment_id":32578332},"translated_abstract":null,"internal_url":"https://www.academia.edu/5458710/Computer_Simulations_of_Alzheimer_s_Amyloid_Protein_Folding_and_Assembly","translated_internal_url":"","created_at":"2013-12-17T06:13:28.191-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":7630487,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":32578332,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/32578332/thumbnails/1.jpg","file_name":"Computer_Simulations_of_Alzheimers_Amyloid_Protein_Folding_and_Assembly.pdf","download_url":"https://www.academia.edu/attachments/32578332/download_file?st=MTczMjM5NTk2Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Computer_Simulations_of_Alzheimer_s_Amyl.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/32578332/Computer_Simulations_of_Alzheimers_Amyloid_Protein_Folding_and_Assembly-libre.pdf?1391186839=\u0026response-content-disposition=attachment%3B+filename%3DComputer_Simulations_of_Alzheimer_s_Amyl.pdf\u0026Expires=1731830881\u0026Signature=W4kioR0CB8Drmu4Mf6KwAKF5eBlKhBUuGlNKEq34ECGe1ZJLfIkyPhix71YDwMhyh0JMrr7490SLajD3Jusc~hEYiyEusmYbIkacLkIa4piXighqmp0Ieom8v-reKxUBfDr8oIWeMlAdnQv1HH~252nNlDM2HgBF5IE9hEDlwt-5njsyX-MpGLoLgkreMp-j51-EhJx--10iiDoyXF9ilosrXWrO7LzfOf1brRJ919ANS1RAqRNS9XGUURe3jf5MfcFYp~wJCrVvhnTW6ROImhnDpr4DObXz4IR56GXIIS~w9ae4gKcX6uRyG44K-dUPgu9dXOUwc9i4u0dtABaPKg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Computer_Simulations_of_Alzheimer_s_Amyloid_Protein_Folding_and_Assembly","translated_slug":"","page_count":12,"language":"en","content_type":"Work","owner":{"id":7630487,"first_name":"Eugene","middle_initials":null,"last_name":"Stanley","page_name":"EugeneStanley","domain_name":"bu","created_at":"2013-12-16T05:51:48.537-08:00","display_name":"Eugene Stanley","url":"https://bu.academia.edu/EugeneStanley"},"attachments":[{"id":32578332,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/32578332/thumbnails/1.jpg","file_name":"Computer_Simulations_of_Alzheimers_Amyloid_Protein_Folding_and_Assembly.pdf","download_url":"https://www.academia.edu/attachments/32578332/download_file?st=MTczMjM5NTk2Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Computer_Simulations_of_Alzheimer_s_Amyl.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/32578332/Computer_Simulations_of_Alzheimers_Amyloid_Protein_Folding_and_Assembly-libre.pdf?1391186839=\u0026response-content-disposition=attachment%3B+filename%3DComputer_Simulations_of_Alzheimer_s_Amyl.pdf\u0026Expires=1731830881\u0026Signature=W4kioR0CB8Drmu4Mf6KwAKF5eBlKhBUuGlNKEq34ECGe1ZJLfIkyPhix71YDwMhyh0JMrr7490SLajD3Jusc~hEYiyEusmYbIkacLkIa4piXighqmp0Ieom8v-reKxUBfDr8oIWeMlAdnQv1HH~252nNlDM2HgBF5IE9hEDlwt-5njsyX-MpGLoLgkreMp-j51-EhJx--10iiDoyXF9ilosrXWrO7LzfOf1brRJ919ANS1RAqRNS9XGUURe3jf5MfcFYp~wJCrVvhnTW6ROImhnDpr4DObXz4IR56GXIIS~w9ae4gKcX6uRyG44K-dUPgu9dXOUwc9i4u0dtABaPKg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="997718" id="papers"><div class="js-work-strip profile--work_container" data-work-id="6474533"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/6474533/Capital_death_in_the_world_market"><img alt="Research paper thumbnail of Capital death in the world market" class="work-thumbnail" src="https://attachments.academia-assets.com/33261083/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/6474533/Capital_death_in_the_world_market">Capital death in the world market</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We study the gross domestic product (GDP) per capita together with the market capitalization (MCA...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We study the gross domestic product (GDP) per capita together with the market capitalization (MCAP) per capita as two indicators of the effect of globalization. We ﬁnd that g, the GDP per capita, as a function of m, the MCAP per capita, follows a power law with average exponent close to 1/3. In addition, the Zipf ranking approach conﬁrms that the m for countries with initially lower values of m tends to grow more rapidly than for countries with initially larger values of m. If the trends over the past 20 years continue to hold in the future, then the Zipf ranking approach leads to the prediction that in about 50 years, all countries participating in globalization will have comparable values of their MCAP per capita. We call this economic state “capital death,” in analogy to the physics state of “heat death” predicted by thermodynamic arguments.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8a471545fbbcf63d1bf4f6cfc6b5220f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":33261083,"asset_id":6474533,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/33261083/download_file?st=MTczMjM5NTk2Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="6474533"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="6474533"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 6474533; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=6474533]").text(description); $(".js-view-count[data-work-id=6474533]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 6474533; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='6474533']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 6474533, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "8a471545fbbcf63d1bf4f6cfc6b5220f" } } $('.js-work-strip[data-work-id=6474533]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":6474533,"title":"Capital death in the world market","translated_title":"","metadata":{"abstract":"We study the gross domestic product (GDP) per capita together with the market capitalization (MCAP) per capita as two indicators of the effect of globalization. We ﬁnd that g, the GDP per capita, as a function of m, the MCAP per capita, follows a power law with average exponent close to 1/3. In addition, the Zipf ranking approach conﬁrms that the m for countries with initially lower values of m tends to grow more rapidly than for countries with initially larger values of m. If the trends over the past 20 years continue to hold in the future, then the Zipf ranking approach leads to the prediction that in about 50 years, all countries participating in globalization will have comparable values of their MCAP per capita. We call this economic state “capital death,” in analogy to the physics state of “heat death” predicted by thermodynamic arguments."},"translated_abstract":"We study the gross domestic product (GDP) per capita together with the market capitalization (MCAP) per capita as two indicators of the effect of globalization. We ﬁnd that g, the GDP per capita, as a function of m, the MCAP per capita, follows a power law with average exponent close to 1/3. In addition, the Zipf ranking approach conﬁrms that the m for countries with initially lower values of m tends to grow more rapidly than for countries with initially larger values of m. If the trends over the past 20 years continue to hold in the future, then the Zipf ranking approach leads to the prediction that in about 50 years, all countries participating in globalization will have comparable values of their MCAP per capita. We call this economic state “capital death,” in analogy to the physics state of “heat death” predicted by thermodynamic arguments.","internal_url":"https://www.academia.edu/6474533/Capital_death_in_the_world_market","translated_internal_url":"","created_at":"2014-03-19T09:33:18.224-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":7630487,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":33261083,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/33261083/thumbnails/1.jpg","file_name":"Capital_death_in_the_world_market.pdf","download_url":"https://www.academia.edu/attachments/33261083/download_file?st=MTczMjM5NTk2Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Capital_death_in_the_world_market.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/33261083/Capital_death_in_the_world_market-libre.pdf?1395247090=\u0026response-content-disposition=attachment%3B+filename%3DCapital_death_in_the_world_market.pdf\u0026Expires=1731454089\u0026Signature=STXjCjbXYSjdJewtffBo~Gt-herPHiTyqBA2j-0tKD-jA2yiZ8Q64dVY6bkfL9dbxUTwEWfcsJ~JBTaLkhFBVIE6PX5w~A56VYpkPGiRhfNz5aU1wymkifnf3e7v3gUHwl40jrdlkHP17tADw1Z0xp6D94eNP-bB6miqrDRQqBH0bWCkeMf~0cI~XDpVL9zvw7Yx8pTk3BQFFgD4qcg7nDyFXJnmRpWkQUFdelQBOH54XwrfKUv7dzmINSjYRy7RXEvXCmZTm0BOFzp~fgKi8ISs57lDx~zxe8MM17UzVGHJpT8IzG14URTJpfGRlI~38n9e-6v11POTJFx1Akq0hw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Capital_death_in_the_world_market","translated_slug":"","page_count":6,"language":"en","content_type":"Work","owner":{"id":7630487,"first_name":"Eugene","middle_initials":null,"last_name":"Stanley","page_name":"EugeneStanley","domain_name":"bu","created_at":"2013-12-16T05:51:48.537-08:00","display_name":"Eugene Stanley","url":"https://bu.academia.edu/EugeneStanley"},"attachments":[{"id":33261083,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/33261083/thumbnails/1.jpg","file_name":"Capital_death_in_the_world_market.pdf","download_url":"https://www.academia.edu/attachments/33261083/download_file?st=MTczMjM5NTk2Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Capital_death_in_the_world_market.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/33261083/Capital_death_in_the_world_market-libre.pdf?1395247090=\u0026response-content-disposition=attachment%3B+filename%3DCapital_death_in_the_world_market.pdf\u0026Expires=1731454089\u0026Signature=STXjCjbXYSjdJewtffBo~Gt-herPHiTyqBA2j-0tKD-jA2yiZ8Q64dVY6bkfL9dbxUTwEWfcsJ~JBTaLkhFBVIE6PX5w~A56VYpkPGiRhfNz5aU1wymkifnf3e7v3gUHwl40jrdlkHP17tADw1Z0xp6D94eNP-bB6miqrDRQqBH0bWCkeMf~0cI~XDpVL9zvw7Yx8pTk3BQFFgD4qcg7nDyFXJnmRpWkQUFdelQBOH54XwrfKUv7dzmINSjYRy7RXEvXCmZTm0BOFzp~fgKi8ISs57lDx~zxe8MM17UzVGHJpT8IzG14URTJpfGRlI~38n9e-6v11POTJFx1Akq0hw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":27708,"name":"Econophysics","url":"https://www.academia.edu/Documents/in/Econophysics"}],"urls":[]}, dispatcherData: dispatcherData }); 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We develop analytical frame...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Percolation theory is an approach to study vulnerability of a system. We develop analytical framework and analyze percolation properties of a network composed of interdependent networks (NetONet). Typically, percolation of a single network shows that the damage in the network due to a failure is a continuous function of the fraction of failed nodes. In sharp contrast, in NetONet, due to the cascading failures, the percolation transition may be discontinuous and even a single node failure may lead to abrupt collapse of the system. We demonstrate our general framework for a NetONet composed of n classic ErdH{o}s-R'{e}nyi (ER) networks, where each network depends on the same number m of other networks, i.e., a random regular network of interdependent ER networks. In contrast to a mph{treelike} NetONet in which the size of the largest connected cluster (mutual component) depends on n, the loops in the RR NetONet cause the largest connected cluster to depend only on m. We also analyzed the extremely vulnerable feedback condition of coupling. In the case of ER networks, the NetONet only exhibits two phases, a second order phase transition and collapse, and there is no first phase transition regime unlike the no feedback condition. In the case of NetONet composed of RR networks, there exists a first order phase transition when q is large and second order phase transition when q is small. Our results can help in designing robust interdependent systems.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ac947c9de39b271a09221dd31739e6ec" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":33040751,"asset_id":6146974,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/33040751/download_file?st=MTczMjM5NTk2Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="6146974"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="6146974"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 6146974; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=6146974]").text(description); $(".js-view-count[data-work-id=6146974]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 6146974; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='6146974']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 6146974, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "ac947c9de39b271a09221dd31739e6ec" } } $('.js-work-strip[data-work-id=6146974]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":6146974,"title":"Percolation of a general network of networks","translated_title":"","metadata":{"abstract":"Percolation theory is an approach to study vulnerability of a system. We develop analytical framework and analyze percolation properties of a network composed of interdependent networks (NetONet). Typically, percolation of a single network shows that the damage in the network due to a failure is a continuous function of the fraction of failed nodes. In sharp contrast, in NetONet, due to the cascading failures, the percolation transition may be discontinuous and even a single node failure may lead to abrupt collapse of the system. We demonstrate our general framework for a NetONet composed of n classic ErdH{o}s-R'{e}nyi (ER) networks, where each network depends on the same number m of other networks, i.e., a random regular network of interdependent ER networks. In contrast to a \u001bmph{treelike} NetONet in which the size of the largest connected cluster (mutual component) depends on n, the loops in the RR NetONet cause the largest connected cluster to depend only on m. We also analyzed the extremely vulnerable feedback condition of coupling. In the case of ER networks, the NetONet only exhibits two phases, a second order phase transition and collapse, and there is no first phase transition regime unlike the no feedback condition. In the case of NetONet composed of RR networks, there exists a first order phase transition when q is large and second order phase transition when q is small. Our results can help in designing robust interdependent systems."},"translated_abstract":"Percolation theory is an approach to study vulnerability of a system. We develop analytical framework and analyze percolation properties of a network composed of interdependent networks (NetONet). Typically, percolation of a single network shows that the damage in the network due to a failure is a continuous function of the fraction of failed nodes. In sharp contrast, in NetONet, due to the cascading failures, the percolation transition may be discontinuous and even a single node failure may lead to abrupt collapse of the system. We demonstrate our general framework for a NetONet composed of n classic ErdH{o}s-R'{e}nyi (ER) networks, where each network depends on the same number m of other networks, i.e., a random regular network of interdependent ER networks. In contrast to a \u001bmph{treelike} NetONet in which the size of the largest connected cluster (mutual component) depends on n, the loops in the RR NetONet cause the largest connected cluster to depend only on m. We also analyzed the extremely vulnerable feedback condition of coupling. In the case of ER networks, the NetONet only exhibits two phases, a second order phase transition and collapse, and there is no first phase transition regime unlike the no feedback condition. In the case of NetONet composed of RR networks, there exists a first order phase transition when q is large and second order phase transition when q is small. Our results can help in designing robust interdependent systems.","internal_url":"https://www.academia.edu/6146974/Percolation_of_a_general_network_of_networks","translated_internal_url":"","created_at":"2014-02-20T04:39:49.656-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":7630487,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":33040751,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/33040751/thumbnails/1.jpg","file_name":"EugeneStanley.pdf","download_url":"https://www.academia.edu/attachments/33040751/download_file?st=MTczMjM5NTk2Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Percolation_of_a_general_network_of_netw.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/33040751/EugeneStanley-libre.pdf?1393945996=\u0026response-content-disposition=attachment%3B+filename%3DPercolation_of_a_general_network_of_netw.pdf\u0026Expires=1731454089\u0026Signature=T39-D4rvlLzNkRtR75WSKnxGn13A6Sz8uKXF1d6~Vwb6W0eGyQva5~L-7zf5-gMyuBpK0vXgWdVoe62-yEruWhSGMkQxN3KLjTBucdB59JTMXpQ3J1qe6zZjfuTtuhHPfbvLU84Iy3JiLhm1aJQawhDJkONJIV98waicf9zH8MBrVX4tAJjlYooZx88FCFO68Bf204u-PtbyBAo4nuvy4VtHyToJX4J5Ruy-AbzT9~sYir8AKrdluKB5ykFQ~HNP4lN7Z-36MAOHe2P7-RFayJyxoY6BUe9QoRP3YdYZn2~Vho6bBT2Sagc8QCkmoVn-PPYed6TyfafULaW2VbsrQg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Percolation_of_a_general_network_of_networks","translated_slug":"","page_count":31,"language":"en","content_type":"Work","owner":{"id":7630487,"first_name":"Eugene","middle_initials":null,"last_name":"Stanley","page_name":"EugeneStanley","domain_name":"bu","created_at":"2013-12-16T05:51:48.537-08:00","display_name":"Eugene Stanley","url":"https://bu.academia.edu/EugeneStanley"},"attachments":[{"id":33040751,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/33040751/thumbnails/1.jpg","file_name":"EugeneStanley.pdf","download_url":"https://www.academia.edu/attachments/33040751/download_file?st=MTczMjM5NTk2Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Percolation_of_a_general_network_of_netw.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/33040751/EugeneStanley-libre.pdf?1393945996=\u0026response-content-disposition=attachment%3B+filename%3DPercolation_of_a_general_network_of_netw.pdf\u0026Expires=1731454089\u0026Signature=T39-D4rvlLzNkRtR75WSKnxGn13A6Sz8uKXF1d6~Vwb6W0eGyQva5~L-7zf5-gMyuBpK0vXgWdVoe62-yEruWhSGMkQxN3KLjTBucdB59JTMXpQ3J1qe6zZjfuTtuhHPfbvLU84Iy3JiLhm1aJQawhDJkONJIV98waicf9zH8MBrVX4tAJjlYooZx88FCFO68Bf204u-PtbyBAo4nuvy4VtHyToJX4J5Ruy-AbzT9~sYir8AKrdluKB5ykFQ~HNP4lN7Z-36MAOHe2P7-RFayJyxoY6BUe9QoRP3YdYZn2~Vho6bBT2Sagc8QCkmoVn-PPYed6TyfafULaW2VbsrQg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="5738240"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/5738240/Spontaneous_recovery_in_dynamical_networks"><img alt="Research paper thumbnail of Spontaneous recovery in dynamical networks" class="work-thumbnail" src="https://attachments.academia-assets.com/32772320/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/5738240/Spontaneous_recovery_in_dynamical_networks">Spontaneous recovery in dynamical networks</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Much research has been carried out to explore the structural properties and vulnerability of comp...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Much research has been carried out to explore the structural properties and vulnerability of complex networks. Of particular interest are abrupt dynamic events that cause networks to irreversibly fail. However, in many real-world phenomena, such as brain seizures in neuroscience or sudden market crashes in finance, after an inactive period of time a significant part of the damaged network is capable of spontaneously becoming active again. The process often occurs repeatedly. To model this marked network recovery, we examine the effect of local node recoveries and stochastic contiguous spreading, and find that they can lead to the spontaneous emergence of macroscopic ‘phase-flipping’ phenomena. As the network is of finite size and is stochastic, the fraction of active nodes z switches back and forth between the two network collective modes characterized by high network activity and low network activity. Furthermore, the system exhibits a strong hysteresis behaviour analogous to phase transitions near a critical point. We present real-world network data exhibiting phase switching behaviour in accord with the predictions of the model.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="548500c25f41be1a1922c3bc5ae41f18" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":32772320,"asset_id":5738240,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/32772320/download_file?st=MTczMjM5NTk2Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="5738240"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="5738240"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 5738240; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=5738240]").text(description); $(".js-view-count[data-work-id=5738240]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 5738240; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='5738240']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 5738240, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "548500c25f41be1a1922c3bc5ae41f18" } } $('.js-work-strip[data-work-id=5738240]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":5738240,"title":"Spontaneous recovery in dynamical networks","translated_title":"","metadata":{"abstract":"Much research has been carried out to explore the structural properties and vulnerability of complex networks. Of particular interest are abrupt dynamic events that cause networks to irreversibly fail. However, in many real-world phenomena, such as brain seizures in neuroscience or sudden market crashes in finance, after an inactive period of time a significant part of the damaged network is capable of spontaneously becoming active again. The process often occurs repeatedly. To model this marked network recovery, we examine the effect of local node recoveries and stochastic contiguous spreading, and find that they can lead to the spontaneous emergence of macroscopic ‘phase-flipping’ phenomena. As the network is of finite size and is stochastic, the fraction of active nodes z switches back and forth between the two network collective modes characterized by high network activity and low network activity. Furthermore, the system exhibits a strong hysteresis behaviour analogous to phase transitions near a critical point. We present real-world network data exhibiting phase switching behaviour in accord with the predictions of the model."},"translated_abstract":"Much research has been carried out to explore the structural properties and vulnerability of complex networks. Of particular interest are abrupt dynamic events that cause networks to irreversibly fail. However, in many real-world phenomena, such as brain seizures in neuroscience or sudden market crashes in finance, after an inactive period of time a significant part of the damaged network is capable of spontaneously becoming active again. The process often occurs repeatedly. To model this marked network recovery, we examine the effect of local node recoveries and stochastic contiguous spreading, and find that they can lead to the spontaneous emergence of macroscopic ‘phase-flipping’ phenomena. As the network is of finite size and is stochastic, the fraction of active nodes z switches back and forth between the two network collective modes characterized by high network activity and low network activity. Furthermore, the system exhibits a strong hysteresis behaviour analogous to phase transitions near a critical point. We present real-world network data exhibiting phase switching behaviour in accord with the predictions of the model.","internal_url":"https://www.academia.edu/5738240/Spontaneous_recovery_in_dynamical_networks","translated_internal_url":"","created_at":"2014-01-16T04:48:08.708-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":7630487,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":32772320,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/32772320/thumbnails/1.jpg","file_name":"Spontaneous_recovery_in_dynamical_networks.pdf","download_url":"https://www.academia.edu/attachments/32772320/download_file?st=MTczMjM5NTk2Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Spontaneous_recovery_in_dynamical_networ.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/32772320/Spontaneous_recovery_in_dynamical_networks-libre.pdf?1391225572=\u0026response-content-disposition=attachment%3B+filename%3DSpontaneous_recovery_in_dynamical_networ.pdf\u0026Expires=1731454089\u0026Signature=eKmuSRz3gQiMRoIPVmK4Tqjr-y-JyVq3o3kPSco~Vo5WsjTOwSPDtUoDjZ1LLXvi6T76huingKui~nmg-S1fFgjOKWcJtJ-iwmk8cKz8RK9sh5PdFqe07CK96rgvxbWW8qcKKlRldpgrayXbx0achz6r~moyZMuK~SYgGZc3skufsVzKxjhLDFJXLWiat1d8FWG9o-jTmqIH-awMnpvvmU49oUiqmXvuz3yAE0uXU7edKiqqa-BECOtf0-2uf33zuYyQQE0vo-yvbBvW3sirZ20XUtlN5rBtcsJFprzTvCw3c0WQt3qDR~rttcv23tQ4qQ6P7fZbSmDf~gTOBmKLUQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Spontaneous_recovery_in_dynamical_networks","translated_slug":"","page_count":5,"language":"en","content_type":"Work","owner":{"id":7630487,"first_name":"Eugene","middle_initials":null,"last_name":"Stanley","page_name":"EugeneStanley","domain_name":"bu","created_at":"2013-12-16T05:51:48.537-08:00","display_name":"Eugene Stanley","url":"https://bu.academia.edu/EugeneStanley"},"attachments":[{"id":32772320,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/32772320/thumbnails/1.jpg","file_name":"Spontaneous_recovery_in_dynamical_networks.pdf","download_url":"https://www.academia.edu/attachments/32772320/download_file?st=MTczMjM5NTk2Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Spontaneous_recovery_in_dynamical_networ.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/32772320/Spontaneous_recovery_in_dynamical_networks-libre.pdf?1391225572=\u0026response-content-disposition=attachment%3B+filename%3DSpontaneous_recovery_in_dynamical_networ.pdf\u0026Expires=1731454089\u0026Signature=eKmuSRz3gQiMRoIPVmK4Tqjr-y-JyVq3o3kPSco~Vo5WsjTOwSPDtUoDjZ1LLXvi6T76huingKui~nmg-S1fFgjOKWcJtJ-iwmk8cKz8RK9sh5PdFqe07CK96rgvxbWW8qcKKlRldpgrayXbx0achz6r~moyZMuK~SYgGZc3skufsVzKxjhLDFJXLWiat1d8FWG9o-jTmqIH-awMnpvvmU49oUiqmXvuz3yAE0uXU7edKiqqa-BECOtf0-2uf33zuYyQQE0vo-yvbBvW3sirZ20XUtlN5rBtcsJFprzTvCw3c0WQt3qDR~rttcv23tQ4qQ6P7fZbSmDf~gTOBmKLUQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); 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The toxicity of Ab oligomers depends on their structure, which is governed by assembly dynamics. Due to limitations of current experimental techniques, a detailed knowledge of oligomer structure at the atomic level is missing. We introduce a molecular dynamics approach to study Ab dimer formation. 1), We use discrete molecular dynamics simulations of a coarse-grained model to identify a variety of dimer conformations; and 2), we employ allatom molecular mechanics simulations to estimate thermodynamic stability of all dimer conformations. Our simulations of a coarse-grained Ab peptide model predicts 10 different planar b-strand dimer conformations. We then estimate the free energies of all dimer conformations in all-atom molecular mechanics simulations with explicit water. We compare the free energies of Ab(1-42) and Ab(1-40) dimers. We find that 1), dimer conformations have higher free energies compared to their corresponding monomeric states; and 2), the free-energy difference between the Ab(1-42) and the corresponding Ab(1-40) dimer conformation is not significant. 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Inhibiting the pathologic folding and oligomerization of A␤ could be effective in the prevention and treatment of Alzheimer's disease. Here, using all-atom molecular dynamics simulations in explicit solvent, we probe the initial stages of folding of a decapeptide segment of A␤, A␤21-30, shown experimentally to nucleate the folding process. In addition, we examine the folding of a homologous decapeptide containing an amino acid substitution linked to hereditary cerebral hemorrhage with amy-","grobid_abstract_attachment_id":32578337},"translated_abstract":null,"internal_url":"https://www.academia.edu/5458719/Solvent_and_mutation_effects_on_the_nucleation_of_amyloid_protein_folding","translated_internal_url":"","created_at":"2013-12-17T06:13:30.168-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":7630487,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":32578337,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/32578337/thumbnails/1.jpg","file_name":"Solvent_and_mutation_effects_on_the_nucleation_of_amyloid_o--protein_folding.pdf","download_url":"https://www.academia.edu/attachments/32578337/download_file?st=MTczMjM5NTk2Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Solvent_and_mutation_effects_on_the_nucl.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/32578337/Solvent_and_mutation_effects_on_the_nucleation_of_amyloid_o--protein_folding-libre.pdf?1391087850=\u0026response-content-disposition=attachment%3B+filename%3DSolvent_and_mutation_effects_on_the_nucl.pdf\u0026Expires=1731830880\u0026Signature=BSOtt1ea6966CtuYnRGnlNVpPDWjhP1AdNTnHHXnllViMxhhV2qcymL5dgJ0qYSoFcQ-BH-jq~Bdmh7vXRnbnHjx2Vx~C2yky~pifUH0x9L9psa-IGeHmV-arQo93k66o4iVImozCpV-KbUSWvQk7LXr17P5Z0AjJ8aHd~bBsaKBWWUNK~WBTxnoZC3M9F2E3ZtHn~UGJc3Xa0eOf0HbX49YxBqp2nVEqzegREZ-i8BDLGfAzFnwUrHEZTm9MfKa6Nz6SWZyKNujiOyQHcRxqQ2W059GUxjFBTfI5XwF2LSnTiZqdj9s~WoI-rJ18KYS3U96uR~bs8tb7P72r-EDng__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Solvent_and_mutation_effects_on_the_nucleation_of_amyloid_protein_folding","translated_slug":"","page_count":6,"language":"en","content_type":"Work","owner":{"id":7630487,"first_name":"Eugene","middle_initials":null,"last_name":"Stanley","page_name":"EugeneStanley","domain_name":"bu","created_at":"2013-12-16T05:51:48.537-08:00","display_name":"Eugene Stanley","url":"https://bu.academia.edu/EugeneStanley"},"attachments":[{"id":32578337,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/32578337/thumbnails/1.jpg","file_name":"Solvent_and_mutation_effects_on_the_nucleation_of_amyloid_o--protein_folding.pdf","download_url":"https://www.academia.edu/attachments/32578337/download_file?st=MTczMjM5NTk2Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Solvent_and_mutation_effects_on_the_nucl.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/32578337/Solvent_and_mutation_effects_on_the_nucleation_of_amyloid_o--protein_folding-libre.pdf?1391087850=\u0026response-content-disposition=attachment%3B+filename%3DSolvent_and_mutation_effects_on_the_nucl.pdf\u0026Expires=1731830880\u0026Signature=BSOtt1ea6966CtuYnRGnlNVpPDWjhP1AdNTnHHXnllViMxhhV2qcymL5dgJ0qYSoFcQ-BH-jq~Bdmh7vXRnbnHjx2Vx~C2yky~pifUH0x9L9psa-IGeHmV-arQo93k66o4iVImozCpV-KbUSWvQk7LXr17P5Z0AjJ8aHd~bBsaKBWWUNK~WBTxnoZC3M9F2E3ZtHn~UGJc3Xa0eOf0HbX49YxBqp2nVEqzegREZ-i8BDLGfAzFnwUrHEZTm9MfKa6Nz6SWZyKNujiOyQHcRxqQ2W059GUxjFBTfI5XwF2LSnTiZqdj9s~WoI-rJ18KYS3U96uR~bs8tb7P72r-EDng__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="5458718"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/5458718/Ab_initio_Discrete_Molecular_Dynamics_Approach_to_Protein_Folding_and_Aggregation"><img alt="Research paper thumbnail of Ab initio Discrete Molecular Dynamics Approach to Protein Folding and Aggregation" class="work-thumbnail" src="https://attachments.academia-assets.com/32578339/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/5458718/Ab_initio_Discrete_Molecular_Dynamics_Approach_to_Protein_Folding_and_Aggregation">Ab initio Discrete Molecular Dynamics Approach to Protein Folding and Aggregation</a></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ea6c800cef8dd518cf0f35756556612d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":32578339,"asset_id":5458718,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/32578339/download_file?st=MTczMjM5NTk2Myw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="5458718"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="5458718"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 5458718; 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(2004). 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Substantial clinical and experimental evidence supports the hypothesis that amyloid β-protein (Aβ) aggregation produces assemblies with potent neurotoxic properties that cause AD. For this reason, it is important to elucidate the structural dynamics of Aβ aggregation at atomic level. We apply the discrete molecular dynamics method coupled with a four-bead protein model to study the aggregation of Aβ16-22, a peptide that contains the Aβ central hydrophobic cluster, Leu 17 -Ala 21 , found to be crucial in mediating Aβ assembly. Backbone hydrogen bond interactions are incorporated into the model. Effective hydrophobic and electrostatic interactions between side-chains are parameterized using amino acid-specific hydropathies and net charges. The aggregation of up to 16 Aβ16-22 peptides is studied. 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Here we test the hypothesis that the scaling properties of the dynamics of healthy physiological systems are more stable than those of pathological systems by studying beat-to-beat fluctuations in the human heart rate. We develop techniques based on the Fano factor and Allan factor functions, as well as on detrended fluctuation analysis, for quantifying deviations from uniform power-law scaling in nonstationary time series. By analyzing extremely long data sets of up to Nϭ10 5 beats for 11 healthy subjects, we find that the fluctuations in the heart rate scale approximately uniformly over several temporal orders of magnitude. By contrast, we find that in data sets of comparable length for 14 subjects with heart disease, the fluctuations grow erratically, indicating a loss of scaling stability.","grobid_abstract_attachment_id":32578330},"translated_abstract":null,"internal_url":"https://www.academia.edu/5458712/Deviations_from_uniform_power_law_scaling_in_nonstationary_time_series","translated_internal_url":"","created_at":"2013-12-17T06:13:28.216-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":7630487,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":32578330,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/32578330/thumbnails/1.jpg","file_name":"Deviations_from_uniform_power_law_scaling_in_nonstationary_time_series.pdf","download_url":"https://www.academia.edu/attachments/32578330/download_file?st=MTczMjM5NTk2Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Deviations_from_uniform_power_law_scalin.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/32578330/Deviations_from_uniform_power_law_scaling_in_nonstationary_time_series-libre.pdf?1391189431=\u0026response-content-disposition=attachment%3B+filename%3DDeviations_from_uniform_power_law_scalin.pdf\u0026Expires=1731830881\u0026Signature=aEUPvCDCykygUOPRt-GYGSZ8F1QgLLsMUv~u30H-qNODqZL-eOUE8~lyGdc31zVbbXAWfZXo4jPhuqAF-zNTkzhlXS2L0GCM9kBwC0M4krz94NwX~zR3VmxvPkqvCR8vez073U57QNf1G7nVo5Ila8tb7WbyMGYOjmmyTpsph5fT50kNxgD2S3VWYNe2-KRyVVkvn9ZWCJZQta1SH6sEpW9T7uTXMhZeNg50RO3jqoKjjkFlBBJlJgohIsdzvLQ2A5NXY9hH3SahyQCsTRHYDqZdWb52lKP6FjeFvM9w9pQ35gt84qU3Ds5jFKt5PQdTVtjwLXlTocTDHra69lDOfQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Deviations_from_uniform_power_law_scaling_in_nonstationary_time_series","translated_slug":"","page_count":5,"language":"en","content_type":"Work","owner":{"id":7630487,"first_name":"Eugene","middle_initials":null,"last_name":"Stanley","page_name":"EugeneStanley","domain_name":"bu","created_at":"2013-12-16T05:51:48.537-08:00","display_name":"Eugene Stanley","url":"https://bu.academia.edu/EugeneStanley"},"attachments":[{"id":32578330,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/32578330/thumbnails/1.jpg","file_name":"Deviations_from_uniform_power_law_scaling_in_nonstationary_time_series.pdf","download_url":"https://www.academia.edu/attachments/32578330/download_file?st=MTczMjM5NTk2Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Deviations_from_uniform_power_law_scalin.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/32578330/Deviations_from_uniform_power_law_scaling_in_nonstationary_time_series-libre.pdf?1391189431=\u0026response-content-disposition=attachment%3B+filename%3DDeviations_from_uniform_power_law_scalin.pdf\u0026Expires=1731830881\u0026Signature=aEUPvCDCykygUOPRt-GYGSZ8F1QgLLsMUv~u30H-qNODqZL-eOUE8~lyGdc31zVbbXAWfZXo4jPhuqAF-zNTkzhlXS2L0GCM9kBwC0M4krz94NwX~zR3VmxvPkqvCR8vez073U57QNf1G7nVo5Ila8tb7WbyMGYOjmmyTpsph5fT50kNxgD2S3VWYNe2-KRyVVkvn9ZWCJZQta1SH6sEpW9T7uTXMhZeNg50RO3jqoKjjkFlBBJlJgohIsdzvLQ2A5NXY9hH3SahyQCsTRHYDqZdWb52lKP6FjeFvM9w9pQ35gt84qU3Ds5jFKt5PQdTVtjwLXlTocTDHra69lDOfQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); 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Development of oligomer-specific therapeutic agents requires a mechanistic understanding of the oligomerization process. This is a daunting task because amyloidogenic protein oligomers often are metastable and comprise structurally heterogeneous populations in equilibrium with monomers and fibrils. A single methodological approach cannot elucidate the entire protein assembly process. An integrated multidisciplinary program is required. We discuss here the synergistic application of in hydro, in vacuo, and in silico methods to the study of the amyloid -protein, the key pathogenetic agent in Alzheimer's disease.","grobid_abstract_attachment_id":32578333},"translated_abstract":null,"internal_url":"https://www.academia.edu/5458713/Elucidating_Amyloid_Protein_Folding_and_Assembly_A_Multidisciplinary_Approach","translated_internal_url":"","created_at":"2013-12-17T06:13:28.223-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":7630487,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":32578333,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/32578333/thumbnails/1.jpg","file_name":"Elucidating_Amyloid_o-Protein_Folding_and_Assembly_A_Multidisciplinary_Approach.pdf","download_url":"https://www.academia.edu/attachments/32578333/download_file?st=MTczMjM5NTk2Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Elucidating_Amyloid_Protein_Folding_and.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/32578333/Elucidating_Amyloid_o-Protein_Folding_and_Assembly_A_Multidisciplinary_Approach-libre.pdf?1391116959=\u0026response-content-disposition=attachment%3B+filename%3DElucidating_Amyloid_Protein_Folding_and.pdf\u0026Expires=1731830881\u0026Signature=D1u5JfrQyZHSvNNmmKNk6voS81214-1FGhYFEiv3fQeWBSVGlzzQeDZsqjvVitJXRq7fy0ZO7REUwaL2Pnz~1FBO6Mli5J8neVKvp1HWljjGlBVjO0fcWfFWCXGtTfJ2q9xOa8BsNnfb3UsSh5rBd3HDfKLwTkV5RGoAXOSHnk5S~jDTqIhdOxoxuf8srPXmkfUSbXlgx1iWKr-Bw8NypfIvP7yofk7DkaHWhi2HiNy75cPbdUNdQE3DwI2qr2NJp0~MUkVo7B8TpmwK6XBF5kYXdbP-cuc2XXjvAA1CJOjDYBIMC3mqfZ891-UntbHXKemb1tBEIpUwPNGZA6IFFA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Elucidating_Amyloid_Protein_Folding_and_Assembly_A_Multidisciplinary_Approach","translated_slug":"","page_count":11,"language":"en","content_type":"Work","owner":{"id":7630487,"first_name":"Eugene","middle_initials":null,"last_name":"Stanley","page_name":"EugeneStanley","domain_name":"bu","created_at":"2013-12-16T05:51:48.537-08:00","display_name":"Eugene Stanley","url":"https://bu.academia.edu/EugeneStanley"},"attachments":[{"id":32578333,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/32578333/thumbnails/1.jpg","file_name":"Elucidating_Amyloid_o-Protein_Folding_and_Assembly_A_Multidisciplinary_Approach.pdf","download_url":"https://www.academia.edu/attachments/32578333/download_file?st=MTczMjM5NTk2Myw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Elucidating_Amyloid_Protein_Folding_and.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/32578333/Elucidating_Amyloid_o-Protein_Folding_and_Assembly_A_Multidisciplinary_Approach-libre.pdf?1391116959=\u0026response-content-disposition=attachment%3B+filename%3DElucidating_Amyloid_Protein_Folding_and.pdf\u0026Expires=1731830881\u0026Signature=D1u5JfrQyZHSvNNmmKNk6voS81214-1FGhYFEiv3fQeWBSVGlzzQeDZsqjvVitJXRq7fy0ZO7REUwaL2Pnz~1FBO6Mli5J8neVKvp1HWljjGlBVjO0fcWfFWCXGtTfJ2q9xOa8BsNnfb3UsSh5rBd3HDfKLwTkV5RGoAXOSHnk5S~jDTqIhdOxoxuf8srPXmkfUSbXlgx1iWKr-Bw8NypfIvP7yofk7DkaHWhi2HiNy75cPbdUNdQE3DwI2qr2NJp0~MUkVo7B8TpmwK6XBF5kYXdbP-cuc2XXjvAA1CJOjDYBIMC3mqfZ891-UntbHXKemb1tBEIpUwPNGZA6IFFA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); 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We use this approach to calculate exactly the first low-density expansion of LBB@) for arbitrary system dimensionality d, where L B B @ ) is the mean number of backbone bonds and p is the bond occupation probability. Standard series extrapolation methods provide estimates of the fractal dimension of the backbone for all d ; these disagree with the Sierpinski gasket model of the backbone. 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