Vast.ai (Vast.ai attempts to create a two-sided market for renting GPU servers from people with idle GPUs, substantially undercutting AWS/GCP. Looks interesting—eg Nvidia 1070s at $2/day.)
Plant Breeding Reviews: Part 1: Long-term Selection: Maize, Volume 24, ed Janick 2004 (an anthology of papers on plant breeding efforts in, primarily, the Midwest, centering mostly on the famous long-term maize selection experiment; I was particularly interested in “Population Size and Long-term Selection”, Weber 2004, but was amazed to learn that the maize selection experiment, despite running for a century and still producing gains on some traits, was founded on a maize population size of just 4! This illustrates the untapped potential in existing genetic variance.)
Newsletter tag: archive of all issues back to 2013 for the gwern.net newsletter (monthly updates, which will include summaries of projects I’ve worked on that month (the same as the changelog), collations of links or discussions from my subreddit, and book/movie reviews.)
This page is a changelog for Gwern.net: a monthly reverse chronological list of recent major writings/changes/additions.
Following my writing can be a little difficult because it is often so incremental. So every month, in addition to my regular /r/Gwern subreddit submissions, I write up reasonably-interesting changes and send it out to the mailing list in addition to a compilation of links & reviews (archives).
A subreddit for posting links of interest and also for announcing updates to gwern.net (which can be used as a RSS feed). Submissions are categorized similar to the monthly newsletter and typically will be collated there.
SMPY (Study of Mathematically Precocious Youth) is a long-running longitudinal survey of extremely mathematically-talented or intelligent youth, which has been following high-IQ cohorts since the 1970s. It has provided the largest and most concrete findings about the correlates and predictive power of screening extremely intelligent children, and revolutionized gifted & talented educational practices.
Because it has been running for over 40 years, SMPY-related publications are difficult to find; many early papers were published only in long-out-of-print books and are not available in any other way. Others are digitized and more accessible, but one must already know they exist. Between these barriers, SMPY information is less widely available & used than it should be given its importance.
To fix this, I have been gradually going through all SMPY citations and making fulltext copies available online with occasional commentary.
Recent advances in genomics are producing powerful DNA predictors of complex traits, especially cognitive abilities. Here, we leveraged summary statistics from the most recent genome-wide association studies of intelligence and educational attainment to build prediction models of general cognitive ability and educational achievement. To this end, we compared the performances of multi-trait genomic and polygenic scoring methods. In a representative UK sample of 7,026 children at age 12 and 16, we show that we can now predict up to 11 percent of the variance in intelligence and 16 percent in educational achievement. We also show that predictive power increases from age 12 to age 16 and that genomic predictions do not differ for girls and boys. Multivariate genomic methods were effective in boosting predictive power and, even though prediction accuracy varied across polygenic scores approaches, results were similar using different multivariate and polygenic score methods. Polygenic scores for educational attainment and intelligence are the most powerful predictors in the behavioural sciences and exceed predictions that can be made from parental phenotypes such as educational attainment and occupational status.
“Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals”, James J. Lee, Robbee Wedow, Aysu Okbay, Edward Kong, Omeed Maghzian, Meghan Zacher, Tuan Anh Nguyen-Viet, Peter Bowers, Julia Sidorenko, Richard Karlsson Linnér, Mark Alan Fontana, Tushar Kundu, Chanwook Lee, Hui Li, Ruoxi Li, Rebecca Royer, Pascal N. Timshel, Raymond K. Walters, Emily A. Willoughby, Loïc Yengo, 23andMe Research Team, COGENT (Cognitive Genomics Consortium), Social Science Genetic Association Consortium, Maris Alver, Yanchun Bao, David W. Clark, Felix R. Day, Nicholas A. Furlotte, Peter K. Joshi, Kathryn E. Kemper, Aaron Kleinman, Claudia Langenberg, Reedik Mägi, Joey W. Trampush, Shefali Setia Verma, Yang Wu, Max Lam, Jing Hua Zhao, Zhili Zheng, Jason D. Boardman, Harry Campbell, Jeremy Freese, Kathleen Mullan Harris, Caroline Hayward, Pamela Herd, Meena Kumari, Todd Lencz, Jian’an Luan, Anil K. Malhotra, Andres Metspalu, Lili Milani, Ken K. Ong, John R. B. Perry, David J. Porteous, Marylyn D. Ritchie, Melissa C. Smart, Blair H. Smith, Joyce Y. Tung, Nicholas J. Wareham, James F. Wilson, Jonathan P. Beauchamp, Dalton C. Conley, Tõnu Esko, Steven F. Lehrer, Patrik K. E. Magnusson, Sven Oskarsson, Tune H. Pers, Matthew R. Robinson, Kevin Thom, Chelsea Watson, Christopher F. Chabris, Michelle N. Meyer, David I. Laibson, Jian Yang, Magnus Johannesson, Philipp D. Koellinger, Patrick Turley, Peter M. Visscher, Daniel J. Benjamin, David Cesarini (10.1038/s41588-018-0147-3):
Here we conducted a large-scale genetic association analysis of educational attainment in a sample of approximately 1.1 million individuals and identify 1,271 independent genome-wide-significant SNPs. For the SNPs taken together, we found evidence of heterogeneous effects across environments. The SNPs implicate genes involved in brain-development processes and neuron-to-neuron communication. In a separate analysis of the X chromosome, we identify 10 independent genome-wide-significant SNPs and estimate a SNP heritability of around 0.3% in both men and women, consistent with partial dosage compensation. A joint (multi-phenotype) analysis of educational attainment and three related cognitive phenotypes generates polygenic scores that explain 11–13% of the variance in educational attainment and 7–10% of the variance in cognitive performance. This prediction accuracy substantially increases the utility of polygenic scores as tools in research.
People who score higher on intelligence tests tend to have larger brains. Twin studies suggest the same genetic factors influence both brain size and intelligence. This has led to the hypothesis that genetics influence intelligence partly by contributing to the development of larger brains. We tested this hypothesis using four large imaging genetics studies (combined N = 7965) with polygenic scores derived from a genome-wide association study (GWAS) of educational attainment, a correlate of intelligence. We conducted meta-analysis to test associations among participants’ genetics, total brain volume (i.e., brain size), and cognitive test performance. Consistent with previous findings, participants with higher polygenic scores achieved higher scores on cognitive tests, as did participants with larger brains. Participants with higher polygenic scores also had larger brains. We found some evidence that brain size partly mediated associations between participants’ education polygenic scores and their cognitive test performance. Effect sizes were larger in the population-based samples than in the convenience-based samples. Recruitment and retention of population-representative samples should be a priority for neuroscience research. Findings suggest promise for studies integrating GWAS discoveries with brain imaging to understand neurobiology linking genetics with cognitive performance.
“The genetic architecture of the human cerebral cortex”, Katrina L. Grasby, Neda Jahanshad, Jodie N. Painter, Lucía Colodro-Conde, Janita Bralten, Derrek P. Hibar, Penelope A. Lind, Fabrizio Pizzagalli, Christopher R. K. Ching, Mary Agnes B. McMahon, Natalia Shatokhina, Leo C. P. Zsembik, Sophia I. Thomopoulos, Alyssa H. Zhu, Lachlan T. Strike, Ingrid Agartz, Saud Alhusaini, Marcio A. A. Almeida, Dag Alnæs, Inge K. Amlien, Micael Andersson, Tyler Ard, Nicola J. Armstrong, Allison Ashley-Koch, Joshua R. Atkins, Manon Bernard, Rachel M. Brouwer, Elizabeth E. L. Buimer, Robin Bülow, Christian Bürger, Dara M. Cannon, Mallar Chakravarty, Qiang Chen, Joshua W. Cheung, Baptiste Couvy-Duchesne, Anders M. Dale, Shareefa Dalvie, Tânia K. de Araujo, Greig I. de Zubicaray, Sonja M. C. de Zwarte, Anouk den Braber, Nhat Trung Doan, Katharina Dohm, Stefan Ehrlich, Hannah-Ruth Engelbrecht, Susanne Erk, Chun Chieh Fan, Iryna O. Fedko, Sonya F. Foley, Judith M. Ford, Masaki Fukunaga, Melanie E. Garrett, Tian Ge, Sudheer Giddaluru, Aaron L. Goldman, Melissa J. Green, Nynke A. Groenewold, Dominik Grotegerd, Tiril P. Gurholt, Boris A. Gutman, Narelle K. Hansell, Mathew A. Harris, Marc B. Harrison, Courtney C. Haswell, Michael Hauser, Stefan Herms, Dirk J. Heslenfeld, New Fei Ho, David Hoehn, Per Hoffmann, Laurena Holleran, Martine Hoogman, Jouke-Jan Hottenga, Masashi Ikeda, Deborah Janowitz, Iris E. Jansen, Tianye Jia, Christiane Jockwitz, Ryota Kanai, Sherif Karama, Dalia Kasperaviciute, Tobias Kaufmann, Sinead Kelly, Masataka Kikuchi, Marieke Klein, Michael Knapp, Annchen R. Knodt, Bernd Krämer, Max Lam, Thomas M. Lancaster, Phil H. Lee, Tristram A. Lett, Lindsay B. Lewis, Iscia Lopes-Cendes, Michelle Luciano, Fabio Macciardi, Andre F. Marquand, Samuel R. Mathias, Tracy R. Melzer, Yuri Milaneschi, Nazanin Mirza-Schreiber, Jose C. V. Moreira, Thomas W. Mühleisen, Bertram Müller-Myhsok, Pablo Najt, Soichiro Nakahara, Kwangsik Nho, Loes M. Olde Loohuis, Dimitri Papadopoulos Orfanos, John F. Pearson, Toni L. Pitcher, Benno Pütz, Yann Quidé, Anjanibhargavi Ragothaman, Faisal M. Rashid, William R. Reay, Ronny Redlich, Céline S. Reinbold, Jonathan Repple, Geneviève Richard, Brandalyn C. Riedel, Shannon L. Risacher, Cristiane S. Rocha, Nina Roth Mota, Lauren Salminen, Arvin Saremi, Andrew J. Saykin, Fenja Schlag, Lianne Schmaal, Peter R. Schofield, Rodrigo Secolin, Chin Yang Shapland, Li Shen, Jean Shin, Elena Shumskaya, Ida E. Sønderby, Emma Sprooten, Katherine E. Tansey, Alexander Teumer, Anbupalam Thalamuthu, Diana Tordesillas-Gutiérrez, Jessica A. Turner, Anne Uhlmann, Costanza Ludovica Vallerga, Dennis van der Meer, Marjolein M. J. van Donkelaar, Liza van Eijk, Theo G. M. van Erp, Neeltje E. M. van Haren, Daan van Rooij, Marie-José van Tol, Jan H. Veldink, Ellen Verhoef, Esther Walton, Mingyuan Wang, Yunpeng Wang, Joanna M. Wardlaw, Wei Wen, Lars T. Westlye, Christopher D. Whelan, Stephanie H. Witt, Katharina Wittfeld, Christiane Wolf, Thomas Wolfers, Jing Qin Wu, Clarissa L. Yasuda, Dario Zaremba, Zuo Zhang, Marcel P. Zwiers, Eric Artiges, Amelia A. Assareh, Rosa Ayesa-Arriola, Aysenil Belger, Christine L. Brandt, Gregory G. Brown, Sven Cichon, Joanne E. Curran, Gareth E. Davies, Franziska Degenhardt, Michelle F. Dennis, Bruno Dietsche, Srdjan Djurovic, Colin P. Doherty, Ryan Espiritu, Daniel Garijo, Yolanda Gil, Penny A. Gowland, Robert C. Green, Alexander N. Häusler, Walter Heindel, Beng-Choon Ho, Wolfgang U. Hoffmann, Florian Holsboer, Georg Homuth, Norbert Hosten, Clifford R. Jack Jr., MiHyun Jang, Andreas Jansen, Nathan A. Kimbrel, Knut Kolskår, Sanne Koops, Axel Krug, Kelvin O. Lim, Jurjen J. Luykx, Daniel H. Mathalon, Karen A. Mather, Venkata S. Mattay, Sarah Matthews, Jaqueline Mayoral Van Son, Sarah C. McEwen, Ingrid Melle, Derek W. Morris, Bryon A. Mueller, Matthias Nauck, Jan E. Nordvik, Markus M. Nöthen, Daniel S. O’Leary, Nils Opel, Marie-Laure Paillère Martinot, G. Bruce Pike, Adrian Preda, Erin B. Quinlan, Paul E. Rasser, Varun Ratnakar, Simone Reppermund, Vidar M. Steen, Paul A. Tooney, Fábio R. Torres, Dick J. Veltman, James T. Voyvodic, Robert Whelan, Tonya White, Hidenaga Yamamori, Hieab H. H. Adams, Joshua C. Bis, Stephanie Debette, Charles Decarli, Myriam Fornage, Vilmundur Gudnason, Edith Hofer, M. Arfan Ikram, Lenore Launer, W. T. Longstreth, Oscar L. Lopez, Bernard Mazoyer, Thomas H. Mosley, Gennady V. Roshchupkin, Claudia L. Satizabal, Reinhold Schmidt, Sudha Seshadri, Qiong Yang, Alzheimer’s Disease Neuroimaging Initiative, CHARGE Consortium, EPIGEN Consortium, IMAGEN Consortium, SYS Consortium, Parkinson’s Progression Markers Initiative, Marina K. M. Alvim, David Ames, Tim J. Anderson, Ole A. Andreassen, Alejandro Arias-Vasquez, Mark E. Bastin, Bernhard T. Baune, Jean C. Beckham, John Blangero, Dorret I. Boomsma, Henry Brodaty, Han G. Brunner, Randy L. Buckner, Jan K. Buitelaar, Juan R. Bustillo, Wiepke Cahn, Murray J. Cairns, Vince Calhoun, Vaughan J. Carr, Xavier Caseras, Svenja Caspers, Gianpiero L. Cavalleri, Fernando Cendes, Aiden Corvin, Benedicto Crespo-Facorro, John C. Dalrymple-Alford, Udo Dannlowski, Eco J. C. de Geus, Ian J. Deary, Norman Delanty, Chantal Depondt, Sylvane Desrivières, Gary Donohoe, Thomas Espeseth, Guillén Fernández, Simon E. Fisher, Herta Flor, Andreas J. Forstner, Clyde Francks, Barbara Franke, David C. Glahn, Randy L. Gollub, Hans J. Grabe, Oliver Gruber, Asta K. Håberg, Ahmad R. Hariri, Catharina A. Hartman, Ryota Hashimoto, Andreas Heinz, Frans A. Henskens, Manon H. J. Hillegers, Pieter J. Hoekstra, Avram J. Holmes, L. Elliot Hong, William D. Hopkins, Hilleke E. Hulshoff Pol, Terry L. Jernigan, Erik G. Jönsson, René S. Kahn, Martin A. Kennedy, Tilo T. J. Kircher, Peter Kochunov, John B. J. Kwok, Stephanie Le Hellard, Carmel M. Loughland, Nicholas G. Martin, Jean-Luc Martinot, Colm McDonald, Katie L. McMahon, Andreas Meyer-Lindenberg, Patricia T. Michie, Rajendra A. Morey, Bryan Mowry, Lars Nyberg, Jaap Oosterlaan, Roel A. Ophoff, Christos Pantelis, Tomas Paus, Zdenka Pausova, Brenda W. J. H. Penninx, Tinca J. C. Polderman, Danielle Posthuma, Marcella Rietschel, Joshua L. Roffman, Laura M. Rowland, Perminder S. Sachdev, Philipp G. Sämann, Ulrich Schall, Gunter Schumann, Rodney J. Scott, Kang Sim, Sanjay M. Sisodiya, Jordan W. Smoller, Iris E. Sommer, Beate St Pourcain, Dan J. Stein, Arthur W. Toga, Julian N. Trollor, Nic J. A. Van der Wee, Dennis van ’t Ent, Henry Völzke, Henrik Walter, Bernd Weber, Daniel R. Weinberger, Margaret J. Wright, Juan Zhou, Jason L. Stein, Paul M. Thompson, Sarah E. Medland, Enhancing NeuroImaging Genetics through Meta-Analysis Consortium (ENIGMA)—Genetics working group (2020-03-20):
The human cerebral cortex is important for cognition, and it is of interest to see how genetic variants affect its structure. Grasby et al. combined genetic data with brain magnetic resonance imaging from more than 50,000 people to generate a genome-wide analysis of how human genetic variation influences human cortical surface area and thickness. From this analysis, they identified variants associated with cortical structure, some of which affect signaling and gene expression. They observed overlap between genetic loci affecting cortical structure, brain development, and neuropsychiatric disease, and the correlation between these phenotypes is of interest for further study.
Introduction: The cerebral cortex underlies our complex cognitive capabilities. Variations in human cortical surface area and thickness are associated with neurological, psychological, and behavioral traits and can be measured in vivo by magnetic resonance imaging (MRI). Studies in model organisms have identified genes that influence cortical structure, but little is known about common genetic variants that affect human cortical structure.
Rationale: To identify genetic variants associated with human cortical structure at both global and regional levels, we conducted a genome-wide association meta-analysis of brain MRI data from 51,665 individuals across 60 cohorts. We analyzed the surface area and average thickness of the whole cortex and 34 cortical regions with known functional specializations.
Results: We identified 306 nominally genome-wide significant loci (p < 5 × 10−8) associated with cortical structure in a discovery sample of 33,992 participants of European ancestry. Of the 299 loci for which replication data were available, 241 loci influencing surface area and 14 influencing thickness remained significant after replication, with 199 loci passing multiple testing correction (p < 8.3 × 10−10; 187 influencing surface area and 12 influencing thickness).
Common genetic variants explained 34% (SE = 3%) of the variation in total surface area and 26% (SE = 2%) in average thickness; surface area and thickness showed a negative genetic correlation (rg = −0.32, SE = 0.05, p = 6.5 × 10−12), which suggests that genetic influences have opposing effects on surface area and thickness. Bioinformatic analyses showed that total surface area is influenced by genetic variants that alter gene regulatory activity in neural progenitor cells during fetal development. By contrast, average thickness is influenced by active regulatory elements in adult brain samples, which may reflect processes that occur after mid-fetal development, such as myelination, branching, or pruning. When considered together, these results support the radial unit hypothesis that different developmental mechanisms promote surface area expansion and increases in thickness.
To identify specific genetic influences on individual cortical regions, we controlled for global measures (total surface area or average thickness) in the regional analyses. After multiple testing correction, we identified 175 loci that influence regional surface area and 10 that influence regional thickness. Loci that affect regional surface area cluster near genes involved in the Wnt signaling pathway, which is known to influence areal identity.
We observed significant positive genetic correlations and evidence of bidirectional causation of total surface area with both general cognitive functioning and educational attainment. We found additional positive genetic correlations between total surface area and Parkinson’s disease but did not find evidence of causation. Negative genetic correlations were evident between total surface area and insomnia, attention deficit hyperactivity disorder, depressive symptoms, major depressive disorder, and neuroticism.
Conclusion: This large-scale collaborative work enhances our understanding of the genetic architecture of the human cerebral cortex and its regional patterning. The highly polygenic architecture of the cortex suggests that distinct genes are involved in the development of specific cortical areas. Moreover, we find evidence that brain structure is a key phenotype along the causal pathway that leads from genetic variation to differences in general cognitive function.
“The genetics of the mood disorder spectrum: genome-wide association analyses of over 185,000 cases and 439,000 controls”, Jonathan R. I Coleman, Héléna A. Gaspar, Julien Bryois, Enda M. Byrne, Andreas J. Forstner, Peter A. Holmans, Christiaan A. de Leeuw, Manuel Mattheisen, Andrew McQuillin, Jennifer M. Whitehead Pavlides, Tune H. Pers, Stephan Ripke, Eli A. Stahl, Stacy Steinberg, Vassily Trubetskoy, Maciej Trzaskowski, Yunpeng Wang, Liam Abbott, Abdel Abdellaoui, Mark J. Adams, Annelie Nordin Adolfsson, Esben Agerbo, Huda Akil, Diego Albani, Ney Alliey-Rodriguez, Thomas D. Als, Till F. M Andlauer, Adebayo Anjorin, Verneri Antilla, Sandra Van der Auwera, Swapnil Awasthi, Silviu-Alin Bacanu, Judith A. Badner, Marie Bækvad-Hansen, Jack D. Barchas, Nicholas Bass, Michael Bauer, Aartjan T. F Beekman, Richard Belliveau, Sarah E. Bergen, Tim B. Bigdeli, Elisabeth B. Binder, Erlend Bøen, Marco Boks, James Boocock, Monika Budde, William Bunney, Margit Burmeister, Henriette N. Buttenschøn, Jonas Bybjerg-Grauholm, William Byerley, Na Cai, Miquel Casas, Enrique Castelao, Felecia Cerrato, Pablo Cervantes, Kimberly Chambert, Alexander W. Charney, Danfeng Chen, Jane Hvarregaard Christensen, Claire Churchhouse, David St Clair, Toni-Kim Clarke, Lucía Colodro-Conde, William Coryell, Baptiste Couvy-Duchesne, David W. Craig, Gregory E. Crawford, Cristiana Cruceanu, Piotr M. Czerski, Anders M. Dale, Gail Davies, Ian J. Deary, Franziska Degenhardt, Jurgen Del-Favero, J. Raymond DePaulo, Eske M. Derks, Nese Direk, Srdjan Djurovic, Amanda L. Dobbyn, Conor V. Dolan, Ashley Dumont, Erin C. Dunn, Thalia C. Eley, Torbjørn Elvsåshagen, Valentina Escott-Price, Chun Chieh Fan, Hilary K. Finucane, Sascha B. Fischer, Matthew Flickinger, Jerome C. Foo, Tatiana M. Foroud, Liz Forty, Josef Frank, Christine Fraser, Nelson B. Freimer, Louise Frisén, Katrin Gade, Diane Gage, Julie Garnham, Claudia Giambartolomei, Fernando S. Goes, Jaqueline Goldstein, Scott D. Gordon, Katherine Gordon-Smith, Elaine K. Green, Melissa J. Green, Tiffany A. Greenwood, Jakob Grove, Weihua Guan, Lynsey S. Hall, Marian L. Hamshere, Christine Søholm Hansen, Thomas F. Hansen, Martin Hautzinger, Urs Heilbronner, Albert M. van Hemert, Stefan Herms, Ian B. Hickie, Maria Hipolito, Per Hoffmann, Dominic Holland, Georg Homuth, Carsten Horn, Jouke-Jan Hottenga, Laura Huckins, Marcus Ising, Stéphane Jamain, Rick Jansen, Jessica S. Johnson, Simone de Jong, Eric Jorgenson, Anders Juréus, Radhika Kandaswamy, Robert Karlsson, James L. Kennedy, Farnush Farhadi Hassan Kiadeh, Sarah Kittel-Schneider, James A. Knowles, Manolis Kogevinas, Isaac S. Kohane, Anna C. Koller, Julia Kraft, Warren W. Kretzschmar, Jesper Krogh, Ralph Kupka, Zoltán Kutalik, Catharina Lavebratt, Jacob Lawrence, William B. Lawson, Markus Leber, Phil H. Lee, Shawn E. Levy, Jun Z. Li, Yihan Li, Penelope A. Lind, Chunyu Liu, Loes M. Olde Loohuis, Anna Maaser, Donald J. MacIntyre, Dean F. MacKinnon, Pamela B. Mahon, Wolfgang Maier, Robert M. Maier, Jonathan Marchini, Lina Martinsson, Hamdi Mbarek, Steve McCarroll, Patrick McGrath, Peter McGuffin, Melvin G. McInnis, James D. McKay, Helena Medeiros, Sarah E. Medland, Divya Mehta, Fan Meng, Christel M. Middeldorp, Evelin Mihailov, Yuri Milaneschi, Lili Milani, Saira Saeed Mirza, Francis M. Mondimore, Grant W. Montgomery, Derek W. Morris, Sara Mostafavi, Thomas W. Mühleisen, Niamh Mullins, Matthias Nauck, Bernard Ng, Hoang Nguyen, Caroline M. Nievergelt, Michel G. Nivard, Evaristus A. Nwulia, Dale R. Nyholt, Claire O’Donovan, Paul F. O’Reilly, Anil P. S Ori, Lilijana Oruc, Urban Ösby, Hogni Oskarsson, Jodie N. Painter, José Guzman Parra, Carsten Bøcker Pedersen, Marianne Giørtz Pedersen, Amy Perry, Roseann E. Peterson, Erik Pettersson, Wouter J. Peyrot, Andrea Pfennig, Giorgio Pistis, Shaun M. Purcell, Jorge A. Quiroz, Per Qvist, Eline J. Regeer, Andreas Reif, Céline S. Reinbold, John P. Rice, Brien P. Riley, Fabio Rivas, Margarita Rivera, Panos Roussos, Douglas M. Ruderfer, Euijung Ryu, Cristina Sánchez-Mora, Alan F. Schatzberg, William A. Scheftner, Robert Schoevers, Nicholas J. Schork, Eva C. Schulte, Tatyana Shehktman, Ling Shen, Jianxin Shi, Paul D. Shilling, Stanley I. Shyn, Engilbert Sigurdsson, Claire Slaney, Olav B. Smeland, Johannes H. Smit, Daniel J. Smith, Janet L. Sobell, Anne T. Spijker, Michael Steffens, John S. Strauss, Fabian Streit, Jana Strohmaier, Szabolcs Szelinger, Katherine E. Tansey, Henning Teismann, Alexander Teumer, Robert C. Thompson, Wesley Thompson, Pippa A. Thomson, Thorgeir E. Thorgeirsson, Matthew Traylor, Jens Treutlein, André G. Uitterlinden, Daniel Umbricht, Helmut Vedder, Alexander Viktorin, Peter M. Visscher, Weiqing Wang, Stanley J. Watson, Bradley T. Webb, Cynthia Shannon Weickert, as W. Weickert, Shantel Marie Weinsheimer, Jürgen Wellmann, Gonneke Willemsen, Stephanie H. Witt, Yang Wu, Hualin S. Xi, Wei Xu, Jian Yang, Allan H. Young, Peter Zandi, Peng Zhang, Futao Zhang, Sebastian Zollner, Rolf Adolfsson, Ingrid Agartz, Martin Alda, Volker Arolt, Lena Backlund, Bernhard T. Baune, Frank Bellivier, Klaus Berger, Wade H. Berrettini, Joanna M. Biernacka, Douglas H. R Blackwood, Michael Boehnke, Dorret I. Boomsma, Aiden Corvin, Nicholas Craddock, Mark J. Daly, Udo Dannlowski, Enrico Domenici, Katharina Domschke, Tõnu Esko, Bruno Etain, Mark Frye, Janice M. Fullerton, Elliot S. Gershon, EJC de Geus, Michael Gill, Fernando Goes, Hans J. Grabe, Maria Grigoroiu-Serbanescu, Steven P. Hamilton, Joanna Hauser, Caroline Hayward, Andrew C. Heath, David M. Hougaard, Christina M. Hultman, Ian Jones, Lisa A. Jones, René S. Kahn, Kenneth S. Kendler, George Kirov, Stefan Kloiber, Mikael Landén, Marion Leboyer, Glyn Lewis, Qingqin S. Li, Jolanta Lissowska, Susanne Lucae, Pamela AF Madden, Patrik K. Magnusson, Nicholas G. Martin, Fermin Mayoral, Susan L. McElroy, Andrew M. McIntosh, Francis J. McMahon, Ingrid Melle, Andres Metspalu, Philip B. Mitchell, Gunnar Morken, Ole Mors, Preben Bo Mortensen, Bertram Müller-Myhsok, Richard M. Myers, Benjamin M. Neale, Vishwajit Nimgaonkar, Merete Nordentoft, Markus M. Nöthen, Michael C. O’Donovan, Ketil J. Oedegaard, Michael J. Owen, Sara A. Paciga, Carlos Pato, Michele T. Pato, Nancy L. Pedersen, Brenda WJH Penninx, Roy H. Perlis, David J. Porteous, Danielle Posthuma, James B. Potash, Martin Preisig, Josep Antoni Ramos-Quiroga, Marta Ribasés, Marcella Rietschel, Guy A. Rouleau, Catherine Schaefer, Martin Schalling, Peter R. Schofield, Thomas G. Schulze, Alessandro Serretti, Jordan W. Smoller, Hreinn Stefansson, Kari Stefansson, Eystein Stordal, Henning Tiemeier, Gustavo Turecki, Rudolf Uher, Arne E. Vaaler, Eduard Vieta, John B. Vincent, Henry Völzke, Myrna M. Weissman, Thomas Werge, Ole A. Andreassen, Anders D. Børglum, Sven Cichon, Howard J. Edenberg, Arianna Di Florio, John Kelsoe, Douglas F. Levinson, Cathryn M. Lewis, John I. Nurnberger, Roel A. Ophoff, Laura J. Scott, Pamela Sklar, Patrick F. Sullivan, Naomi R. Wray, Gerome Breen (2018-08-13):
Mood disorders affect 10-20% of the population, ranging from brief, mild episodes to severe, incapacitating conditions that markedly impact lives. Multiple approaches have shown considerable sharing of genetic risk factors between unipolar and bipolar mood disorders. We use data from the largest genome-wide association studies of major depression (MD) and bipolar disorder (BD) to investigate the molecular basis of the shared genetic liability to mood disorders. We meta-analysed the Psychiatric Genomics Consortium (PGC) MD and BD cohorts, and an additional MD cohort from UK Biobank (185,285 cases, 439,741 controls, non-overlapping N = 609,424). 73 loci reached genome-wide significance in the meta-analysis, with additional loci significant in subtype and depression-only analyses. More genome-wide significant loci from PGC MD (39/44, 89% of the PGC MD loci) than PGC BD (4/19, 21%) reached genome-wide significance in the meta-analysis. Genetic correlations calculated between MD and BD subtypes revealed that type II BD correlates strongly with MD. Integrating the results with systems biology information, we implicate pathways and neuronal subtypes which highlight similarities but also potential differences between MD and BD. Our results reflected MD more than BD, perhaps due to the larger sample size for MD, but also perhaps because depression is their predominant common feature. Overall, these results provide evidence for a genetic mood disorders spectrum.
“Machine Learning to Predict Osteoporotic Fracture Risk from Genotypes”, Vincenzo Forgetta, Julyan Keller-Baruch, Marie Forest, Audrey Durand, Sahir Bhatnagar, John Kemp, John A. Morris, John A. Kanis, Douglas P. Kiel, Eugene V. McCloskey, Fernando Rivadeneira, Helena Johannson, Nicholas Harvey, Cyrus Cooper, David M. Evans, Joelle Pineau, William D. Leslie, Celia MT Greenwood, J. Brent Richards (2018-09-11):
Background: Genomics-based prediction could be useful since genome-wide genotyping costs less than many clinical tests. We tested whether machine learning methods could provide a clinically-relevant genomic prediction of quantitative ultrasound speed of sound (SOS)—a risk factor for osteoporotic fracture.
Methods: We used 341,449 individuals from UK Biobank with SOS measures to develop genomically-predicted SOS (gSOS) using machine learning algorithms. We selected the optimal algorithm in 5,335 independent individuals and then validated it and its ability to predict incident fracture in an independent test dataset (N = 80,027). Finally, we explored whether genomic pre-screening could complement a UK-based osteoporosis screening strategy, based on the validated tool FRAX.
Results: gSOS explained 4.8-fold more variance in SOS than FRAX clinical risk factors (CRF) alone (r2 = 23% vs. 4.8%). A standard deviation decrease in gSOS, adjusting for the CRF-FRAX score was associated with a higher increased odds of incident major osteoporotic fracture (1,491 cases / 78,536 controls, OR = 1.91 [1.70-2.14], p = 10-28) than that for measured SOS (OR = 1.60 [1.50-1.69], p = 10-52) and femoral neck bone mineral density (147 cases / 4,594 controls, OR = 1.53 [1.27-1.83], p = 10-6). Individuals in the bottom decile of the gSOS distribution had a 3.25-fold increased risk of major osteoporotic fracture (P = 10-18) compared to the top decile. A gSOS-based FRAX score, identified individuals at high risk for incident major osteoporotic fractures better than the CRF-FRAX score (P = 10-14). Introducing a genomic pre-screening step into osteoporosis screening in 4,741 individuals reduced the number of required clinical visits from 2,455 to 1,273 and the number of BMD tests from 1,013 to 473, while only reducing the sensitivity to identify individuals eligible for therapy from 99% to 95%.
The use of genotypes in a machine learning algorithm resulted in a clinically-relevant prediction of SOS and fracture, with potential to impact healthcare resource utilization.
Research in Context
Evidence Before this Study
Genome-wide association studies have identified many loci associated with risk of clinically-relevant fracture risk factors, such as SOS. Yet, it is unclear if such information can be leveraged to identify those at risk for disease outcomes, such as osteoporotic fractures. Most previous attempts to predict disease risk from genotypes have used polygenic risk scores, which may not be optimal for genomic-prediction. Despite these obstacles, genomic-prediction could enable screening programs to be more efficient since most people screened in a population are not determined to have a level of risk that would prompt a change in clinical care. Genomic pre-screening could help identify individuals whose risk of disease is low enough that they are unlikely to benefit from screening.
Added Value of this Study
Using a large dataset of 426,811 individuals we trained and tested a machine learning algorithm to genomically-predict SOS. This metric, gSOS, had performance characteristics for predicting fracture risk that were similar to measured SOS and femoral neck BMD. Implementing a gSOS-based pre-screening step into the UK-based osteoporosis treatment guidelines reduced the number of individuals who would require screening clinical visits and skeletal testing by approximately 50%, while having little impact on the sensitivity to identify individuals at high risk for osteoporotic fracture.
Implications of all of the Available Evidence
Clinically-relevant genomic prediction of heritable traits is feasible using the machine learning algorithm presented here in large sample sizes. Genome-wide genotyping is now less expensive than many clinical tests, needs to be performed once over a lifetime and could risk stratify for multiple heritable traits and diseases years prior to disease onset, providing an opportunity for prevention. The implementation of such algorithms could improve screening efficiency, yet their cost-effectiveness will need to be ascertained in subsequent analyses.
“Common risk variants identified in autism spectrum disorder”, Jakob Grove, Stephan Ripke, Thomas D. Als, Manuel Mattheisen, Raymond Walters, Hyejung Won, Jonatan Pallesen, Esben Agerbo, Ole A. Andreassen, Richard Anney, Rich Belliveau, Francesco Bettella, Joseph D. Buxbaum, Jonas Bybjerg-Grauholm, Marie Bækved-Hansen, Felecia Cerrato, Kimberly Chambert, Jane H. Christensen, Claire Churchhouse, Karin Dellenvall, Ditte Demontis, Silvia De Rubeis, Bernie Devlin, Srdjan Djurovic, Ashle Dumont, Jacqueline Goldstein, Christine S. Hansen, Mads Engel Hauberg, Mads V. Hollegaard, Sigrun Hope, Daniel P. Howrigan, Hailiang Huang, Christina Hultman, Lambertus Klei, Julian Maller, Joanna Martin, Alicia R. Martin, Jennifer Moran, Mette Nyegaard, Terje Nærland, Duncan S. Palmer, Aarno Palotie, Carsten B. Pedersen, Marianne G. Pedersen, Timothy Poterba, Jesper B. Poulsen, Beate St Pourcain, Per Qvist, Karola Rehnström, Avi Reichenberg, Jennifer Reichert, Elise B. Robinson, Kathryn Roeder, Panos Roussos, Evald Saemundsen, Sven Sandin, F. Kyle Satterstrom, George D. Smith, Hreinn Stefansson, Kari Stefansson, Stacy Steinberg, Christine Stevens, Patrick F. Sullivan, Patrick Turley, G. Bragi Walters, Xinyi Xu, Autism Spectrum Disorders Working Group of The Psychiatric Genomics Consortium, BUPGEN, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, 23andMe Research Team, Daniel Geschwind, Merete Nordentoft, David M. Hougaard, Thomas Werge, Ole Mors, Preben Bo Mortensen, Benjamin M. Neale, Mark J. Daly, Anders D. Børglum (2017-11-25):
Autism spectrum disorder (ASD) is a highly heritable and heterogeneous group of neurodevelopmental phenotypes diagnosed in more than 1% of children. Common genetic variants contribute substantially to ASD susceptibility, but to date no individual variants have been robustly associated with ASD. With a marked sample size increase from a unique Danish population resource, we report a genome-wide association meta-analysis of 18,381 ASD cases and 27,969 controls that identifies five genome-wide significant loci. Leveraging GWAS results from three phenotypes with significantly overlapping genetic architectures (schizophrenia, major depression, and educational attainment), seven additional loci shared with other traits are identified at equally strict significance levels. Dissecting the polygenic architecture we find both quantitative and qualitative polygenic heterogeneity across ASD subtypes, in contrast to what is typically seen in other complex disorders. These results highlight biological insights, particularly relating to neuronal function and corticogenesis and establish that GWAS performed at scale will be much more productive in the near term in ASD, just as it has been in a broad range of important psychiatric and diverse medical phenotypes.
The parental feeding practices (PFPs) of excessive restriction of food intake (‘restriction’) and pressure to increase food consumption (‘pressure’) have been argued to causally influence child weight in opposite directions (high restriction causing overweight; high pressure causing underweight). However child weight could also ‘elicit’ PFPs. A novel approach is to investigate gene-environment correlation between child genetic influences on BMI and PFPs. Genome-wide polygenic scores (GPS) combining BMI-associated variants were created for 10,346 children (including 3,320 DZ twin pairs) from the Twins Early Development Study using results from an independent genome-wide association study meta-analysis. Parental ‘restriction’ and ‘pressure’ were assessed using the Child Feeding Questionnaire. Child BMI standard deviation scores (BMI-SDS) were calculated from children’s height and weight at age 10. Linear regression and fixed family effect models were used to test between-(n = 4,445 individuals) and within-family (n = 2,164 DZ pairs) associations between the GPS and PFPs. In addition, we performed multivariate twin analyses (n = 4,375 twin pairs) to estimate the heritabilities of PFPs and the genetic correlations between BMI-SDS and PFPs. The GPS was correlated with BMI-SDS (β=0.20, p=2.41×10-38). Consistent with the gene-environment correlation hypothesis, child BMI GPS was positively associated with ‘restriction’ (β=0.05, p=4.19×10-4), and negatively associated with ‘pressure’ (β=-0.08, p=2.70×10-7). These results remained consistent after controlling for parental BMI, and after controlling for overall family contributions (within-family analyses). Heritabilities for ‘restriction’ (43% [40-47%]) and ‘pressure’ (54% [50-59%]) were moderate-to-high. Twin-based genetic correlations were moderate and positive between BMI-SDS and ‘restriction’ (rA=0.28 [0.23-0.32]), and substantial and negative between BMI-SDS and ‘pressure’ (rA=-0.48 [-0.52 --0.44]. Results suggest that the degree to which parents limit or encourage children’s food intake is partly influenced by children’s genetic predispositions to higher or lower BMI. These findings point to an evocative gene-environment correlation in which heritable characteristics in the child elicit parental feeding behaviour.
It is widely believed that parents influence their child’s BMI via certain feeding practices. For example, rigid restriction has been argued to cause overweight, and pressuring to eat to cause underweight. However, recent longitudinal research has not supported this model. An alternative hypothesis is that child BMI, which has a strong genetic basis, evokes parental feeding practices (‘gene-environment correlation’). To test this, we applied two genetic methods in a large sample of 10-year-old children from the Twins Early Development Study: a polygenic score analysis (DNA-based score of common genetic variants robustly associated with BMI in genome-wide meta-analyses), and a twin analysis (comparing resemblance between identical and non-identical twin pairs). Polygenic scores correlated positively with parental restriction of food intake (‘restriction’; β=0.05, p=4.19×10-4), and negatively with parental pressure to increase food intake (‘pressure’; β=-0.08, p=2.70×10-7). Associations were unchanged after controlling for all genetic and environmental effects shared within families. Results from twin analyses were consistent. ‘Restriction’ (43%) and ‘pressure’ (54%) were substantially heritable, and a positive genetic correlation between child BMI and ‘restriction’ (rA=0.28), and negative genetic correlation between child BMI and ‘pressure’ (rA=-0.48) emerged. These findings challenge the prevailing view that parental behaviours are the sole cause of child BMI by supporting an alternate hypothesis that child BMI also causes parental feeding behaviour.
Despite intensive study, most genetic factors that contribute to variation in human height remain undiscovered. We conducted a family-based linkage study of height in a unique cohort of very large nuclear families from a founder (Jewish) population. This design allowed for increased power to detect linkage, compared to previous family-based studies. We identified loci that together explain an estimated 6% of the variance in height. We showed that these loci are not tagging known common variants associated with height. Rather, we suggest that the observed signals arise from variants with large effects that are rare globally but elevated in frequency in the Jewish population.
Shawn Paul Bradley is a German-American former professional basketball player who played center for the Philadelphia 76ers, New Jersey Nets, and Dallas Mavericks of the National Basketball Association (NBA). Nicknamed "the Stormin' Mormon", Bradley was one of the tallest players in NBA history at 7 ft 6 in (2.29 m). Bradley was born in Landstuhl, West Germany as his family was stationed at the U.S. Military base medical facility, and grew up in Castle Dale, Utah. He also holds German citizenship.
Adaptive evolution in humans has rarely been characterized for its whole set of components, i.e. selective pressure, adaptive phenotype, beneficial alleles and realized fitness differential. We combined approaches for detecting polygenic adaptations and for mapping the genetic bases of physiological and fertility phenotypes in approximately 1000 indigenous ethnically Tibetan women from Nepal, adapted to high altitude. The results of genome-wide association analyses and tests for polygenic adaptations showed evidence of positive selection for alleles associated with more pregnancies and live births and evidence of negative selection for those associated with higher offspring mortality. Lower hemoglobin level did not show clear evidence for polygenic adaptation, despite its strong association with an EPAS1 haplotype carrying selective sweep signals.
Author summary: The adaptations to high altitude environments in Tibetan populations have long been highlighted as an important case study of adaptive evolution in our species. Recent genetic studies found two genes, EGLN1 and EPAS1, the genetic variants in which were swept to high frequency in Tibetans due to strong positive natural selection. However, it still remains unclear if and how these and other genetic variants are connected to adaptive phenotypes and ultimately to fitness advantage. In this study, we collected genotype and phenotype information of 1,000 ethnically Tibetan women from the high Himalayan valleys in Nepal. Using both genome-wide association analysis and test for polygenic adaptations, we show that natural selection systematically altered frequency of alleles associated with reproductive outcomes to the direction of increasing fitness. That is, alleles associated with more pregnancies and live births, as well as those associated with lower offspring mortality, were under positive selection. Omitting the EPAS1 haplotype under selective sweep, the other variants associated with lower hemoglobin did not collectively show a clear signal for polygenic adaptation. Our study highlights the polygenic nature of human adaptive traits.
“The palaeogenetics of cat dispersal in the ancient world”, Claudio Ottoni, Wim Van Neer, Bea De Cupere, Julien Daligault, Silvia Guimaraes, Joris Peters, Nikolai Spassov, Mary E. Prendergast, Nicole Boivin, Arturo Morales-Muñiz, Adrian Bălăşescu, Cornelia Becker, Norbert Benecke, Adina Boroneant, Hijlke Buitenhuis, Jwana Chahoud, Alison Crowther, Laura Llorente, Nina Manaseryan, Hervé Monchot, Vedat Onar, Marta Osypińska, Olivier Putelat, Eréndira M. Quintana Morales, Jacqueline Studer, Ursula Wierer, Ronny Decorte, Thierry Grange, Eva-Maria Geigl (2017-06-19):
The cat has long been important to human societies as a pest-control agent, object of symbolic value and companion animal, but little is known about its domestication process and early anthropogenic dispersal. Here we show, using ancient DNA analysis of geographically and temporally widespread archaeological cat remains, that both the Near Eastern and Egyptian populations of Felis silvestris lybica contributed to the gene pool of the domestic cat at different historical times. While the cat’s worldwide conquest began during the Neolithic period in the Near East, its dispersal gained momentum during the Classical period, when the Egyptian cat successfully spread throughout the Old World. The expansion patterns and ranges suggest dispersal along human maritime and terrestrial routes of trade and connectivity. A coat-colour variant was found at high frequency only after the Middle Ages, suggesting that directed breeding of cats occurred later than with most other domesticated animals.
Phenylketonuria (PKU) is an inborn error of metabolism that results in decreased metabolism of the amino acid phenylalanine. Untreated, PKU can lead to intellectual disability, seizures, behavioral problems, and mental disorders. It may also result in a musty smell and lighter skin. A baby born to a mother who has poorly treated PKU may have heart problems, a small head, and low birth weight.
In 1961, the National Institutes of Health (NIH) began to circulate biological preprints in a forgotten experiment called the Information Exchange Groups (IEGs). This system eventually attracted over 3,600 participants and saw the production of over 2,500 different documents, but by 1967, it was effectively shut down following the refusal of journals to accept articles that had been circulated as preprints. This article charts the rise and fall of the IEGs and explores the parallels with the 1990s and the biomedical preprint movement of today.
Several species of fish are claimed to produce hallucinogenic effects when consumed. For example, Sarpa salpa, a species of sea bream, is commonly claimed to be hallucinogenic. These widely distributed coastal fish are normally found in the Mediterranean and around Spain, and along the west and south coasts of Africa. Occasionally they are found in British waters. They may induce hallucinogenic effects similar to LSD if eaten. However, based on the reports of exposure they are more likely to resemble hallucinogenic effects of deliriants than the effects of serotonergic psychedelics such as LSD. In 2006, two men who apparently ate the fish experienced hallucinations lasting for several days. The likelihood of hallucinations depends on the season. Sarpa salpa is known as "the fish that makes dreams" in Arabic.
Web users are increasingly turning to ad blockers to avoid ads, which are often perceived as annoying or an invasion of privacy. While there has been significant research into the factors driving ad blocker adoption and the detrimental effect to ad publishers on the Web, the resulting effects of ad blocker usage on Web users' browsing experience is not well understood. To approach this problem, we conduct a retrospective natural field experiment using Firefox browser usage data, with the goal of estimating the effect of adblocking on user engagement with the Web. We focus on new users who installed an ad blocker after a baseline observation period, to avoid comparing different populations. Their subsequent browser activity is compared against that of a control group, whose members do not use ad blockers, over a corresponding observation period, controlling for prior baseline usage. In order to estimate causal effects, we employ propensity score matching on a number of other features recorded during the baseline period. In the group that installed an ad blocker, we find significant increases in both active time spent in the browser (+28% over control) and the number of pages viewed (+15% over control), while seeing no change in the number of searches. Additionally, by reapplying the same methodology to other popular Firefox browser extensions, we show that these effects are specific to ad blockers. We conclude that ad blocking has a positive impact on user engagement with the Web, suggesting that any costs of using ad blockers to users' browsing experience are largely drowned out by the utility that they offer.
Design: A decision analysis of revenue vs readers yields an maximum acceptable total traffic loss of ~3%. Power analysis of historical Gwern.net traffic data demonstrates that the high autocorrelation yields low statistical power with standard tests & regressions but acceptable power with ARIMA models. I design a long-term Bayesian ARIMA(4,0,1) time-series model in which an A/B-test running January–October 2017 in randomized paired 2-day blocks of ads/no-ads uses client-local JS to determine whether to load & display ads, with total traffic data collected in Google Analytics & ad exposure data in Google AdSense. The A/B test ran from 2017-01-01 to 2017-10-15, affecting 288 days with collectively 380,140 pageviews in 251,164 sessions.
Correcting for a flaw in the randomization, the final results yield a surprisingly large estimate of an expected traffic loss of −9.7% (driven by the subset of users without adblock), with an implied −14% traffic loss if all traffic were exposed to ads (95% credible interval: −13–16%), exceeding my decision threshold for disabling ads & strongly ruling out the possibility of acceptably small losses which might justify further experimentation.
Thus, banner ads on Gwern.net appear to be harmful and AdSense has been removed. If these results generalize to other blogs and personal websites, an important implication is that many websites may be harmed by their use of banner ad advertising without realizing it.
A randomized experiment with almost 35 million Pandora listeners enables us to measure the sensitivity of consumers to advertising, an important topic of study in the era of ad-supported digital content provision. The experiment randomized listeners into 9 treatment groups, each of which received a different level of audio advertising interrupting their music listening, with the highest treatment group receiving more than twice as many ads as the lowest treatment group. By keeping consistent treatment assignment for 21 months, we are able to measure long-run demand effects, with three times as much ad-load sensitivity as we would have obtained if we had run a month-long experiment. We estimate a demand curve that is strikingly linear, with the number of hours listened decreasing linearly in the number of ads per hour (also known as the price of ad-supported listening). We also show the negative impact on the number of days listened and on the probability of listening at all in the final month. Using an experimental design that separately varies the number of commercial interruptions per hour and the number of ads per commercial interruption, we find that neither makes much difference to listeners beyond their impact on the total number of ads per hour. Lastly, we find that increased ad load causes a significant increase in the number of paid ad-free subscriptions to Pandora, particularly among older listeners.
We report the first active acoustic side-channel attack. Speakers are used to emit human inaudible acoustic signals and the echo is recorded via microphones, turning the acoustic system of a smart phone into a sonar system. The echo signal can be used to profile user interaction with the device. For example, a victim’s finger movements can be inferred to steal Android phone unlock patterns. In our empirical study, the number of candidate unlock patterns that an attacker must try to authenticate herself to a Samsung S4 Android phone can be reduced by up to 70 can be easily applied to other application scenarios and device types. Overall, our work highlights a new family of security threats.
William Leonard Pickard is one of two people convicted in the largest lysergic acid diethylamide (LSD) manufacturing case in history. In 2000, while moving their LSD laboratory across Kansas, Pickard and Clyde Apperson were pulled over while driving a Ryder rental truck and a follow car. The laboratory had been stored near a renovated Atlas-E missile silo near Wamego, Kansas. Gordon Todd Skinner, one of the men intimately involved in the case but not charged due to his cooperation, owned the property where the laboratory equipment was stored.
El Ten Eleven is an American, Los Angeles-based, post-rock duo, known for combining guitar/bass doubleneck or fretless bass, with heavy looping, or vamping, and the utility of an effects pedal, over acoustic or electric drumming. They have released seven full-length albums, 2 EPs and a remix album, earning generally positive reviews.
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