Understanding Terrorist Organizations with a Dynamic Model

Terrorist organizations change over time because of processes such as recruitment and training as well as counter-terrorism (CT) measures, but the effects of these processes are typically studied qualitatively and in separation from each other. Seeking a more quantitative and integrated understanding, we constructed a simple dynamic model where equations describe how these processes change an organization's membership. Analysis of the model yields a number of intuitive as well as novel findings. Most importantly it becomes possible to predict whether counter-terrorism measures would be sufficient to defeat the organization. Furthermore, we can prove in general that an organization would collapse if its strength and its pool of foot soldiers decline simultaneously. In contrast, a simultaneous decline in its strength and its pool of leaders is often insufficient and short-termed. These results and other like them demonstrate the great potential of dynamic models for informing terrorism scholarship and counter-terrorism policy making.Comment: To appear as Springer Lecture Notes in Computer Science v2: vectorized 4 figures, fixed two typos, more detailed bibliograph

THE ELEVENTH AND TWELFTH DATA RELEASES OF THE SLOAN DIGITAL SKY SURVEY: FINAL DATA FROM SDSS-III

Citation: Alam, S., Albareti, F. D., Prieto, C. A., Anders, F., Anderson, S. F., Anderton, T., . . . Zhu, G. T. (2015). THE ELEVENTH AND TWELFTH DATA RELEASES OF THE SLOAN DIGITAL SKY SURVEY: FINAL DATA FROM SDSS-III. Astrophysical Journal Supplement Series, 219(1), 27. doi:10.1088/0067-0049/219/1/12The third generation of the Sloan Digital Sky Survey (SDSS-III) took data from 2008 to 2014 using the original SDSS wide-field imager, the original and an upgraded multi-object fiber-fed optical spectrograph, a new near-infrared high-resolution spectrograph, and a novel optical interferometer. All of the data from SDSS-III are now made public. In particular, this paper describes Data Release 11 (DR11) including all data acquired through 2013 July, and Data Release 12 (DR12) adding data acquired through 2014 July (including all data included in previous data releases), marking the end of SDSS-III observing. Relative to our previous public release (DR10), DR12 adds one million new spectra of galaxies and quasars from the Baryon Oscillation Spectroscopic Survey (BOSS) over an additional 3000 deg(2) of sky, more than triples the number of H-band spectra of stars as part of the Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE), and includes repeated accurate radial velocity measurements of 5500 stars from the Multi-object APO Radial Velocity Exoplanet Large-area Survey (MARVELS). The APOGEE outputs now include the measured abundances of 15 different elements for each star. In total, SDSS-III added 5200 deg(2) of ugriz imaging; 155,520 spectra of 138,099 stars as part of the Sloan Exploration of Galactic Understanding and Evolution 2 (SEGUE-2) survey; 2,497,484 BOSS spectra of 1,372,737 galaxies, 294,512 quasars, and 247,216 stars over 9376 deg(2); 618,080 APOGEE spectra of 156,593 stars; and 197,040 MARVELS spectra of 5513 stars. Since its first light in 1998, SDSS has imaged over 1/3 of the Celestial sphere in five bands and obtained over five million astronomical spectra.Additional Authors: Berlind, A. A.;Beutler, F.;Bhardwaj, V.;Bird, J. C.;Bizyaev, D.;Blake, C. H.;Blanton, M. R.;Blomqvist, M.;Bochanski, J. J.;Bolton, A. S.;Bovy, J.;Bradley, A. S.;Brandt, W. N.;Brauer, D. E.;Brinkmann, J.;Brown, P. J.;Brownstein, J. R.;Burden, A.;Burtin, E.;Busca, N. G.;Cai, Z.;Capozzi, D.;Rosell, A. C.;Carr, M. A.;Carrera, R.;Chambers, K. C.;Chaplin, W. J.;Chen, Y. C.;Chiappini, C.;Chojnowski, S. D.;Chuang, C. H.;Clerc, N.;Comparat, J.;Covey, K.;Croft, R. A. C.;Cuesta, A. J.;Cunha, K.;da Costa, L. N.;Da Rio, N.;Davenport, J. R. A.;Dawson, K. S.;De Lee, N.;Delubac, T.;Deshpande, R.;Dhital, S.;Dutra-Ferreira, L.;Dwelly, T.;Ealet, A.;Ebelke, G. L.;Edmondson, E. M.;Eisenstein, D. J.;Ellsworth, T.;Elsworth, Y.;Epstein, C. R.;Eracleous, M.;Escoffier, S.;Esposito, M.;Evans, M. L.;Fan, X. H.;Fernandez-Alvar, E.;Feuillet, D.;Ak, N. F.;Finley, H.;Finoguenov, A.;Flaherty, K.;Fleming, S. W.;Font-Ribera, A.;Foster, J.;Frinchaboy, P. M.;Galbraith-Frew, J. G.;Garcia, R. A.;Garcia-Hernandez, D. A.;Perez, A. E. G.;Gaulme, P.;Ge, J.;Genova-Santos, R.;Georgakakis, A.;Ghezzi, L.;Gillespie, B. A.;Girardi, L.;Goddard, D.;Gontcho, S. G. A.;Hernandez, J. I. G.;Grebel, E. K.;Green, P. J.;Grieb, J. N.;Grieves, N.;Gunn, J. E.;Guo, H.;Harding, P.;Hasselquist, S.;Hawley, S. L.;Hayden, M.;Hearty, F. R.;Hekker, S.;Ho, S.;Hogg, D. W.;Holley-Bockelmann, K.;Holtzman, J. A.;Honscheid, K.;Huber, D.;Huehnerhoff, J.;Ivans, II;Jiang, L. H.;Johnson, J. A.;Kinemuchi, K.;Kirkby, D.;Kitaura, F.;Klaene, M. A.;Knapp, G. R.;Kneib, J. P.;Koenig, X. P.;Lam, C. R.;Lan, T. W.;Lang, D. T.;Laurent, P.;Le Goff, J. M.;Leauthaud, A.;Lee, K. G.;Lee, Y. S.;Licquia, T. C.;Liu, J.;Long, D. C.;Lopez-Corredoira, M.;Lorenzo-Oliveira, D.;Lucatello, S.;Lundgren, B.;Lupton, R. H.;Mack, C. E.;Mahadevan, S.;Maia, M. A. G.;Majewski, S. R.;Malanushenko, E.;Malanushenko, V.;Manchado, A.;Manera, M.;Mao, Q. Q.;Maraston, C.;Marchwinski, R. C.;Margala, D.;Martell, S. L.;Martig, M.;Masters, K. L.;Mathur, S.;McBride, C. K.;McGehee, P. M.;McGreer, I. D.;McMahon, R. G.;Menard, B.;Menzel, M. L.;Merloni, A.;Meszaros, S.;Miller, A. A.;Miralda-Escude, J.;Miyatake, H.;Montero-Dorta, A. D.;More, S.;Morganson, E.;Morice-Atkinson, X.;Morrison, H. L.;Mosser, B.;Muna, D.;Myers, A. D.;Nandra, K.;Newman, J. A.;Neyrinck, M.;Nguyen, D. C.;Nichol, R. C.;Nidever, D. L.;Noterdaeme, P.;Nuza, S. E.;O'Connell, J. E.;O'Connell, R. W.;O'Connell, R.;Ogando, R. L. C.;Olmstead, M. D.;Oravetz, A. E.;Oravetz, D. J.;Osumi, K.;Owen, R.;Padgett, D. L.;Padmanabhan, N.;Paegert, M.;Palanque-Delabrouille, N.;Pan, K. K.;Parejko, J. K.;Paris, I.;Park, C.;Pattarakijwanich, P.;Pellejero-Ibanez, M.;Pepper, J.;Percival, W. J.;Perez-Fournon, I.;Perez-Rafols, I.;Petitjean, P.;Pieri, M. M.;Pinsonneault, M. H.;de Mello, G. F. P.;Prada, F.;Prakash, A.;Price-Whelan, A. M.;Protopapas, P.;Raddick, M. J.;Rahman, M.;Reid, B. A.;Rich, J.;Rix, H. W.;Robin, A. C.;Rockosi, C. M.;Rodrigues, T. S.;Rodriguez-Torres, S.;Roe, N. A.;Ross, A. J.;Ross, N. P.;Rossi, G.;Ruan, J. J.;Rubino-Martin, J. A.;Rykoff, E. S.;Salazar-Albornoz, S.;Salvato, M.;Samushia, L.;Sanchez, A. G.;Santiago, B.;Sayres, C.;Schiavon, R. P.;Schlegel, D. J.;Schmidt, S. J.;Schneider, D. P.;Schultheis, M.;Schwope, A. D.;Scoccola, C. G.;Scott, C.;Sellgren, K.;Seo, H. J.;Serenelli, A.;Shane, N.;Shen, Y.;Shetrone, M.;Shu, Y. P.;Aguirre, V. S.;Sivarani, T.;Skrutskie, M. F.;Slosar, A.;Smith, V. V.;Sobreira, F.;Souto, D.;Stassun, K. G.;Steinmetz, M.;Stello, D.;Strauss, M. A.;Streblyanska, A.;Suzuki, N.;Swanson, M. E. C.;Tan, J. C.;Tayar, J.;Terrien, R. C.;Thakar, A. R.;Thomas, D.;Thomas, N.;Thompson, B. A.;Tinker, J. L.;Tojeiro, R.;Troup, N. W.;Vargas-Magana, M.;Vazquez, J. A.;Verde, L.;Viel, M.;Vogt, N. P.;Wake, D. A.;Wang, J.;Weaver, B. A.;Weinberg, D. H.;Weiner, B. J.;White, M.;Wilson, J. C.;Wisniewski, J. P.;Wood-Vasey, W. M.;Yeche, C.;York, D. G.;Zakamska, N. L.;Zamora, O.;Zasowski, G.;Zehavi, I.;Zhao, G. B.;Zheng, Z.;Zhou, X.;Zhou, Z. M.;Zou, H.;Zhu, G. T

TLR-4 ligation of dendritic cells is sufficient to drive pathogenic T cell function in experimental autoimmune encephalomyelitis

<p>Abstract</p> <p>Background</p> <p>Experimental autoimmune encephalomyelitis (EAE) depends on the initial activation of CD4⁺ T cells responsive to myelin autoantigens. The key antigen presenting cell (APC) population that drives the activation of naïve T cells most efficiently is the dendritic cell (DC). As such, we should be able to trigger EAE by transfer of DC that can present the relevant autoantigen(s). Despite some sporadic reports, however, models of DC-driven EAE have not been widely adopted. We sought to test the feasibility of this approach and whether activation of the DC by toll-like receptor (TLR)-4 ligation was a sufficient stimulus to drive EAE.</p> <p>Findings</p> <p>Host mice were seeded with myelin basic protein (MBP)-reactive CD4+ T cells and then were injected with DC that could present the relevant MBP peptide which had been exposed to lipopolysaccharide as a TLR-4 agonist. We found that this approach induced robust clinical signs of EAE.</p> <p>Conclusions</p> <p>DC are sufficient as APC to effectively drive the differentiation of naïve myelin-responsive T cells into autoaggressive effector T cells. TLR-4-stimulation can activate the DC sufficiently to deliver the signals required to drive the pathogenic function of the T cell. These models will allow the dissection of the molecular requirements of the initial DC-T cell interaction in the lymphoid organs that ultimately leads to autoimmune pathology in the central nervous system.</p

Modulation of enhancer looping and differential gene targeting by Epstein-Barr virus transcription factors directs cellular reprogramming

Epstein-Barr virus (EBV) epigenetically reprogrammes B-lymphocytes to drive immortalization and facilitate viral persistence. Host-cell transcription is perturbed principally through the actions of EBV EBNA 2, 3A, 3B and 3C, with cellular genes deregulated by specific combinations of these EBNAs through unknown mechanisms. Comparing human genome binding by these viral transcription factors, we discovered that 25% of binding sites were shared by EBNA 2 and the EBNA 3s and were located predominantly in enhancers. Moreover, 80% of potential EBNA 3A, 3B or 3C target genes were also targeted by EBNA 2, implicating extensive interplay between EBNA 2 and 3 proteins in cellular reprogramming. Investigating shared enhancer sites neighbouring two new targets (WEE1 and CTBP2) we discovered that EBNA 3 proteins repress transcription by modulating enhancer-promoter loop formation to establish repressive chromatin hubs or prevent assembly of active hubs. Re-ChIP analysis revealed that EBNA 2 and 3 proteins do not bind simultaneously at shared sites but compete for binding thereby modulating enhancer-promoter interactions. At an EBNA 3-only intergenic enhancer site between ADAM28 and ADAMDEC1 EBNA 3C was also able to independently direct epigenetic repression of both genes through enhancer-promoter looping. Significantly, studying shared or unique EBNA 3 binding sites at WEE1, CTBP2, ITGAL (LFA-1 alpha chain), BCL2L11 (Bim) and the ADAMs, we also discovered that different sets of EBNA 3 proteins bind regulatory elements in a gene and cell-type specific manner. Binding profiles correlated with the effects of individual EBNA 3 proteins on the expression of these genes, providing a molecular basis for the targeting of different sets of cellular genes by the EBNA 3s. Our results therefore highlight the influence of the genomic and cellular context in determining the specificity of gene deregulation by EBV and provide a paradigm for host-cell reprogramming through modulation of enhancer-promoter interactions by viral transcription factors

The IUPHAR/BPS Guide to PHARMACOLOGY in 2018: updates and expansion to encompass the new guide to IMMUNOPHARMACOLOGY.

The IUPHAR/BPS Guide to PHARMACOLOGY (GtoPdb, www.guidetopharmacology.org) and its precursor IUPHAR-DB, have captured expert-curated interactions between targets and ligands from selected papers in pharmacology and drug discovery since 2003. This resource continues to be developed in conjunction with the International Union of Basic and Clinical Pharmacology (IUPHAR) and the British Pharmacological Society (BPS). As previously described, our unique model of content selection and quality control is based on 96 target-class subcommittees comprising 512 scientists collaborating with in-house curators. This update describes content expansion, new features and interoperability improvements introduced in the 10 releases since August 2015. Our relationship matrix now describes ∼9000 ligands, ∼15 000 binding constants, ∼6000 papers and ∼1700 human proteins. As an important addition, we also introduce our newly funded project for the Guide to IMMUNOPHARMACOLOGY (GtoImmuPdb, www.guidetoimmunopharmacology.org). This has been 'forked' from the well-established GtoPdb data model and expanded into new types of data related to the immune system and inflammatory processes. This includes new ligands, targets, pathways, cell types and diseases for which we are recruiting new IUPHAR expert committees. Designed as an immunopharmacological gateway, it also has an emphasis on potential therapeutic interventions

Sheep Updates 2005 - Part 4

This session covers twelve papers from different authors: REPRODUCTION 1. Is it worth increasing investment to increase lambing percentages? Lucy Anderton Department of Agriculture Western Australia. 2. What value is a lamb? John Young, Farming Systems Analysis Service, Kojonup, WA 3. Providing twin-bearing ewes with extra energy at lambing produces heavier lambs at marking. Rob Davidson WAMMCO International,, formerly University of Western Australia; Keith Croker, Ken Hart, Department of Agriculture Western Australia, Tim Wiese, Chuckem , Highbury, Western Australia. GENETICS 4. Underlying biological cause of trade-off between meat and wool. Part 1. Wool and muscle glycogen, BM Thomson, I Williams, University of WA, Crawley, JRBriegel, CSIRO Livestock Industries, Floreat Park WA &CRC for the Australian Sheep Industry, JC Greeff, Department of Agriculture Western Australia &CRC for the Australian Sheep Industry. 5. Underlying biological cause of trade-off between meat and wool. Part 2. Wool and fatness, NR Adams1,3, EN Bermingham1,3, JR Briegel1,3, JC Greeff2,3 1CSIRO Livestock Industries, Floreat Park WA 2Department of Agriculture Western Australia, 3CRC for the Australian Sheep Industry 6. Genetic trade-offs between lamb and wool production in Merino breeding programs, Johan Greeff, Department of Agriculture, Western Australia. 7. Clean fleece weight is no phenotypically independent of other traits. Sue Hatcherac and Gordon Refshaugebc aNSWDPI Orange Agricultural Institute, Orange NSW 2800 bUNE c/- NSWDPI Cowra AR&AS Cowra NSW 2794 cAustralian Sheep Industry CRC. 8. When you\u27re on a good thing, do it better: An economic analysis of sheep breed profitability. Emma Kopke, Ross Kingwell, Department of Agriculture, Western Australia, John Young, Farming Systems Analysis Service, Kojonup, WA. 9. Selection Demonstration Flocks: Demonstrating improvementsin productivity of merinos, K.E. Kemper, M.L. Hebart, F.D. Brien, K.S. Jaensch, R.J. Grimson, D.H. Smith South Australian Research and Development Institute 10. You are compromising yield by using Dust Penetration and GFW in breeding programs, Melanie Dowling, Department of Agriculture, Western Australia, A. (Tony) Schlink, CSIRO Livestock Industries, Wembley, Johan Greeff, Department of Agriculture Western Australia. 11. Merino Sheep can be bred for resistance to breech strike. Johan Greeff , John Karlsson, Department of Agriculture Western Australia 12. Parasite resistant sheep and hypersensitivity diarrhoea, L.J.E. Karlsson & J.C. Greeff, Department of Agriculture Western Australi

Loss of Let-7 Up-Regulates EZH2 in Prostate Cancer Consistent with the Acquisition of Cancer Stem Cell Signatures That Are Attenuated by BR-DIM

The emergence of castrate-resistant prostate cancer (CRPC) contributes to the high mortality of patients diagnosed with prostate cancer (PCa), which in part could be attributed to the existence and the emergence of cancer stem cells (CSCs). Recent studies have shown that deregulated expression of microRNAs (miRNAs) contributes to the initiation and progression of PCa. Among several known miRNAs, let-7 family appears to play a key role in the recurrence and progression of PCa by regulating CSCs; however, the mechanism by which let-7 family contributes to PCa aggressiveness is unclear. Enhancer of Zeste homolog 2 (EZH2), a putative target of let-7 family, was demonstrated to control stem cell function. In this study, we found loss of let-7 family with corresponding over-expression of EZH2 in human PCa tissue specimens, especially in higher Gleason grade tumors. Overexpression of let-7 by transfection of let-7 precursors decreased EZH2 expression and repressed clonogenic ability and sphere-forming capacity of PCa cells, which was consistent with inhibition of EZH2 3′UTR luciferase activity. We also found that the treatment of PCa cells with BR-DIM (formulated DIM: 3,3′-diindolylmethane by Bio Response, Boulder, CO, abbreviated as BR-DIM) up-regulated let-7 and down-regulated EZH2 expression, consistent with inhibition of self-renewal and clonogenic capacity. Moreover, BR-DIM intervention in our on-going phase II clinical trial in patients prior to radical prostatectomy showed upregulation of let-7 consistent with down-regulation of EZH2 expression in PCa tissue specimens after BR-DIM intervention. These results suggest that the loss of let-7 mediated increased expression of EZH2 contributes to PCa aggressiveness, which could be attenuated by BR-DIM treatment, and thus BR-DIM is likely to have clinical impact

Upregulation of the cell-cycle regulator RGC-32 in Epstein-Barr virus-immortalized cells

Epstein-Barr virus (EBV) is implicated in the pathogenesis of multiple human tumours of lymphoid and epithelial origin. The virus infects and immortalizes B cells establishing a persistent latent infection characterized by varying patterns of EBV latent gene expression (latency 0, I, II and III). The CDK1 activator, Response Gene to Complement-32 (RGC-32, C13ORF15), is overexpressed in colon, breast and ovarian cancer tissues and we have detected selective high-level RGC-32 protein expression in EBV-immortalized latency III cells. Significantly, we show that overexpression of RGC-32 in B cells is sufficient to disrupt G2 cell-cycle arrest consistent with activation of CDK1, implicating RGC-32 in the EBV transformation process. Surprisingly, RGC-32 mRNA is expressed at high levels in latency I Burkitt's lymphoma (BL) cells and in some EBV-negative BL cell-lines, although RGC-32 protein expression is not detectable. We show that RGC-32 mRNA expression is elevated in latency I cells due to transcriptional activation by high levels of the differentially expressed RUNX1c transcription factor. We found that proteosomal degradation or blocked cytoplasmic export of the RGC-32 message were not responsible for the lack of RGC-32 protein expression in latency I cells. Significantly, analysis of the ribosomal association of the RGC-32 mRNA in latency I and latency III cells revealed that RGC-32 transcripts were associated with multiple ribosomes in both cell-types implicating post-initiation translational repression mechanisms in the block to RGC-32 protein production in latency I cells. In summary, our results are the first to demonstrate RGC-32 protein upregulation in cells transformed by a human tumour virus and to identify post-initiation translational mechanisms as an expression control point for this key cell-cycle regulator

The Ninth Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the SDSS-III Baryon Oscillation Spectroscopic Survey

The Sloan Digital Sky Survey III (SDSS-III) presents the first spectroscopic data from the Baryon Oscillation Spectroscopic Survey (BOSS). This ninth data release (DR9) of the SDSS project includes 535,995 new galaxy spectra (median z=0.52), 102,100 new quasar spectra (median z=2.32), and 90,897 new stellar spectra, along with the data presented in previous data releases. These spectra were obtained with the new BOSS spectrograph and were taken between 2009 December and 2011 July. In addition, the stellar parameters pipeline, which determines radial velocities, surface temperatures, surface gravities, and metallicities of stars, has been updated and refined with improvements in temperature estimates for stars with T_eff<5000 K and in metallicity estimates for stars with [Fe/H]>-0.5. DR9 includes new stellar parameters for all stars presented in DR8, including stars from SDSS-I and II, as well as those observed as part of the SDSS-III Sloan Extension for Galactic Understanding and Exploration-2 (SEGUE-2). The astrometry error introduced in the DR8 imaging catalogs has been corrected in the DR9 data products. The next data release for SDSS-III will be in Summer 2013, which will present the first data from the Apache Point Observatory Galactic Evolution Experiment (APOGEE) along with another year of data from BOSS, followed by the final SDSS-III data release in December 2014.Comment: 9 figures; 2 tables. Submitted to ApJS. DR9 is available at http://www.sdss3.org/dr