Boys and their toys : status inconsistency in non-democratic regimes and the import of major weapon systems

Major weapon system imports are significant as they are useful for domestic and international security. However, states regularly imported weapons they want in addition to weapons they need. One explanation is that states import unnecessary weapons to gain status. We argue that states suffering from higher levels of negative status inconsistency (SI) import a greater proportion of status symbol weapons. To account for differing security motives, we also separate non-democratic regime types – strongman, junta, boss, and machine – as they vary in their international conflict propensity and domestic stability. Due to the differences across these regimes, we further argue that non-democratic personalist regimes will import more status symbol weapons. Using data covering 1965–1999, we find that negatively status inconsistent regimes import more status symbol weapons

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Supplemental Material, JCR-17-0111.R2 - The Dyadic Militarized Interstate Disputes (MIDs) Dataset Version 3.0: Logic, Characteristics, and Comparisons to Alternative Datasets

<p>Supplemental Material, JCR-17-0111.R2 for The Dyadic Militarized Interstate Disputes (MIDs) Dataset Version 3.0: Logic, Characteristics, and Comparisons to Alternative Datasets by Zeev Maoz, Paul L. Johnson, Jasper Kaplan, Fiona Ogunkoya, and Aaron P. Shreve in Journal of Conflict Resolution</p

A Personalized Clinical-Decision Tool to Improve the Diagnostic Accuracy of Myelodysplastic Syndromes

Background While histo- and cytomorphological examinations are central to the diagnosis of myelodysplastic syndromes (MDS), significant inter-observer variability exists. The diagnosis can be challenging in pancytopenic patients (pts) without evidence of dysplasia and is contingent on observer expertise. We developed and externally validated a geno-clinical model that uses mutational data and peripheral blood counts/clinical variables to distinguish MDS from other myeloid malignancies. Methods Clinical and genomic data, including commercially available next-generation sequencing panels, were obtained for patients (pts) treated at the Cleveland Clinic (CC; 652 pts), Munich Leukemia Laboratory (MLL; 1509 pts), and the University of Pavia in Italy (UP, 536 pts). All patients had carried a diagnosis of MDS, chronic myelomonocytic leukemia (CMML), MDS/myeloproliferative neoplasm overlap (MDS/MPN), myeloproliferative neoplasm (MPN; either polycythemia vera, essential thrombocythemia, or myelofibrosis), clonal cytopenia of undetermined significance (CCUS), or idiopathic cytopenia of undetermined significance (ICUS). All diagnoses were established with bone marrow aspiration and according to World Health Organization 2017 criteria. The training cohort included data from CC and UP and randomly divided into learner (80%) and test (20%) cohorts. The final model was independently validated in the MLL cohort. A machine learning algorithm was used to build the model; multiple extraction algorithms were used to extract genomic/clinical variables on both the cohort and individual levels. Performance was evaluated according to the area under the curve of the receiver operating characteristic (ROC-AUC) and accuracy matrices. Results Among the 2697 pts included from all sites, the median age was 70 years [36 - 86]. Median hemoglobin (Hb) was 10.4g/dl [6.9 - 15.7], median platelet count (PLT) was 132 k/dL [14 - 722], median WBC count was 5.3 k/dL [1.4 - 49.9], median ANC was 2.8 k/dL [0.3 - 27.7], median monocyte count was 0.3 k/dL [0 - 9.9], and median lymphocyte count (ALC) was 1.1 k/dL [0.1 - 5.4], and median peripheral blast percentage 0% [0 - 8]. The most commonly mutated genes in all patients were (list top 5 genes) and among pts with MDS were SF3B1 (27%), TET2 (25%), ASXL1 (19%), SRSF2 (16%), and DNMT3A (11%); among patients with MDS-MPN/CMML, the most commonly mutated genes were MDS-MPN/CMML (TET2 46%, ASXL1 34%, SRSF2 29%, RUNX1 13%, CBL 12%) ; among patients with MPNs, the most commonly mutated genes were (JAK2 64%, ASXL1 27%, TET2 14%, DNMT3A 8%, U2AF1 7%); among patients with CCUS the most commonly mutated genes were (TET2 41%, DNMT3A 27%, ASXL1 19%, SRSF2 17%, ZRSR2 10%). The most important features for model predictions (ranked from the most to the least important) included: number of mutations detected/sample, peripheral blast percentage, AMC, JAK2 status, Hb, basophil count, age, eosinophil count, ALC, WBC, EZH2 mutation status, ANC, mutation status of KRAS and SF3B1, platelets, and gender. The final model achieved an average AUROC of 0.95 (95% CI 0.93-0.96) when applied to the test cohort and 0.93 (95% CI 0.91 - 0.94) when it was applied to the MLL cohort. The model also provides individual-level explanations for predictions, providing top differential diagnoses and individual-level explanations of how features influence a putative diagnosis (Figure 1b). Conclusions We developed and externally validated a highly accurate and interpretable model that can distinguish MDS from other myeloid malignancies using clinical and mutational data from a large international cohort. The model can provide personalized interpretations of its outcome and can aid physicians and hematopathologists in recognizing MDS with high accuracy when encountering pts with pancytopenia and with a suspected diagnosis of MDS. Disclosures Sekeres: Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda/Millenium: Consultancy, Membership on an entity's Board of Directors or advisory committees; BMS: Consultancy, Membership on an entity's Board of Directors or advisory committees. Mukherjee:Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Partnership for Health Analytic Research, LLC (PHAR, LLC): Honoraria; Bristol Myers Squib: Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Aplastic Anemia and MDS International Foundation: Honoraria; Celgene/Acceleron: Membership on an entity's Board of Directors or advisory committees; EUSA Pharma: Consultancy. Gerds:Sierra Oncology: Research Funding; Imago Biosciences: Research Funding; Apexx Oncology: Consultancy; Celgene: Consultancy, Research Funding; Incyte Corporation: Consultancy, Research Funding; Roche/Genentech: Research Funding; CTI Biopharma: Consultancy, Research Funding; AstraZeneca/MedImmune: Consultancy; Gilead Sciences: Research Funding; Pfizer: Research Funding. Maciejewski:Alexion, BMS: Speakers Bureau; Novartis, Roche: Consultancy, Honoraria. Nazha:Jazz: Research Funding; Incyte: Speakers Bureau; Novartis: Speakers Bureau; MEI: Other: Data monitoring Committee

Geno-Clinical Model for the Diagnosis of Bone Marrow Myeloid Neoplasms

Background Myelodysplastic syndromes (MDS) and other myeloid neoplasms are mainly diagnosed based on morphological changes in the bone marrow. Diagnosis can be challenging in patients (pts) with pancytopenia with minimal dysplasia, and is subject to inter-observer variability, with up to 40% disagreement in diagnosis (Zhang, ASH 2018). Somatic mutations can be identified in all myeloid neoplasms, but no gene or set of genes are diagnostic for each disease phenotype. We developed a geno-clinical model that uses mutational data, peripheral blood values, and clinical variables to distinguish among several bone marrow disorders that include: MDS, idiopathic cytopenia of undetermined significance (ICUS), clonal cytopenia of undetermined significance (CCUS), MDS/myeloproliferative neoplasm (MPN) overlaps including chronic myelomonocytic leukemia (CMML), and MPNs such as polycythemia vera (PV), essential thrombocythemia (ET), and myelofibrosis (PMF). Methods We combined genomic and clinical data from 2471 pts treated at our institution (684) and the Munich Leukemia Laboratory (1787). Pts were diagnosed with MDS, ICUS, CCUS, CMML, MDS/MPN, PV, ET, and PMF according to 2016 WHO criteria. Diagnoses were confirmed by independent hematopathologists not associated with the study. A panel of 60 genes commonly mutated in myeloid malignancies was included. The cohort was randomly divided into learner (80%) and validation (20%) cohorts. Machine learning algorithms were applied to predict the phenotype. Feature extraction algorithms were used to extract genomic/clinical variables that impacted the algorithm decision and to visualize the impact of each variable on phenotype. Prediction performance was evaluated according to the area under the curve of the receiver operator characteristic (ROC-AUC). Results Of 2471 pts, 1306 had MDS, 223 had ICUS, 107 had CCUS, 478 had CMML, 89 had MDS/MPN, 79 had PV, 90 had ET, and 99 had PMF. The median age for the entire cohort was 71 years (range, 9-102); 38% were female. The median white blood cell count (WBC) was 3.2x10^9/L (range, 0.00-179), absolute monocyte count (AMC) 0.21x10^9/L (range, 0-96), absolute lymphocyte count (ALC) 0.88x10^9/L (range, 0-357), absolute neutrophil count (ANC) 0.60x10^9/L (range, 0-170), and hemoglobin (Hgb) 10.50 g/dL (range, 3.9-24.0). The most commonly mutated genes in all pts were: TET2 (28%), ASXL1 (23%), SF3B1 (15%). In MDS, they were: TET2 (26%), SF3B1 (24%), ASXL1 (21%). In CCUS: TET2 (46%), SRSF2 (24%), ASXL1 (23%). In CMML, TET2 (51%), ASXL1 (43 %), SRSF2 (25%). In MDS/MPN: SF3B1 (39%), JAK2 (37%), TET2 (20%). In PV, JAK2 (94%), TET2 (22%), DNMT3A (8%). In ET: JAK2 (44%), TET2 (13%), DNMT3A (8%). In PMF: JAK2 (67%), ASXL1 (43%), SRSF2 (17%). 71 genomic/clinical variables were evaluated. Feature extraction algorithms were used to identify the variables with the most significant impacts on prediction. The top variables are shown in the Figure 1. Overall, the most important variables were: age, AMC, ANC, Hgb, Plt, ALC, total number of mutations, JAK2, ASXL1, TET2, U2AF1, SRSF2, SF3B1, BCOR, EZH2, and DNMT3A. The top variables for each disease were different, see Figure. When applying the model to the validation cohort, AUC performance was as follows (a perfect predictor has an AUC of 1, and AUC ≥ 0.90 are generally considered excellent): MDS: 0.95 +/- 0.04, ICUS: 0.96 +/- 0.05, CCUS: 0.95 +/- 0.05, CMML: 0.95 +/- 0.05, MDS/MPN: 0.95 +/- 0.05, PV: 0.95 +/- 0.05, ET: 0.96 +/- 0.05, PMF: 0.95 +/- 0.05. When the analysis was restricted to MDS, ICUS, and CCUS, the AUC remained high, 0.95 +/- 0.4. The model can also provide personalized explanations of the variables supporting the prediction and the impact of each variable on the outcome (Figure). Conclusions We propose a new approach using interpretable, individualized modeling to predict myeloid neoplasm phenotypes based on genomic and clinical data without bone marrow biopsy data. This approach can aid clinicians and hematopathologists when encountering pts with cytopenias and suspicion for these disorders. The model also provides feature attributions that allow for quantitative understanding of the complex interplay among genotypes, clinical variables, and phenotypes. A web application to facilitate the translation of this model into the clinic is under development and will be presented at the meeting. Figure 1 Disclosures Meggendorfer: MLL Munich Leukemia Laboratory: Employment. Sekeres:Syros: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Millenium: Membership on an entity's Board of Directors or advisory committees. Walter:MLL Munich Leukemia Laboratory: Employment. Hutter:MLL Munich Leukemia Laboratory: Employment. Savona:Incyte Corporation: Membership on an entity's Board of Directors or advisory committees, Research Funding; Karyopharm Therapeutics: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Selvita: Membership on an entity's Board of Directors or advisory committees; Sunesis: Research Funding; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees; Boehringer Ingelheim: Patents & Royalties; Celgene Corporation: Membership on an entity's Board of Directors or advisory committees. Gerds:Incyte: Consultancy, Research Funding; Roche: Research Funding; Imago Biosciences: Research Funding; CTI Biopharma: Consultancy, Research Funding; Pfizer: Consultancy; Celgene Corporation: Consultancy, Research Funding; Sierra Oncology: Research Funding. Mukherjee:Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Projects in Knowledge: Honoraria; Celgene Corporation: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Partnership for Health Analytic Research, LLC (PHAR, LLC): Consultancy; McGraw Hill Hematology Oncology Board Review: Other: Editor; Pfizer: Honoraria; Bristol-Myers Squibb: Speakers Bureau; Takeda: Membership on an entity's Board of Directors or advisory committees. Komrokji:JAZZ: Speakers Bureau; Agios: Consultancy; Incyte: Consultancy; DSI: Consultancy; pfizer: Consultancy; celgene: Consultancy; JAZZ: Consultancy; Novartis: Speakers Bureau. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Maciejewski:Alexion: Consultancy; Novartis: Consultancy. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Nazha:Tolero, Karyopharma: Honoraria; MEI: Other: Data monitoring Committee; Novartis: Speakers Bureau; Jazz Pharmacutical: Research Funding; Incyte: Speakers Bureau; Daiichi Sankyo: Consultancy; Abbvie: Consultancy

A geno-clinical decision model for the diagnosis of myelodysplastic syndromes

Abstract The differential diagnosis of myeloid malignancies is challenging and subject to interobserver variability. We used clinical and next-generation sequencing (NGS) data to develop a machine learning model for the diagnosis of myeloid malignancies independent of bone marrow biopsy data based on a 3-institution, international cohort of patients. The model achieves high performance, with model interpretations indicating that it relies on factors similar to those used by clinicians. In addition, we describe associations between NGS findings and clinically important phenotypes and introduce the use of machine learning algorithms to elucidate clinicogenomic relationships

Diblock Copolymer Micelles and Supported Films with Noncovalently Incorporated Chromophores: A Modular Platform for Efficient Energy Transfer

We report generation of modular, artificial light-harvesting assemblies where an amphiphilic diblock copolymer, poly(ethylene oxide)-block-poly(butadiene), serves as the framework for noncovalent organization of BODIPY-based energy donor and bacteriochlorin-based energy acceptor chromophores. The assemblies are adaptive and form well-defined micelles in aqueous solution and high-quality monolayer and bilayer films on solid supports, with the latter showing greater than 90% energy transfer efficiency. This study lays the groundwork for further development of modular, polymer-based materials for light harvesting and other photonic applications