Iterative Schedule Optimization for Parallelization in the Polyhedron Model

In high-performance computing, one primary objective is to exploit the performance that the given target hardware can deliver to the fullest. Compilers that have the ability to automatically optimize programs for a specific target hardware can be highly useful in this context. Iterative (or search-based) compilation requires little or no prior knowledge and can adapt more easily to concrete programs and target hardware than static cost models and heuristics. Thereby, iterative compilation helps in situations in which static heuristics do not reflect the combination of input program and target hardware well. Moreover, iterative compilation may enable the derivation of more accurate cost models and heuristics for optimizing compilers. In this context, the polyhedron model is of help as it provides not only a mathematical representation of programs but, more importantly, a uniform representation of complex sequences of program transformations by schedule functions. The latter facilitates the systematic exploration of the set of legal transformations of a given program. Early approaches to purely iterative schedule optimization in the polyhedron model do not limit their search to schedules that preserve program semantics and, thereby, suffer from the need to explore numbers of illegal schedules. More recent research ensures the legality of program transformations but presumes a sequential rather than a parallel execution of the transformed program. Other approaches do not perform a purely iterative optimization. We propose an approach to iterative schedule optimization for parallelization and tiling in the polyhedron model. Our approach targets loop programs that profit from data locality optimization and coarse-grained loop parallelization. The schedule search space can be explored either randomly or by means of a genetic algorithm. To determine a schedule's profitability, we rely primarily on measuring the transformed code's execution time. While benchmarking is accurate, it increases the time and resource consumption of program optimization tremendously and can even make it impractical. We address this limitation by proposing to learn surrogate models from schedules generated and evaluated in previous runs of the iterative optimization and to replace benchmarking by performance prediction to the extent possible. Our evaluation on the PolyBench 4.1 benchmark set reveals that, in a given setting, iterative schedule optimization yields significantly higher speedups in the execution of the program to be optimized. Surrogate performance models learned from training data that was generated during previous iterative optimizations can reduce the benchmarking effort without strongly impairing the optimization result. A prerequisite for this approach is a sufficient similarity between the training programs and the program to be optimized

Corporate Security Responsibility: Towards a Conceptual Framework for a Comparative Research Agenda

The political debate about the role of business in armed conflicts has increasingly raised expectations as to governance contributions by private corporations in the fields of conflict prevention, peace-keeping and postconflict peace-building. This political agenda seems far ahead of the research agenda, in which the negative image of business in conflicts, seen as fuelling, prolonging and taking commercial advantage of violent conflicts,still prevails. So far the scientific community has been reluctant to extend the scope of research on ‘corporate social responsibility’ to the area of security in general and to intra-state armed conflicts in particular. As a consequence, there is no basis from which systematic knowledge can be generated about the conditions and the extent to which private corporations can fulfil the role expected of them in the political discourse. The research on positive contributions of private corporations to security amounts to unconnected in-depth case studies of specific corporations in specific conflict settings. Given this state of research, we develop a framework for a comparative research agenda to address the question: Under which circumstances and to what extent can private corporations be expected to contribute to public security

A TNF-Regulated Recombinatorial Macrophage Immune Receptor Implicated in Granuloma Formation in Tuberculosis

Macrophages play a central role in host defense against mycobacterial infection and anti- TNF therapy is associated with granuloma disorganization and reactivation of tuberculosis in humans. Here, we provide evidence for the presence of a T cell receptor (TCR) αβ based recombinatorial immune receptor in subpopulations of human and mouse monocytes and macrophages. In vitro, we find that the macrophage-TCRαβ induces the release of CCL2 and modulates phagocytosis. TNF blockade suppresses macrophage-TCRαβ expression. Infection of macrophages from healthy individuals with mycobacteria triggers formation of clusters that express restricted TCR Vβ repertoires. In vivo, TCRαβ bearing macrophages abundantly accumulate at the inner host-pathogen contact zone of caseous granulomas from patients with lung tuberculosis. In chimeric mouse models, deletion of the variable macrophage-TCRαβ or TNF is associated with structurally compromised granulomas of pulmonary tuberculosis even in the presence of intact T cells. These results uncover a TNF-regulated recombinatorial immune receptor in monocytes/macrophages and demonstrate its implication in granuloma formation in tuberculosis

Reduced 8-Gray compared to standard 12-Gray Total Body Irradiation for allogeneic transplantation in first remission acute lymphoblastic leukemia: a study of the Acute Leukemia Working Party of the EBMT

In this registry-based study, we compared outcomes of allogeneic hematopoietic cell transplantation (allo-HCT) in adult patients with acute lymphoblastic leukemia (ALL) transplanted in first complete remission (CR-1), following conditioning with total body irradiation (TBI) at a standard 12-Gray or at a lower 8-Gray total dose. Patients received fludarabine (flu) as the sole chemotherapy complementing TBI. Eight-Gray TBI/flu was used in 494 patients and 12-Gray TBI/flu in 145 patients. Eighty-eight (23.1%) and 36 (29%) of the patients had Ph-negative B-ALL, 222 (58.3%) and 53 (42.7%) had Ph-positive B-ALL, 71 (18.6%) and 35 (28.2%) T-ALL, respectively (P = 0.008). Patients treated with 8-Gray were older than ones received 12-Gray (median 55.7 versus 40.3 years, P < 0.0001) and were more frequently administered in vivo T-cell depletion (71% versus 40%, P <0.0001). In a multivariate model adjusted for age, type of ALL, and other prognostic factors, leukemia-free survival (primary endpoint) as well as relapse, nonrelapse mortality, overall survival, and GVHD-free, relapse-free survival were not influenced by the TBI dose. These results were confirmed when we focused on patients <55 years of age (median 47 years). Patients with Ph-positive ALL or T-ALL had significantly better survival outcomes than ones with Ph-negative B-ALL, mainly due to significantly fewer relapses. We conclude that 8-Gray TBI is sufficient for adult patients with ALL transplanted in CR-1 with no additional benefit of augmenting the conditioning intensity to 12-Gray

A database of naturally occurring human urinary peptides and proteins for use in clinical applications

Owing to its availability, ease of collection and correlation with (patho-) physiology, urine is an attractive source for clinical proteomics. However, the lack of comparable datasets from large cohorts has greatly hindered development in this field. Here we report the establishment of a high resolution proteome database of naturally occurring human urinary peptides and proteins - ranging from 800-17,000 Da - from over 3,600 individual samples using capillary electrophoresis coupled to mass spectrometry, yielding an average of 1,500 peptides per sample. All processed data were deposited in an SQL database, currently containing 5,010 relevant unique urinary peptides that serve as classifiers for diagnosis and monitoring of diseases, including kidney and vascular diseases. Of these, 352 have been sequenced to date. To demonstrate the applicability of this database, two examples of disease diagnosis were provided: For renal damage diagnosis, patients with a specific renal disease were identified with high specificity and sensitivity in a blinded cohort of 131 individuals. We further show definition of biomarkers specific for immunosuppression and complications after transplantation (Kaposi's sarcoma). Due to its high information content, this database will be a powerful tool for the validation of biomarkers for both renal and non-renal diseases

Abdominal aortic aneurysm is associated with a variant in low-density lipoprotein receptor-related protein 1

Abdominal aortic aneurysm (AAA) is a common cause of morbidity and mortality and has a significant heritability. We carried out a genome-wide association discovery study of 1866 patients with AAA and 5435 controls and replication of promising signals (lead SNP with a p value &lt; 1 × 10-5) in 2871 additional cases and 32,687 controls and performed further follow-up in 1491 AAA and 11,060 controls. In the discovery study, nine loci demonstrated association with AAA (p &lt; 1 × 10-5). In the replication sample, the lead SNP at one of these loci, rs1466535, located within intron 1 of low-density-lipoprotein receptor-related protein 1 (LRP1) demonstrated significant association (p = 0.0042). We confirmed the association of rs1466535 and AAA in our follow-up study (p = 0.035). In a combined analysis (6228 AAA and 49182 controls), rs1466535 had a consistent effect size and direction in all sample sets (combined p = 4.52 × 10-10, odds ratio 1.15 [1.10-1.21]). No associations were seen for either rs1466535 or the 12q13.3 locus in independent association studies of coronary artery disease, blood pressure, diabetes, or hyperlipidaemia, suggesting that this locus is specific to AAA. Gene-expression studies demonstrated a trend toward increased LRP1 expression for the rs1466535 CC genotype in arterial tissues; there was a significant (p = 0.029) 1.19-fold (1.04-1.36) increase in LRP1 expression in CC homozygotes compared to TT homozygotes in aortic adventitia. Functional studies demonstrated that rs1466535 might alter a SREBP-1 binding site and influence enhancer activity at the locus. In conclusion, this study has identified a biologically plausible genetic variant associated specifically with AAA, and we suggest that this variant has a possible functional role in LRP1 expression

Genome-wide association identifies nine common variants associated with fasting proinsulin levels and provides new insights into the pathophysiology of type 2 diabetes.

OBJECTIVE: Proinsulin is a precursor of mature insulin and C-peptide. Higher circulating proinsulin levels are associated with impaired β-cell function, raised glucose levels, insulin resistance, and type 2 diabetes (T2D). Studies of the insulin processing pathway could provide new insights about T2D pathophysiology. RESEARCH DESIGN AND METHODS: We have conducted a meta-analysis of genome-wide association tests of ∼2.5 million genotyped or imputed single nucleotide polymorphisms (SNPs) and fasting proinsulin levels in 10,701 nondiabetic adults of European ancestry, with follow-up of 23 loci in up to 16,378 individuals, using additive genetic models adjusted for age, sex, fasting insulin, and study-specific covariates. RESULTS: Nine SNPs at eight loci were associated with proinsulin levels (P < 5 × 10(-8)). Two loci (LARP6 and SGSM2) have not been previously related to metabolic traits, one (MADD) has been associated with fasting glucose, one (PCSK1) has been implicated in obesity, and four (TCF7L2, SLC30A8, VPS13C/C2CD4A/B, and ARAP1, formerly CENTD2) increase T2D risk. The proinsulin-raising allele of ARAP1 was associated with a lower fasting glucose (P = 1.7 × 10(-4)), improved β-cell function (P = 1.1 × 10(-5)), and lower risk of T2D (odds ratio 0.88; P = 7.8 × 10(-6)). Notably, PCSK1 encodes the protein prohormone convertase 1/3, the first enzyme in the insulin processing pathway. A genotype score composed of the nine proinsulin-raising alleles was not associated with coronary disease in two large case-control datasets. CONCLUSIONS: We have identified nine genetic variants associated with fasting proinsulin. Our findings illuminate the biology underlying glucose homeostasis and T2D development in humans and argue against a direct role of proinsulin in coronary artery disease pathogenesis

A genome-wide association search for type 2 diabetes genes in African Americans.

African Americans are disproportionately affected by type 2 diabetes (T2DM) yet few studies have examined T2DM using genome-wide association approaches in this ethnicity. The aim of this study was to identify genes associated with T2DM in the African American population. We performed a Genome Wide Association Study (GWAS) using the Affymetrix 6.0 array in 965 African-American cases with T2DM and end-stage renal disease (T2DM-ESRD) and 1029 population-based controls. The most significant SNPs (n = 550 independent loci) were genotyped in a replication cohort and 122 SNPs (n = 98 independent loci) were further tested through genotyping three additional validation cohorts followed by meta-analysis in all five cohorts totaling 3,132 cases and 3,317 controls. Twelve SNPs had evidence of association in the GWAS (P<0.0071), were directionally consistent in the Replication cohort and were associated with T2DM in subjects without nephropathy (P<0.05). Meta-analysis in all cases and controls revealed a single SNP reaching genome-wide significance (P<2.5×10(-8)). SNP rs7560163 (P = 7.0×10(-9), OR (95% CI) = 0.75 (0.67-0.84)) is located intergenically between RND3 and RBM43. Four additional loci (rs7542900, rs4659485, rs2722769 and rs7107217) were associated with T2DM (P<0.05) and reached more nominal levels of significance (P<2.5×10(-5)) in the overall analysis and may represent novel loci that contribute to T2DM. We have identified novel T2DM-susceptibility variants in the African-American population. Notably, T2DM risk was associated with the major allele and implies an interesting genetic architecture in this population. These results suggest that multiple loci underlie T2DM susceptibility in the African-American population and that these loci are distinct from those identified in other ethnic populations