Formation of public strategic planning in Ukraine

It is sometimes the case that a theory proposes that the population means on two variables should have the same rank order across a set of experimental conditions. This paper presents a test of this hypothesis. The test statistic is based on the coupled monotonic regression algorithm developed by the authors. The significance of the test statistic is determined by comparison to an empirical distribution specific to each case, obtained via non-parametric or semi-parametric bootstrap. We present an analysis of the power and Type I error control of the test based on numerical simulation. Partial order constraints placed on the variables may sometimes be theoretically justified. These constraints are easily incorporated into the computation of the test statistic and are shown to have substantial effects on power. The test can be applied to any form of data, as long as an appropriate statistical model can be specified.free access is valid until January 8, 2016:http://authors.elsevier.com/a/1S3XC53naPWGhFunding agencies: Australian Research Council [0877510, 0878630, 110100751, 130101535]; National Science Foundation [1256959]; Linkoping University</p

Divergent HIV and Simian Immunodeficiency Virus Surveillance, Zaire

Recent HIV infection or divergent HIV or simian immunodeficiency virus (SIV) strains may be responsible for Western blot–indeterminate results on 70 serum samples from Zairian hospital employees that were reactive in an enzyme immunoassay. Using universal polymerase chain reaction HIV-1, HIV-2, and SIV primers, we detected 1 (1.4%) HIV-1 sequence. Except for 1 sample, no molecular evidence for unusual HIV- or SIV-like strains in this sampling was found

Central African Hunters Exposed to Simian Immunodeficiency Virus

HIV-seronegative Cameroonians with exposure to nonhuman primates were tested for simian immunodeficiency virus (SIV) infection. Seroreactivity was correlated with exposure risk (p<0.001). One person had strong humoral and weak cellular immune reactivity to SIVcol peptides. Humans are exposed to and possibly infected with SIV, which has major public health implications

Statistics in the service of science : don’t let the tail wag the dog

Statistical modeling is generally meant to describe patterns in data in service of the broader scientific goal of developing theories to explain those patterns. Statistical models support meaningful inferences when models are built so as to align parameters of the model with potential causal mechanisms and how they manifest in data. When statistical models are instead based on assumptions chosen by default, attempts to draw inferences can be uninformative or even paradoxical—in essence, the tail is trying to wag the dog. These issues are illustrated by van Doorn et al. (this issue) in the context of using Bayes Factors to identify effects and interactions in linear mixed models. We show that the problems identified in their applications (along with other problems identified here) can be circumvented by using priors over inherently meaningful units instead of default priors on standardized scales. This case study illustrates how researchers must directly engage with a number of substantive issues in order to support meaningful inferences, of which we highlight two: The first is the problem of coordination, which requires a researcher to specify how the theoretical constructs postulated by a model are functionally related to observable variables. The second is the problem of generalization, which requires a researcher to consider how a model may represent theoretical constructs shared across similar but non-identical situations, along with the fact that model comparison metrics like Bayes Factors do not directly address this form of generalization. For statistical modeling to serve the goals of science, models cannot be based on default assumptions, but should instead be based on an understanding of their coordination function and on how they represent causal mechanisms that may be expected to generalize to other related scenarios

PerR Confers Phagocytic Killing Resistance and Allows Pharyngeal Colonization by Group A Streptococcus

The peroxide response transcriptional regulator, PerR, is thought to contribute to virulence of group A Streptococcus (GAS); however, the specific mechanism through which it enhances adaptation for survival in the human host remains unknown. Here, we identify a critical role of PerR-regulated gene expression in GAS phagocytosis resistance and in virulence during pharyngeal infection. Deletion of perR in M-type 3 strain 003Sm was associated with reduced resistance to phagocytic killing in human blood and by murine macrophages in vitro. The increased phagocytic killing of the perR mutant was abrogated in the presence of the general oxidative burst inhibitor diphenyleneiodonium chloride (DPI), a result that suggests PerR-dependent gene expression counteracts the phagocyte oxidative burst. Moreover, an isogenic perR mutant was severely attenuated in a baboon model of GAS pharyngitis. In competitive infection experiments, the perR mutant was cleared from two animals at 24 h and from four of five animals by day 14, in sharp contrast to wild-type bacteria that persisted in the same five animals for 28 to 42 d. GAS genomic microarrays were used to compare wild-type and perR mutant transcriptomes in order to characterize the PerR regulon of GAS. These studies identified 42 PerR-dependent loci, the majority of which had not been previously recognized. Surprisingly, a large proportion of these loci are involved in sugar utilization and transport, in addition to oxidative stress adaptive responses and virulence. This finding suggests a novel role for PerR in mediating sugar uptake and utilization that, together with phagocytic killing resistance, may contribute to GAS fitness in the infected host. We conclude that PerR controls expression of a diverse regulon that enhances GAS resistance to phagocytic killing and allows adaptation for survival in the pharynx