Inca expansion and parasitism in the Lluta Valley: preliminary data

Assessing the impact of cultural change on parasitism has been a central goal in archaeoparasitology. The influence of civilization and the development of empires on parasitism has not been evaluated. Presented here is a preliminary analysis of the change in human parasitism associated with the Inca conquest of the Lluta Valley in Northern Chile. Changes in parasite prevalence are described. It can be seen that the change in life imposed on the inhabitants of the Lluta Valley by the Incas caused an increase in parasitism.

Modular analysis of gene expression data with R.

SUMMARY: Large sets of data, such as expression profiles from many samples, require analytic tools to reduce their complexity. The Iterative Signature Algorithm (ISA) is a biclustering algorithm. It was designed to decompose a large set of data into so-called 'modules'. In the context of gene expression data, these modules consist of subsets of genes that exhibit a coherent expression profile only over a subset of microarray experiments. Genes and arrays may be attributed to multiple modules and the level of required coherence can be varied resulting in different 'resolutions' of the modular mapping. In this short note, we introduce two BioConductor software packages written in GNU R: The isa2 package includes an optimized implementation of the ISA and the eisa package provides a convenient interface to run the ISA, visualize its output and put the biclusters into biological context. Potential users of these packages are all R and BioConductor users dealing with tabular (e.g. gene expression) data. AVAILABILITY: http://www.unil.ch/cbg/ISA CONTACT: sven.bergmann@unil.ch

Biologic data of Macaca mulatta, Macaca fascicularis, and Saimiri sciureusused for research at the fiocruz primate center

Physiological parameters of laboratory animals used for biomedical research is crucial for following several experimental procedures. With the intent to establish baseline biologic parameters for non-human primates held in closed colonies, hematological and morphometric data of captive monkeys were determined. Data of clinically healthy rhesus macaques (Macaca mulatta), cynomolgus monkeys (Macaca fascicularis), and squirrel monkeys (Saimiri sciureus) were collected over a period of five years. Animals were separated according to sex and divided into five age groups. Hematological data were compared with those in the literature by Student's t test. Discrepancies with significance levels of 0.1, 1 or 5% were found in the hematological studies. Growth curves showed that the sexual dimorphism of rhesus monkeys appeared at an age of four years. In earlier ages, the differences between sexes could not be distinguished (p < 0.05). Sexual dimorphism in both squirrel monkeys and cynomolgus monkeys occurred at an age of about 32 months. Data presented in this paper could be useful for comparative studies using primates under similar conditions.

Improvement of the HIV/AIDS vaccine candidate NYVAC-C by deletion of the viral type I IFN inhibitor and/or acquisition of replication competence

Background: Recombinant viruses based on the attenuated vaccinia virus strain NYVAC are promising HIV vaccine candidates as phase I/II clinical trials have shown good safety and immunogenicity profiles. However, this NYVAC strain is non-replicating in most human cell lines and encodes viral inhibitors of the immune system. Methods: With the aim to increase the immune potency of the current NYVAC-C vector (expressing the codon optimized clade C HIV-1 genes encoding gp120 and Gag-Pol-Nef polyprotein), we have generated and characterized three NYVAC-C-based vectors by, 1) deletion of the viral type I IFN inhibitor gene (NYVAC-CdeltaB19R), 2) restoration of virus replication competence in human cells by re-inserting K1L and C7L host range genes (NYVAC-C-KC) and, 3) combination of both strategies (NYVACC- KC-deltaB19R). Results: Insertion of the KC fragment restored the replication competence of the viruses in human cells (HeLa cells and primary dermal fibroblasts and keratinocytes), increased the expression of HIV antigens by more than 3-fold compared to the non-replicating homologs, inhibited apoptosis induced by the parental NYVAC-C and retained attenuation in a newborn mouse model. In adult mice, replication-competent viruses showed a limited capacity to replicate in tissues surrounding the inoculation site (ovaries and lymph nodes). After infection of keratinocytes, PBMCs and dendritic cells these viruses induced differential modulation in specific host cell signal transduction pathways, triggering genes important in immune modulation. Conclusion: We have developed improved NYVAC-C-based vectors with enhanced HIV-1 antigen expression, with the ability to replicate in cultured human cells and partially in some tissues, with an induced expression of cellular genes relevant to immune system activation, and which trigger IFN-dependent and independent signalling pathways, while maintaining a safety phenotype. These new vectors are promising new HIV vaccine candidates. These studies were performed within the Poxvirus Tcell Vaccine Discovery Consortium (PTVDC) which is part of the CAVD program.

Current genetic data do not improve the prediction of type 2 diabetes mellitus: the CoLaus study.

CONTEXT: Several genetic risk scores to identify asymptomatic subjects at high risk of developing type 2 diabetes mellitus (T2DM) have been proposed, but it is unclear whether they add extra information to risk scores based on clinical and biological data. OBJECTIVE: The objective of the study was to assess the extra clinical value of genetic risk scores in predicting the occurrence of T2DM. DESIGN: This was a prospective study, with a mean follow-up time of 5 yr. SETTING AND SUBJECTS: The study included 2824 nondiabetic participants (1548 women, 52 ± 10 yr). MAIN OUTCOME MEASURE: Six genetic risk scores for T2DM were tested. Four were derived from the literature and two were created combining all (n = 24) or shared (n = 9) single-nucleotide polymorphisms of the previous scores. A previously validated clinic + biological risk score for T2DM was used as reference. RESULTS: Two hundred seven participants (7.3%) developed T2DM during follow-up. On bivariate analysis, no differences were found for all but one genetic score between nondiabetic and diabetic participants. After adjusting for the validated clinic + biological risk score, none of the genetic scores improved discrimination, as assessed by changes in the area under the receiver-operating characteristic curve (range -0.4 to -0.1%), sensitivity (-2.9 to -1.0%), specificity (0.0-0.1%), and positive (-6.6 to +0.7%) and negative (-0.2 to 0.0%) predictive values. Similarly, no improvement in T2DM risk prediction was found: net reclassification index ranging from -5.3 to -1.6% and nonsignificant (P ≥ 0.49) integrated discrimination improvement. CONCLUSIONS: In this study, adding genetic information to a previously validated clinic + biological score does not seem to improve the prediction of T2DM.

2006 National report (2005 data) to the EMCDDA by the Reitox National Focal Point: Norway: New developments, trends and in-depth information on selected issues.

This is a 2006 national report to the EMCDDA, using 2005 data. It is compiled by the Reitox national focal point and covers epidemiology, policing, strategy, drugs markets, drug-related infectious diseases, drug-related death and problem drug use in Norway.This resource was contributed by The National Documentation Centre on Drug Use.

Data Envelopment Analysis (DEA) : un guide pédagogique à l'intention des décideurs dans le secteur public

Ce guide présente la méthode Data Envelopment Analysis (DEA), une méthode d'évaluation de la performance . Il est destiné aux responsables d'organisations publiques qui ne sont pas familiers avec les notions d'optimisation mathématique, autrement dit de recherche opérationnelle. L'utilisation des mathématiques est par conséquent réduite au minimum. Ce guide est fortement orienté vers la pratique. Il permet aux décideurs de réaliser leurs propres analyses d'efficience et d'interpréter facilement les résultats obtenus. La méthode DEA est un outil d'analyse et d'aide à la décision dans les domaines suivants : - en calculant un score d'efficience, elle indique si une organisation dispose d'une marge d'amélioration ; - en fixant des valeurs-cibles, elle indique de combien les inputs doivent être réduits et les outputs augmentés pour qu'une organisation devienne efficiente ; - en identifiant le type de rendements d'échelle, elle indique si une organisation doit augmenter ou au contraire réduire sa taille pour minimiser son coût moyen de production ; - en identifiant les pairs de référence, elle désigne quelles organisations disposent des best practice à analyser.

Mechanisms governing lentivirus integration site selection.

An essential step of the life cycle of retroviruses is the stable insertion of a copy of their DNA genome into the host cell genome, and lentiviruses are no exception. This integration step, catalyzed by the viral-encoded integrase, ensures long-term expression of the viral genes, thus allowing a productive viral replication and rendering retroviral vectors also attractive for the field of gene therapy. At the same time, this ability to integrate into the host genome raises safety concerns regarding the use of retroviral-based gene therapy vectors, due to the genomic locations of integration sites. The availability of the human genome sequence made possible the analysis of the integration site preferences, which revealed to be nonrandom and retrovirus-specific, i.e. all lentiviruses studied so far favor integration in active transcription units, while other retroviruses have a different integration site distribution. Several mechanisms have been proposed that may influence integration targeting, which include (i) chromatin accessibility, (ii) cell cycle effects, and (iii) tethering proteins. Recent data provide evidence that integration site selection can occur via a tethering mechanism, through the recruitment of the lentiviral integrase by the cellular LEDGF/p75 protein, both proteins being the two major players in lentiviral integration targeting.

Quantitative genetic modeling and inference in the presence of nonignorable missing data.

Natural selection is typically exerted at some specific life stages. If natural selection takes place before a trait can be measured, using conventional models can cause wrong inference about population parameters. When the missing data process relates to the trait of interest, a valid inference requires explicit modeling of the missing process. We propose a joint modeling approach, a shared parameter model, to account for nonrandom missing data. It consists of an animal model for the phenotypic data and a logistic model for the missing process, linked by the additive genetic effects. A Bayesian approach is taken and inference is made using integrated nested Laplace approximations. From a simulation study we find that wrongly assuming that missing data are missing at random can result in severely biased estimates of additive genetic variance. Using real data from a wild population of Swiss barn owls Tyto alba, our model indicates that the missing individuals would display large black spots; and we conclude that genes affecting this trait are already under selection before it is expressed. Our model is a tool to correctly estimate the magnitude of both natural selection and additive genetic variance.

Regional-scale integration of multi-scale hydrological and geophysical data using a two-step Bayesian sequential simulation approach

Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale for the purpose of improving predictions of groundwater flow and solute transport. However, extending corresponding approaches to the regional scale still represents one of the major challenges in the domain of hydrogeophysics. To address this problem, we have developed a regional-scale data integration methodology based on a two-step Bayesian sequential simulation approach. Our objective is to generate high-resolution stochastic realizations of the regional-scale hydraulic conductivity field in the common case where there exist spatially exhaustive but poorly resolved measurements of a related geophysical parameter, as well as highly resolved but spatially sparse collocated measurements of this geophysical parameter and the hydraulic conductivity. To integrate this multi-scale, multi-parameter database, we first link the low- and high-resolution geophysical data via a stochastic downscaling procedure. This is followed by relating the downscaled geophysical data to the high-resolution hydraulic conductivity distribution. After outlining the general methodology of the approach, we demonstrate its application to a realistic synthetic example where we consider as data high-resolution measurements of the hydraulic and electrical conductivities at a small number of borehole locations, as well as spatially exhaustive, low-resolution estimates of the electrical conductivity obtained from surface-based electrical resistivity tomography. The different stochastic realizations of the hydraulic conductivity field obtained using our procedure are validated by comparing their solute transport behaviour with that of the underlying ?true? hydraulic conductivity field. We find that, even in the presence of strong subsurface heterogeneity, our proposed procedure allows for the generation of faithful representations of the regional-scale hydraulic conductivity structure and reliable predictions of solute transport over long, regional-scale distances.

Clustering and hot spot detection in socio-economic spatio-temporal data

Distribution of socio-economic features in urban space is an important source of information for land and transportation planning. The metropolization phenomenon has changed the distribution of types of professions in space and has given birth to different spatial patterns that the urban planner must know in order to plan a sustainable city. Such distributions can be discovered by statistical and learning algorithms through different methods. In this paper, an unsupervised classification method and a cluster detection method are discussed and applied to analyze the socio-economic structure of Switzerland. The unsupervised classification method, based on Ward's classification and self-organized maps, is used to classify the municipalities of the country and allows to reduce a highly-dimensional input information to interpret the socio-economic landscape. The cluster detection method, the spatial scan statistics, is used in a more specific manner in order to detect hot spots of certain types of service activities. The method is applied to the distribution services in the agglomeration of Lausanne. Results show the emergence of new centralities and can be analyzed in both transportation and social terms.