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.

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.

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.

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.

The UniProtKB/Swiss-Prot Tox-Prot program: A central hub of integrated venom protein data.

Animal toxins are of interest to a wide range of scientists, due to their numerous applications in pharmacology, neurology, hematology, medicine, and drug research. This, and to a lesser extent the development of new performing tools in transcriptomics and proteomics, has led to an increase in toxin discovery. In this context, providing publicly available data on animal toxins has become essential. The UniProtKB/Swiss-Prot Tox-Prot program (http://www.uniprot.org/program/Toxins) plays a crucial role by providing such an access to venom protein sequences and functions from all venomous species. This program has up to now curated more than 5000 venom proteins to the high-quality standards of UniProtKB/Swiss-Prot (release 2012_02). Proteins targeted by these toxins are also available in the knowledgebase. This paper describes in details the type of information provided by UniProtKB/Swiss-Prot for toxins, as well as the structured format of the knowledgebase.

Alcohol-related and hepatocellular cancer deaths by country of birth in England and Wales: analysis of mortality and census data.

BACKGROUND: The incidence of and mortality from alcohol-related conditions, liver disease and hepatocellular cancer (HCC) are increasing in the UK. We compared mortality rates by country of birth to explore potential inequalities and inform clinical and preventive care. DESIGN: Analysis of mortality for people aged 20 years and over using the 2001 Census data and death data from 1999 and 2001-2003. SETTING: England and Wales. MAIN OUTCOME MEASURES: Standardized mortality ratios (SMRs) for alcohol-related deaths and HCC. RESULTS: Mortality from alcohol-related deaths (23 502 deaths) was particularly high for people born in Ireland (SMR for men [M]: 236, 95% confidence interval [CI]: 219-254; SMR for women [F]: 212, 95% CI: 191-235) and Scotland (SMR-M: 187, CI: 173-213; SMR-F 182, CI: 163-205) and men born in India (SMR-M: 161, CI: 144-181). Low alcohol-related mortality was found in women born in other countries and men born in Bangladesh, Middle East, West Africa, Pakistan, China and Hong Kong, and the West Indies. Similar mortality patterns were observed by country of birth for alcoholic liver disease and other liver diseases. Mortality from HCC (8266 deaths) was particularly high for people born in Bangladesh (SMR-M: 523, CI: 380-701; SMR-F: 319, CI: 146-605), China and Hong Kong (SMR-M: 492, CI: 168-667; SMR-F: 323, CI: 184-524), West Africa (SMR-M: 440, CI, 308-609; SMR-F: 319, CI: 165-557) and Pakistan (SMR-M: 216, CI: 113-287; SMR-F: 215, CI: 133-319). CONCLUSIONS: These findings show persistent differences in mortality by country of birth for both alcohol-related and HCC deaths and have important clinical and public health implications. New policy, research and practical action are required to address these differences.This resource was contributed by The National Documentation Centre on Drug Use.

Interpretation of array comparative genome hybridization data: a major challenge.

The advent and application of high-resolution array-based comparative genome hybridization (array CGH) has led to the detection of large numbers of copy number variants (CNVs) in patients with developmental delay and/or multiple congenital anomalies as well as in healthy individuals. The notion that CNVs are also abundantly present in the normal population challenges the interpretation of the clinical significance of detected CNVs in patients. In this review we will illustrate a general clinical workflow based on our own experience that can be used in routine diagnostics for the interpretation of CNVs.

Catherine Comiskey Describing Data

Introducing and describing data and understanding the normal distribution.

Archiving and Accessing Qualitative Data Programme

There is a growing body of qualitative research data being made available in data archives in both Ireland and the UK. Examples of Irish qualitative data currently available for further analysis include Growing Up in Ireland (GUI) and Life Histories and Social Change in 20th Century Ireland. The Timescapes Research Programme, at the University of Leeds, hosts a wide variety of economic and social research data relevant to child and family researchers; data which is available to researchers in both Northern Ireland and the Republic of Ireland. Access to this data provides researchers with greater opportunities to explore the issues that affect children, their families and their  communities and which may ultimately contribute to informing children's policy. In this the second master class of the Children's Research Network for Ireland and Northern Ireland, participants will learn about the various qualitativ edatasets that are publicly-available for research purposes in the Republic of Ireland and Northern Ireland.

Growing up in Ireland data in the Irish Qualitative Data Archive

Brian Merriman's presentation on the Growing up in Ireland data in the Irish Qualitative Data Archive

CDI Tallaght RACCER data in the Irish Qualitative Data Archive

Tara Murphy's presentation on preparing RACCER data for the Irish Qualitative Data Archive