Toxoplasma gondii: flat-mounting of retina as a new tool for the observation of ocular infection in mice.

Ocular toxoplasmosis is the principal cause of posterior uveitis and a leading cause of blindness. Animal models are required to improve our understanding of the pathogenesis of this disease. The method currently used for the detection of retinal cysts in animals involves the observation, under a microscope, of all the sections from infected eyes. However, this method is time-consuming and lacks sensitivity. We have developed a rapid, sensitive method for observing retinal cysts in mice infected with Toxoplasma gondii. This method involves combining the flat-mounting of retina - a compromise between macroscopic observation and global analysis of this tissue - and the use of an avirulent recombinant strain of T. gondii expressing the Escherichia coli beta-galactosidase gene, visually detectable at the submacroscopic level. Single cyst unilateral infection was found in six out of 17 mice killed within 28 days of infection, whereas a bilateral infection was found in only one mouse. There was no correlation between brain cysts number and ocular infection.

MORM--a Petri net based model for assessing OH&S risks in industrial processes: modeling qualitative aspects.

Because of the increase in workplace automation and the diversification of industrial processes, workplaces have become more and more complex. The classical approaches used to address workplace hazard concerns, such as checklists or sequence models, are, therefore, of limited use in such complex systems. Moreover, because of the multifaceted nature of workplaces, the use of single-oriented methods, such as AEA (man oriented), FMEA (system oriented), or HAZOP (process oriented), is not satisfactory. The use of a dynamic modeling approach in order to allow multiple-oriented analyses may constitute an alternative to overcome this limitation. The qualitative modeling aspects of the MORM (man-machine occupational risk modeling) model are discussed in this article. The model, realized on an object-oriented Petri net tool (CO-OPN), has been developed to simulate and analyze industrial processes in an OH&S perspective. The industrial process is modeled as a set of interconnected subnets (state spaces), which describe its constitutive machines. Process-related factors are introduced, in an explicit way, through machine interconnections and flow properties. While man-machine interactions are modeled as triggering events for the state spaces of the machines, the CREAM cognitive behavior model is used in order to establish the relevant triggering events. In the CO-OPN formalism, the model is expressed as a set of interconnected CO-OPN objects defined over data types expressing the measure attached to the flow of entities transiting through the machines. Constraints on the measures assigned to these entities are used to determine the state changes in each machine. Interconnecting machines implies the composition of such flow and consequently the interconnection of the measure constraints. This is reflected by the construction of constraint enrichment hierarchies, which can be used for simulation and analysis optimization in a clear mathematical framework. The use of Petri nets to perform multiple-oriented analysis opens perspectives in the field of industrial risk management. It may significantly reduce the duration of the assessment process. But, most of all, it opens perspectives in the field of risk comparisons and integrated risk management. Moreover, because of the generic nature of the model and tool used, the same concepts and patterns may be used to model a wide range of systems and application fields.

Calprotectine fécale: outil diagnostique dans les maladies inflammatoires chroniques de l'intestin [Fecal calprotectin: a diagnostic tool for inflammatory bowel disease].

Fecal calprotectin (FC) is a valid biomarker to discriminate with a good sensitivity and specificity the presence of mucosal lesions of the gastrointestinal tube (e.g. ulcers in the context of inflammatory bowel disease (IBD)) from functional disorders (e.g. irritable bowel syndrome). FC is not specific for IBD and can be elevated also in gastrointestinal infections, ischemic colitis or neoplasia. An elevated FC should stimulate further investigations, notably an endoscopic workup. The level of FC correlates with the endoscopic score in Crohn's disease and ulcerative colitis. The correlation of FC and the endoscopic severity is better than the one of CRP or blood leukocytes. Thus, FC can also be used in the follow-up of IBD patients.

Simultaneous determination of Pu and Am radioisotopes in environmental samples: a tool for determining origin and release date

A radiochemical procedure was developed for the sequential determination of Pu and Am radioisotopes in environmental samples. The radioisotope activities were then used to assess the origin and release date of the environmental plutonium. The radioanalytical procedure is based on the separation of Pu and Am on selective extraction chromatographic resins (Eichrom TEVA and DGA). Alpha sources were prepared by electrodeposition on stainless steel discs, and the alpha emitting radionuclides (238Pu, 239,240Pu and 241Am) were measured by alpha spectrometry. For the determination of the beta emitting 241Pu, the Pu alpha source was leached in hot concentrated nitric acid and the Pu fraction further purified by extraction chromatography on a small column of TEVA resin (100 μg of resin in a pipette tip). 241Pu is then measured by ultra low level liquid scintillation counting. Due to the lack of reference material for 241Pu, the proposed radiochemical method was nevertheless validated using four IAEA reference sediments with information values of 241Pu. The proposed method was then used to determine the 238Pu, 239,240Pu, 241Pu and 241Am activity concentrations in alpine soils of France and Switzerland. The soil is the primary receptor of the atmospheric radioactive fallout and, because of the strong binding interaction with soils particles, the isotopes are little fractionated. Therefore, the activity ratios 241Pu/239+240Pu and 238Pu/239,240Pu in soil samples were used to determine the origin (source) and date of the Pu contamination in the investigated alpine sites. The 241Pu/239,240Pu and 238Pu/239,240Pu activity ratios confirmed that the main origin of Pu in the alpine soils was the global fallout from the nuclear bomb tests (NBT) in the fifties and sixties. Furthermore, the 241Pu/241Am activity ratios were used to determine the age of the Pu contamination, which is also an important data for distinguishing the Pu sources. The estimation of the date of the contamination, by the 241Pu/241Am age-dating method, further confirmed the NBT as the Pu source. However, the 241Pu/241Am dating method was limited to samples where Pu-Am fractionation was insignificant. If any, the contribution of the Chernobyl accident in the studied sites is negligible.

Kinetic resolution: a powerful tool for the synthesis of planar-chiral ferrocenes

Since the serendipitous discovery of ferrocene by Pauson and Kealy in 1951, it has become one of the most important structures in Organic Chemistry. Lately, kinetic resolution has emerged as a useful tool for the synthesis of planar chiral ferrocenes. This review aims to cover and discuss the development of this topic.

Auditing the management of vaccine-preventable disease outbreaks: the need for a tool

Public health activities, especially infectious disease control, depend on effective teamwork. We present the results of a pilot audit questionnaire aimed at assessing the quality of public health services in the management of VPD outbreaks. Audit questionnaire with three main areas indicators (structure, process and results) was developed. Guidelines were set and each indicator was assessed by three auditors. Differences in indicator scores according to median size of outbreaks were determined by ANOVA (significance at p (greater than or equal to) 0.05). Of 154 outbreaks; eighteen indicators had a satisfactory mean score, indicator "updated guidelines" and "timely reporting" had a poor mean score (2.84±106 and 2.44±1.67, respectively). Statistically significant differences were found according to outbreak size, in the indicators "availability of guidelines/protocol updated less than 3 years ago" (p = 0.03) and "days needed for outbreak control" (p = 0.04). Improving availability of updated guidelines, enhancing timely reporting and adequate recording of control procedures taken is needed to allow for management assessment and improvement.

Phytolith and spherulite studies of herbivores dung from the African Savannah as a tool for palaeocological reconstruction.

Los restos fecales están compuestos mayoritariamente por materia orgánica, la cual se degrada con el tiempo despareciendo finalmente del registro arqueológico. Sin embargo, estos restos fecales también contienen ciertos elementos resistentes al paso del tiempo y a los efectos postdeposicionales. Las esferulitas son cristales de carbonato cálcico formadas en los intestinos de ciertos animales herbívoros, principalmente rumiantes y que posteriormente son depositados en los restos fecales. Los fitolitos de sílice, aunque se forman en las plantas, son también comúnmente identificados en los restos fecales de animales herbívoros. Su número y morfología dependerá de la dieta vegetal de estos animales. El estudio que aquí se presenta se centra en el análisis microscópico de ambos elementos, fitolitos y esferulitas, identificados en restos fecales, de varios animales herbívoros, recolectados durante la estación seca en la Garganta de Olduvai en Tanzania. Los fitolitos y las esferulitas fueron identificados y analizados siguiendo un método morfológico y cuantitativo. Los fitolitos fueron luego comparados con una colección de referencia de plantas modernas de la misma zona geográfica con el propósito de estudiar la dieta de cada uno de los animales analizados. Finalmente los resultados fueron relacionados con los obtenidos del estudio de esferulitas, con el propósito de analizar la relación entre morfología y número de fitolitos y morfología y número de esferulitas para cada uno de los restos fecales analizados. El objetivo de este trabajo consiste en evaluar la utilidad de combinar ambas técnicas para identificar restos fecales en el registro arqueológico y, consecuentemente, responder a cuestiones relacionadas con el animal productor de estos restos, su dieta y movimientos migratorios y, paralelamente, la paleovegetación y el paleopaisaje en una región determinada.

Machine learning analysis and modeling of interest rate curves

The present research deals with the review of the analysis and modeling of Swiss franc interest rate curves (IRC) by using unsupervised (SOM, Gaussian Mixtures) and supervised machine (MLP) learning algorithms. IRC are considered as objects embedded into different feature spaces: maturities; maturity-date, parameters of Nelson-Siegel model (NSM). Analysis of NSM parameters and their temporal and clustering structures helps to understand the relevance of model and its potential use for the forecasting. Mapping of IRC in a maturity-date feature space is presented and analyzed for the visualization and forecasting purposes.

Development of a GPS-Based Sign Inventory Tool for Iowa’s Statewide Management System, May 2011

This report is on state-of-the-art research efforts specific to infrastructure inventory/data collection with sign inventory as a case study. The development of an agency-wide sign inventory is based on feature inventory and location information. Specific to location, a quick and simple location acquisition tool is critical to tying assets to an accurate location-referencing system. This research effort provides a contrast between legacy referencing systems (route and milepost) and global positioning system- (GPS-) based techniques (latitude and longitude) integrated into a geographic information system (GIS) database. A summary comparison of field accuracies using a variety of consumer grade devices is also provided. This research, and the data collection tools developed, are critical in supporting the Iowa Department of Transportation (DOT) Statewide Sign Management System development effort. For the last two years, a Task Force has embarked on a comprehensive effort to develop a sign management system to improve sign quality, as well as to manage all aspects of signage, from request, ordering, fabricating, installing, maintaining, and ultimately removing, and to provide the ability to budget for these key assets on a statewide basis. This effort supported the development of a sign inventory tool and is the beginning of the development of a sign management system to support the Iowa DOT efforts in the consistent, cost effective, and objective decision making process when it comes to signs and their maintenance.

Compliance Monitoring Report Technical Assistance Tool, 2008

This form requests the minimum information required to demonstrate the extent of compliance with section 223(a)(11), 223(a)(12), 223(a)(13), 223(a)(14) of the JJDP Act of 2002. Many of these reporting requirements may be found in 28 C.F.R. 31.303(f)(5).

Compliance Monitoring Report Technical Assistance Tool, 2009

This form requests the minimum information required to demonstrate the extent of compliance with section 223(a)(11), 223(a)(12), 223(a)(13), 223(a)(14) of the JJDP Act of 2002. Many of these reporting requirements may be found in 28 C.F.R. 31.303(f)(5).

Advanced geostatistical and machine-learning models for spatial data analysis of radioactively contaminated regions

Radioactive soil-contamination mapping and risk assessment is a vital issue for decision makers. Traditional approaches for mapping the spatial concentration of radionuclides employ various regression-based models, which usually provide a single-value prediction realization accompanied (in some cases) by estimation error. Such approaches do not provide the capability for rigorous uncertainty quantification or probabilistic mapping. Machine learning is a recent and fast-developing approach based on learning patterns and information from data. Artificial neural networks for prediction mapping have been especially powerful in combination with spatial statistics. A data-driven approach provides the opportunity to integrate additional relevant information about spatial phenomena into a prediction model for more accurate spatial estimates and associated uncertainty. Machine-learning algorithms can also be used for a wider spectrum of problems than before: classification, probability density estimation, and so forth. Stochastic simulations are used to model spatial variability and uncertainty. Unlike regression models, they provide multiple realizations of a particular spatial pattern that allow uncertainty and risk quantification. This paper reviews the most recent methods of spatial data analysis, prediction, and risk mapping, based on machine learning and stochastic simulations in comparison with more traditional regression models. The radioactive fallout from the Chernobyl Nuclear Power Plant accident is used to illustrate the application of the models for prediction and classification problems. This fallout is a unique case study that provides the challenging task of analyzing huge amounts of data ('hard' direct measurements, as well as supplementary information and expert estimates) and solving particular decision-oriented problems.