Sinanodonta woodiana (mollusca: bivalvia: unionidae): isolation and characterization of the first microsatellite markers

Sinanodonta woodiana (Lea, 1834) is a large Unionid species with a real invasion success. It colonized Europe, Central America, the Indonesian Islands and recently North America. The species life cycle involves a larval parasitic stage on freshwater fish species which contributes to the spread of the mussel. In this paper we describe, for the first time, eight polymorphic microsatellite loci for the species Sinanodonta woodiana. The genetic screening of individuals confirmed that all loci were highly polymorphic. The number of alleles per locus ranged from 7 to 14 and the observed heterozygosity ranged from 0.650 to 0.950. These loci should prove useful to study the species population genetics which could help to infer important aspects of the invasion process.     

Resident Memory T Cells and Their Role within the Liver

Immunological memory is fundamental to maintain immunity against re-invading pathogens. It is the basis for prolonged protection induced by vaccines and can be mediated by humoral or cellular responses—the latter largely mediated by T cells. Memory T cells belong to different subsets with specialized functions and distributions within the body. They can be broadly separated into circulating memory cells, which pace the entire body through the lymphatics and blood, and tissue-resident memory T (T_RM) cells, which are constrained to peripheral tissues. Retained in the tissues where they form, T_RM cells provide a frontline defense against reinfection. Here, we review this population of cells with specific attention to the liver, where T_RM cells have been found to protect against infections, in particular those by Plasmodium species that cause malaria.     

Energy efficiency improvements of post-combustion CO2 capture based on reactive gas–liquid absorption applied for super-critical circulating fluidized bed combustion (CFBC) power plants

Clean Technologies and Environmental Policy - The environmental impact represents a significant constraint for fossil fuel-intensive industrial processes in transition to a low-carbon scenario....     

Soft object deformation monitoring and learning for model-based robotic hand manipulation

This paper discusses the design and implementation of a framework that automatically extracts and monitors the shape deformations of soft objects from a video sequence and maps them with force measurements with the goal of providing the necessary information to the controller of a robotic hand to ensure safe model-based deformable object manipulation. Measurements corresponding to the interaction force at the level of the fingertips and to the position of the fingertips of a three-finger robotic hand are associated with the contours of a deformed object tracked in a series of images using neural-network approaches. The resulting model captures the behavior of the object and is able to predict its behavior for previously unseen interactions without any assumption on the object's material. The availability of such models can contribute to the improvement of a robotic hand controller, therefore allowing more accurate and stable grasp while providing more elaborate manipulation capabilities for deformable objects. Experiments performed for different objects, made of various materials, reveal that the method accurately captures and predicts the object's shape deformation while the object is submitted to external forces applied by the robot fingers. The proposed method is also fast and insensitive to severe contour deformations, as well as to smooth changes in lighting, contrast, and background.     

Unsupervised named-entity extraction from the Web: An experimental study

The KnowItAll system aims to automate the tedious process of extracting large collections of facts (e.g., names of scientists or politicians) from the Web in an unsupervised, domain-independent, and scalable manner. The paper presents an overview of KnowItAll's novel architecture and design principles, emphasizing its distinctive ability to extract information without any hand-labeled training examples. In its first major run, KnowItAll extracted over 50,000 class instances, but suggested a challenge: How can we improve KnowItAll's recall and extraction rate without sacrificing precision?This paper presents three distinct ways to address this challenge and evaluates their performance. Pattern Learning learns domain-specific extraction rules, which enable additional extractions. Subclass Extraction automatically identifies sub-classes in order to boost recall (e.g., “chemist” and “biologist” are identified as sub-classes of “scientist”). List Extraction locates lists of class instances, learns a “wrapper” for each list, and extracts elements of each list. Since each method bootstraps from KnowItAll's domain-independent methods, the methods also obviate hand-labeled training examples. The paper reports on experiments, focused on building lists of named entities, that measure the relative efficacy of each method and demonstrate their synergy. In concert, our methods gave KnowItAll a 4-fold to 8-fold increase in recall at precision of 0.90, and discovered over 10,000 cities missing from the Tipster Gazetteer.     

On sovereign credit migration: A study of alternative estimators and rating dynamics

Different estimators of rating transition matrices have been proposed in the literature but their behaviour has been studied mainly in the context of corporate ratings. The finite-sample bias and variability of three sovereign credit migration estimators is investigated through bootstrap simulations. These are a discrete multinomial estimator and two continuous-time hazard rate methods, one of which neglects time heterogeneity in the rating process whereas the other accounts for it. Panel logit models and spectral analysis are utilized to study the properties of the rating process. The sample consists of Moody's ratings 1981-2004 for 72 industrialized and emerging economies. Hazard rate estimators yield more accurate default probabilities. The time homogeneity assumption leads to underestimating the default probability and greater migration risk is inferred upon relaxing it. There is evidence of duration dependence and downgrade momentum effects in the rating process. These findings have important implications for economic and regulatory capital allocation and for the pricing of credit sensitive instruments.