Registry of non-native species in the Two Seas region countries (Great Britain, France, Belgium and the Netherlands)

This dataset represents a registry of species that are not native but recorded to live in the wild of at least one of the four countries that comprise the Two Seas Area, i.e. Great Britain, France, Belgium and the Netherlands. For each of the 6,661 species, subspecies and hybrids listed, we provide detailed information on its status in each country, taxonomic affiliation and environment inhabited. The data were collected by review of 36 web- and print-based sources over an eight-month period. Further systematic scanning of three of the most relevant scientific journals, i.e. Neobiota, Aquatic Invasions and BioInvasions Records, recovered 19 additional relevant publications from which information was included in the registry. As a result, the registry will serve as a basis for developing effective, cross-boundary strategies to manage and control non-native species, which can have severe ecological and economic impacts. The registry can further be used as a general reference for both scientists and practitioners, as well as a tool to assess reliability and comprehensiveness of other well-known databases such as the DAISIE portal

Development of an empirically based dynamic biomechanical strength model

The focus here is on the development of a dynamic strength model for humans. Our model is based on empirical data. The shoulder, elbow, and wrist joints are characterized in terms of maximum isolated torque, position, and velocity in all rotational planes. This information is reduced by a least squares regression technique into a table of single variable second degree polynomial equations determining the torque as a function of position and velocity. The isolated joint torque equations are then used to compute forces resulting from a composite motion, which in this case is a ratchet wrench push and pull operation. What is presented here is a comparison of the computed or predicted results of the model with the actual measured values for the composite motion

Tools to develop antibiotic combinations that target drug tolerance in Mycobacterium tuberculosis

Combination therapy is necessary to treat tuberculosis to decrease the rate of disease relapse and prevent the acquisition of drug resistance, and shorter regimens are urgently needed. The adaptation of Mycobacterium tuberculosis to various lesion microenvironments in infection induces various states of slow replication and non-replication and subsequent antibiotic tolerance. This non-heritable tolerance to treatment necessitates lengthy combination therapy. Therefore, it is critical to develop combination therapies that specifically target the different types of drug-tolerant cells in infection. As new tools to study drug combinations earlier in the drug development pipeline are being actively developed, we must consider how to best model the drug-tolerant cells to use these tools to design the best antibiotic combinations that target those cells and shorten tuberculosis therapy. In this review, we discuss the factors underlying types of drug tolerance, how combination therapy targets these populations of bacteria, and how drug tolerance is currently modeled for the development of tuberculosis multidrug therapy. We highlight areas for future studies to develop new tools that better model drug tolerance in tuberculosis infection specifically for combination therapy testing to bring the best drug regimens forward to the clinic

The Knowledge Filter and Economic Growth: The Role of Scientist Entrepreneurship

Assesses the prevalence of and trends in the commercialization of research by university scientists funded by the National Cancer Institute. Analyzes levels of entrepreneurship in patenting choices and the role of university technology transfer offices

The Knowledge Filter and Economic Growth: The Role of Scientist Entrepreneurship

Assesses the prevalence of and trends in the commercialization of research by university scientists funded by the National Cancer Institute. Analyzes levels of entrepreneurship in patenting choices and the role of university technology transfer offices

Large-Scale Integration of Nanoelectromechanical Systems for Gas Sensing Applications

We have developed arrays of nanomechanical systems (NEMS) by large-scale integration, comprising thousands of individual nanoresonators with densities of up to 6 million NEMS per square centimeter. The individual NEMS devices are electrically coupled using a combined series-parallel configuration that is extremely robust with respect to lithographical defects and mechanical or electrostatic-discharge damage. Given the large number of connected nanoresonators, the arrays are able to handle extremely high input powers (>1 W per array, corresponding to <1 mW per nanoresonator) without excessive heating or deterioration of resonance response. We demonstrate the utility of integrated NEMS arrays as high-performance chemical vapor sensors, detecting a part-per-billion concentration of a chemical warfare simulant within only a 2 s exposure period

Fuzzy Description of Skin Lesions

We propose a system for describing skin lesions images based on a human perception model. Pigmented skin lesions including melanoma and other types of skin cancer as well as non-malignant lesions are used. Works on classification of skin lesions already exist but they mainly concentrate on melanoma. The novelty of our work is that our system gives to skin lesion images a semantic label in a manner similar to humans. This work consists of two parts: first we capture they way users perceive each lesion, second we train a machine learning system that simulates how people describe images. For the first part, we choose 5 attributes: colour (light to dark), colour uniformity (uniform to non-uniform), symmetry (symmetric to non-symmetric), border (regular to irregular), texture (smooth to rough). Using a web based form we asked people to pick a value of each attribute for each lesion. In the second part, we extract 93 features from each lesions and we trained a machine learning algorithm using such features as input and the values of the human attributes as output. Results are quite promising, especially for the colour related attributes, where our system classifies over 80 % of the lesions into the same semantic classes as humans

Nanoelectromechanical Resonator Arrays for Ultrafast, Gas-Phase Chromatographic Chemical Analysis

Miniaturized gas chromatography (GC) systems can provide fast, quantitative analysis of chemical vapors in an ultrasmall package. We describe a chemical sensor technology based on resonant nanoelectromechanical systems (NEMS) mass detectors that provides the speed, sensitivity, specificity, and size required by the microscale GC paradigm. Such NEMS sensors have demonstrated detection of subparts per billion (ppb) concentrations of a phosphonate analyte. By combining two channels of NEMS detection with an ultrafast GC front-end, chromatographic analysis of 13 chemicals was performed within a 5 s time window