Mosquito surveillance for West Nile virus in Connecticut, 2000: isolation from Culex pipiens, Cx. restuans, Cx. salinarius, and Culiseta melanura.

Fourteen isolations of West Nile (WN) virus were obtained from four mosquito species (Culex pipiens [5], Cx. restuans [4], Cx. salinarius [2], and Culiseta melanura [3]) in statewide surveillance conducted from June through October 2000. Most isolates were obtained from mosquitoes collected in densely populated residential locales in Fairfield and New Haven counties, where the highest rates of dead crow sightings were reported and where WN virus was detected in 1999. Minimum field infection rates per 1,000 mosquitoes ranged from 0.5 to 1.8 (county based) and from 1.3 to 76.9 (site specific). Cx. restuans appears to be important in initiating WN virus transmission among birds in early summer; Cx. pipiens appears to play a greater role in amplifying virus later in the season. Cs. melanura could be important in the circulation of WN virus among birds in sylvan environments; Cx. salinarius is a suspected vector of WN virus to humans and horses

Nucleon to Delta Weak Excitation Amplitudes in the Non-relativistic Quark Model

We investigate the nucleon to Delta(1232) vector and axial vector amplitudes in the non-relativistic quark model of the Isgur-Karl variety. A particular interest is to investigate the SU(6) symmetry breaking, due to color hyperfine interaction. We compare the theoretical estimates to recent experimental investigation of the Adler amplitudes by neutrino scattering.Comment: \documentstyle[aps]{revtex}, 21pages; 11 postscript figures. Accepted for publication by Phys. Rev.

Verified and potential pathogens of predatory mites (Acari: Phytoseiidae)

Several species of phytoseiid mites (Acari: Phytoseiidae), including species of the genera Amblyseius, Galendromus, Metaseiulus, Neoseiulus, Phytoseiulus and Typhlodromus, are currently reared for biological control of various crop pests and/or as model organisms for the study of predator¿prey interactions. Pathogen-free phytoseiid mites are important to obtain high efficacy in biological pest control and to get reliable data in mite research, as pathogens may affect the performance of their host or alter their reproduction and behaviour. Potential and verified pathogens have been reported for phytoseiid mites during the past 25 years. The present review provides an overview, including potential pathogens with unknown host effects (17 reports), endosymbiotic Wolbachia (seven reports), other bacteria (including Cardinium and Spiroplasma) (four reports), cases of unidentified diseases (three reports) and cases of verified pathogens (six reports). From the latter group four reports refer to Microsporidia, one to a fungus and one to a bacterium. Only five entities have been studied in detail, including Wolbachia infecting seven predatory mite species, other endosymbiotic bacteria infecting Metaseiulus (Galendromus, Typhlodromus) occidentalis (Nesbitt), the bacterium Acaricomes phytoseiuli infecting Phytoseiulus persimilis Athias-Henriot, the microsporidium Microsporidium phytoseiuli infecting P. persimilis and the microsporidium Oligosproridium occidentalis infecting M. occidentalis. In four cases (Wolbachia, A. phytoseiuli, M. phytoseiuli and O. occidentalis) an infection may be connected with fitness costs of the host. Moreover, infection is not always readily visible as no obvious gross symptoms are present. Monitoring of these entities on a routine and continuous basis should therefore get more attention, especially in commercial mass-production. Special attention should be paid to field-collected mites before introduction into the laboratory or mass rearing, and to mites that are exchanged among rearing facilities. However, at present general pathogen monitoring is not yet practical as effects of many entities are unknown. More research effort is needed concerning verified and potential pathogens of commercially reared arthropods and those used as model organisms in research

Human adipose stem cells cell sheet constructs impact epidermal morphogenesis in full-thickness excisional wounds

Among the wide range of strategies to target skin repair/regeneration, tissue engineering (TE) with stem cells at the forefront, remains as the most promising route. Cell sheet (CS) engineering is herein proposed, taking advantage of particular cell-cell and cell-extracellular matrix (ECM) interactions and subsequent cellular milieu, to create 3D TE constructs to promote full-thickness skin wound regeneration. Human adipose derived stem cells (hASCs) CS were obtained within five days using both thermoresponsive and standard cell culture surfaces. hASCs-based constructs were then built by superimposing three CS and transplanted into full-thickness excisional mice skin wounds with delayed healing. Constructs obtained using thermoresponsive surfaces were more stable than the ones from standard cell culture surfaces due to the natural adhesive character of the respective CS. Both CS-generating strategies lead to prolonged hASCs engraftment, although no transdifferentiation phenomena were observed. Moreover, our findings suggest that the transplanted hASCs might be promoting neotissue vascularization and extensively influencing epidermal morphogenesis, mainly through paracrine actions with the resident cells. The thicker epidermis, with a higher degree of maturation characterized by the presence of rete ridges-like structures, as well as a significant number of hair follicles observed after transplantation of the constructs combining the CS obtained from the thermoresponsive surfaces, reinforced the assumptions of the influence of the transplanted hASCs and the importance of the higher stability of these constructs promoted by cohesive cell-cell and cell-ECM interactions. Overall, this study confirmed the potential of hASCs CS-based constructs to treat full-thickness excisional skin wounds and that their fabrication conditions impact different aspects of skin regeneration, such as neovascularisation, but mainly epidermal morphogenesis.We would like to thank Hospital da Prelada (Porto), in particular, to Dr. Paulo Costa for the lipoaspirates collection and for financial support by Skingineering (PTDC/SAU-OSM/099422/2008), Portuguese Foundation for Science and Technology (FCT) funded project. The research leading to these results has also received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under Grant Agreement No. REGPOT-CT2012-316331-POLARIS

Morea: A GPU-accelerated evolutionary algorithm for multi-objective deformable registration of 3d medical images

Finding a realistic deformation that transforms one image into another, in case large deformations are required, is considered a key challenge in medical image analysis. Having a proper image registration approach to achieve this could unleash a number of applications requiring information to be transferred between images. Clinical adoption is currently hampered by many existing methods requiring extensive configuration effort before each use, or not being able to (realistically) capture large deformations. A recent multi-objective approach that uses the Multi-Objective Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm (MO-RV-GOMEA) and a dual-dynamic mesh transformation model has shown promise, exposing the trade-offs inherent to image registration problems and modeling large deformations in 2D. This work builds on this promise and introduces MOREA: the first evolutionary algorithm-based multi-objective approach to deformable registration of 3D images capable of tackling large deformations. MOREA includes a 3D biomechanical mesh model for physical plausibility and is fully GPU-accelerated. We compare MOREA to two state-of-the-art approaches on abdominal CT scans of 4 cervical cancer patients, with the latter two approaches configured for the best results per patient. Without requiring per-patient configuration, MOREA significantly outperforms these approaches on 3 of the 4 patients that represent the most difficult cases

Multi-objective dual simplex-mesh based deformable image registration for 3D medical images – proof of concept

Reliably and physically accurately transferring information between images through deformable image registration with large anatomical differences is an open challenge in medical image analysis. Most existing methods have two key shortcomings: first, they require extensive up-front parameter tuning to each specific registration problem, and second, they have difficulty capturing large deformations and content mismatches between images. There have however been developments that have laid the foundation for potential solutions to both shortcomings. Towards the first shortcoming, a multi-objective optimization approach using the Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm (RV-GOMEA) has been shown to be capable of producing a diverse set of registrations for 2D images in one run of the algorithm, representing different trade-offs between conflicting objectives in the registration problem. This allows the user to select a registration afterwards and removes the need for up-front tuning. Towards the second shortcoming, a dual-dynamic grid transformation model has proven effective at capturing large differences in 2D images. These two developments have recently been accelerated through GPU parallelization, delivering large speed-ups. Based on this accelerated version, it is now possible to extend the approach to 3D images. Concordantly, this work introduces the first method for multi-objective 3D deformable image registration, using a 3D dual-dynamic grid transformation model based on simplex meshes while still supporting the incorporation of annotated guidance information and multi-resolution schemes. Our proof-of-concept prototype shows promising results on synthetic and clinical 3D registration problems, forming the foundation for a new, insightful method that can include bio-mechanical properties in the registration

Fitness-based linkage learning and maximum-clique conditional linkage modelling for gray-box optimization with RV-GOMEA

For many real-world optimization problems it is possible to perform partial evaluations, meaning that the impact of changing a few variables on a solution's fitness can be computed very efficiently. It has been shown that such partial evaluations can be excellently leveraged by the Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm (RV-GOMEA) that uses a linkage model to capture dependencies between problem variables. Recently, conditional linkage models were introduced for RV-GOMEA, expanding its state-of-the-art performance even to problems with overlapping dependencies. However, that work assumed that the dependency structure is known a priori. Fitness-based linkage learning techniques have previously been used to detect dependencies during optimization, but only for non-conditional linkage models. In this work, we combine fitness-based linkage learning and conditional linkage modelling in RV-GOMEA. In addition, we propose a new way to model overlapping dependencies in conditional linkage models to maximize the joint sampling of fully interdependent groups of variables. We compare the resulting novel variant of RV-GOMEA to other variants of RV-GOMEA and VkD-CMA on 12 problems with varying degree of overlapping dependencies. We find that the new RV-GOMEA not only performs best on most problems, also the overhead of learning the conditional linkage models during optimization is often negligible