Growing The Fields of Opportunity for Children and Adults with Autism in Iowa, 2009-2010

Many would argue that the dramatic rise in autism has reached critical mass, and this council echoes that statement. Iowa, like many states in the nation, is currently ill equipped to handle the large influx of children and adults with autism. When this council was initially formed we were facing diagnosis rates of 1 in 150 and currently the diagnosis rate is 1 in 91. Current resource strains in education, qualified trained professionals, access to care, and financial services are rapidly deteriorating Iowa’s ability to deliver quality services to children, adults, and families affected by autism. If Iowa leadership fails to act quickly the already strained system will face a breaking point in the following areas: financing, coordination of care, educational resources, early identification, adult services, and access to service delivery - just to name a few. This council has taken the past 12 plus months hearing testimony from state officials, providers, and caregivers to ensure that care for those with autism is effective, cost efficient, and accessible. This council will be making recommendations on three major areas; early identification, seamless support/coordination of care, and financing of care. While these areas will be highlighted in this first annual report it in no way minimizes other areas that need to be addressed such as early intervention, special education, training, in-home support services, financing options, and data collection. Implementing the initial recommendations of this council will lay foundational support for the areas mentioned above. Often those in position to help ask what can be done to help families in Iowa. This council has provided a roadmap to help facilitate effective and proven treatments to children and adults with autism.

Monitoring network optimisation for spatial data classification using support vector machines

The paper presents a novel method for monitoring network optimisation, based on a recent machine learning technique known as support vector machine. It is problem-oriented in the sense that it directly answers the question of whether the advised spatial location is important for the classification model. The method can be used to increase the accuracy of classification models by taking a small number of additional measurements. Traditionally, network optimisation is performed by means of the analysis of the kriging variances. The comparison of the method with the traditional approach is presented on a real case study with climate data.

Individual prediction of cognitive decline in mild cognitive impairment using support vector machine-based analysis of diffusion tensor imaging data.

Although cross-sectional diffusion tensor imaging (DTI) studies revealed significant white matter changes in mild cognitive impairment (MCI), the utility of this technique in predicting further cognitive decline is debated. Thirty-five healthy controls (HC) and 67 MCI subjects with DTI baseline data were neuropsychologically assessed at one year. Among them, there were 40 stable (sMCI; 9 single domain amnestic, 7 single domain frontal, 24 multiple domain) and 27 were progressive (pMCI; 7 single domain amnestic, 4 single domain frontal, 16 multiple domain). Fractional anisotropy (FA) and longitudinal, radial, and mean diffusivity were measured using Tract-Based Spatial Statistics. Statistics included group comparisons and individual classification of MCI cases using support vector machines (SVM). FA was significantly higher in HC compared to MCI in a distributed network including the ventral part of the corpus callosum, right temporal and frontal pathways. There were no significant group-level differences between sMCI versus pMCI or between MCI subtypes after correction for multiple comparisons. However, SVM analysis allowed for an individual classification with accuracies up to 91.4% (HC versus MCI) and 98.4% (sMCI versus pMCI). When considering the MCI subgroups separately, the minimum SVM classification accuracy for stable versus progressive cognitive decline was 97.5% in the multiple domain MCI group. SVM analysis of DTI data provided highly accurate individual classification of stable versus progressive MCI regardless of MCI subtype, indicating that this method may become an easily applicable tool for early individual detection of MCI subjects evolving to dementia.

Fast Geodesic Active Fields for Image Registration Based on Splitting and Augmented Lagrangian Approaches.

In this paper, we present an efficient numerical scheme for the recently introduced geodesic active fields (GAF) framework for geometric image registration. This framework considers the registration task as a weighted minimal surface problem. Hence, the data-term and the regularization-term are combined through multiplication in a single, parametrization invariant and geometric cost functional. The multiplicative coupling provides an intrinsic, spatially varying and data-dependent tuning of the regularization strength, and the parametrization invariance allows working with images of nonflat geometry, generally defined on any smoothly parametrizable manifold. The resulting energy-minimizing flow, however, has poor numerical properties. Here, we provide an efficient numerical scheme that uses a splitting approach; data and regularity terms are optimized over two distinct deformation fields that are constrained to be equal via an augmented Lagrangian approach. Our approach is more flexible than standard Gaussian regularization, since one can interpolate freely between isotropic Gaussian and anisotropic TV-like smoothing. In this paper, we compare the geodesic active fields method with the popular Demons method and three more recent state-of-the-art algorithms: NL-optical flow, MRF image registration, and landmark-enhanced large displacement optical flow. Thus, we can show the advantages of the proposed FastGAF method. It compares favorably against Demons, both in terms of registration speed and quality. Over the range of example applications, it also consistently produces results not far from more dedicated state-of-the-art methods, illustrating the flexibility of the proposed framework.

Temporal changes in mosquito abundance (Culex pipiens), avian malaria prevalence and lineage composition

Background: Knowledge on the temporal dynamics of host/vector/parasite interactions is a pre-requisite to further address relevant questions in the fields of epidemiology and evolutionary ecology of infectious diseases. In studies of avian malaria, the natural history of Plasmodium parasites with their natural mosquito vectors, however, is mostly unknown. Methods: Using artificial water containers placed in the field, we monitored the relative abundance of parous females of Culex pipiens mosquitoes during two years (2010-2011), in a population in western Switzerland. Additionally, we used molecular tools to examine changes in avian malaria prevalence and Plasmodium lineage composition in female C. pipiens caught throughout one field season (April-August) in 2011. Results: C. pipiens relative abundance varied both between years and months, and was associated with temperature fluctuations. Total Plasmodium prevalence was high and increased from spring to summer months (13.1-20.3%). The Plasmodium community was composed of seven different lineages including P. relictum (SGS1, GRW11 and PADOM02 lineages), P. vaughani (lineage SYAT05) and other Plasmodium spp. (AFTRU5, PADOM1, COLL1). The most prevalent lineages, P. vaughani (lineage SYAT05) and P. relictum (lineage SGS1), were consistently found between years, although they had antagonistic dominance patterns during the season survey. Conclusions: Our results suggest that the time window of analysis is critical in evaluating changes in the community of avian malaria lineages infecting mosquitoes. The potential determinants of the observed changes as well as their implications for future prospects on avian malaria are discussed.

Optimal priming of poxvirus vector (NYVAC)-based HIV vaccine regimens requires 3 DNA injections. Results of the EV03/ANRS Vac20 Phase I/II Trial

Background: Two or three DNA primes have been used in previous smaller clinical trials, but the number required for optimal priming of viral vectors has never been assessed in adequately powered clinical trials. The EV03/ANRS Vac20 phase I/II trial investigated this issue using the DNA prime/poxvirus NYVAC boost combination, both expressing a common HIV-1 clade C immunogen consisting of Env and Gag-Pol-Nef polypeptide. Methods: 147 healthy volunteers were randomly allocated through 8 European centres to either 3xDNA plus 1xNYVAC (weeks 0, 4, 8 plus 24; n¼74) or to 2xDNA plus 2xNYVAC (weeks 0, 4 plus 20, 24; n¼73), stratified by geographical region and sex. T cell responses were quantified using the interferon g Elispot assay and 8 peptide pools; samples from weeks 0, 26 and 28 (time points for primary immunogenicity endpoint), 48 and 72 were considered for this analysis. Results: 140 of 147 participants were evaluable at weeks 26 and/ or 28. 64/70 (91%) in the 3xDNA arm compared to 56/70 (80%) in the 2xDNA arm developed a T cell response (P¼0.053). 26 (37%) participants of the 3xDNA arm developed a broader T cell response (Env plus at least to one of the Gag, Pol, Nef peptide pools) versus 15 (22%) in the 2xDNA arm (P¼0.047). At week 26, the overall magnitude of responses was also higher in the 3xDNA than in the 2xDNA arm (similar at week 28), with a median of 545 versus 328 SFUs/106 cells at week 26 (P<0.001). Preliminary overall evaluation showed that participants still developed T-cell response at weeks 48 (78%, n¼67) and 72 (70%, n¼66). Conclusion: This large clinical trial demonstrates that optimal priming of poxvirus-based vaccine regimens requires 3 DNA regimens and further confirms that the DNA/NYVAC prime boost vaccine combination is highly immunogenic and induced durable T-cell responses.

Nutritional status in commercial currant fields

Selostus: Herukkaviljelmien ravinnetila

Weeds in spring cereal fields in Finland - a third survey

Selostus: Kolmas kevätviljapeltojen rikkakasvikartoitus

Ensemble methods for environmental data modelling with support vector regression

This paper investigates the use of ensemble of predictors in order to improve the performance of spatial prediction methods. Support vector regression (SVR), a popular method from the field of statistical machine learning, is used. Several instances of SVR are combined using different data sampling schemes (bagging and boosting). Bagging shows good performance, and proves to be more computationally efficient than training a single SVR model while reducing error. Boosting, however, does not improve results on this specific problem.

Poxvirus vector-based HIV vaccines.

PURPOSE OF REVIEW: In this review, we will provide the scientific rationale for the use of poxvirus vectors in the field of HIV vaccines, the immunological profile of the vaccine-induced immune responses, an update on the current use of poxvirus vector-based vaccines in HIV vaccine clinical trials, and the development of new modified poxvirus vectors with improved immunological profile. RECENT FINDINGS: An Ad5-HIV vaccine was tested in a phase IIb clinical trial (known as the Step trial). Vaccinations in the Step trial were discontinued because the vaccine did not show any effect on acquisition of infection and on viral load. After the disappointing failure of the Step trial, the field of HIV vaccine has regained enthusiasm and vigour due to the promising protective effect observed in the phase III efficacy trial (known as RV-144) performed in Thailand which has tested a poxvirus-gp120 combination. SUMMARY: The RV-144 phase III has provided for the first time evidence that an HIV vaccine can prevent HIV infection. The results from the RV-144 trial are providing the scientific rationale for the future development of the HIV vaccine field and for designing future efficacy trials.

Stress intensity factor of wood from crack-tip displacement fields obtained from digital image processing

Abstract