Steady-state equilibrium phase inversion recovery ON-resonant water suppression (IRON) MR angiography in conjunction with superparamagnetic nanoparticles. A robust technique for imaging within a wide range of contrast agent dosages.

PURPOSE: To investigate the ability of inversion recovery ON-resonant water suppression (IRON) in conjunction with P904 (superparamagnetic nanoparticles which consisting of a maghemite core coated with a low-molecular-weight amino-alcohol derivative of glucose) to perform steady-state equilibrium phase MR angiography (MRA) over a wide dose range. MATERIALS AND METHODS: Experiments were approved by the institutional animal care committee. Rabbits (n = 12) were imaged at baseline and serially after the administration of 10 incremental dosages of 0.57-5.7 mgFe/Kg P904. Conventional T1-weighted and IRON MRA were obtained on a clinical 1.5 Tesla (T) scanner to image the thoracic and abdominal aorta, and peripheral vessels. Contrast-to-noise ratios (CNR) and vessel sharpness were quantified. RESULTS: Using IRON MRA, CNR and vessel sharpness progressively increased with incremental dosages of the contrast agent P904, exhibiting constantly higher contrast values than T1 -weighted MRA over a very wide range of contrast agent doses (CNR of 18.8 ± 5.6 for IRON versus 11.1 ± 2.8 for T1 -weighted MRA at 1.71 mgFe/kg, P = 0.02 and 19.8 ± 5.9 for IRON versus -0.8 ± 1.4 for T1-weighted MRA at 3.99 mgFe/kg, P = 0.0002). Similar results were obtained for vessel sharpness in peripheral vessels, (Vessel sharpness of 46.76 ± 6.48% for IRON versus 33.20 ± 3.53% for T1-weighted MRA at 1.71 mgFe/Kg, P = 0.002, and of 48.66 ± 5.50% for IRON versus 19.00 ± 7.41% for T1-weighted MRA at 3.99 mgFe/Kg, P = 0.003). CONCLUSION: Our study suggests that quantitative CNR and vessel sharpness after the injection of P904 are consistently higher for IRON MRA when compared with conventional T1-weighted MRA. These findings apply for a wide range of contrast agent dosages.

Robust volume-targeted balanced steady-state free-precession coronary magnetic resonance angiography in a breathhold at 3.0 Tesla: a reproducibility study.

BACKGROUND: Transient balanced steady-state free-precession (bSSFP) has shown substantial promise for noninvasive assessment of coronary arteries but its utilization at 3.0 T and above has been hampered by susceptibility to field inhomogeneities that degrade image quality. The purpose of this work was to refine, implement, and test a robust, practical single-breathhold bSSFP coronary MRA sequence at 3.0 T and to test the reproducibility of the technique. METHODS: A 3D, volume-targeted, high-resolution bSSFP sequence was implemented. Localized image-based shimming was performed to minimize inhomogeneities of both the static magnetic field and the radio frequency excitation field. Fifteen healthy volunteers and three patients with coronary artery disease underwent examination with the bSSFP sequence (scan time = 20.5 ± 2.0 seconds), and acquisitions were repeated in nine subjects. The images were quantitatively analyzed using a semi-automated software tool, and the repeatability and reproducibility of measurements were determined using regression analysis and intra-class correlation coefficient (ICC), in a blinded manner. RESULTS: The 3D bSSFP sequence provided uniform, high-quality depiction of coronary arteries (n = 20). The average visible vessel length of 100.5 ± 6.3 mm and sharpness of 55 ± 2% compared favorably with earlier reported navigator-gated bSSFP and gradient echo sequences at 3.0 T. Length measurements demonstrated a highly statistically significant degree of inter-observer (r = 0.994, ICC = 0.993), intra-observer (r = 0.894, ICC = 0.896), and inter-scan concordance (r = 0.980, ICC = 0.974). Furthermore, ICC values demonstrated excellent intra-observer, inter-observer, and inter-scan agreement for vessel diameter measurements (ICC = 0.987, 0.976, and 0.961, respectively), and vessel sharpness values (ICC = 0.989, 0.938, and 0.904, respectively). CONCLUSIONS: The 3D bSSFP acquisition, using a state-of-the-art MR scanner equipped with recently available technologies such as multi-transmit, 32-channel cardiac coil, and localized B0 and B1+ shimming, allows accelerated and reproducible multi-segment assessment of the major coronary arteries at 3.0 T in a single breathhold. This rapid sequence may be especially useful for functional imaging of the coronaries where the acquisition time is limited by the stress duration and in cases where low navigator-gating efficiency prohibits acquisition of a free breathing scan in a reasonable time period.

Cross-presenting human gammadelta T cells induce robust CD8+ alphabeta T cell responses.

Gammadelta T cells are implicated in host defense against microbes and tumors but their mode of function remains largely unresolved. Here, we have investigated the ability of activated human Vgamma9Vdelta2(+) T cells (termed gammadelta T-APCs) to cross-present microbial and tumor antigens to CD8(+) alphabeta T cells. Although this process is thought to be mediated best by DCs, adoptive transfer of ex vivo antigen-loaded, human DCs during immunotherapy of cancer patients has shown limited success. We report that gammadelta T-APCs take up and process soluble proteins and induce proliferation, target cell killing and cytokine production responses in antigen-experienced and naïve CD8(+) alphabeta T cells. Induction of APC functions in Vgamma9Vdelta2(+) T cells was accompanied by the up-regulation of costimulatory and MHC class I molecules. In contrast, the functional predominance of the immunoproteasome was a characteristic of gammadelta T cells irrespective of their state of activation. Gammadelta T-APCs were more efficient in antigen cross-presentation than monocyte-derived DCs, which is in contrast to the strong induction of CD4(+) alphabeta T cell responses by both types of APCs. Our study reveals unexpected properties of human gammadelta T-APCs in the induction of CD8(+) alphabeta T effector cells, and justifies their further exploration in immunotherapy research.

Accelerated Microstructure Imaging via Convex Optimization (AMICO) from diffusion MRI data.

Microstructure imaging from diffusion magnetic resonance (MR) data represents an invaluable tool to study non-invasively the morphology of tissues and to provide a biological insight into their microstructural organization. In recent years, a variety of biophysical models have been proposed to associate particular patterns observed in the measured signal with specific microstructural properties of the neuronal tissue, such as axon diameter and fiber density. Despite very appealing results showing that the estimated microstructure indices agree very well with histological examinations, existing techniques require computationally very expensive non-linear procedures to fit the models to the data which, in practice, demand the use of powerful computer clusters for large-scale applications. In this work, we present a general framework for Accelerated Microstructure Imaging via Convex Optimization (AMICO) and show how to re-formulate this class of techniques as convenient linear systems which, then, can be efficiently solved using very fast algorithms. We demonstrate this linearization of the fitting problem for two specific models, i.e. ActiveAx and NODDI, providing a very attractive alternative for parameter estimation in those techniques; however, the AMICO framework is general and flexible enough to work also for the wider space of microstructure imaging methods. Results demonstrate that AMICO represents an effective means to accelerate the fit of existing techniques drastically (up to four orders of magnitude faster) while preserving accuracy and precision in the estimated model parameters (correlation above 0.9). We believe that the availability of such ultrafast algorithms will help to accelerate the spread of microstructure imaging to larger cohorts of patients and to study a wider spectrum of neurological disorders.

Optimization of preservation conditions of As (III) bioreporter bacteria.

Long-term preservation of bioreporter bacteria is essential for the functioning of cell-based detection devices, particularly when field application, e.g., in developing countries, is intended. We varied the culture conditions (i.e., the NaCl content of the medium), storage protection media, and preservation methods (vacuum drying vs. encapsulation gels remaining hydrated) in order to achieve optimal preservation of the activity of As (III) bioreporter bacteria during up to 12 weeks of storage at 4 degrees C. The presence of 2% sodium chloride during the cultivation improved the response intensity of some bioreporters upon reconstitution, particularly of those that had been dried and stored in the presence of sucrose or trehalose and 10% gelatin. The most satisfying, stable response to arsenite after 12 weeks storage was obtained with cells that had been dried in the presence of 34% trehalose and 1.5% polyvinylpyrrolidone. Amendments of peptone, meat extract, sodium ascorbate, and sodium glutamate preserved the bioreporter activity only for the first 2 weeks, but not during long-term storage. Only short-term stability was also achieved when bioreporter bacteria were encapsulated in gels remaining hydrated during storage.

CT dose optimization when changing to CT multi-detector row technology

The purpose of this article was to review the strategies to control patient dose in adult and pediatric computed tomography (CT), taking into account the change of technology from single-detector row CT to multi-detector row CT. First the relationships between computed tomography dose index, dose length product, and effective dose in adult and pediatric CT are revised, along with the diagnostic reference level concept. Then the effect of image noise as a function of volume computed tomography dose index, reconstructed slice thickness, and the size of the patient are described. Finally, the potential of tube current modulation CT is discussed.

Optimization of snowmaking in high mountains ski resort

Ski resorts are deploying more and more systems of artificial snow. These tools are necessary to ensure an important economic activity for the high alpine valleys. However, artificial snow raises important environmental issues that can be reduced by an optimization of its production. This paper presents a software prototype based on artificial intelligence to help ski resorts better manage their snowpack. It combines on one hand a General Neural Network for the analysis of the snow cover and the spatial prediction, with on the other hand a multiagent simulation of skiers for the analysis of the spatial impact of ski practice. The prototype has been tested on the ski resort of Verbier (Switzerland).

Learning wind fields with multiple kernels

This paper presents multiple kernel learning (MKL) regression as an exploratory spatial data analysis and modelling tool. The MKL approach is introduced as an extension of support vector regression, where MKL uses dedicated kernels to divide a given task into sub-problems and to treat them separately in an effective way. It provides better interpretability to non-linear robust kernel regression at the cost of a more complex numerical optimization. In particular, we investigate the use of MKL as a tool that allows us to avoid using ad-hoc topographic indices as covariables in statistical models in complex terrains. Instead, MKL learns these relationships from the data in a non-parametric fashion. A study on data simulated from real terrain features confirms the ability of MKL to enhance the interpretability of data-driven models and to aid feature selection without degrading predictive performances. Here we examine the stability of the MKL algorithm with respect to the number of training data samples and to the presence of noise. The results of a real case study are also presented, where MKL is able to exploit a large set of terrain features computed at multiple spatial scales, when predicting mean wind speed in an Alpine region.

How Much Does It Cost? Optimization of Costs in Sequence Analysis of Social Science Data

One major methodological problem in analysis of sequence data is the determination of costs from which distances between sequences are derived. Although this problem is currently not optimally dealt with in the social sciences, it has some similarity with problems that have been solved in bioinformatics for three decades. In this article, the authors propose an optimization of substitution and deletion/insertion costs based on computational methods. The authors provide an empirical way of determining costs for cases, frequent in the social sciences, in which theory does not clearly promote one cost scheme over another. Using three distinct data sets, the authors tested the distances and cluster solutions produced by the new cost scheme in comparison with solutions based on cost schemes associated with other research strategies. The proposed method performs well compared with other cost-setting strategies, while it alleviates the justification problem of cost schemes.