Imputation in data fusion of heterogeneous data sets a model-based numerical experiment

Given the very large amount of data obtained everyday through population surveys, much of the new research again could use this information instead of collecting new samples. Unfortunately, relevant data are often disseminated into different files obtained through different sampling designs. Data fusion is a set of methods used to combine information from different sources into a single dataset. In this article, we are interested in a specific problem: the fusion of two data files, one of which being quite small. We propose a model-based procedure combining a logistic regression with an Expectation-Maximization algorithm. Results show that despite the lack of data, this procedure can perform better than standard matching procedures.

Probability distribution of the radionuclide half-lives

Power law distributions, a well-known model in the theory of real random variables, characterize a wide variety of natural and man made phenomena. The intensity of earthquakes, the word frequencies, the solar ares and the sizes of power outages are distributed according to a power law distribution. Recently, given the usage of power laws in the scientific community, several articles have been published criticizing the statistical methods used to estimate the power law behaviour and establishing new techniques to their estimation with proven reliability. The main object of the present study is to go in deep understanding of this kind of distribution and its analysis, and introduce the half-lives of the radioactive isotopes as a new candidate in the nature following a power law distribution, as well as a \canonical laboratory" to test statistical methods appropriate for long-tailed distributions.

Sustained activation and tumor targeting of NKT cells using a CD1d-anti-HER2-scFv fusion protein induce antitumor effects in mice

Invariant NKT (iNKT) cells are potent activators of DCs, NK cells, and T cells, and their antitumor activity has been well demonstrated. A single injection of the high-affinity CD1d ligand alpha-galactosylceramide (alphaGalCer) leads to short-lived iNKT cell activation followed, however, by long-term anergy, limiting its therapeutic use. In contrast, we demonstrated here that when alphaGalCer was loaded on a recombinant soluble CD1d molecule (alphaGalCer/sCD1d), repeated injections led to sustained iNKT and NK cell activation associated with IFN-gamma secretion as well as DC maturation in mice. Most importantly, when alphaGalCer/sCD1d was fused to a HER2-specific scFv antibody fragment, potent inhibition of experimental lung metastasis and established s.c. tumors was obtained when systemic treatment was started 2-7 days after the injection of HER2-expressing B16 melanoma cells. In contrast, administration of free alphaGalCer at this time had no effect. The antitumor activity of the CD1d-anti-HER2 fusion protein was associated with HER2-specific tumor localization and accumulation of iNKT, NK, and T cells at the tumor site. Targeting iNKT cells to the tumor site thus may activate a combined innate and adaptive immune response that may prove to be effective in cancer immunotherapy

Decision fusion for the classification of hyperspectral data: Outcome of the 2008 GRS-S data fusion contest

The 2008 Data Fusion Contest organized by the IEEE Geoscience and Remote Sensing Data Fusion Technical Committee deals with the classification of high-resolution hyperspectral data from an urban area. Unlike in the previous issues of the contest, the goal was not only to identify the best algorithm but also to provide a collaborative effort: The decision fusion of the best individual algorithms was aiming at further improving the classification performances, and the best algorithms were ranked according to their relative contribution to the decision fusion. This paper presents the five awarded algorithms and the conclusions of the contest, stressing the importance of decision fusion, dimension reduction, and supervised classification methods, such as neural networks and support vector machines.

The subjectivist interpretation of probability and the problem of individualisation in forensic science

This paper presents and discusses further aspects of the subjectivist interpretation of probability (also known as the 'personalist' view of probabilities) as initiated in earlier forensic and legal literature. It shows that operational devices to elicit subjective probabilities - in particular the so-called scoring rules - provide additional arguments in support of the standpoint according to which categorical claims of forensic individualisation do not follow from a formal analysis under that view of probability theory.

Sequential analysis of trans-SNARE formation in intracellular membrane fusion.

SNARE complexes are required for membrane fusion in the endomembrane system. They contain coiled-coil bundles of four helices, three (Q(a), Q(b), and Q(c)) from target (t)-SNAREs and one (R) from the vesicular (v)-SNARE. NSF/Sec18 disrupts these cis-SNARE complexes, allowing reassembly of their subunits into trans-SNARE complexes and subsequent fusion. Studying these reactions in native yeast vacuoles, we found that NSF/Sec18 activates the vacuolar cis-SNARE complex by selectively displacing the vacuolar Q(a) SNARE, leaving behind a Q(bc)R subcomplex. This subcomplex serves as an acceptor for a Q(a) SNARE from the opposite membrane, leading to Q(a)-Q(bc)R trans-complexes. Activity tests of vacuoles with diagnostic distributions of inactivating mutations over the two fusion partners confirm that this distribution accounts for a major share of the fusion activity. The persistence of the Q(bc)R cis-complex and the formation of the Q(a)-Q(bc)R trans-complex are both sensitive to the Rab-GTPase inhibitor, GDI, and to mutations in the vacuolar tether complex, HOPS (HOmotypic fusion and vacuolar Protein Sorting complex). This suggests that the vacuolar Rab-GTPase, Ypt7, and HOPS restrict cis-SNARE disassembly and thereby bias trans-SNARE assembly into a preferred topology.

Multi-modal Change Detection, Application to the Detection of Flooded Areas: Outcome of the 2009-2010 data fusion contest

The 2009-2010 Data Fusion Contest organized by the Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society was focused on the detection of flooded areas using multi-temporal and multi-modal images. Both high spatial resolution optical and synthetic aperture radar data were provided. The goal was not only to identify the best algorithms (in terms of accuracy), but also to investigate the further improvement derived from decision fusion. This paper presents the four awarded algorithms and the conclusions of the contest, investigating both supervised and unsupervised methods and the use of multi-modal data for flood detection. Interestingly, a simple unsupervised change detection method provided similar accuracy as supervised approaches, and a digital elevation model-based predictive method yielded a comparable projected change detection map without using post-event data.

Fusion telomérique de deux chromosomes X par leurs bras courts chez une patiente présentant un syndrome de Turner atypique. [Telomeric fusion of the short arms of both X chromosomes in a patient presenting an atypical Turner syndrome.]

A case of atypical Turner's syndrome with unusual karyotype is reported. The chromosome complements of the patient, studied with different banding techniques, is 45,XO/46,X,dic(X)(Xqter leads to p22::p22 leads to qter). In the literature 8 similar cases have been reported. Short stature and amenorrhea are the most constant findings. The mechanisms by which the observed chromosomal "rearrangement" can be produced are briefly discussed.

Weighted Shape-based Averaging with Neighborhood Prior Model for Multiple Atlas Fusion-based Medical Image Segmentation

In medical imaging, merging automated segmentations obtained from multiple atlases has become a standard practice for improving the accuracy. In this letter, we propose two new fusion methods: "Global Weighted Shape-Based Averaging" (GWSBA) and "Local Weighted Shape-Based Averaging" (LWSBA). These methods extend the well known Shape-Based Averaging (SBA) by additionally incorporating the similarity information between the reference (i.e., atlas) images and the target image to be segmented. We also propose a new spatially-varying similarity-weighted neighborhood prior model, and an edge-preserving smoothness term that can be used with many of the existing fusion methods. We first present our new Markov Random Field (MRF) based fusion framework that models the above mentioned information. The proposed methods are evaluated in the context of segmentation of lymph nodes in the head and neck 3D CT images, and they resulted in more accurate segmentations compared to the existing SBA.

T-cell activation by treatment of cancer patients with EMD 521873 (Selectikine), an IL-2/anti-DNA fusion protein.

ABSTRACT: BACKGROUND: EMD 521873 (Selectikine or NHS-IL2LT) is a fusion protein consisting of modified human IL-2 which binds specifically to the high-affinity IL-2 receptor, and an antibody specific for both single- and double-stranded DNA, designed to facilitate the enrichment of IL-2 in tumor tissue. METHODS: An extensive analysis of pharmacodynamic (PD) markers associated with target modulation was assessed during a first-in-human phase I dose-escalation trial of Selectikine. RESULTS: Thirty-nine patients with metastatic or locally advanced tumors refractory to standard treatments were treated with increasing doses of Selectikine, and nine further patients received additional cyclophosphamide. PD analysis, assessed during the first two treatment cycles, revealed strong activation of both CD4+ and CD8+ T-cells and only weak NK cell activation. No dose response was observed. As expected, Treg cells responded actively to Selectikine but remained at lower frequency than effector CD4+ T-cells. Interestingly, patient survival correlated positively with both high lymphocyte counts and low levels of activated CD8+ T-cells at baseline, the latter of which was associated with enhanced T-cell responses to the treatment. CONCLUSIONS: The results confirm the selectivity of Selectikine with predominant T-cell and low NK cell activation, supporting follow-up studies assessing the clinical efficacy of Selectikine for cancer patients.