Pigment Melanin: Pattern for Iris Recognition

Recognition of iris based on Visible Light (VL) imaging is a difficult problem because of the light reflection from the cornea. Nonetheless, pigment melanin provides a rich feature source in VL, unavailable in Near-Infrared (NIR) imaging. This is due to biological spectroscopy of eumelanin, a chemical not stimulated in NIR. In this case, a plausible solution to observe such patterns may be provided by an adaptive procedure using a variational technique on the image histogram. To describe the patterns, a shape analysis method is used to derive feature-code for each subject. An important question is how much the melanin patterns, extracted from VL, are independent of iris texture in NIR. With this question in mind, the present investigation proposes fusion of features extracted from NIR and VL to boost the recognition performance. We have collected our own database (UTIRIS) consisting of both NIR and VL images of 158 eyes of 79 individuals. This investigation demonstrates that the proposed algorithm is highly sensitive to the patterns of cromophores and improves the iris recognition rate.Comment: To be Published on Special Issue on Biometrics, IEEE Transaction on Instruments and Measurements, Volume 59, Issue number 4, April 201

Prediction of Glioblastoma Multiform Response to Bevacizumab Treatment Using Multi-Parametric MRI

Glioblastoma multiform (GBM) is a highly malignant brain tumor. Bevacizumab is a recent therapy for stopping tumor growth and even shrinking tumor through inhibition of vascular development (angiogenesis). This paper presents a non-invasive approach based on image analysis of multi-parametric magnetic resonance images (MRI) to predict response of GBM to this treatment. The resulting prediction system has potential to be used by physicians to optimize treatment plans of the GBM patients. The proposed method applies signal decomposition and histogram analysis methods to extract statistical features from Gd-enhanced regions of tumor that quantify its microstructural characteristics. MRI studies of 12 patients at multiple time points before and up to four months after treatment are used in this work. Changes in the Gd-enhancement as well as necrosis and edema after treatment are used to evaluate the response. Leave-one-out cross validation method is applied to evaluate prediction quality of the models. Predictive models developed in this work have large regression coefficients (maximum R2 = 0.95) indicating their capability to predict response to therapy

Dependence of the flux creep activation energy on current density and magnetic field for MgB2 superconductor

Systematic ac susceptibility measurements have been performed on a MgB$_2$ bulk sample. We demonstrate that the flux creep activation energy is a nonlinear function of the current density $U(j)\propto j^{-0.2}$, indicating a nonlogarithmic relaxation of the current density in this material. The dependence of the activation energy on the magnetic field is determined to be a power law $U(B)\propto B^{-1.33}$, showing a steep decline in the activation energy with the magnetic field, which accounts for the steep drop in the critical current density with magnetic field that is observed in MgB$_2$. The irreversibility field is also found to be rather low, therefore, the pinning properties of this new material will need to be enhanced for practical applications.Comment: 11 pages, 6 figures, Revtex forma

Carbon substitution in MgB2 single crystals: structural and superconducting properties

Growth of carbon substituted magnesium diboride Mg(B1-xCx)2 single crystals with 0<x<0.15 is reported and the structural, transport, and magnetization data are presented. The superconducting transition temperature decreases monotonically with increasing carbon content in the full investigated range of substitution. By adjusting the nominal composition, Tc of substituted crystals can be tuned in a wide temperature range between 10 and 39 K. Simultaneous introduction of disorder by carbon substitution and significant increase of the upper critical field Hc2 is observed. Comparing with the non-substituted compound, Hc2 at 15K for x=0.05 is enhanced by more then a factor of 2 for H oriented both perpendicular and parallel to the ab-plane. This enhancement is accompanied by a reduction of the Hc2-anisotropy coefficient gamma from 4.5 (for non-substituted compound) to 3.4 and 2.8 for the crystals with x = 0.05 and 0.095, respectively. At temperatures below 10 K, the single crystal with larger carbon content shows Hc2 (defined at zero resistance) higher than 7 and 24 T for H oriented perpendicular and parallel to the ab-plane, respectively. Observed increase of Hc2 cannot be explained by the change in the coherence length due to disorder-induced decrease of the mean free path only.Comment: 22 pages, 9 figures, 4 table

Dietary b-glucan (MacroGard®) enhances survival of first feeding turbot (Scophthalmus maximus) larvae by altering immunity, metabolism and microbiota

Reflecting the natural biology of mass spawning fish aquaculture production of fish larvae is often hampered by high and unpredictable mortality rates. The present study aimed to enhance larval performance and immunity via the oral administration of an immunomodulator, β-glucan (MacroGard®) in turbot (Scophthalmus maximus). Rotifers (Brachionus plicatilis) were incubated with or without yeast β-1,3/1,6-glucan in form of MacroGard® at a concentration of 0.5 g/L. Rotifers were fed to first feeding turbot larvae once a day. From day 13 dph onwards all tanks were additionally fed untreated Artemia sp. nauplii (1 nauplius ml/L). Daily mortality was monitored and larvae were sampled at 11 and 24 dph for expression of 30 genes, microbiota analysis, trypsin activity and size measurements. Along with the feeding of β-glucan daily mortality was significantly reduced by ca. 15% and an alteration of the larval microbiota was observed. At 11 dph gene expression of trypsin and chymotrypsin was elevated in the MacroGard® fed fish, which resulted in heightened tryptic enzyme activity. No effect on genes encoding antioxidative proteins was observed, whilst the immune response was clearly modulated by β-glucan. At 11 dph complement component c3 was elevated whilst cytokines, antimicrobial peptides, toll like receptor 3 and heat shock protein 70 were not affected. At the later time point (24 dph) an anti-inflammatory effect in form of a down-regulation of hsp 70, tnf-α and il-1β was observed. We conclude that the administration of MacroGard® induced an immunomodulatory response and could be used as an effective measure to increase survival in rearing of turbot

Effects of Ferumoxides – Protamine Sulfate Labeling on Immunomodulatory Characteristics of Macrophage-like THP-1 Cells

Superparamagnetic Iron Oxide (SPIO) complexed with cationic transfection agent is used to label various mammalian cells. Labeled cells can then be utilized as an in vivo magnetic resonance imaging (MRI) probes. However, certain number of in vivo administered labeled cells may be cleared from tissues by the host's macrophages. For successful translation to routine clinical application of SPIO labeling method it is important that this mode of in vivo clearance of iron does not elicit any diverse immunological effects. The purpose of this study was to demonstrate that SPIO agent ferumoxides-protamine sulfate (FePro) incorporation into macrophages does not alter immunological properties of these cells with regard to differentiation, chemotaxis, and ability to respond to the activation stimuli and to modulate T cell response. We used THP-1 cell line as a model for studying macrophage cell type. THP-1 cells were magnetically labeled with FePro, differentiated with 100 nM of phorbol ester, 12-Myristate-13-acetate (TPA) and stimulated with 100 ng/ml of LPS. The results showed 1) FePro labeling had no effect on the changes in morphology and expression of cell surface proteins associated with TPA induced differentiation; 2) FePro labeled cells responded to LPS with slightly higher levels of NFκB pathway activation, as shown by immunobloting; TNF-α secretion and cell surface expression levels of CD54 and CD83 activation markers, under these conditions, were still comparable to the levels observed in non-labeled cells; 3) FePro labeling exhibited differential, chemokine dependent, effect on THP-1 chemotaxis with a decrease in cell directional migration to MCP-1; 4) FePro labeling did not affect the ability of THP-1 cells to down-regulate T cell expression of CD4 and CD8 and to induce T cell proliferation. Our study demonstrated that intracellular incorporation of FePro complexes does not alter overall immunological properties of THP-1 cells. The described experiments provide the model for studying the effects of in vivo clearance of iron particles via incorporation into the host's macrophages that may follow after in vivo application of any type of magnetically labeled mammalian cells. To better mimic the complex in vivo scenario, this model may be further exploited by introducing additional cellular and biological, immunologically relevant, components

Artificial intelligence for classification of temporal lobe epilepsy with ROI-level MRI data: A worldwide ENIGMA-Epilepsy study

Artificial intelligence has recently gained popularity across different medical fields to aid in the detection of diseases based on pathology samples or medical imaging findings. Brain magnetic resonance imaging (MRI) is a key assessment tool for patients with temporal lobe epilepsy (TLE). The role of machine learning and artificial intelligence to increase detection of brain abnormalities in TLE remains inconclusive. We used support vector machine (SV) and deep learning (DL) models based on region of interest (ROI-based) structural (n = 336) and diffusion (n = 863) brain MRI data from patients with TLE with (“lesional”) and without (“non-lesional”) radiographic features suggestive of underlying hippocampal sclerosis from the multinational (multi-center) ENIGMA-Epilepsy consortium. Our data showed that models to identify TLE performed better or similar (68–75%) compared to models to lateralize the side of TLE (56–73%, except structural-based) based on diffusion data with the opposite pattern seen for structural data (67–75% to diagnose vs. 83% to lateralize). In other aspects, structural and diffusion-based models showed similar classification accuracies. Our classification models for patients with hippocampal sclerosis were more accurate (68–76%) than models that stratified non-lesional patients (53–62%). Overall, SV and DL models performed similarly with several instances in which SV mildly outperformed DL. We discuss the relative performance of these models with ROI-level data and the implications for future applications of machine learning and artificial intelligence in epilepsy care