An efficient approximation for accelerating convergence of the numerical power series. Results for the 1D Schr\"odinger's equation

The numerical matrix Numerov algorithm is used to solve the stationary Schr\"odinger equation for central Coulomb potentials. An efficient approximation for accelerating the convergence is proposed. The Numerov method is error-prone if the magnitude of grid$-$size is not chosen properly. A number of rules so far, have been devised. The effectiveness of these rules decrease for more complicated equations. Efficiency of the technique used for accelerating the convergence is tested by allowing the grid-sizes to have variationally optimum values. The method presented in this study eliminates the increased margin of error while calculating the excited states. The results obtained for energy eigenvalues are compared with the literature. It is observed that, once the values of grid-sizes for hydrogen energy eigenvalues are obtained, they can simply be determined for the hydrogen iso-electronic series as, $h_{\varepsilon}(Z)=h_{\varepsilon}(1)/Z$.Comment: 1 figure six page

Metal nanoring and tube formation on carbon nanotubes

The structural and electronic properties of aluminum covered single wall carbon nanotubes (SWNT) are studied from first-principles for a large number of coverage. Aluminum-aluminum interaction that is stronger than aluminum-tube interaction, prevents uniform metal coverage, and hence gives rise to the clustering. However, a stable aluminum ring and aluminum nanotube with well defined patterns can also form around the semiconducting SWNT and lead to metallization. The persistent current in the Al nanoring is discussed to show that a high magnetic field can be induced at the center of SWNT.Comment: Submitted to Physical Review

Learning Optimal Deep Projection of 18^{18}F-FDG PET Imaging for Early Differential Diagnosis of Parkinsonian Syndromes

Several diseases of parkinsonian syndromes present similar symptoms at early stage and no objective widely used diagnostic methods have been approved until now. Positron emission tomography (PET) with $^{18}$F-FDG was shown to be able to assess early neuronal dysfunction of synucleinopathies and tauopathies. Tensor factorization (TF) based approaches have been applied to identify characteristic metabolic patterns for differential diagnosis. However, these conventional dimension-reduction strategies assume linear or multi-linear relationships inside data, and are therefore insufficient to distinguish nonlinear metabolic differences between various parkinsonian syndromes. In this paper, we propose a Deep Projection Neural Network (DPNN) to identify characteristic metabolic pattern for early differential diagnosis of parkinsonian syndromes. We draw our inspiration from the existing TF methods. The network consists of a (i) compression part: which uses a deep network to learn optimal 2D projections of 3D scans, and a (ii) classification part: which maps the 2D projections to labels. The compression part can be pre-trained using surplus unlabelled datasets. Also, as the classification part operates on these 2D projections, it can be trained end-to-end effectively with limited labelled data, in contrast to 3D approaches. We show that DPNN is more effective in comparison to existing state-of-the-art and plausible baselines.Comment: 8 pages, 3 figures, conference, MICCAI DLMIA, 201

In vivo examination of healthy human skin after short-time treatment with moisturizers using confocal Raman spectroscopy and optical coherence tomography: Preliminary observations

Skin is our barrier against environmental damage. Moisturizers are widely used to increase hydration and barrier integrity of the skin; however, there are contrasting observations on their in vivo effects in real-life settings. In cosmetic studies, cor- neometers and tewameters are traditionally used to assess skin hydration. In this study, two novel noninvasive diagnostic techniques, optical coherence tomography (OCT) and confocal Raman spectroscopy, were used to analyze stratum corneum and epidermal thickness (ET), water content, blood flow in function of depth, skin roughness, attenuation coefficient, natural moisturizing factor, ceramides and free fatty acids, cholesterol, urea, and lactates in 20 female subjects aged between 30 and 45 before and after 2 weeks application of a commercially available mois- turizing lotion on one forearm. The untreated forearm served as control. A third measurement was conducted 1 week after cessation of moisturizing to verify whether the changes in the analyzed parameters persisted. We noticed a reduction in skin roughness, an increase in ceramides and free fatty acids and a not statistically sig- nificant increase in ET. As a conclusion, short time moisturizing appears insufficient to provide significant changes in skin morphology and composition, as assessed by OCT and RS. Novel noninvasive imaging methods are suitable for the evalu- ation of skin response to topical moisturizers. Further studies on larger sample size and longer treatment schedules are needed to analyze changes under treat- ment with moisturizers and to standardize the use of novel noninvasive diagnostic techniques

Semi-Supervised Deep Learning for Multi-Tissue Segmentation from Multi-Contrast MRI

Segmentation of thigh tissues (muscle, fat, inter-muscular adipose tissue (IMAT), bone, and bone marrow) from magnetic resonance imaging (MRI) scans is useful for clinical and research investigations in various conditions such as aging, diabetes mellitus, obesity, metabolic syndrome, and their associated comorbidities. Towards a fully automated, robust, and precise quantification of thigh tissues, herein we designed a novel semi-supervised segmentation algorithm based on deep network architectures. Built upon Tiramisu segmentation engine, our proposed deep networks use variational and specially designed targeted dropouts for faster and robust convergence, and utilize multi-contrast MRI scans as input data. In our experiments, we have used 150 scans from 50 distinct subjects from the Baltimore Longitudinal Study of Aging (BLSA). The proposed system made use of both labeled and unlabeled data with high efficacy for training, and outperformed the current state-of-the-art methods with dice scores of 97.52%, 94.61%, 80.14%, 95.93%, and 96.83% for muscle, fat, IMAT, bone, and bone marrow tissues, respectively. Our results indicate that the proposed system can be useful for clinical research studies where volumetric and distributional tissue quantification is pivotal and labeling is a significant issue. To the best of our knowledge, the proposed system is the first attempt at multi-tissue segmentation using a single end-to-end semi-supervised deep learning framework for multi-contrast thigh MRI scans.Comment: 20 pages, 9 figures, Journal of Signal Processing System

Combined In Silico, In Vivo, and In Vitro Studies Shed Insights into the Acute Inflammatory Response in Middle-Aged Mice

We combined in silico, in vivo, and in vitro studies to gain insights into age-dependent changes in acute inflammation in response to bacterial endotoxin (LPS). Time-course cytokine, chemokine, and NO2-/NO3- data from "middle-aged" (6-8 months old) C57BL/6 mice were used to re-parameterize a mechanistic mathematical model of acute inflammation originally calibrated for "young" (2-3 months old) mice. These studies suggested that macrophages from middle-aged mice are more susceptible to cell death, as well as producing higher levels of pro-inflammatory cytokines, vs. macrophages from young mice. In support of the in silico-derived hypotheses, resident peritoneal cells from endotoxemic middle-aged mice exhibited reduced viability and produced elevated levels of TNF-α, IL-6, IL-10, and KC/CXCL1 as compared to cells from young mice. Our studies demonstrate the utility of a combined in silico, in vivo, and in vitro approach to the study of acute inflammation in shock states, and suggest hypotheses with regard to the changes in the cytokine milieu that accompany aging. © 2013 Namas et al

Divergent in situ expression of IL-31 and IL-31RA between bullous pemphigoid and pemphigus vulgaris

Bullous pemphigoid (BP) and pemphigus vulgaris (PV) are two major autoimmune blistering skin diseases. Unlike PV, BP is accompanied by intense pruritus, suggesting possible involvement of the pruritogenic cytokine IL-31. However, the underlying mechanisms of the clinical difference between BP and PV in terms of pruritus are not fully understood. To compare the expression levels of IL-31 and its receptor IL-31RA in the lesional skin, including peripheral nerves in BP and PV patients, immunohistochemical staining for IL-31 and IL-31RA was performed in skin samples of BP and PV patients and healthy controls (HC). The IL-31RA-expressing area in epidermis and peripheral nerves was analysed using ImageJ and the percentage of positive cells for IL-31/IL-31RA in dermal infiltrating cells was manually quantified. Quantitative analyses revealed that IL-31/IL-31RA expressions in the epidermis and dermal infiltrate were significantly increased in BP compared to PV and HC. The difference between BP and PV became more obvious when advanced bullous lesions were compared. Peripheral nerves in BP lesions presented significantly higher IL-31RA expression compared to PV lesions. In conclusion, we found significantly augmented expressions of IL-31/IL-31RA in BP lesions, including peripheral nerves, in comparison to PV. These results suggest a possible contribution of IL-31/IL-31RA signalling to the difference between BP and PV in the facilitation of pruritus and local skin inflammation, raising the possibility of therapeutic targeting of the IL-31/IL-31RA pathway in BP patients

Horizontal DNA transfer mechanisms of bacteria as weapons of intragenomic conflict

Horizontal DNA transfer (HDT) is a pervasive mechanism of diversification in many microbial species, but its primary evolutionary role remains controversial. Much recent research has emphasised the adaptive benefit of acquiring novel DNA, but here we argue instead that intragenomic conflict provides a coherent framework for understanding the evolutionary origins of HDT. To test this hypothesis, we developed a mathematical model of a clonally descended bacterial population undergoing HDT through transmission of mobile genetic elements (MGEs) and genetic transformation. Including the known bias of transformation toward the acquisition of shorter alleles into the model suggested it could be an effective means of counteracting the spread of MGEs. Both constitutive and transient competence for transformation were found to provide an effective defence against parasitic MGEs; transient competence could also be effective at permitting the selective spread of MGEs conferring a benefit on their host bacterium. The coordination of transient competence with cell-cell killing, observed in multiple species, was found to result in synergistic blocking of MGE transmission through releasing genomic DNA for homologous recombination while simultaneously reducing horizontal MGE spread by lowering the local cell density. To evaluate the feasibility of the functions suggested by the modelling analysis, we analysed genomic data from longitudinal sampling of individuals carrying Streptococcus pneumoniae. This revealed the frequent within-host coexistence of clonally descended cells that differed in their MGE infection status, a necessary condition for the proposed mechanism to operate. Additionally, we found multiple examples of MGEs inhibiting transformation through integrative disruption of genes encoding the competence machinery across many species, providing evidence of an ongoing "arms race." Reduced rates of transformation have also been observed in cells infected by MGEs that reduce the concentration of extracellular DNA through secretion of DNases. Simulations predicted that either mechanism of limiting transformation would benefit individual MGEs, but also that this tactic's effectiveness was limited by competition with other MGEs coinfecting the same cell. A further observed behaviour we hypothesised to reduce elimination by transformation was MGE activation when cells become competent. Our model predicted that this response was effective at counteracting transformation independently of competing MGEs. Therefore, this framework is able to explain both common properties of MGEs, and the seemingly paradoxical bacterial behaviours of transformation and cell-cell killing within clonally related populations, as the consequences of intragenomic conflict between self-replicating chromosomes and parasitic MGEs. The antagonistic nature of the different mechanisms of HDT over short timescales means their contribution to bacterial evolution is likely to be substantially greater than previously appreciated

Computational Insights on the Competing Effects of Nitric Oxide in Regulating Apoptosis

Despite the establishment of the important role of nitric oxide (NO) on apoptosis, a molecular- level understanding of the origin of its dichotomous pro- and anti-apoptotic effects has been elusive. We propose a new mathematical model for simulating the effects of nitric oxide (NO) on apoptosis. The new model integrates mitochondria-dependent apoptotic pathways with NO-related reactions, to gain insights into the regulatory effect of the reactive NO species N2O3, non-heme iron nitrosyl species (FeLnNO), and peroxynitrite (ONOO−). The biochemical pathways of apoptosis coupled with NO-related reactions are described by ordinary differential equations using mass-action kinetics. In the absence of NO, the model predicts either cell survival or apoptosis (a bistable behavior) with shifts in the onset time of apoptotic response depending on the strength of extracellular stimuli. Computations demonstrate that the relative concentrations of anti- and pro-apoptotic reactive NO species, and their interplay with glutathione, determine the net anti- or pro-apoptotic effects at long time points. Interestingly, transient effects on apoptosis are also observed in these simulations, the duration of which may reach up to hours, despite the eventual convergence to an anti-apoptotic state. Our computations point to the importance of precise timing of NO production and external stimulation in determining the eventual pro- or anti-apoptotic role of NO

TRAIL Death Receptor-4, Decoy Receptor-1 and Decoy Receptor-2 Expression on CD8+ T Cells Correlate with the Disease Severity in Patients with Rheumatoid Arthritis

BACKGROUND: Rheumatoid Arthritis (RA) is a chronic autoimmune inflammatory disorder. Although the pathogenesis of disease is unclear, it is well known that T cells play a major role in both development and perpetuation of RA through activating macrophages and B cells. Since the lack of TNF-Related Apoptosis Inducing Ligand (TRAIL) expression resulted in defective thymocyte apoptosis leading to an autoimmune disease, we explored evidence for alterations in TRAIL/TRAIL receptor expression on peripheral T lymphocytes in the molecular mechanism of RA development. METHODS: The expression of TRAIL/TRAIL receptors on T cells in 20 RA patients and 12 control individuals were analyzed using flow cytometry. The correlation of TRAIL and its receptor expression profile was compared with clinical RA parameters (RA activity scored as per DAS28) using Spearman Rho Analysis. RESULTS: While no change was detected in the ratio of CD4+ to CD8+ T cells between controls and RA patient groups, upregulation of TRAIL and its receptors (both death and decoy) was detected on both CD4+ and CD8+ T cells in RA patients compared to control individuals. Death Receptor-4 (DR4) and the decoy receptors DcR1 and DcR2 on CD8+ T cells, but not on CD4+ T cells, were positively correlated with patients' DAS scores. CONCLUSIONS: Our data suggest that TRAIL/TRAIL receptor expression profiles on T cells might be important in revelation of RA pathogenesis