118 resultados para Nodal admittance matrices
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In a weighted spatial network, as specified by an exchange matrix, the variances of the spatial values are inversely proportional to the size of the regions. Spatial values are no more exchangeable under independence, thus weakening the rationale for ordinary permutation and bootstrap tests of spatial autocorrelation. We propose an alternative permutation test for spatial autocorrelation, based upon exchangeable spatial modes, constructed as linear orthogonal combinations of spatial values. The coefficients obtain as eigenvectors of the standardised exchange matrix appearing in spectral clustering, and generalise to the weighted case the concept of spatial filtering for connectivity matrices. Also, two proposals aimed at transforming an acessibility matrix into a exchange matrix with with a priori fixed margins are presented. Two examples (inter-regional migratory flows and binary adjacency networks) illustrate the formalism, rooted in the theory of spectral decomposition for reversible Markov chains.
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One-hundred patients treated with curative radiotherapy (RT) ± chemotherapy (CT) for an anal canal carcinoma (T1-4N0-3M0) were retrospectively analyzed. Five- and 10-year local control (LC) rates were 73% and 67%, respectively. Acute and late G3-G4 toxicity rates were 32% and 12%, respectively. Two patients underwent a colostomy for a G4 anal toxicity. This study confirms the outcomes of RT ± CT in the treatment of anal canal cancer. Concomitant CT and LC statistically influenced Overall Survival and Colostomy-Free Survival. CT also statistically reduced the risk of nodal relapse. High rates of acute skin toxicity impose tailored volumes and techniques of irradiation.
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Purpose: To assess the outcome in patients with olfactory neuroblastoma (ONB). Methods and Materials: Seventy-seven patients treated for nonmetastatic ONB between 1971 and 2004 were included. According to Kadish classification, there were 11 patients with Stage A, 29 with Stage B, and 37 with Stage C. T-classification included 9 patients with T1, 26 with T2, 16 with T3, 15 with T4a, and 11 with T4b tumors. Sixty-eight patients presented with N0 (88%) disease. Results: Most of the patients (n = 56, 73 %) benefited from surgery (S), and total excision was possible in 44 patients (R0 in 32, R1 in 13, R2 in 11). All but five patients benefited from RT, and chemotherapy was given in 21(27%). Median follow-up period was 72 months (range, 6-315). The 5-year overall survival (OS), disease-free survival (DES), locoregional control, and local control were 64%, 57%, 62%, and 70%, respectively. In univariate analyses, favorable factors were Kadish A or B disease, T1 T3 tumors, no nodal involvement, curative surgery, R0/R1 resection, and RT-dose 54 Gy or higher. Multivariate analysis revealed that the best independent factors predicting the outcome were T1 T3, N0, R0/R1 resection, and total RT dose (54 Gy or higher). Conclusion: In this multicenter retrospective study, patients with ONB treated with R0 or R1 surgical resection followed by at least 54-Gy postoperative RT had the best outcome. Novel strategies including concomitant chemotherapy and/or higher dose RT should be prospectively investigated in this rare disease for which local failure remains a problem.
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The class of Schoenberg transformations, embedding Euclidean distances into higher dimensional Euclidean spaces, is presented, and derived from theorems on positive definite and conditionally negative definite matrices. Original results on the arc lengths, angles and curvature of the transformations are proposed, and visualized on artificial data sets by classical multidimensional scaling. A distance-based discriminant algorithm and a robust multidimensional centroid estimate illustrate the theory, closely connected to the Gaussian kernels of Machine Learning.
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Purpose: Primary bone lymphoma (PBL) accounts for less than 1% of all malignant lymphomas, and 4-5% of all extra-nodal lymphomas. In this study, the disease profile, outcome, and prognostic factors were assessed in patients with stage I and II PBL.Patients and Methods: Thirteen Rare Cancer Network (RCN) institutions enrolled 116 consecutive patients with PBL treated between 1987 and 2008 in this study. Inclusion criteria were age > 16 years, stage I and II, minimum 6 months follow-up and a biopsy-proven confirmation of non-Hodgkin's lymphoma (NHL). Eighty-seven patients underwent chemoradiotherapy (CXRT), 15 radiotherapy (RT) without (13) or with (2) surgery, 14 chemotherapy (CXT) without (9) or with (5) surgery. Median RT dose was 40 Gy (range: 4-60). The median number of CXT cycles was 6 (range: 2-8). Median follow-up was 41 months (range: 6-242).Results: The overall response rate at the end of treatment was 91% (CR 74%, PR 17%). Local recurrence or progression was observed in 12 (10%) patients, and systemic recurrence in 17 (15%). Causes of death included disease progression in 21, unrelated in 5, CXT-related toxicity in 1, and second primary cancer in 2 patients. The 5-yr overall survival (OS), lymphoma-specific survival (LSS), and local control (LC) were 76%, 78% and 92%, respectively. In univariate analyses (log-rank test), favorable prognostic factors for OS were age <50 years (P=0.008), international prognostic index (IPI) score ≤1 (P=0.009), high grade histology (P=0.04), CXRT (P=0.05), CXT (P=0,0004), complete response (CR) (P<0.0001), number of CXT cycles ( ≥6 ) (P=0.01), and RT dose > 40 Gy (P=0.005). All above-mentioned parameters were also significant for LSS except for age and number of chemotherapy cycles. For LC, only CR and stage I were favorable factors. In multivariate analysis, IPI score, RT dose, complete response, and chemotherapy were independently influencing the outcome (OS and LSS). Complete response at the end of treatment was the only predicting factor for LC. Six patients developed grade 3 or more toxicities, according to Common Terminology Criteria for Adverse Events (CTCAE) V3.0.Conclusion: This large multicenter study confirms the relatively good prognosis of early stage PBL treated with combined CXRT. Local control was excellent, while systemic failures were rare. An adequate dose of RT (40 Gy or more) and complete CXT regime (≥ 6 cycles) were associated with better outcome.
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OBJECTIVE: To assess porcine urothelial cell cultures and the in vitro induction of urothelial stratification in long-term cultures, to study their morphological, functional and genetic behaviour, and thus provide potential autologous urothelium for tissue-engineered substitutes for demucosalized gastric or colonic tissue. MATERIALS AND METHODS: Primary cultures of porcine urothelium were established and the cells passaged thereafter. Cell specificity was confirmed by cytokeratin analysis, cell membrane stability assessed using lactate dehydrogenase leakage, cell de-differentiation by gamma-glutamyl transferase activity and genomic stability by karyotype investigations. Histology and scanning electron microscopy were performed to study the cultured cells and the stratified constructs. Furthermore, collagen matrices were tested as cell scaffolds. RESULTS: The cells were cultured for 180 days; 10 subcultures were established during this period. Stratification was induced in a culture flask and on a collagen matrix. Cytokeratins 7, 8, 17 and 18 were expressed in all cultures, and cell membranes were stable, with no evident de-differentiation. The cultures were stable in their genotype and no chromosomal aberrations were found. The histology and immunohistochemistry of the stratified porcine constructs, and cell membrane stability and cell de-differentiation, were compared with those in the human system. CONCLUSION: Pig and human urothelial cells can be cultured over a long period with no signs of senescence. Urothelial stratification can be induced in vitro. The collagen matrix seems to be an excellent scaffold, allowing cell adherence and growth.
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Abstract : The human body is composed of a huge number of cells acting together in a concerted manner. The current understanding is that proteins perform most of the necessary activities in keeping a cell alive. The DNA, on the other hand, stores the information on how to produce the different proteins in the genome. Regulating gene transcription is the first important step that can thus affect the life of a cell, modify its functions and its responses to the environment. Regulation is a complex operation that involves specialized proteins, the transcription factors. Transcription factors (TFs) can bind to DNA and activate the processes leading to the expression of genes into new proteins. Errors in this process may lead to diseases. In particular, some transcription factors have been associated with a lethal pathological state, commonly known as cancer, associated with uncontrolled cellular proliferation, invasiveness of healthy tissues and abnormal responses to stimuli. Understanding cancer-related regulatory programs is a difficult task, often involving several TFs interacting together and influencing each other's activity. This Thesis presents new computational methodologies to study gene regulation. In addition we present applications of our methods to the understanding of cancer-related regulatory programs. The understanding of transcriptional regulation is a major challenge. We address this difficult question combining computational approaches with large collections of heterogeneous experimental data. In detail, we design signal processing tools to recover transcription factors binding sites on the DNA from genome-wide surveys like chromatin immunoprecipitation assays on tiling arrays (ChIP-chip). We then use the localization about the binding of TFs to explain expression levels of regulated genes. In this way we identify a regulatory synergy between two TFs, the oncogene C-MYC and SP1. C-MYC and SP1 bind preferentially at promoters and when SP1 binds next to C-NIYC on the DNA, the nearby gene is strongly expressed. The association between the two TFs at promoters is reflected by the binding sites conservation across mammals, by the permissive underlying chromatin states 'it represents an important control mechanism involved in cellular proliferation, thereby involved in cancer. Secondly, we identify the characteristics of TF estrogen receptor alpha (hERa) target genes and we study the influence of hERa in regulating transcription. hERa, upon hormone estrogen signaling, binds to DNA to regulate transcription of its targets in concert with its co-factors. To overcome the scarce experimental data about the binding sites of other TFs that may interact with hERa, we conduct in silico analysis of the sequences underlying the ChIP sites using the collection of position weight matrices (PWMs) of hERa partners, TFs FOXA1 and SP1. We combine ChIP-chip and ChIP-paired-end-diTags (ChIP-pet) data about hERa binding on DNA with the sequence information to explain gene expression levels in a large collection of cancer tissue samples and also on studies about the response of cells to estrogen. We confirm that hERa binding sites are distributed anywhere on the genome. However, we distinguish between binding sites near promoters and binding sites along the transcripts. The first group shows weak binding of hERa and high occurrence of SP1 motifs, in particular near estrogen responsive genes. The second group shows strong binding of hERa and significant correlation between the number of binding sites along a gene and the strength of gene induction in presence of estrogen. Some binding sites of the second group also show presence of FOXA1, but the role of this TF still needs to be investigated. Different mechanisms have been proposed to explain hERa-mediated induction of gene expression. Our work supports the model of hERa activating gene expression from distal binding sites by interacting with promoter bound TFs, like SP1. hERa has been associated with survival rates of breast cancer patients, though explanatory models are still incomplete: this result is important to better understand how hERa can control gene expression. Thirdly, we address the difficult question of regulatory network inference. We tackle this problem analyzing time-series of biological measurements such as quantification of mRNA levels or protein concentrations. Our approach uses the well-established penalized linear regression models where we impose sparseness on the connectivity of the regulatory network. We extend this method enforcing the coherence of the regulatory dependencies: a TF must coherently behave as an activator, or a repressor on all its targets. This requirement is implemented as constraints on the signs of the regressed coefficients in the penalized linear regression model. Our approach is better at reconstructing meaningful biological networks than previous methods based on penalized regression. The method is tested on the DREAM2 challenge of reconstructing a five-genes/TFs regulatory network obtaining the best performance in the "undirected signed excitatory" category. Thus, these bioinformatics methods, which are reliable, interpretable and fast enough to cover large biological dataset, have enabled us to better understand gene regulation in humans.
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CE is a powerful analytical tool used to separate intact biomolecules such as proteins. The coupling of CE with TOF/MS produces a very promising method that can be used to detect and identify proteins in different matrices. This paper describes an efficient, rapid, and simple CE-ESI-TOF/MS procedure for the analysis of endogenous human growth hormone and recombinant human growth hormone without sample preparation. Operational factors were optimized using an experimental design, and the method was successfully applied to distinguish human growth hormone and recombinant human growth hormone in unknown samples.
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Rats with periodontitis and catheter-induced aortic valve vegetations underwent dental extractions. Cultures of blood obtained 1 min later showed polymicrobial bacteremia in 19 of 19 rats, mostly due to viridans streptococci (18 of 19), Morganella (15 of 19), group G streptococci (13 of 19), and Staphylococcus aureus (10 of 19). Viridans streptococci circulated in higher numbers than did group G streptococci and S. aureus (P less than .01). Three days after dental extractions, 18 of 20 rats had endocarditis. Fifteen (83%) of 18 infections were due to group G streptococci, 9 (50%) of 18 were due to S. aureus, and 2 (11%) of 18 were due to viridans streptococci (P less than .05). In vitro, adherence to platelet-fibrin matrices of endocarditis strain 8 of group G streptococcus was two times greater than that of endocarditis strain S. aureus 23 and three to four times greater than that of Streptococcus sanguis 44 and Morganella morganii 93 (P less than 10(-5)). The inoculum size that produced endocarditis in 90% of rats after iv challenge was 10(5) cfu for group G streptococcus strain 8 and 10(7) for S. sanguis 44.
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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.
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INTRODUCTION: Breast cancer subtyping and prognosis have been studied extensively by gene expression profiling, resulting in disparate signatures with little overlap in their constituent genes. Although a previous study demonstrated a prognostic concordance among gene expression signatures, it was limited to only one dataset and did not fully elucidate how the different genes were related to one another nor did it examine the contribution of well-known biological processes of breast cancer tumorigenesis to their prognostic performance. METHOD: To address the above issues and to further validate these initial findings, we performed the largest meta-analysis of publicly available breast cancer gene expression and clinical data, which are comprised of 2,833 breast tumors. Gene coexpression modules of three key biological processes in breast cancer (namely, proliferation, estrogen receptor [ER], and HER2 signaling) were used to dissect the role of constituent genes of nine prognostic signatures. RESULTS: Using a meta-analytical approach, we consolidated the signatures associated with ER signaling, ERBB2 amplification, and proliferation. Previously published expression-based nomenclature of breast cancer 'intrinsic' subtypes can be mapped to the three modules, namely, the ER-/HER2- (basal-like), the HER2+ (HER2-like), and the low- and high-proliferation ER+/HER2- subtypes (luminal A and B). We showed that all nine prognostic signatures exhibited a similar prognostic performance in the entire dataset. Their prognostic abilities are due mostly to the detection of proliferation activity. Although ER- status (basal-like) and ERBB2+ expression status correspond to bad outcome, they seem to act through elevated expression of proliferation genes and thus contain only indirect information about prognosis. Clinical variables measuring the extent of tumor progression, such as tumor size and nodal status, still add independent prognostic information to proliferation genes. CONCLUSION: This meta-analysis unifies various results of previous gene expression studies in breast cancer. It reveals connections between traditional prognostic factors, expression-based subtyping, and prognostic signatures, highlighting the important role of proliferation in breast cancer prognosis.
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PURPOSE: Mutations within the KRAS proto-oncogene have predictive value but are of uncertain prognostic value in the treatment of advanced colorectal cancer. We took advantage of PETACC-3, an adjuvant trial with 3,278 patients with stage II to III colon cancer, to evaluate the prognostic value of KRAS and BRAF tumor mutation status in this setting. PATIENTS AND METHODS: Formalin-fixed paraffin-embedded tissue blocks (n = 1,564) were prospectively collected and DNA was extracted from tissue sections from 1,404 cases. Planned analysis of KRAS exon 2 and BRAF exon 15 mutations was performed by allele-specific real-time polymerase chain reaction. Survival analyses were based on univariate and multivariate proportional hazard regression models. RESULTS: KRAS and BRAF tumor mutation rates were 37.0% and 7.9%, respectively, and were not significantly different according to tumor stage. In a multivariate analysis containing stage, tumor site, nodal status, sex, age, grade, and microsatellite instability (MSI) status, KRAS mutation was associated with grade (P = .0016), while BRAF mutation was significantly associated with female sex (P = .017), and highly significantly associated with right-sided tumors, older age, high grade, and MSI-high tumors (all P < 10(-4)). In univariate and multivariate analysis, KRAS mutations did not have a major prognostic value regarding relapse-free survival (RFS) or overall survival (OS). BRAF mutation was not prognostic for RFS, but was for OS, particularly in patients with MSI-low (MSI-L) and stable (MSI-S) tumors (hazard ratio, 2.2; 95% CI, 1.4 to 3.4; P = .0003). CONCLUSION: In stage II-III colon cancer, the KRAS mutation status does not have major prognostic value. BRAF is prognostic for OS in MS-L/S tumors.
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Introduction: Survival of children born prematurely or with very low birth weight has increased dramatically, but the long term developmental outcome remains unknown. Many children have deficits in cognitive capacities, in particular involving executive domains and those disabilities are likely to involve a central nervous system deficit. To understand their neurostructural origin, we use DTI. Structurally segregated and functionally regions of the cerebral cortex are interconnected by a dense network of axonal pathways. We noninvasively map these pathways across cortical hemispheres and construct normalized structural connection matrices derived from DTI MR tractography. Group comparisons of brain connectivity reveal significant changes in fiber density in case of children with poor intrauterine grown and extremely premature children (gestational age<28 weeks at birth) compared to control subjects. This changes suggest a link between cortico-axonal pathways and the central nervous system deficit. Methods: Sixty premature born infants (5-6 years old) were scanned on clinical 3T scanner (Magnetom Trio, Siemens Medical Solutions, Erlangen, Germany) at two hospitals (HUG, Geneva and CHUV, Lausanne). For each subject, T1-weighted MPRAGE images (TR/TE=2500/2.91,TI=1100, resolution=1x1x1mm, matrix=256x154) and DTI images (30 directions, TR/TE=10200/107, in-plane resolution=1.8x1.8x2mm, 64 axial, matrix=112x112) were acquired. Parent(s) provided written consent on prior ethical board approval. The extraction of the Whole Brain Structural Connectivity Matrix was performed following (Cammoun, 2009 and Hagmann, 2008). The MPARGE images were registered using an affine registration to the non-weighted-DTI and WM-GM segmentation performed on it. In order to have equal anatomical localization among subjects, 66 cortical regions with anatomical landmarks were created using the curvature information, i.e. sulcus and gyrus (Cammoun et al, 2007; Fischl et al, 2004; Desikan et al, 2006) with freesurfer software (http://surfer.nmr.mgh.harvard.edu/). Tractography was performed in WM using an algorithm especially designed for DTI/DSI data (Hagmann et al., 2007) and both information were then combined in a matrix. Each row and column of the matrix corresponds to a particular ROI. Each cell of index (i,j) represents the fiber density of the bundle connecting the ROIs i and j. Subdividing each cortical region, we obtained 4 Connectivity Matrices of different resolution (33, 66, 125 and 250 ROI/hemisphere) for each subject . Subjects were sorted in 3 different groups, namely (1) control, (2) Intrauterine Growth Restriction (IUGR), (3) Extreme Prematurity (EP), depending on their gestational age, weight and percentile-weight score at birth. Group-to-group comparisons were performed between groups (1)-(2) and (1)-(3). The mean age at examination of the three groups were similar. Results: Quantitative analysis were performed between groups to determine fibers density differences. For each group, a mean connectivity matrix with 33ROI/hemisphere resolution was computed. On the other hand, for all matrix resolutions (33,66,125,250 ROI/hemisphere), the number of bundles were computed and averaged. As seen in figure 1, EP and IUGR subjects present an overall reduction of fibers density in both interhemispherical and intrahemispherical connections. This is given quantitatively in table 1. IUGR subjects presents a higher percentage of missing fiber bundles than EP when compared to control subjects (~16% against 11%). When comparing both groups to control subjects, for the EP subjects, the occipito-parietal regions seem less interhemispherically connected whilst the intrahemispherical networks present lack of fiber density in the lymbic system. Children born with IUGR, have similar reductions in interhemispherical connections than the EP. However, the cuneus and precuneus connections with the precentral and paracentral lobe are even lower than in the case of the EP. For the intrahemispherical connections the IUGR group preset a loss of fiber density between the deep gray matter structures (striatum) and the frontal and middlefrontal poles, connections typically involved in the control of executive functions. For the qualitative analysis, a t-test comparing number of bundles (p-value<0.05) gave some preliminary significant results (figure 2). Again, even if both IUGR and EP appear to have significantly less connections comparing to the control subjects, the IUGR cohort seems to present a higher lack of fiber density specially relying the cuneus, precuneus and parietal areas. In terms of fiber density, preliminary Wilcoxon tests seem to validate the hypothesis set by the previous analysis. Conclusions: The goal of this study was to determine the effect of extreme prematurity and poor intrauterine growth on neurostructural development at the age of 6 years-old. This data indicates that differences in connectivity may well be the basis for the neurostructural and neuropsychological deficit described in these populations in the absence of overt brain lesions (Inder TE, 2005; Borradori-Tolsa, 2004; Dubois, 2008). Indeed, we suggest that IUGR and prematurity leads to alteration of connectivity between brain structures, especially in occipito-parietal and frontal lobes for EP and frontal and middletemporal poles for IUGR. Overall, IUGR children have a higher loss of connectivity in the overall connectivity matrix than EP children. In both cases, the localized alteration of connectivity suggests a direct link between cortico-axonal pathways and the central nervous system deficit. Our next step is to link these connectivity alterations to the performance in executive function tests.
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Diabetic peripheral neuropathy (DPN) is a common complication affecting more than one third of diabetes mellitus (DM) patients. Although all cellular components participating in peripheral nerve function are exposed to and affected by the metabolic consequences of DM, nodal regions, areas of intense interactions between Schwann cells and axons, may be particularly sensitive to DM-induced alterations. Nodes are enriched in insulin receptors, glucose transporters, Na(+) and K(+) channels, and mitochondria, all implicated in the development and progression of DPN. Latest results particularly reinforce the idea that changes in ion-channel function and energy metabolism, both of which depend on axon-glia crosstalk, are among the important contributors to DPN. These insights provide a basis for new therapeutic approaches aimed at delaying or reversing DPN.
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Peripheral T-cell lymphomas (PTCLs) represent a heterogeneous group of more than 20 neoplastic entities derived from mature T cells and natural killer (NK) cells involved in innate and adaptive immunity. With few exceptions these malignancies, which may present as disseminated, predominantly extranodal or cutaneous, or predominantly nodal diseases, are clinically aggressive and have a dismal prognosis. Their diagnosis and classification is hampered by several difficulties, including a significant morphological and immunophenotypic overlap across different entities, and the lack of characteristic genetic alterations for most of them. Although there is increasing evidence that the cell of origin is a major determinant for the delineation of several PTCL entities, however, the cellular derivation of most entities remains poorly characterized and/or may be heterogeneous. The complexity of the biology and pathophysiology of PTCLs has been only partly deciphered. In recent years, novel insights have been gained from genome-wide profiling analyses. In this review, we will summarize the current knowledge on the pathobiological features of peripheral NK/T-cell neoplasms, with a focus on selected disease entities manifesting as tissue infiltrates primarily in extranodal sites and lymph nodes.