Polyakov Loop Percolation and Deconfinement in SU(2) Gauge Theory

The deconfinement transition in SU(2) gauge theory and the magnetization transition in the Ising model belong to the same universality class. The critical behaviour of the Ising model can be characterized either as spontaneous breaking of the Z_2 symmetry of spin states or as percolation of appropriately defined spin clusters. We show that deconfinement in SU(2) gauge theory can be specified as percolation of Polyakov loop clusters with Fortuin-Kasteleyn bond weights, leading to the same (Onsager) critical exponents as the conventional order-disorder description based on the Polyakov loop expectation value.Comment: revised versio

Percolation and Deconfinement in SU(2) Gauge Theory

We show that deconfinement in SU(2) gauge theory can be described by the percolation of site-bond clusters of like-sign Polyakov loops. In particular, we find that in 2+1 dimensions the percolation variables coincide with those of the 2-dimensional Ising model.Comment: 3 pages, latex, 2 figures; proceedings of Lattice '99 (Pisa,Italy

Percolation and Magnetization in the Continuous Spin Ising Model

In the strong coupling limit the partition function of SU(2) gauge theory can be reduced to that of the continuous spin Ising model with nearest neighbour pair-interactions. The random cluster representation of the continuous spin Ising model in two dimensions is derived through a Fortuin-Kasteleyn transformation, and the properties of the corresponding cluster distribution are analyzed. It is shown that for this model, the magnetic transition is equivalent to the percolation transition of Fortuin-Kasteleyn clusters, using local bond weights. These results are also illustrated by means of numerical simulations

Percolation and Magnetization for Generalized Continuous Spin Models

For the Ising model, the spin magnetization transition is equivalent to the percolation transition of Fortuin-Kasteleyn clusters; this result remains valid also for the conventional continuous spin Ising model. The investigation of more general continuous spin models may help to obtain a percolation formulation for the critical behaviour in SU(2) gauge theory. We therefore study a broad class of theories, introducing spin distribution functions, longer range interactions and self-interaction terms. The thermal behaviour of each model turns out to be in the Ising universality class. The corresponding percolation formulations are then obtained by extending the Fortuin-Kasteleyn cluster definition; in several cases they illustrate recent rigorous results.Comment: Abstract and references partially change

Cluster Percolation and Pseudocritical Behaviour in Spin Models

The critical behaviour of many spin models can be equivalently formulated as percolation of specific site-bond clusters. In the presence of an external magnetic field, such clusters remain well-defined and lead to a percolation transition, even though the system no longer shows thermal critical behaviour. We investigate the 2-dimensional Ising model and the 3-dimensional O(2) model by means of Monte Carlo simulations. We find for small fields that the line of percolation critical points has the same functional form as the line of thermal pseudocritical points.Comment: 11 pages, 7 figures, 2 tables, abstract partially changed, references adde

Mcl-1 Antisense Therapy Chemosensitizes Human Melanoma in a SCID Mouse Xenotransplantation Model

It is well established that high expression of the antiapoptotic Bcl-2 family proteins Bcl-2 and Bcl-xL can significantly contribute to chemoresistance in a number of human malignancies. Much less is known about the role the more recently described Bcl-2 family member Mcl-1 might play in tumor biology and resistance to chemotherapy. Using an antisense strategy, we here address this issue in melanoma, a paradigm of a treatment-resistant malignancy. After in vitro proof of principle supporting an antisense mechanism of action with specific reduction of Mcl-1 protein as a consequence of nuclear uptake of the Mcl-1 antisense oligonucleotides employed, antisense and universal control oligonucleotides were administered systemically in combination with dacarbazine in a human melanoma SCID mouse xenotransplantation model. Dacarbazine, available now for more than three decades, still remains the most active single agent for treatment of advanced melanoma. Mcl-1 antisense oligonucleotides specifically reduced target protein expression as well as the apoptotic threshold of melanoma xenotransplants. Combined Mcl-1 antisense oligonucleotide plus dacarbazine treatment resulted in enhanced tumor cell apoptosis and led to a significantly reduced mean tumor weight (mean 0.16 g, 95% confidence interval 0.08–0.26) compared to the tumor weight in universal control oligonucleotide plus dacarbazine treated animals (mean 0.35 g, 95% confidence interval 0.2–0.44) or saline plus dacarbazine treated animals (mean 0.39 g, 95% confidence interval 0.25–0.53). We thus show that Mcl-1 is an important factor contributing to the chemoresistance of human melanoma in vivo. Antisense therapy against the Mcl-1 gene product, possibly in combination with antisense strategies targeting other antiapoptotic Bcl-2 family members, appears to be a rational and promising approach to help overcome treatment resistance of malignant melanoma

Evaluation of the biomarker candidate MFAP4 for non-invasive assessment of hepatic fibrosis in hepatitis C patients

$\textbf {Background:}$ The human microfibrillar-associated protein 4 (MFAP4) is located to extracellular matrix fibers and plays a role in disease-related tissue remodeling. Previously, we identified MFAP4 as a serum biomarker candidate for hepatic fibrosis and cirrhosis in hepatitis C patients. The aim of the present study was to elucidate the potential of MFAP4 as biomarker for hepatic fibrosis with a focus on the differentiation of no to moderate (F0–F2) and severe fibrosis stages and cirrhosis (F3 and F4, Desmet-Scheuer scoring system). $\textbf {Methods:}$ MFAP4 levels were measured using an AlphaLISA immunoassay in a retrospective study including $\it n$ = 542 hepatitis C patients. We applied a univariate logistic regression model based on MFAP4 serum levels and furthermore derived a multivariate model including also age and gender. Youden-optimal cutoffs for binary classification were determined for both models without restrictions and considering a lower limit of 80% sensitivity (correct classification of F3 and F4), respectively. To assess the generalization error, leave-one-out cross validation (LOOCV ) was performed. $\textbf {Results:}$ MFAP4 levels were shown to differ between no to moderate fibrosis stages F0–F2 and severe stages (F3 and F4) with high statistical significance ($\it t$ test on log scale, $\it p$ value $<2.2·10^{-16}$). In the LOOCV, the univariate classification resulted in 85.8% sensitivity and 54.9% specificity while the multivariate model yielded 81.3% sensitivity and 61.5% specificity (restricted approaches). $\textbf {Conclusions:}$ We confirmed the applicability of MFAP4 as a novel serum biomarker for assessment of hepatic fibrosis and identification of high-risk patients with severe fibrosis stages in hepatitis C. The combination of MFAP4 with existing tests might lead to a more accurate non-invasive diagnosis of hepatic fibrosis and allow a cost-effective disease management in the era of new direct acting antivirals

Estimation of Mortalities

If a linear regression is fit to log-transformed mortalities and the estimate is back-transformed according to the formula Ee^Y = e^{\mu + \sigma^2/2} a systematic bias occurs unless the error distribution is normal and the scale estimate is gauged to normal variance. This result is a consequence of the uniqueness theorem for the Laplace transform. We determine the systematic bias of minimum-L_2 and minimum-L_1 estimation with sample variance and interquartile range of the residuals as scale estimates under a uniform and four contaminated normal error distributions. Already under innocent looking contaminations the true mortalities may be underestimated by 50% in the long run. Moreover, the logarithmic transformation introduces an instability into the model that results in a large discrepancy between rg_Huber estimates as the tuning constant regulating the degree of robustness varies. Contrary to the logarithm the square root stabilizes variance, diminishes the influence of outliers, automatically copes with observed zeros, allows the `nonparametric' back-transformation formula E Y^2 = \mue^2 + \sigma^2, and in the homoskedastic case avoids a systematic bias of minimum-L_2 estimation with sample variance. For the company-specific table 3 of [Loeb94], in the age range of 20-65 years, we fit a parabola to root mortalities by minimum-L_2 , minimum-L_1, and robust rg_Huber regression estimates, and a cubic and exponential by least squares. The fits thus obtained in the original model are excellent and practically indistinguishable by a \chi^2 goodness-of-fit test. Finally , dispensing with the transformation of observations, we employ a Poisson generalized linear model and fit an exponential and a cubic by maximum likelihood

Familie und Schule: zwei Orte der Erziehung.

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