Thermodynamical Properties and Quasi-localized Energy of the Stringy Dyonic Black Hole Solution

In this article, we calculate the heat flux passing through the horizon $. {\bf TS}|_{r_h}$ and the difference of energy between the Einstein and M{\o}ller prescription within the region ${\cal M}$, in which is the region between outer horizon ${\cal H}_+$ and inner horizon ${\cal H}_-$, for the modified GHS solution, KLOPP solution and CLH solution. The formula . E_{\rm Einstein}|_{\cal M} = . E_{\rm M{\o}ller}|_{\cal M} - \sum_{\partial {\cal M}} {\bf TS}$ is obeyed for the mGHS solution and the KLOPP solution, but not for the CLH solution. Also, we suggest a RN-like stringy dyonic black hole solution, which comes from the KLOPP solution under a dual transformation, and its thermodynamical properties are the same as the KLOPP solution

Comparing a new risk prediction model with prostate cancer risk calculator apps in a Taiwanese population

PURPOSE: To develop a novel Taiwanese prostate cancer (PCa) risk model for predicting PCa, comparing its predictive performance with that of two well-established PCa risk calculator apps. METHODS: 1545 men undergoing prostate biopsies in a Taiwanese tertiary medical center between 2012 and 2019 were identified retrospectively. A five-fold cross-validated logistic regression risk model was created to calculate the probabilities of PCa and high-grade PCa (Gleason score ≧ 7), to compare those of the Rotterdam and Coral apps. Discrimination was analyzed using the area under the receiver operator characteristic curve (AUC). Calibration was graphically evaluated with the goodness-of-fit test. Decision-curve analysis was performed for clinical utility. At different risk thresholds to biopsy, the proportion of biopsies saved versus low- and high-grade PCa missed were presented. RESULTS: Overall, 278/1309 (21.2%) patients were diagnosed with PCa, and 181 out of 278 (65.1%) patients had high-grade PCa. Both our model and the Rotterdam app demonstrated better discriminative ability than the Coral app for detection of PCa (AUC: 0.795 vs 0.792 vs 0.697, DeLong's method: P < 0.001) and high-grade PCa (AUC: 0.869 vs 0.873 vs 0.767, P < 0.001). Using a ≥ 10% risk threshold for high-grade PCa to biopsy, our model could save 67.2% of total biopsies; among these saved biopsies, only 3.4% high-grade PCa would be missed. CONCLUSION: Our new logistic regression model, similar to the Rotterdam app, outperformed the Coral app in the prediction of PCa and high-grade PCa. Additionally, our model could save unnecessary biopsies and avoid missing clinically significant PCa in the Taiwanese population

Study of the Potts Model on the Honeycomb and Triangular Lattices: Low-Temperature Series and Partition Function Zeros

We present and analyze low-temperature series and complex-temperature partition function zeros for the $q$-state Potts model with $q=4$ on the honeycomb lattice and $q=3,4$ on the triangular lattice. A discussion is given as to how the locations of the singularities obtained from the series analysis correlate with the complex-temperature phase boundary. Extending our earlier work, we include a similar discussion for the Potts model with $q=3$ on the honeycomb lattice and with $q=3,4$ on the kagom\'e lattice.Comment: 33 pages, Latex, 9 encapsulated postscript figures, J. Phys. A, in pres

Predicting serum levels of lithium-treated patients: A supervised machine learning approach

Routine monitoring of lithium levels is common clinical practice. This is because the lithium prediction strategies available developed by previous studies are still limited due to insufficient prediction performance. Thus, we used machine learning approaches to predict lithium concentration in a large real-world dataset. Real-world data from multicenter electronic medical records were used in different machine learning algorithms to predict: (1) whether the serum level was 0.6-1.2 mmol/L or 0.0-0.6 mmol/L (binary prediction), and (2) its concentration value (continuous prediction). We developed models from 1505 samples through 5-fold cross-validation and used 204 independent samples to test their performance by evaluating their accuracy. Moreover, we ranked the most important clinical features in different models and reconstructed three reduced models with fewer clinical features. For binary and continuous predictions, the average accuracy of these models was 0.70-0.73 and 0.68-0.75, respectively. Seven features were listed as important features related to serum lithium levels of 0.6-1.2 mmol/L or higher lithium concentration, namely older age, lower systolic blood pressure, higher daily and last doses of lithium prescription, concomitant psychotropic drugs with valproic acid and -pine drugs, and comorbid substance-related disorders. After reducing the features in the three new predictive models, the binary or continuous models still had an average accuracy of 0.67-0.74. Machine learning processes complex clinical data and provides a potential tool for predicting lithium concentration. This may help in clinical decision-making and reduce the frequency of serum level monitoring

Gene selection with multiple ordering criteria

BACKGROUND: A microarray study may select different differentially expressed gene sets because of different selection criteria. For example, the fold-change and p-value are two commonly known criteria to select differentially expressed genes under two experimental conditions. These two selection criteria often result in incompatible selected gene sets. Also, in a two-factor, say, treatment by time experiment, the investigator may be interested in one gene list that responds to both treatment and time effects. RESULTS: We propose three layer ranking algorithms, point-admissible, line-admissible (convex), and Pareto, to provide a preference gene list from multiple gene lists generated by different ranking criteria. Using the public colon data as an example, the layer ranking algorithms are applied to the three univariate ranking criteria, fold-change, p-value, and frequency of selections by the SVM-RFE classifier. A simulation experiment shows that for experiments with small or moderate sample sizes (less than 20 per group) and detecting a 4-fold change or less, the two-dimensional (p-value and fold-change) convex layer ranking selects differentially expressed genes with generally lower FDR and higher power than the standard p-value ranking. Three applications are presented. The first application illustrates a use of the layer rankings to potentially improve predictive accuracy. The second application illustrates an application to a two-factor experiment involving two dose levels and two time points. The layer rankings are applied to selecting differentially expressed genes relating to the dose and time effects. In the third application, the layer rankings are applied to a benchmark data set consisting of three dilution concentrations to provide a ranking system from a long list of differentially expressed genes generated from the three dilution concentrations. CONCLUSION: The layer ranking algorithms are useful to help investigators in selecting the most promising genes from multiple gene lists generated by different filter, normalization, or analysis methods for various objectives

A single residue substitution in the receptor-binding domain of H5N1 hemagglutinin is critical for packaging into pseudotyped lentiviral particles

© 2012 Tang et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Background: Serological studies for influenza infection and vaccine response often involve microneutralization and hemagglutination inhibition assays to evaluate neutralizing antibodies against human and avian influenza viruses, including H5N1. We have previously characterized lentiviral particles pseudotyped with H5-HA (H5pp) and validated an H5pp-based assay as a safe alternative for high-throughput serological studies in BSL-2 facilities. Here we show that H5-HAs from different clades do not always give rise to efficient production of H5pp and the underlying mechanisms are addressed. Methodology/Findings: We have carried out mutational analysis to delineate the molecular determinants responsible for efficient packaging of HA from A/Cambodia/40808/2005 (H5Cam) and A/Anhui/1/2005 (H5Anh) into H5pp. Our results demonstrate that a single A134V mutation in the 130-loop of the receptor binding domain is sufficient to render H5Anh the ability to generate H5Anh-pp efficiently, whereas the reverse V134A mutation greatly hampers production of H5Cam-pp. Although protein expression in total cell lysates is similar for H5Anh and H5Cam, cell surface expression of H5Cam is detected at a significantly higher level than that of H5Anh. We further demonstrate by several independent lines of evidence that the behaviour of H5Anh can be explained by a stronger binding to sialic acid receptors implicating residue 134. Conclusions: We have identified a single A134V mutation as the molecular determinant in H5-HA for efficient incorporation into H5pp envelope and delineated the underlying mechanism. The reduced binding to sialic acid receptors as a result of the A134V mutation not only exerts a critical influence in pseudotyping efficiency of H5-HA, but has also an impact at the whole virus level. Because A134V substitution has been reported as a naturally occurring mutation in human host, our results may have implications for the understanding of human host adaptation of avian influenza H5N1 virusesThis work was supported by grants from the Research Fund for the Control of Infectious Diseases of Hong Kong (RFCID#08070972), the Area of Excellence Scheme of the University Grants Committee (grant AoE/M-12/-06 of the Hong Kong Special Administrative Region, China), the French Ministry of Health, and the RESPARI project of the Institut Pasteur International Network

Pretreatment with a Heat-Killed Probiotic Modulates the NLRP3 Inflammasome and Attenuates Colitis-Associated Colorectal Cancer in Mice.

Colorectal cancer (CRC) is one of the most common malignancies worldwide. Inflammation contributes to cancer development and inflammatory bowel disease is an important risk factor for CRC. The aim of this study is to assess whether a widely used probiotic Enterococcus faecalis can modulate the NLRP3 inflammasome and protect against colitis and colitis-associated CRC. We studied the effect of heat-killed cells of E. faecalis on NLRP3 inflammasome activation in THP-1-derived macrophages. Pretreatment of E. faecalis or NLRP3 siRNA can inhibit NLRP3 inflammasome activation in macrophages in response to fecal content or commensal microbes, P. mirabilis or E. coli, according to the reduction of caspase-1 activation and IL-1β maturation. Mechanistically, E. faecalis attenuates the phagocytosis that is required for the full activation of the NLRP3 inflammasome. In in vivo mouse experiments, E. faecalis can ameliorate the severity of intestinal inflammation and thereby protect mice from dextran sodium sulfate (DSS)-induced colitis and the formation of CRC in wild type mice. On the other hand, E. faecalis cannot prevent DSS-induced colitis in NLRP3 knockout mice. Our findings indicate that application of the inactivated probiotic, E. faecalis, may be a useful and safe strategy for attenuation of NLRP3-mediated colitis and inflammation-associated colon carcinogenesis

Design and construction of the MicroBooNE Cosmic Ray Tagger system

The MicroBooNE detector utilizes a liquid argon time projection chamber (LArTPC) with an 85 t active mass to study neutrino interactions along the Booster Neutrino Beam (BNB) at Fermilab. With a deployment location near ground level, the detector records many cosmic muon tracks in each beam-related detector trigger that can be misidentified as signals of interest. To reduce these cosmogenic backgrounds, we have designed and constructed a TPC-external Cosmic Ray Tagger (CRT). This sub-system was developed by the Laboratory for High Energy Physics (LHEP), Albert Einstein center for fundamental physics, University of Bern. The system utilizes plastic scintillation modules to provide precise time and position information for TPC-traversing particles. Successful matching of TPC tracks and CRT data will allow us to reduce cosmogenic background and better characterize the light collection system and LArTPC data using cosmic muons. In this paper we describe the design and installation of the MicroBooNE CRT system and provide an overview of a series of tests done to verify the proper operation of the system and its components during installation, commissioning, and physics data-taking

The Mitochondrial Ca(2+) Uniporter: Structure, Function, and Pharmacology.

Mitochondrial Ca(2+) uptake is crucial for an array of cellular functions while an imbalance can elicit cell death. In this chapter, we briefly reviewed the various modes of mitochondrial Ca(2+) uptake and our current understanding of mitochondrial Ca(2+) homeostasis in regards to cell physiology and pathophysiology. Further, this chapter focuses on the molecular identities, intracellular regulators as well as the pharmacology of mitochondrial Ca(2+) uniporter complex