Analytical models of probability distribution and excess noise factor of Solid State Photomultiplier signals with crosstalk

Silicon Photomultipliers (SiPM), also so-called Solid State Photomultipliers (SSPM), are based on Geiger mode avalanche breakdown limited by strong negative feedback. SSPM can detect and resolve single photons due to high gain and ultra-low excess noise of avalanche multiplication in this mode. Crosstalk and afterpulsing processes associated with the high gain introduce specific excess noise and deteriorate photon number resolution of the SSPM. Probabilistic features of these processes are widely studied because of its high importance for the SSPM design, characterization, optimization and application, but the process modeling is mostly based on Monte Carlo simulations and numerical methods. In this study, crosstalk is considered to be a branching Poisson process, and analytical models of probability distribution and excess noise factor (ENF) of SSPM signals based on the Borel distribution as an advance on the geometric distribution models are presented and discussed. The models are found to be in a good agreement with the experimental probability distributions for dark counts and a few photon spectrums in a wide range of fired pixels number as well as with observed super-linear behavior of crosstalk ENF.Comment: 10 pages, 2 tables, 3 figures, Reported at 6th International Conference on "New Developments In Photodetection - NDIP11

Evolution of surname distribution under gender-equality measurements

We consider a model for the evolution of the surnames distribution under a gender-equality measurement presently discussed in the Spanish parliament (the children take the surname of the father or the mother according to alphabetical order). We quantify how this would bias the alphabetical distribution of surnames, and analyze its effect on the present distribution of the surnames in Spain

Self-Titrating Anticoagulant Nanocomplexes That Restore Homeostatic Regulation of the Coagulation Cascade

Antithrombotic therapy is a critical portion of the treatment regime for a number of life-threatening conditions, including cardiovascular disease, stroke, and cancer; yet, proper clinical management of anticoagulation remains a challenge because existing agents increase the propensity for bleeding in patients. Here, we describe the development of a bioresponsive peptide–polysaccharide nanocomplex that utilizes a negative feedback mechanism to self-titrate the release of anticoagulant in response to varying levels of coagulation activity. This nanoscale self-titrating activatable therapeutic, or nanoSTAT, consists of a cationic thrombin-cleavable peptide and heparin, an anionic polysaccharide and widely used clinical anticoagulant. Under nonthrombotic conditions, nanoSTATs circulate inactively, neither releasing anticoagulant nor significantly prolonging bleeding time. However, in response to life-threatening pulmonary embolism, nanoSTATs locally release their drug payload and prevent thrombosis. This autonomous negative feedback regulator may improve antithrombotic therapy by increasing the therapeutic window and decreasing the bleeding risk of anticoagulants.National Institutes of Health (U.S.) (R01CA124427-01)National Cancer Institute (U.S.) (U54CA119349)National Cancer Institute (U.S.) (U54CA119335)National Cancer Institute (U.S.) (Center of Cancer Nanotechnology Excellence at MIT-Harvard U54CA151884)David & Lucile Packard Foundation (Fellowship)David H. Koch Institute for Integrative Cancer Research at MIT (Marie D. and Pierre Casimir-Lambert Fund)National Cancer Institute (U.S.) (Koch Institute Support (Core) Grant P30-CA14051)MIT-Harvard Center of Cancer Nanotechnology Excellence (5 U54 CA151884-03)National Institutes of Health (U.S.). Medical Scientist Training Program (T32GM007753)National Institutes of Health (U.S.) (Ruth L. Kirschstein National Research Service Award F32CA159496-02)Burroughs Wellcome Fund (Career Award at the Scientific Interface

Traveling wave solutions in a half-space for boundary reactions

We prove the existence and uniqueness of a traveling front and of its speed for the homogeneous heat equation in the half-plane with a Neumann boundary reaction term of unbalanced bistable type or of combustion type. We also establish the monotonicity of the front and, in the bistable case, its behavior at infinity. In contrast with the classical bistable interior reaction model, its behavior at the side of the invading state is of power type, while at the side of the invaded state its decay is exponential. These decay results rely on the construction of a family of explicit bistable traveling fronts. Our existence results are obtained via a variational method, while the uniqueness of the speed and of the front rely on a comparison principle and the sliding method.Peer ReviewedPostprint (author’s final draft

An application of the generalized Poisson difference distribution to the Bayesian modelling of football scores

The analysis of sports data, in particular football match outcomes, has always produced an immense interest among the statisticians. In this paper, we adopt the generalised Poisson difference distribution (GPDD) to model the goal difference of football matches. We discuss the advantages of the proposed model over the Poisson difference (PD) model which was also used for the same purpose. The GPDD model, like the PD model, is based on the goal difference in each game which allows us to account for the correlation without explicitly modelling it. The main advantage of the GPDD model is its flexibility in the tails by considering shorter as well as longer tails than the PD distribution. We carry out the analysis in a Bayesian framework in order to incorporate external information, such as historical knowledge or data, through the prior distributions. We model both the mean and the variance of the goal difference and show that such a model performs considerably better than a model with a fixed variance. Finally, the proposed model is fitted to the 2012-13 Italian Serie A football data and various model diagnostics are carried out to evaluate the performance of the model

Letter from Swiss Consul General Shanghai to American, British, and Dutch Nationals in Peking.

Typewritten letter with seal of Representative of the Swiss Consul General, Peking. With envelope addressed to Rev. William A. Amrhein.https://digitalcommons.whitworth.edu/amrhein_corr/1002/thumbnail.jp

A two-parameter generalized Poisson model to improve the analysis of RNA-seq data

Deep sequencing of RNAs (RNA-seq) has been a useful tool to characterize and quantify transcriptomes. However, there are significant challenges in the analysis of RNA-seq data, such as how to separate signals from sequencing bias and how to perform reasonable normalization. Here, we focus on a fundamental question in RNA-seq analysis: the distribution of the position-level read counts. Specifically, we propose a two-parameter generalized Poisson (GP) model to the position-level read counts. We show that the GP model fits the data much better than the traditional Poisson model. Based on the GP model, we can better estimate gene or exon expression, perform a more reasonable normalization across different samples, and improve the identification of differentially expressed genes and the identification of differentially spliced exons. The usefulness of the GP model is demonstrated by applications to multiple RNA-seq data sets