Fast Hamiltonian sampling for large scale structure inference

In this work we present a new and efficient Bayesian method for nonlinear three dimensional large scale structure inference. We employ a Hamiltonian Monte Carlo (HMC) sampler to obtain samples from a multivariate highly non-Gaussian lognormal Poissonian density posterior given a set of observations. The HMC allows us to take into account the nonlinear relations between the observations and the underlying density field which we seek to recover. As the HMC provides a sampled representation of the density posterior any desired statistical summary, such as the mean, mode or variance, can be calculated from the set of samples. Further, it permits us to seamlessly propagate non-Gaussian uncertainty information to any final quantity inferred from the set of samples. The developed method is extensively tested in a variety of test scenarios, taking into account a highly structured survey geometry and selection effects. Tests with a mock galaxy catalog based on the millennium run show that the method is able to recover the filamentary structure of the nonlinear density field. The results further demonstrate the feasibility of non-Gaussian sampling in high dimensional spaces, as required for precision nonlinear large scale structure inference. The HMC is a flexible and efficient method, which permits for simple extension and incorporation of additional observational constraints. Thus, the method presented here provides an efficient and flexible basis for future high precision large scale structure inference.Comment: 14 pages, 7 figure

Treatment with 5-Fluorouracil and Celecoxib Displays Synergistic Regression of Ultraviolet Light B-Induced Skin Tumors

Standard chemotherapeutic agents used for the treatment of pre-cancerous skin lesions and non-melanoma skin cancers are not completely effective. Several studies have suggested that repeated inflammatory sunburn reactions, which include the induction of cyclooxygenase-2 (COX-2) and the subsequent production of prostaglandins, play a role in skin cancer development. COX-2 inhibition has been demonstrated to be a potent means of preventing skin cancer development in mice; however, COX-2 inhibitors alone are not effective as chemotherapeutic agents. Data in a variety of cancer types suggest greater efficacy in treating tumors with combination chemotherapies. Therefore, we hypothesized that a combination of the chemotherapeutic agent 5-fluorouracil (5-FU) and the COX-2 inhibitor and anti-inflammatory drug celecoxib would act synergistically to regress tumors in a murine model of ultraviolet light B- (UVB-) induced carcinogenesis. We found that topical treatment with 5-FU and celecoxib together was up to 70% more effective in reducing the number of UVB-induced skin tumors than 5-FU treatment alone. Our data suggest that more effective chemotherapy regimens can be developed to treat the millions of pre-cancerous and cancerous skin lesions that arise every year, which could ultimately lead to a significant reduction in costs and cosmetic defects (scarring) associated with surgical interventions

Bayesian Multimodel Inference for Geostatistical Regression Models

The problem of simultaneous covariate selection and parameter inference for spatial regression models is considered. Previous research has shown that failure to take spatial correlation into account can influence the outcome of standard model selection methods. A Markov chain Monte Carlo (MCMC) method is investigated for the calculation of parameter estimates and posterior model probabilities for spatial regression models. The method can accommodate normal and non-normal response data and a large number of covariates. Thus the method is very flexible and can be used to fit spatial linear models, spatial linear mixed models, and spatial generalized linear mixed models (GLMMs). The Bayesian MCMC method also allows a priori unequal weighting of covariates, which is not possible with many model selection methods such as Akaike's information criterion (AIC). The proposed method is demonstrated on two data sets. The first is the whiptail lizard data set which has been previously analyzed by other researchers investigating model selection methods. Our results confirmed the previous analysis suggesting that sandy soil and ant abundance were strongly associated with lizard abundance. The second data set concerned pollution tolerant fish abundance in relation to several environmental factors. Results indicate that abundance is positively related to Strahler stream order and a habitat quality index. Abundance is negatively related to percent watershed disturbance

Superparamagnetic properties of hemozoin

We report that hemozoin nanocrystals demonstrate superparamagnetic properties, with direct measurements of the synthetic hemozoin magnetization. The results show that the magnetic permeability constant varies from mu = 4585 (at -20 degrees C) to 3843 (+20 degrees C), with the values corresponding to a superparamagnetic system. Similar results were obtained from the analysis of the diffusion separation of natural hemozoin nanocrystals in the magnetic field gradient, with mu = 6783 exceeding the value obtained in direct measurements by the factor of 1.8. This difference is interpreted in terms of structural differences between the synthetic and natural hemozoin. The ab initio analysis of the hemozoin elementary cell showed that the Fe3+ ion is in the high-spin state (S = 5/2), while the exchange interaction between Fe3+ electron-spin states was much stronger than k(B)T at room temperature. Thus, the spin dynamics of the neighboring Fe3+ ions are strongly correlated, lending support to the superparamagnetism

Functional Coupling of Ca2+ Channels to Ryanodine Receptors at Presynaptic Terminals: Amplification of Exocytosis and Plasticity

Ca2+-induced Ca2+ release (CICR) enhances a variety of cellular Ca2+ signaling and functions. How CICR affects impulse-evoked transmitter release is unknown. At frog motor nerve terminals, repetitive Ca2+ entries slowly prime and subsequently activate the mechanism of CICR via ryanodine receptors and asynchronous exocytosis of transmitters. Further Ca2+ entry inactivates the CICR mechanism and the absence of Ca2+ entry for >1 min results in its slow depriming. We now report here that the activation of this unique CICR markedly enhances impulse-evoked exocytosis of transmitter. The conditioning nerve stimulation (10–20 Hz, 2–10 min) that primes the CICR mechanism produced the marked enhancement of the amplitude and quantal content of end-plate potentials (EPPs) that decayed double exponentially with time constants of 1.85 and 10 min. The enhancement was blocked by inhibitors of ryanodine receptors and was accompanied by a slight prolongation of the peak times of EPP and the end-plate currents estimated from deconvolution of EPP. The conditioning nerve stimulation also enhanced single impulse- and tetanus-induced rises in intracellular Ca2+ in the terminals with little change in time course. There was no change in the rate of growth of the amplitudes of EPPs in a short train after the conditioning stimulation. On the other hand, the augmentation and potentiation of EPP were enhanced, and then decreased in parallel with changes in intraterminal Ca2+ during repetition of tetani. The results suggest that ryanodine receptors exist close to voltage-gated Ca2+ channels in the presynaptic terminals and amplify the impulse-evoked exocytosis and its plasticity via CICR after Ca2+-dependent priming

Virtual Reality Based Simulation of Hysteroscopic Interventions

Virtual reality based simulation is an appealing option to supplement traditional clinical education. However, the formal integration of training simulators into the medical curriculum is still lacking. Especially, the lack of a reasonable level of realism supposedly hinders the widespread use of this technology. Therefore, we try to tackle this situation with a reference surgical simulator of the highest possible fidelity for procedural training. This overview describes all elements that have been combined into our training system as well as first results of simulator validation. Our framework allows the rehearsal of several aspects of hysteroscopy—for instance, correct fluid management, handling of excessive bleeding, appropriate removal of intrauterine tumors, or the use of the surgical instrument

Spatial extremes of wildfire sizes: Bayesian hieralquical models for extremes

In Portugal, due to the combination of climatological and ecological factors, large wildfires are a constant threat and due to their economic impact, a big policy issue. In order to organize efficient fire fighting capacity and resource management, correct quantification of the risk of large wildfires are needed. In this paper, we quantify the regional risk of large wildfire sizes, by fitting a Generalized Pareto distribution to excesses over a suitably chosen high threshold. Spatio-temporal variations are introduced into the model through model parameters with suitably chosen link functions. The inference on these models are carried using Bayesian Hierarchical Models and Markov chain Monte Carlo methods