Generalized Bayesian model selection for speckle on remote sensing images

Synthetic aperture radar (SAR) and ultrasound (US) are two important active imaging techniques for remote sensing, both of which are subject to speckle noise caused by coherent summation of back-scattered waves and subsequent nonlinear envelope transformations. Estimating the characteristics of this multiplicative noise is crucial to develop denoising methods and to improve statistical inference from remote sensing images. In this paper, reversible jump Markov chain Monte Carlo (RJMCMC) algorithm has been used with a wider interpretation and a recently proposed RJMCMC-based Bayesian approach, trans-space RJMCMC, has been utilized. The proposed method provides an automatic model class selection mechanism for remote sensing images of SAR and US where the model class space consists of popular envelope distribution families. The proposed method estimates the correct distribution family, as well as the shape and the scale parameters, avoiding performing an exhaustive search. For the experimental analysis, different SAR images of urban, forest and agricultural scenes, and two different US images of a human heart have been used. Simulation results show the efficiency of the proposed method in finding statistical models for speckle

How does a biopsy of endoscopically normal terminal ileum contribute to the diagnosis? Which patients should undergo biopsy?

Background: Terminal ileum endoscopy and biopsy are the diagnostic tools of diseases attacking the ileum. However, abnormal histological findings can be found in endoscopically normal terminal ileum.Objective: This study was performed to evaluate the histopathological results of biopsies from endoscopically normal terminal ileum in order to determine pre-procedure clinical and laboratory factors predicting abnormal histopathological results, if any.Methods: A total of 297 patients who underwent colonoscopy and terminal ileum biopsy and had normal terminal ileum or a few aphthous ulcers in the terminal ileum together with completely normal colon mucosa were included in the study. The patients were grouped into two arms as normal cases and cases with aphthous ulcers. Histopathological and pre-procedural laboratory results of patients were analyzed according to their indications.Results: The terminal ileum was endoscopically normal in 200 patients, and 97 patients had aphthous ulcers. Chronic ileitis rate was present in 5.5% of those with endoscopically normal terminal ileum and in 39.2% of the patients with aphthous ulcers. In both groups, the highest rate of chronic ileitis was detected in the patients with known inflammatory bowel disease (IBD) (15.4 and 50%, respectively), anemia (9.5 and 43.5%, respectively), and in the patients having chronic diarrhea together with abdominal pain (7.7 and 44.8%, respectively). We found that the sensitivity of mean platelet volume for predicting chronic ileitis was 87% and the specificity was 45% at a cut-off value lower than 9.35 fl.Conclusion: In anemia indication or chronic diarrhea together with abdominal pain, the frequency of aphthous ulcers detected by ileoscopy and the frequency of chronic ileitis detected histopathologically despite a normal-appearing ileum were elevated.Keywords: Terminal ileum; ileoscopy; chronic ileitis; inflammatory bowel diseas

Cauchy-Rician model for backscattering in urban SAR images

This paper presents a new statistical model for urban scene SAR images by combining the Cauchy distribution, which is heavy-tailed, with the Rician back-scattering. The literature spans various well-known models most of which are derived under the assumption that the scene consists of multitudes of random reflectors. This idea specifically fails for urban scenes since they accommodate a heterogeneous collection of strong scatterers such as buildings, cars, wall corners. Moreover, when it comes to analysing their statistical behaviour, due to these strong reflectors, urban scenes include a high number of high amplitude samples, which implies that urban scenes are mostly heavy-tailed. The proposed Cauchy-Rician model contributes to the literature by leveraging non-zero location (Rician) heavy-tailed (Cauchy) signal components. In the experimental analysis, the Cauchy-Rician model is investigated in comparison to state-of-the-art statistical models that include G0, generalized gamma, and the lognormal distribution. The numerical analysis demonstrates the superior performance and flexibility of the proposed distribution for modelling urban scenes

Anti-cancer effects and mechanism of actions of aspirin analogues in the treatment of glioma cancer

INTRODUCTION: In the past 25 years only modest advancements in glioma treatment have been made, with patient prognosis and median survival time following diagnosis only increasing from 3 to 7 months. A substantial body of clinical and preclinical evidence has suggested a role for aspirin in the treatment of cancer with multiple mechanisms of action proposed including COX 2 inhibition, down regulation of EGFR expression, and NF-κB signaling affecting Bcl-2 expression. However, with serious side effects such as stroke and gastrointestinal bleeding, aspirin analogues with improved potency and side effect profiles are being developed. METHOD: Effects on cell viability following 24 hr incubation of four aspirin derivatives (PN508, 517, 526 and 529) were compared to cisplatin, aspirin and di-aspirin in four glioma cell lines (U87 MG, SVG P12, GOS – 3, and 1321N1), using the PrestoBlue assay, establishing IC50 and examining the time course of drug effects. RESULTS: All compounds were found to decrease cell viability in a concentration and time dependant manner. Significantly, the analogue PN517 (IC50 2mM) showed approximately a twofold increase in potency when compared to aspirin (3.7mM) and cisplatin (4.3mM) in U87 cells, with similar increased potency in SVG P12 cells. Other analogues demonstrated similar potency to aspirin and cisplatin. CONCLUSION: These results support the further development and characterization of novel NSAID derivatives for the treatment of glioma

Population genomics of post-glacial western Eurasia.

Western Eurasia witnessed several large-scale human migrations during the Holocene <sup>1-5</sup> . Here, to investigate the cross-continental effects of these migrations, we shotgun-sequenced 317 genomes-mainly from the Mesolithic and Neolithic periods-from across northern and western Eurasia. These were imputed alongside published data to obtain diploid genotypes from more than 1,600 ancient humans. Our analyses revealed a 'great divide' genomic boundary extending from the Black Sea to the Baltic. Mesolithic hunter-gatherers were highly genetically differentiated east and west of this zone, and the effect of the neolithization was equally disparate. Large-scale ancestry shifts occurred in the west as farming was introduced, including near-total replacement of hunter-gatherers in many areas, whereas no substantial ancestry shifts happened east of the zone during the same period. Similarly, relatedness decreased in the west from the Neolithic transition onwards, whereas, east of the Urals, relatedness remained high until around 4,000 BP, consistent with the persistence of localized groups of hunter-gatherers. The boundary dissolved when Yamnaya-related ancestry spread across western Eurasia around 5,000 BP, resulting in a second major turnover that reached most parts of Europe within a 1,000-year span. The genetic origin and fate of the Yamnaya have remained elusive, but we show that hunter-gatherers from the Middle Don region contributed ancestry to them. Yamnaya groups later admixed with individuals associated with the Globular Amphora culture before expanding into Europe. Similar turnovers occurred in western Siberia, where we report new genomic data from a 'Neolithic steppe' cline spanning the Siberian forest steppe to Lake Baikal. These prehistoric migrations had profound and lasting effects on the genetic diversity of Eurasian populations

Long term wind speed prediction with polynomial autoregressive model

Wind energy is one of the preferred energy generation methods because wind is an important renewable energy source. Prediction of wind speed in a time period, is important due to the one-to-one relationship between wind speed and wind power. Due to the nonlinear character of the wind speed data, nonlinear methods are known to produce better results compared to linear time series methods like Autoregressive (AR), Autoregressive Moving Average (ARMA) in predicting in a period longer than 12 hours. A method is proposed to apply a 48-hour ahead wind speed prediction by using the past wind speed measurements of the Çeşme Peninsula. We proposed to model wind speed data with a Polynomial AR (PAR) model. Coefficients of the models are estimated via linear Least Squares (LS) method and up to 48 hours ahead wind speed prediction is calculated for different models. In conclusion, a better performance is observed for higher than 12-hour ahead wind speed predictions of wind speed data which is modelled with PAR model, than AR and ARMA models

One-day ahead wind speed/power prediction based on polynomial autoregressive model

Wind has been one of the popular renewable energy generation methods in the last decades. Foreknowledge of power to be generated from wind is crucial especially for planning and storing the power. It is evident in various experimental data that wind speed time series has non-linear characteristics. It has been reported in the literature that nonlinear prediction methods such as artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS) perform better than linear autoregressive (AR) and AR moving average models. Polynomial AR (PAR) models, despite being non-linear, are simpler to implement when compared with other non-linear AR models due to their linear-in-the-parameters property. In this study, a PAR model is used for one-day ahead wind speed prediction by using the past hourly average wind speed measurements of Ceşme and Bandon and performance comparison studies between PAR and ANN-ANFIS models are performed. In addition, wind power data which was published for Global Energy Forecasting Competition 2012 has been used to make power predictions. Despite having lower number of model parameters, PAR models outperform all other models for both of the locations in speed predictions as well as in power predictions when the prediction horizon is longer than 12 h

BAYESIAN ESTIN ATION OF POLYNOMIAL MOVING AVERAGE MODELS WITH UNKNOWN DEGREE OF NONLINEARITY

24th European Signal Processing Conference (EUSIPCO) -- AUG 28-SEP 02, 2016 -- Budapest, HUNGARYAltinkaya, Mustafa A/0000-0001-8048-5850; Karakus, Oktay/0000-0001-8009-9319WOS: 000391891900296Various real world phenomena such as optical communication channels, power amplifiers and movement of sea vessels exhibit nonlinear characteristics. The nonlinearity degree of such systems is assumed to be known as a general intention. In this paper, we contribute to the literature with a Bayesian estimation method based on reversible jump Markov chain Monte Carlo (RJMCMC) for polynomial moving average (PMA) models. Our use of RJMCMC is novel and unique in the way of estimating both model memory and the nonlinearity degree. This offers greater flexibility to characterize the models which reflect different nonlinear characters of the measured data. In this study, we aim to demonstrate the potentials of RJMCMC in the identification for PMA models due to its potential of exploring nonlinear spaces of different degrees by sampling