Automatic Detection and Classification of Glioma Tumors using Statistical Features

The characterization and grading of glioma tumors, via image derived features, for diagnosis, prognosis, and treatment response has been an active research area in medical image computing. This paper presents a novel method for automatic detection and classification of glioma from conventional T2 weighted MR images. Automatic detection of the tumor was established using newly developed method called Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA).Statistical Features were extracted from the detected tumor texture using first order statistics and gray level co-occurrence matrix (GLCM) based second order statistical methods. Statistical significance of the features was determined by t-test and its corresponding p-value. A decision system was developed for the grade detection of glioma using these selected features and its p-value. The detection performance of the decision system was validated using the receiver operating characteristic (ROC) curve. The diagnosis and grading of glioma using this non-invasive method can contribute promising results in medical image computing

Early osteogenic response of osteoprogenitor cells on modified titanium implant surfaces leads to improved osteogenesis

Background: Implant surface micro-roughness and hydrophilicity are known to improve the osteogenic differentiation potential of osteoprogenitor cells. This study was aimed to determine whether topographically and chemically modified titanium implant surfaces stimulate an initial osteogenic response in osteoprogenitor cells, which leads to their improved osteogenesis. ----- ----- Methods: Statistical analysis of microarray gene expression profiling data available from studies (at 72 hours) on sand-blasted, large grit acid etched (SLA) titanium surfaces was performed. Subsequently, human osteoprogenitor cells were cultured on SLActive (hydrophilic SLA), SLA and polished titanium surfaces for 24 hours, 3 days and 7 days. The expression of BMP2, BMP6, BMP2K, SP1, ACVR1, FZD6, WNT5A, PDLIM7, ITGB1, ITGA2, OCN, OPN, ALP and RUNX2 were studied using qPCR. ----- ----- Results: Several functional clusters related to osteogenesis were highlighted when genes showing statistically significant differences (from microarray data at 72 hours) in expression on SLA surface (compared with control surface) were analysed using DAVID (online tool). This indicates that differentiation begins very early in response to modified titanium surfaces. At 24 hours, ACVR1 (BMP pathway), FZD6 (Wnt pathway) and SP1 (TGF-β pathway) were significantly up-regulated in cultures on the SLActive surface compared to the other surfaces. WNT5A and ITGB1 also showed higher expression on the modified surfaces. Gene expression patterns on Day 3 and Day 7 did not reveal any significant differences.----- ----- Conclusion: These results suggest that the initial molecular response of osteoprogenitor cells to modified titanium surfaces may be responsible for an improved osteogenic response via the BMP and Wnt signalling pathways.

Cost effectiveness of nutrition support in the prevention of pressure ulcer in hospitals

Background/objectives This study estimates the economic outcomes of a nutrition intervention to at-risk patients compared with standard care in the prevention of pressure ulcer. Subjects/methods Statistical models were developed to predict ‘cases of pressure ulcer avoided’, ‘number of bed days gained’ and ‘change to economic costs’ in public hospitals in 2002–2003 in Queensland, Australia. Input parameters were specified and appropriate probability distributions fitted for: number of discharges per annum; incidence rate for pressure ulcer; independent effect of pressure ulcer on length of stay; cost of a bed day; change in risk in developing a pressure ulcer associated with nutrition support; annual cost of the provision of a nutrition support intervention for at-risk patients. A total of 1000 random re-samples were made and the results expressed as output probability distributions. Results The model predicts a mean 2896 (s.d. 632) cases of pressure ulcer avoided; 12 397 (s.d. 4491) bed days released and corresponding mean economic cost saving of euros 2 869 526 (s.d. 2 078 715) with a nutrition support intervention, compared with standard care. Conclusion Nutrition intervention is predicted to be a cost-effective approach in the prevention of pressure ulcer in at-risk patients.

Incidence and mortality of female breast cancer in the Asia-Pacific region

Objective: To provide an overview of the incidence and mortality of female breast cancer for countries in the Asia-Pacific region. Methods: Statistical information about breast cancer was obtained from publicly available cancer registry and mortality databases (such as GLOBOCAN), and supplemented with data requested from individual cancer registries. Rates were directly age-standardised to the Segi World Standard population and trends were analysed using joinpoint models. Results: Breast cancer was the most common type of cancer among females in the region, accounting for 18% of all cases in 2012, and was the fourth most common cause of cancer-related deaths (9%). Although incidence rates remain much higher in New Zealand and Australia, rapid rises in recent years were observed in several Asian countries. Large increases in breast cancer mortality rates also occurred in many areas, particularly Malaysia and Thailand, in contrast to stabilising trends in Hong Kong and Singapore, while decreases have been recorded in Australia and New Zealand. Mortality trends tended to be more favourable for women aged under 50 compared to those who were 50 years or older. Conclusion: It is anticipated that incidence rates of breast cancer in developing countries throughout the Asia-Pacific region will continue to increase. Early detection and access to optimal treatment are the keys to reducing breast cancer-related mortality, but cultural and economic obstacles persist. Consequently, the challenge is to customise breast cancer control initiatives to the particular needs of each country to ensure the best possible outcomes.

Bayesian model comparison in cosmology with Population Monte Carlo

We use Bayesian model selection techniques to test extensions of the standard flat LambdaCDM paradigm. Dark-energy and curvature scenarios, and primordial perturbation models are considered. To that end, we calculate the Bayesian evidence in favour of each model using Population Monte Carlo (PMC), a new adaptive sampling technique which was recently applied in a cosmological context. The Bayesian evidence is immediately available from the PMC sample used for parameter estimation without further computational effort, and it comes with an associated error evaluation. Besides, it provides an unbiased estimator of the evidence after any fixed number of iterations and it is naturally parallelizable, in contrast with MCMC and nested sampling methods. By comparison with analytical predictions for simulated data, we show that our results obtained with PMC are reliable and robust. The variability in the evidence evaluation and the stability for various cases are estimated both from simulations and from data. For the cases we consider, the log-evidence is calculated with a precision of better than 0.08. Using a combined set of recent CMB, SNIa and BAO data, we find inconclusive evidence between flat LambdaCDM and simple dark-energy models. A curved Universe is moderately to strongly disfavoured with respect to a flat cosmology. Using physically well-motivated priors within the slow-roll approximation of inflation, we find a weak preference for a running spectral index. A Harrison-Zel'dovich spectrum is weakly disfavoured. With the current data, tensor modes are not detected; the large prior volume on the tensor-to-scalar ratio r results in moderate evidence in favour of r=0.

Every method counts

In this study we explore the concurrent, combined use of three research methods, statistical corpus analysis and two psycholinguistic experiments (a forced-choice and an acceptability rating task), using verbal synonymy in Finnish as a case in point. In addition to supporting conclusions from earlier studies concerning the relationships between corpus-based and ex- perimental data (e. g., Featherston 2005), we show that each method adds to our understanding of the studied phenomenon, in a way which could not be achieved through any single method by itself. Most importantly, whereas relative rareness in a corpus is associated with dispreference in selection, such infrequency does not categorically always entail substantially lower acceptability. Furthermore, we show that forced-choice and acceptability rating tasks pertain to distinct linguistic processes, with category-wise in- commensurable scales of measurement, and should therefore be merged with caution, if at all.

Metalak eta metaloideak itsasadarretan: kutsadura jarraitzeko erreminta analitikoen

265 p.

Vibration and stress analysis in the presence of structural uncertainty

At medium to high frequencies the dynamic response of a built-up engineering system, such as an automobile, can be sensitive to small random manufacturing imperfections. Ideally the statistics of the system response in the presence of these uncertainties should be computed at the design stage, but in practice this is an extremely difficult task. In this paper a brief review of the methods available for the analysis of systems with uncertainty is presented, and attention is then focused on two particular "non- parametric" methods: statistical energy analysis (SEA), and the hybrid method. The main governing equations are presented, and a number of example applications are considered, ranging from academic benchmark studies to industrial design studies. © 2009 IOP Publishing Ltd.

Biomonitoring of polycyclic aromatic hydrocarbons (PAH) and heavy metals Ni, V in sediments and anodonts of the Anzali Lagoon and applicability of netural red retention assay (NRR) as biomarkers of these pollutants

Biochemical ecotoxicology and biomarkers using are a new sciences that are used for biomonitoring in aquatic environment. Biomonitoring plays a vital role in strategies to identify, assess, and control contaminants. On the other hands in recent year's attention to polycyclic Aromatic Hydrocarbons (PAHs) and heavy metals increased in aquatic environments because of their carcinogenic and mutagenic properties combined with their nearly ubiquitous distribution in depositional environments by oil pollution or industrial waste waters. The present research aimed to assess PAHs and Ni, V levels in surface sediments and bivalves (Anodonta cygnea)and the effects of PAHs and heavy metals (Ni,V) on the hemocyte of the Anodonta cygnea were investigated in 2 stations (Mahrozeh, Selke in Anzali Lagoon, North of Iran). Samples were collected during at 2 different periods of the year, Dry and rain seasons, (June & September) and to confirm our first observations, Cage station is added. The bivalves hemocytes were monitored for membrane injury by NRR methods (neutral red retention assay). Heavy metal (Ni, V) concentrations were determined by Atomic Absorption in Anodonta cygnea and the sediments in Anzali Lagoon. The vanadium concentration in bivalves and sediments was ND(not detect )-0.4231 μg/g and 1.4381-306.9603 μg/g dry weight respectively. Nickel concentration in bivalves and sediments was 0.0231-1.3351, 0.4024-19.3561 μg/g dry weight respectively. PAHs concentrations were determined by GC-Mass in Anodonta cygnea and the sediments. Average concentration of PAHs is 115-373.788 ng/g dry weight in bivalves and average concentration of PAHs is 34.85-1339.839 ng/g dry weight in sediments. Bioaccumulation sediments factor(BASF) is high about PAHs (>1) and BASF is low for Ni, V (<1) . Internal Damage mechanisms of bivalves hemocytes (cell mortality, dye leakage, decreased membrane stability, are observed (Lowe Methods). Statistical analysis was used to explore the relationship between altered cellular and above contaminants. There are power and negative correlations between PAHs and NRR method for hemocytes in Anodonta cygnea (P<0.0005), but good correlation is not observed between Ni, V and NRR method for hemocytes in every time. This research indicates that the NRR assay is a useful screening technique able to discriminate polluted sites and at first we announce that Anodonta cygnea hemocytes are efficient biomarker for PAHs pollutants in fresh water.

A Bayesian method for detecting stellar flares

We present a Bayesian-odds-ratio-based algorithm for detecting stellar flares in light-curve data. We assume flares are described by a model in which there is a rapid rise with a half-Gaussian profile, followed by an exponential decay. Our signal model also contains a polynomial background model required to fit underlying light-curve variations in the data, which could otherwise partially mimic a flare. We characterize the false alarm probability and efficiency of this method under the assumption that any unmodelled noise in the data is Gaussian, and compare it with a simpler thresholding method based on that used in Walkowicz et al. We find our method has a significant increase in detection efficiency for low signal-to-noise ratio (S/N) flares. For a conservative false alarm probability our method can detect 95 per cent of flares with S/N less than 20, as compared to S/N of 25 for the simpler method. We also test how well the assumption of Gaussian noise holds by applying the method to a selection of 'quiet' Kepler stars. As an example we have applied our method to a selection of stars in Kepler Quarter 1 data. The method finds 687 flaring stars with a total of 1873 flares after vetos have been applied. For these flares we have made preliminary characterizations of their durations and and S/N.

Machine learning for transient discovery in Pan-STARRS1 difference imaging

Efficient identification and follow-up of astronomical transients is hindered by the need for humans to manually select promising candidates from data streams that contain many false positives. These artefacts arise in the difference images that are produced by most major ground-based time-domain surveys with large format CCD cameras. This dependence on humans to reject bogus detections is unsustainable for next generation all-sky surveys and significant effort is now being invested to solve the problem computationally. In this paper, we explore a simple machine learning approach to real-bogus classification by constructing a training set from the image data of similar to 32 000 real astrophysical transients and bogus detections from the Pan-STARRS1 Medium Deep Survey. We derive our feature representation from the pixel intensity values of a 20 x 20 pixel stamp around the centre of the candidates. This differs from previous work in that it works directly on the pixels rather than catalogued domain knowledge for feature design or selection. Three machine learning algorithms are trained (artificial neural networks, support vector machines and random forests) and their performances are tested on a held-out subset of 25 per cent of the training data. We find the best results from the random forest classifier and demonstrate that by accepting a false positive rate of 1 per cent, the classifier initially suggests a missed detection rate of around 10 per cent. However, we also find that a combination of bright star variability, nuclear transients and uncertainty in human labelling means that our best estimate of the missed detection rate is approximately 6 per cent.

Selection of burst-like transients and stochastic variables using multi-brand image differencing in the Pan-STARRS1 Medium Deep Survey

We present a novel method for the light-curve characterization of Pan-STARRS1 Medium Deep Survey (PS1 MDS) extragalactic sources into stochastic variables (SVs) and burst-like (BL) transients, using multi-band image-differencing time-series data. We select detections in difference images associated with galaxy hosts using a star/galaxy catalog extracted from the deep PS1 MDS stacked images, and adopt a maximum a posteriori formulation to model their difference-flux time-series in four Pan-STARRS1 photometric bands gP1, rP1, iP1, and zP1. We use three deterministic light-curve models to fit BL transients; a Gaussian, a Gamma distribution, and an analytic supernova (SN) model, and one stochastic light-curve model, the Ornstein-Uhlenbeck process, in order to fit variability that is characteristic of active galactic nuclei (AGNs). We assess the quality of fit of the models band-wise and source-wise, using their estimated leave-out-one cross-validation likelihoods and corrected Akaike information criteria. We then apply a K-means clustering algorithm on these statistics, to determine the source classification in each band. The final source classification is derived as a combination of the individual filter classifications, resulting in two measures of classification quality, from the averages across the photometric filters of (1) the classifications determined from the closest K-means cluster centers, and (2) the square distances from the clustering centers in the K-means clustering spaces. For a verification set of AGNs and SNe, we show that SV and BL occupy distinct regions in the plane constituted by these measures. We use our clustering method to characterize 4361 extragalactic image difference detected sources, in the first 2.5 yr of the PS1 MDS, into 1529 BL, and 2262 SV, with a purity of 95.00% for AGNs, and 90.97% for SN based on our verification sets. We combine our light-curve classifications with their nuclear or off-nuclear host galaxy offsets, to define a robust photometric sample of 1233 AGNs and 812 SNe. With these two samples, we characterize their variability and host galaxy properties, and identify simple photometric priors that would enable their real-time identification in future wide-field synoptic surveys.

The ejected mass distribution of Type Ia supernovae: a significant rate of non-Chandrasekhar-mass progenitors

The ejected mass distribution of Type Ia supernovae (SNe Ia) directly probes progenitor evolutionary history and explosion mechanisms, with implications for their use as cosmological probes. Although the Chandrasekhar mass is a natural mass scale for the explosion of white dwarfs as SNe Ia, models allowing SNe Ia to explode at other masses have attracted much recent attention. Using an empirical relation between the ejected mass and the light-curve width, we derive ejected masses Mej and 56Ni masses MNi for a sample of 337 SNe Ia with redshifts z <0.7 used in recent cosmological analyses. We use hierarchical Bayesian inference to reconstruct the joint Mej-MNi distribution, accounting for measurement errors. The inferred marginal distribution of Mej has a long tail towards sub-Chandrasekhar masses, but cuts off sharply above 1.4 M⊙. Our results imply that 25-50 per cent of normal SNe Ia are inconsistent with Chandrasekhar-mass explosions, with almost all of these being sub-Chandrasekhar mass; super-Chandrasekhar-mass explosions make up no more than 1 per cent of all spectroscopically normal SNe Ia. We interpret the SN Ia width-luminosity relation as an underlying relation between Mej and MNi, and show that the inferred relation is not naturally explained by the predictions of any single known explosion mechanism.

Locations of peculiar supernovae as a diagnostic of their origins

We put constraints on the properties of the progenitors of peculiar calcium-rich transients using the distribution of locations within their host galaxies. We confirm that this class of transients do not follow the galaxy stellar mass profile and are more likely to be found in remote locations of their apparent hosts. We test the hypothesis that these transients are from low-metallicity progenitors by comparing their spatial distributions with the predictions of self-consistent cosmological simulations that include star formation and chemical enrichment. We find that while metal-poor stars and our transient sample show a consistent preference for large offsets, metallicity alone cannot explain the extreme cases. Invoking a lower age limit on the progenitor helps to improve the match, indicating these events may result from a very old metal-poor population. We also investigate the radial distribution of globular cluster systems, and show that they too are consistent with the class of calcium-rich transients. Because photometric upper limits exist for globular clusters for some members of the class, a production mechanism related to the dense environment of globular clusters is not favoured for the calcium-rich events. However, the methods developed in this paper may be used in the future to constrain the effects of low metallicity on radially distant core-collapse events or help establish a correlation with globular clusters for other classes of peculiar explosions.