Добірка наукової літератури з теми "Semi-automatic summary statistics"

Abstract. The semi-arid regions of Central Asia crucially depend on the water resources supplied by the mountainous areas of the Tien Shan and Pamir and Altai mountains. During the summer months the snow-melt- and glacier-melt-dominated river discharge originating in the mountains provides the main water resource available for agricultural production, but also for storage in reservoirs for energy generation during the winter months. Thus a reliable seasonal forecast of the water resources is crucial for sustainable management and planning of water resources. In fact, seasonal forecasts are mandatory tasks of all national hydro-meteorological services in the region. In order to support the operational seasonal forecast procedures of hydro-meteorological services, this study aims to develop a generic tool for deriving statistical forecast models of seasonal river discharge based solely on observational records. The generic model structure is kept as simple as possible in order to be driven by meteorological and hydrological data readily available at the hydro-meteorological services, and to be applicable for all catchments in the region. As snow melt dominates summer runoff, the main meteorological predictors for the forecast models are monthly values of winter precipitation and temperature, satellite-based snow cover data, and antecedent discharge. This basic predictor set was further extended by multi-monthly means of the individual predictors, as well as composites of the predictors. Forecast models are derived based on these predictors as linear combinations of up to four predictors. A user-selectable number of the best models is extracted automatically by the developed model fitting algorithm, which includes a test for robustness by a leave-one-out cross-validation. Based on the cross-validation the predictive uncertainty was quantified for every prediction model. Forecasts of the mean seasonal discharge of the period April to September are derived every month from January until June. The application of the model for several catchments in Central Asia – ranging from small to the largest rivers (240 to 290 000 km2 catchment area) – for the period 2000–2015 provided skilful forecasts for most catchments already in January, with adjusted R2 values of the best model in the range of 0.6–0.8 for most of the catchments. The skill of the prediction increased every following month, i.e. with reduced lead time, with adjusted R2 values usually in the range 0.8–0.9 for the best and 0.7–0.8 on average for the set of models in April just before the prediction period. The later forecasts in May and June improve further due to the high predictive power of the discharge in the first 2 months of the snow melt period. The improved skill of the set of forecast models with decreasing lead time resulted in narrow predictive uncertainty bands at the beginning of the snow melt period. In summary, the proposed generic automatic forecast model development tool provides robust predictions for seasonal water availability in Central Asia, which will be tested against the official forecasts in the upcoming years, with the vision of operational implementation.

Abstract Summary Fourier ptychographic microscopy (FPM) is a computational microscopy technique that enables large field of view and high-resolution microscopic imaging of biological samples. However, the FPM does not yet have an adequately capable open-source software. In order to fill this gap we are presenting novel, simple, universal, semi-automatic and highly intuitive graphical user interface (GUI) open-source application called the FPM app enabling wide-scale robust FPM reconstruction. Apart from implementing the FPM in accessible GUI app, we also made several improvements in the FPM image reconstruction process itself, making the FPM more automatic, noise-robust and faster. Availability and implementation FPM app was implemented in MATLAB and all MATLAB codes along with standalone executable version of the FPM app and the online documentation are freely accessible at https://github.com/MRogalski96/FPM-app. Our exemplary FPM datasets may be downloaded at https://bit.ly/2MxNpGb. Supplementary information Supplementary data are available at Bioinformatics online.

Abstract Summary Limited by spatial resolution and visual contrast, bone scintigraphy interpretation is susceptible to subjective factors, which considerably affects the accuracy and repeatability of lesion detection and anatomical localization. In this work, we design and implement an end-to-end multi-task deep learning model to perform automatic lesion detection and anatomical localization in whole-body bone scintigraphy. A total of 617 whole-body bone scintigraphy cases including anterior and posterior views were retrospectively analyzed. The proposed semi-supervised model consists of two task flows. The first one, the lesion segmentation flow, received image patches and were trained in a supervised way. The other one, skeleton segmentation flow, was trained on as few as five labeled images in conjunction with the multi-atlas approach, in a semi-supervised way. The two flows joint in their encoder layers so each flow can capture more generalized distribution of the sample space and extract more abstract deep features. The experimental results show that the architecture achieved the highest precision in the finest bone segmentation task in both anterior and posterior images of whole-body scintigraphy. Such an end-to-end approach with very few manual annotation requirement would be suitable for algorithm deployment. Moreover, the proposed approach reliably balances unsupervised labels construction and supervised learning, providing useful insight for weakly labeled image analysis. Supplementary information Supplementary data are available at Bioinformatics online.

Population genetics is a discipline within the biological sciences that is concerned with the change in frequency of types of individuals in a population due to natural selection, mutation, genetic drift and gene flow. Genetic drift is the part of this process explained by random sampling. Important to the process of genetic drift is population structure and so we focus on the recovery of population sizes over time, given a set of DNA sequences. With recent advances in computational power and a growth in the amount of data available, increasingly powerful techniques are being developed for the study of sequence data. Key advances in the early 1980's centred around `the coalescent', a continuous time approximation to the Wright-Fisher model of reproduction, and these advances resulted in Skyline Plot methods for recovering population size estimates over time. Skyline Plots suffer from large variances for the `coalescent' event times, and sources of error common to DNA sequence sampling schemes. Approximate Bayesian Computation (ABC) is a class of likelihood-free methods for statistical inference. ABC techniques can trace their genesis back to the biological sciences due to the complexity of the models for reproduction (and hence the intractability of likelihood calculations). Unfortunately, like Skyline Plots, ABC also suffers from many sources of error, not least of which occurs when we can not use sufficient summary statistics. To considerably reduce the effect of the error related with the use of insufficient summary statistics, we explore a process of semi-automatic summary statistic calculation through the use of `training data' (simulated under the coalescent model). We obtain a training set of data, and fit a linear model (under a Box-Cox transformation) for each parameter of interest, using common summary statistics for DNA sequences as predictor variables. We call these linear combinations of (insufficient) summary statistics the semi-automatic summary statistics, and using a new set of simulations, we perform ABC where a simulation is retained if the predicted parameter values are `close enough' to the predicted parameters for the observed data. We analyse three sets of coalescent simulated data from three population models; the Constant, Exponential and Migration Models, and compare our findings with the corresponding Skyline Plot analyses performed in BEAST. When we simulate data for training our linear model, we must specify a model of population size dynamics, and we explore methods to select a population model, given our data. A common means of model comparison used with ABC analyses is called Bayes Factors. We show that Bayes Factors perform poorly for our data, and highlight a fundamental bias inherent in any model comparison where the probability of a model, given an observed summary statistic, is employed. As an alternative to Bayes Factors, we apply multiple logistic regression (MLR) to classify our observed data into one of a candidate set of possible models. In conjunction with the MLR analysis, we use principal component analysis for visualisation, and introduce a method for attempting to identify when the correct model is not in the candidate model set, or when a classification seems reasonable. We show that this method of classification performs well for the three observed data sets using sensitivity analysis. Due to the early stage of development of our work, we can not use real world data, and so we use a different type of simulation since our method uses coalescent simulations to train the model. We obtain sequence data simulated under a `forward simulation' framework, a type of sequence simulation that looks forward in time. We define a two-step process for analysis that begins with MLR classification, and then, under a model chosen by the MLR classification, uses semi-automatic summary statistic calculation for parameter estimation via ABC. We correctly identify this model of population dynamics, and perform parameter estimation on the data, comparing our results with the corresponding BEAST Skyline Plot analysis.
Thesis (M.Phil.) -- University of Adelaide, School of Mathematical Sciences, 2014

Quantitative measurements of wall thickness in human abdominal aortic aneurysms (AAAs) may provide useful information to predict rupture risk. Our procedure for estimating wall thickness in AAAs includes medical image segmentation and wall thickness detection. Image segmentation requires identifying and segmenting the luminal and outer wall boundaries of the blood vessels and wall thickness can be calculated by using intensity histograms and neural networks. The goal of this study is to develop an image-based, semi-automated method to trace the contours of the vessel wall and measure the wall thickness of the abdominal aorta from in-vivo, contrast-enhanced, CT images. An algorithm for the lumen and inner wall segmentations, and wall thickness detection was developed and tested on 10 ruptured and 10 unruptured AAAs. Reproducibility and repeatability of the algorithm were determined by comparing manual tracings made by two observers to contours made automatically by the algorithm itself. There was a high correspondence between automatic and manual area measurements for the lumen (r = 0.96) and between users (r = 0.98). Based on statistical analyses, the algorithm tends to underestimate the lumen area when compared to both observers.