varunr14/newsummary huggingface dataset:
Enhancing 2D angiography while maintaining a low radiation dose has become an important research topic . However it is difficult to enhance images while preserving vessel structure details because X ray noise and contrast blood vessels in 2D angiography have similar intensity distributions which can lead to ambiguous images of vessel structures . In this paper we propose a novel and fast vessel enhancement method for 2D angiography . We apply filtering in the principal component analysis domain for vessel regions and background regions separately using assumptions based on energy compaction . First we identify an approximate vessel region using a Hessian based method . Vessel and non vessel regions are then represented sparsely by calculating their optimal bases separately . This is achieved by identifying periodic motion in the vessel region caused by the flow of the contrast medium through the blood vessels when viewed on the time axis . Finally we obtain noise free images by removing noise in the new coordinate domain for the optimal bases . Our method was validated for an X ray system using 10 low dose sets for training and 20 low dose sets for testing . The results were compared with those for a high dose dataset with respect to noise free images . The average enhancement rate was 93.11 0.71 . The average processing time for enhancing video comprising 50 70 frames was 0.80 0.35s which is much faster than the previously proposed technique . Our method is applicable to 2D angiography procedures such as catheterization which requires rapid and natural vessel enhancement . 


Summary: A novel and fast vessel enhancement method for 2D angiography method is proposed. The results showed that we obtain nature enhanced X ray images using our method and the processing time is very small. Our method is applicable to 2D angiography procedures such as catheterization which requires rapid and natural vessel enhancement.
Alternative splicing plays a key role in the regulation of the central dogma . Four major types of alternative splicing have been classified as intron retention exon skipping alternative 5 splice sites or alternative donor sites and alternative 3 splice sites or alternative acceptor sites . A few algorithms have been developed to detect splice junctions from RNA Seq reads . However there are few tools targeting at the major alternative splicing types at the exon intron level . This type of analysis may reveal subtle yet important events of alternative splicing and thus help gain deeper understanding of the mechanism of alternative splicing . This paper describes a user friendly R package extracting annotating and analyzing alternative splicing types for sequence alignment files from RNA Seq . SplicingTypesAnno can provide annotation for major alternative splicing at exon intron level . By comparing the annotation from GTF GFF file it identifies the novel alternative splicing sites offer a convenient two level analysis genome scale annotation for users with high performance computing environment and gene scale annotation for users with personal computers generate a user friendly web report and additional BED files for IGV visualization . SplicingTypesAnno is a user friendly R package for extracting annotating and analyzing alternative splicing types at exon intron level for sequence alignment files from RNA Seq . It is publically available at https sourceforge.net projects splicingtypes files or http genome.sdau.edu.cn research software SplicingTypesAnno.html . 


Summary: User friendly R package for identifcation annotating and analyzing alternative splicing events from RNA Seq. Provide annotation for major alternative splicing at exon intron level and identifies novel alternative splicing events using the GTF GFF file. Generates user friendly web report and additional BED files for IGV visualization. Provides comprehensive genome scale analysis for users with High Performance Computing HPC and Gene level analysis for memory efficient personal computers.
A judgment post stratified sample is used in order to develop statistical inference for population quantiles and variance . For the th order of the population quantile a test is constructed an estimator is developed and a distribution free confidence interval is provided . An unbiased estimator for the population variance is also derived . For finite sample sizes it is shown that the proposed inferential procedures for quantiles are more efficient than corresponding simple random sampling procedures but less efficient than corresponding ranked set sampling procedures . The variance estimator is less efficient as efficient as or more efficient than a simple random sample variance estimator for small moderately small and large sample sizes respectively . Furthermore it is shown that JPS sample quantile estimators and tests are asymptotically equivalent to RSS estimators and tests in their efficiency comparison . 


Summary: A quantile inference is developed based on a judgment post stratified JPS sample. A variance estimator is constructed for population variance based on a JPS sample. The procedures have higher efficiencies than the ones in a simple random sample. The procedures have slightly lower efficiencies than the ones in a ranked set sample. Inference in a JPS sample is asymptotically equal to the one in a ranked set sample.
Background and objective Wireless Capsule Endoscopy can image the portions of the human gastrointestinal tract that were previously unreachable for conventional endoscopy examinations . A major drawback of this technology is that a large volume of data are to be analyzed in order to detect a disease which can be time consuming and burdensome for the clinicians . Consequently there is a dire need of computer aided disease detection schemes to assist the clinicians . In this paper we propose a real time computationally efficient and effective computerized bleeding detection technique applicable for WCE technology . Methods The development of our proposed technique is based on the observation that characteristic patterns appear in the frequency spectrum of the WCE frames due to the presence of bleeding region . Discovering these discriminating patterns we develop a texture feature descriptor based algorithm that operates on the Normalized Gray Level Co occurrence Matrix of the magnitude spectrum of the images . A new local texture descriptor called difference average that operates on NGLCM is also proposed . We also perform statistical validation of the proposed scheme . Results The proposed algorithm was evaluated using a publicly available WCE database . The training set consisted of 600 bleeding and 600 non bleeding frames . This set was used to train the SVM classifier . On the other hand 860 bleeding and 860 non bleeding images were selected from the rest of the extracted images to form the test set . The accuracy sensitivity and specificity obtained from our method are 99.19 99.41 and 98.95 respectively which are significantly higher than state of the art methods . In addition the low computational cost of our method makes it suitable for real time implementation . Conclusion This work proposes a bleeding detection algorithm that employs textural features from the magnitude spectrum of the WCE images . Experimental outcomes backed by statistical validations prove that the proposed algorithm is superior to the existing ones in terms of accuracy sensitivity specificity and computational cost . 


Summary: An automated GI hemorrhage detection scheme from WCE images is proposed.. Features from the NGLCM of the spectrum of the frames and SVM are employed. We introduce difference average a new feature that operates on NGLCM. Validity of the method is confirmed by statistical and graphical analyses. The performance of the proposed scheme compared to the existing ones is promising.
The generalized Pareto distribution has been widely used in modelling heavy tail phenomena in many applications . The standard practice is to fit the tail region of the dataset to the GPD separately a framework known as the peaks over threshold in the extreme value literature . In this paper we propose a new GPD parameter estimator under the POT framework to estimate common tail risk measures the Value at Risk and Conditional Tail Expectation for heavy tailed losses . The proposed estimator is based on a nonlinear weighted least squares method that minimizes the sum of squared deviations between the empirical distribution function and the theoretical GPD for the data exceeding the tail threshold . The proposed method properly addresses a caveat of a similar estimator previously advocated and further improves the performance by introducing appropriate weights in the optimization procedure . Using various simulation studies and a realistic heavy tailed model we compare alternative estimators and show that the new estimator is highly competitive especially when the tail risk measures are concerned with extreme confidence levels . 


Summary: A new GPD parameter estimator is proposed. It is based on a nonlinear weighted least squares method. Under the POT framework we estimate tail risk measures. Extensive simulation studies show the new method works well.
Active contours are image segmentation methods that minimize the total energy of the contour to be segmented . Among the active contour methods the radial methods have lower computational complexity and can be applied in real time . This work aims to present a new radial active contour technique called pSnakes using the 1D Hilbert transform as external energy . The pSnakes method is based on the fact that the beams in ultrasound equipment diverge from a single point of the probe thus enabling the use of polar coordinates in the segmentation . The control points or nodes of the active contour are obtained in pairs and are called twin nodes . The internal energies as well as the external one Hilbertian energy are redefined . The results showed that pSnakes can be used in image segmentation of short axis echocardiogram images and that they were effective in image segmentation of the left ventricle . The echo cardiologist s golden standard showed that the pSnakes was the best method when compared with other methods . The main contributions of this work are the use of pSnakes and Hilbertian energy as the external energy in image segmentation . The Hilbertian energy is calculated by the 1D Hilbert transform . Compared with traditional methods the pSnakes method is more suitable for ultrasound images because it is not affected by variations in image contrast such as noise . The experimental results obtained by the left ventricle segmentation of echocardiographic images demonstrated the advantages of the proposed model . The results presented in this paper are justified due to an improved performance of the Hilbert energy in the presence of speckle noise . 


Summary: To propose a new method called pSnakes applied and evaluated in the segmentation of the left ventricle from echocardiographic images. To make a comparative study of methods based on active contour methods. To apply the Hilbert transform to calculate the external energy of the proposed pSnakes method.
In the dependency rule mining the goal is to discover the most significant statistical dependencies among all possible collapsed contingency tables . Fisher s exact test is a robust method to estimate the significance and it enables efficient pruning of the search space . The problem is that evaluating the required value can be very laborious and the worst case time complexity is where is the data size . The traditional solution is to approximate the significance with the measure which can be estimated in a constant time . However the measure can produce unreliable results . Furthermore it does not support efficient pruning of the search space . As a solution a family of tight upper bounds for Fisher s is introduced . The new upper bounds are fast to calculate and approximate Fisher s value accurately . In addition the new approximations are not sensitive to the data size distribution or smallest expected counts like the based approximation . In practice the execution time depends on the desired accuracy level . According to experimental evaluation the simplest upper bounds are already sufficiently accurate for dependency rule mining purposes and they can be estimated in 0.004 0.1 of the time needed for exact calculation . For other purposes one may need more accurate approximations but even they can be calculated in less than 1 of the exact calculation time . 


Summary: A family of new tight upper bounds to approximate Fisher s is introduced. The new approximations suit for data mining purposes because they are much faster to evaluate than the exact value. Theoretical analysis and empirical evaluation show that the new approximations are very accurate for all practical purposes. The approximations are not sensitive to the data size distribution or small expected counts.
Multivariate methods often rely on a sample covariance matrix . The conventional estimators of a covariance matrix require complete data vectors on all subjects an assumption that can frequently not be met . For example in many fields of life sciences that are utilizing modern measuring technology such as mass spectrometry left censored values caused by denoising the data are a commonplace phenomena . Left censored values are low level concentrations that are considered too imprecise to be reported as a single number but known to exist somewhere between zero and the laboratory s lower limit of detection . Maximum likelihood based covariance matrix estimators that allow the presence of the left censored values without substituting them with a constant or ignoring them completely are considered . The presented estimators efficiently use all the information available and thus based on simulation studies produce the least biased estimates compared to often used competing estimators . As the genuine maximum likelihood estimate can be solved fast only in low dimensions it is suggested to estimate the covariance matrix element wise and then adjust the resulting covariance matrix to achieve positive semi definiteness . It is shown that the new approach succeeds in decreasing the computation times substantially and still produces accurate estimates . Finally as an example a left censored data set of toxic chemicals is explored . 


Summary: ML based covariance matrix estimator for left censored data is introduced. Computation times are decreased considerably with parallelized pairwise estimation. The proposed estimators produce unbiased estimates with reasonable variation.
Supply chains are increasingly virtualised in response to market challenges and to opportunities offered by nowadays affordable new technologies . Virtual supply chain management does no longer require physical proximity which implies that control and coordination can take place in other locations and by other partners . This paper assesses how the Internet of Things concept can be used to enhance virtualisation of supply chains in the floricultural sector . Virtualisation is expected to have a big impact in this sector where currently still most products physically pass through auction houses on their fixed routes from national growers to national customers . The paper defines the concept of virtualisation and describes different perspectives on virtualisation in literature i.e . the organisational team information technology virtual reality and virtual things perspectives . Subsequently it develops a conceptual framework for analysis of virtualisation in supply chains . This framework is applied in the Dutch floriculture to investigate the existing situation and to define future challenges for virtualisation in this sector . 


Summary: We assess how the Internet of Things can enhance virtualisation in floriculture. We define the concept of virtualisation and different perspectives in literature. A conceptual framework for analysis of virtual supply chains is developed. We analyse the existing situation and future challenges in Dutch floriculture.
The purpose of this study was to develop automatic classifiers to simplify the clinical use and increase the accuracy of the forced oscillation technique in the categorisation of airway obstruction level in patients with chronic obstructive pulmonary disease . The data consisted of FOT parameters obtained from 168 volunteers . The first part of this study showed that FOT parameters do not provide adequate accuracy in identifying COPD subjects in the first levels of obstruction as well as in discriminating between close levels of obstruction . In the second part of this study different supervised machine learning techniques were investigated including k nearest neighbour random forest and support vector machines with linear and radial basis function kernels . These algorithms were applied only in situations where high categorisation accuracy 0.9 was not achieved with the FOT parameter alone . It was observed that KNN and RF classifiers improved categorisation accuracy . Notably in four of the six cases studied an AUC 0.9 was achieved . Even in situations where an AUC 0.9 was not achieved there was a significant improvement in categorisation performance . In conclusion machine learning classifiers can help in the categorisation of COPD airway obstruction . They can assist clinicians in tracking disease progression evaluating the risk of future disease exacerbations and guiding therapy . 


Summary: Our aim was to develop automatic classifiers to help the categorisation of airway obstruction level in patients with chronic obstructive pulmonary disease COPD . We used different techniques including k nearest neighbour KNN random forest RF and support vector machines with linear and radial basis function kernels. Feature selection methods were also used to improve the performance of the classifiers. Our findings revealed that KNN and RF classifiers improved categorisation accuracy. Our study and findings will contribute to improvement of tracking COPD progression and guiding therapy.
Studies on health domain have shown that health websites provide imperfect information and give recommendations which are not up to date with the recent literature even when their last modified dates are quite recent . In this paper we propose a framework which assesses the timeliness of the content of health websites automatically by evidence based medicine . Our aim is to assess the accordance of website contents with the current literature and information timeliness disregarding the update time stated on the websites . The proposed method is based on automatic term recognition relevance feedback and information retrieval techniques in order to generate time aware structured queries . We tested the framework on diabetes health web sites which were archived between 2006 and 2013 by Archive it using American Diabetes Association s guidelines . The results showed that the proposed framework achieves 65 and 77 accuracy in detecting the timeliness of the web content according to years and pre determined time intervals respectively . Information seekers and web site owners may benefit from the proposed framework in finding relevant and up to date diabetes web sites . 


Summary: The proposed method predicts the time period that a given content belongs to. The archives of high quality diabetes web sites between 2006 and 2013 were utilized. The method s accuracy is 65 in detecting the timeliness according to years. The accuracy increases when detecting timeliness according to time intervals.
A confocal microscope provides a sequence of images of the corneal layers and structures at different depths from which medical clinicians can extract clinical information on the state of health of the patient s cornea . A hybrid model based on snake and particle swarm optimisation is proposed in this paper to analyse the confocal endothelium images . The proposed system is able to pre process images detect cells measure cell densities and identify abnormalities in the analysed data sets . Three normal corneal data sets acquired using a confocal microscope and three abnormal confocal endothelium images associated with diseases have been investigated in the proposed system . Promising results are presented and the performance of this system is compared with manual and two morphological based approaches . The average differences between the manual and the automatic cell densities calculated using S PSO and two other morphological based approaches is 5 7 and 13 respectively . The developed system will be deployable as a clinical tool to underpin the expertise of ophthalmologists in analysing confocal corneal images . 


Summary: A new intelligent system to tackle the challenges of confocal corneal endothelium images is developed. This system underpins the expertise of ophthalmologists. It provides clinically useful factors saves a useful amount of clinician time in the process. It is able to model endothelial cells for better evaluation and fast analysis. Useful system for busy clinic and patient care.
Anomalous modulation characteristics of Josephson current I c through niobium tunnel junctions at liquid helium temperature were first measured after applying the external magnetic field in perpendicular direction . Josephson current I c was modulated by applying the external magnetic fields parallel and Hz vertical to the junction plane . Modulation characteristics of the I c value upon Hz had hysteresis . Before applying the vertical field modulation characteristics I c were the product of the two Fraunhofer diffraction patterns in Hx and Hy directions which were parallel to the junction edges of the square shape junction respectively . Under the perpendicular magnetic field as much as 4000A m the maximum I c value did not appear at point . After removing this vertical field the I c modulation pattern changed from the product of the two Fraunhofer diffraction patterns to the deformed I c characteristics whose anomalous shape was explained by assuming the extremely low current density at four edges of the square shape except the four corner regions . Some magnetic flux would be trapped perpendicularly inside the junction electrodes within the junction area . After the junction was heated to the room temperature and was again cooled to the liquid He temperature these modulation characteristics again became the normal modulation pattern in which the trapped flux would be released by this thermal cycle . 


Summary: Through Nb superconducting junction with Al oxide current flow without voltage drop. Josephson current I c dependence on Hx Hy and Hz magnetic field was obtained. c dependence on Hx Hy before applying Hz was Fraunhofer pattern. c dependence on Hx Hy after applying Hz was not Fraunhofer pattern. Numerical simulation explained the measured dependence.
Let be a cell with a single interior vertex and n boundary vertices . Say that has the interpolation property if for every there is a spline such that for all i . We investigate under what conditions does a cell fail the interpolation property . The question is related to an open problem posed by Alfeld Piper and Schumaker in 1987 about characterization of unconfinable vertices . For hexagonal cells we obtain a geometric criterion characterizing the failure of the interpolation property . As a corollary we conclude that a hexagonal cell such that its six interior edges lie on three lines fails the interpolation property if and only if the cell is projectively equivalent to a regular hexagonal cell . Along the way we obtain an explicit basis for the vector space for 


Summary: Geometric characterization of hexagonal cells without the interpolation property. New examples of unconfinable interior vertices. Interpolation properties of splines depend on the geometry of cells.