varunr14/newsummary huggingface dataset:
Breast thermography still has inherent limitations that prevent it from being fully accepted as a breast screening modality in medicine . The main challenges of breast thermography are to reduce false positive results and to increase the sensitivity of a thermogram . Further it is still difficult to obtain information about tumour parameters such as metabolic heat tumour depth and diameter from a thermogram . However infrared technology and image processing have advanced significantly and recent clinical studies have shown increased sensitivity of thermography in cancer diagnosis . The aim of this paper is to study numerically the possibilities of extracting information about the tumour depth from steady state thermography and transient thermography after cold stress with no need to use any specific inversion technique . Both methods are based on the numerical solution of Pennes bioheat equation for a simple three dimensional breast model . The effectiveness of two approaches used for depth detection from steady state thermography is assessed . The effect of breast density on the steady state thermal contrast has also been studied . The use of a cold stress test and the recording of transient contrasts during rewarming were found to be potentially suitable for tumour depth detection during the rewarming process . Sensitivity to parameters such as cold stress temperature and cooling time is investigated using the numerical model and simulation results reveal two prominent depth related characteristic times which do not strongly depend on the temperature of the cold stress or on the cooling period . 


Summary: Steady state thermal contrast magnitude depends on the tumour diameter and depth as well as on the breast density. The Full Width at Half Maximum calculated at the surface of the breast enlarges as the tumour depth increases but also depends on the tumour diameter. Transient thermal contrast trends present three important characteristics including the response time the transient peak and its corresponding observation time. The observation time is likely a potential time for tumour depth detection as it does not strongly depend on the tumour diameter or on the chilling temperature and its duration.
Change point models seek to fit a piecewise regression model with unknown breakpoints to a data set whose parameters are suspected to change through time . However the exponential number of possible solutions to a multiple change point problem requires an efficient algorithm if long time series are to be analyzed . A sequential Bayesian change point algorithm is introduced that provides uncertainty bounds on both the number and location of change points . The algorithm is able to quickly update itself in linear time as each new data point is recorded and uses the exact posterior distribution to infer whether or not a change point has been observed . Simulation studies illustrate how the algorithm performs under various parameter settings including detection speeds and error rates and allow for comparison with several existing multiple change point algorithms . The algorithm is then used to analyze two real data sets including global surface temperature anomalies over the last 130 years . 


Summary: Algorithm quickly updates its inference in linear time with each new observation. Derives uncertainty bounds on the number and location of change points in a data set. Explores potential detection criteria associated with posterior distribution. Simulation studies show high detection rate low false positive rate. Analysis of two real data sets illustrate wide range of potential applications.
Background and objectives Post genomic clinical trials require the participation of multiple institutions and collecting data from several hospitals laboratories and research facilities . This paper presents a standard based solution to provide a uniform access endpoint to patient data involved in current clinical research . Methods The proposed approach exploits well established standards such as HL7 v3 or SPARQL and medical vocabularies such as SNOMED CT LOINC and HGNC . A novel mechanism to exploit semantic normalization among HL7 based data models and biomedical ontologies has been created by using Semantic Web technologies . Results Different types of queries have been used for testing the semantic interoperability solution described in this paper . The execution times obtained in the tests enable the development of end user tools within a framework that requires efficient retrieval of integrated data . Conclusions The proposed approach has been successfully tested by applications within the INTEGRATE and EURECA EU projects . These applications have been deployed and tested for patient screening trial recruitment and retrospective analysis exploiting semantically interoperable access to clinical patient data from heterogeneous data sources . 


Summary: We present a standard based semantic interoperability solution. This solution uses a mechanism that exploits semantic normalization over HL7 model. It has been used for testing in trial recruitment or trial feasibility. Execution times obtained in testing empower its utilization for end user tools.
Background and objective Early childhood caries is a potentially severe disease affecting children all over the world . The available findings are mostly based on a logistic regression model but data mining in particular association rule mining could be used to extract more information from the same data set . Methods ECC data was collected in a cross sectional analytical study of the 10 sample of preschool children in the South Ba ka area . Association rules were extracted from the data by association rule mining . Risk factors were extracted from the highly ranked association rules . Results Discovered dominant risk factors include male gender frequent breastfeeding high birth order language and low body weight at birth . Low health awareness of parents was significantly associated to ECC only in male children . Conclusions The discovered risk factors are mostly confirmed by the literature which corroborates the value of the methods . 


Summary: We look for an approach to massive risk factor discovery for early childhood caries. Association rule mining with adequate rule pruning and ranking performs well. Literature supports most risk factors identified in associative analysis. When coupled with other risk factors frequent breastfeeding confirmed as a risk. Parent health awareness significant only for boys.
For constructing simultaneous confidence intervals for the ratios of means of several lognormal distributions we propose a new parametric bootstrap method which is different from an inaccurate parametric bootstrap method previously considered in the literature . Our proposed method is conceptually simpler than other proposed methods which are based on the concepts of generalized pivotal quantities and fiducial generalized pivotal quantities . Also our extensive simulation results indicate that our proposed method consistently performs better than other methods its coverage probability is close to the nominal confidence level and the resulting intervals are typically shorter than the intervals produced by other methods . 


Summary: We present parametric bootstrap PB simultaneous confidence intervals. Our PB procedure is different from a previous PB procedure which performs poorly. Using simulation we compare the PB procedure with three other procedures. The coverage probability of the PB method is close to the nominal confidence level. The PB procedure consistently outperforms other procedures.
Gait function is traditionally assessed using well lit unobstructed walkways with minimal distractions . In patients with subclinical physiological abnormalities these conditions may not provide enough stress on their ability to adapt to walking . The introduction of challenging walking conditions in gait can induce responses in physiological systems in addition to the locomotor system . There is a need for a device that is capable of monitoring multiple physiological systems in various walking conditions . To address this need an Android based gait monitoring device was developed that enabled the recording of a patient s physiological systems during walking . The gait monitoring device was tested during self regulated overground walking sessions of fifteen healthy subjects that included 6 females and 9 males aged 18 35 years . The gait monitoring device measures the patient s stride interval acceleration electrocardiogram skin conductance and respiratory rate . The data is stored on an Android phone and is analyzed offline through the extraction of features in the time frequency and time frequency domains . The analysis of the data depicted multisystem physiological interactions during overground walking in healthy subjects . These interactions included locomotion electrodermal locomotion respiratory and cardiolocomotion couplings . The current results depicting strong interactions between the locomotion system and the other considered systems warrant further investigation into multisystem interactions during walking particularly in challenging walking conditions with older adults . 


Summary: We developed a smart phone based gait monitor. The proposed device records several physiological signals simultaneously. ECG skin conductance respiration strides and gait acceleration signals were measured. The devices enable us to understand physiological interactions during walking.
Metabolic Engineering aims to design microbial cell factories towards the production of valuable compounds . In this endeavor one important task relates to the search for the most suitable heterologous pathway to add to the selected host . Different algorithms have been developed in the past towards this goal following distinct approaches spanning constraint based modeling graph based methods and knowledge based systems based on chemical rules . While some of these methods search for pathways optimizing specific objective functions here the focus will be on methods that address the enumeration of pathways that are able to convert a set of source compounds into desired targets and their posterior evaluation according to different criteria . Two pathway enumeration algorithms based on graph based representations are selected as the most promising ones and are analyzed in more detail the Solution Structure Generation and the Find Path algorithms . Their capabilities and limitations are evaluated when designing novel heterologous pathways by applying these methods on three case studies of synthetic ME related to the production of non native compounds in E. coli and S. cerevisiae 1 butanol curcumin and vanillin . Some targeted improvements are implemented extending both methods to address limitations identified that impair their scalability improving their ability to extract potential pathways over large scale databases . In all case studies the algorithms were able to find already described pathways for the production of the target compounds but also alternative pathways that can represent novel ME solutions after further evaluation . 


Summary: Paper reviews the main hyper graph based algorithms for metabolic pathway enumeration. The FindPath and SSG revealed limitations in metabolic engineering case studies. These algorithms were improved to boost computational efficiency and scalability. Improved versions of the algorithms were able to find previously known pathways from literature. There is still room for improvement in the computational efficiency of the algorithms.
We study the property of the Fused Lasso Signal Approximator for estimating a blocky signal sequence with additive noise . We transform the FLSA to an ordinary Lasso problem and find that in general the resulting design matrix does not satisfy the irrepresentable condition that is known as an almost necessary and sufficient condition for exact pattern recovery . We give necessary and sufficient conditions on the expected signal pattern such that the irrepresentable condition holds in the transformed Lasso problem . However these conditions turn out to be very restrictive . We apply the newly developed preconditioning method Puffer Transformation to the transformed Lasso and call the new procedure the preconditioned fused Lasso . We give non asymptotic results for this method showing that as long as the signal to noise ratio is not too small our preconditioned fused Lasso estimator always recovers the correct pattern with high probability . Theoretical results give insight into what controls the ability of recovering the pattern it is the noise level instead of the length of the signal sequence . Simulations further confirm our theorems and visualize the significant improvement of the preconditioned fused Lasso estimator over the vanilla FLSA in exact pattern recovery . 


Summary: We provided necessary and sufficient conditions such that fused Lasso consistently recovers the piecewise constant pattern. We found that in general the fused Lasso is not consistent. We proposed a preconditioned fused Lasso to overcome the non consistent issue. Simulation studies support our findings.
Retinal image registration is a necessary step in diagnosis and monitoring of Diabetes Retinopathy which is one of the leading causes of blindness . Long term diabetes affects the retinal blood vessels and capillaries eventually causing blindness . This progressive damage to retina and subsequent blindness can be prevented by periodic retinal screening . The extent of damage caused by DR can be assessed by comparing retinal images captured during periodic retinal screenings . During image acquisition at the time of periodic screenings translation rotation and scale are introduced in the retinal images . Therefore retinal image registration is an essential step in automated system for screening diagnosis treatment and evaluation of DR . This paper presents an algorithm for registration of retinal images using orthogonal moment invariants as features for determining the correspondence between the dominant points in the reference and test retinal images . As orthogonal moments are invariant to TRS moment invariants features around a vessel bifurcation are unaltered due to TRS and can be used to determine the correspondence between reference and test retinal images . The vessel bifurcation points are located in segmented thinned retinal images and labeled in corresponding grayscale retinal images . The correspondence between vessel bifurcations in reference and test retinal image is established based on moment invariants features . Further the TRS in test retinal image with respect to reference retinal image is estimated using similarity transformation . The test retinal image is aligned with reference retinal image using the estimated registration parameters . The accuracy of registration is evaluated in terms of mean error and standard deviation of the labeled vessel bifurcation points in the aligned images . The experimentation is carried out on DRIVE database STARE database VARIA database and database provided by local government hospital in Pune India . The experimental results exhibit effectiveness of the proposed algorithm for registration of retinal images . 


Summary: This paper presents an algorithm for registration of retinal images using orthogonal moment invariants as features. As orthogonal moments are invariant to translation rotation and scale TRS moment invariants features around a vessel bifurcation are unaltered due to TRS and are used to determine the correspondence between reference and test retinal images. The TRS in test retinal image with respect to reference retinal image is estimated using Similarity transformation. The test retinal image is aligned with reference retinal image using the estimated registration parameters. The experimentation is carried out on 4 databases as DRIVE STARE VARIA and database provided by local government hospital.
In this paper the model predictive control technology is used for tackling the optimal drug administration problem . The important advantage of MPC compared to other control technologies is that it explicitly takes into account the constraints of the system . In particular for drug treatments of living organisms MPC can guarantee satisfaction of the minimum toxic concentration constraints . A whole body physiologically based pharmacokinetic model serves as the dynamic prediction model of the system after it is formulated as a discrete time state space model . Only plasma measurements are assumed to be measured on line . The rest of the states are estimated in real time by designing an artificial observer . The complete system is able to drive the drug concentration to the desired levels at the organs of interest while satisfying the imposed constraints even in the presence of modelling errors disturbances and noise . A case study on a PBPK model with 7 compartments constraints on 5 tissues and a variable drug concentration set point illustrates the efficiency of the methodology in drug dosing control applications . The proposed methodology is also tested in an uncertain setting and proves successful in presence of modelling errors and inaccurate measurements . 


Summary: The model predictive control methodology was used to design an optimal drug administration scheme for continuous drug administration. A PBPK model served as the predictive model after it is formulated as a state space system. The methodology can impose constraints on all different compartments even if drug concentration measurements are not possible by augmenting the control system with an observer. Through the application to the problem of distributing DMA in mice it was shown that the method is not sensitive to the presence of measurement noise and intra patient variability.
Automatic detection of the QRS complexes R peaks in an electrocardiogram signal is the most important step preceding any kind of ECG processing and analysis . The performance of these systems heavily relies on the accuracy of the QRS detector . The objective of present work is to drive a new robust method based on stationary wavelet transform for R peaks detection . The decimation of the coefficients at each level of the transformation algorithm is omitted more samples in the coefficient sequences are available and hence a better outlier detection can be performed . Using the information of local maxima minima and zero crossings of the fourth SWT coefficient detail the proposed algorithm identifies the significant points for detection and delineation of the QRS complexes as well as detection and identification of the QRS individual waves peaks of the pre processed ECG signal . Various experimental results show that the proposed algorithm exhibits reliable QRS detection as well as accurate ECG delineation achieving excellent performance on different databases on the MIT BIH database on the QT Database and on MIT BIH Noise Stress Test Database . Reliability and accuracy are close to the highest among the ones obtained in other studies . Experiments results being satisfactory the SWT may represent a novel QRS detection tool for a robust ECG signal analysis . 


Summary: This work makes a R peaks detection based on stationary wavelet transform SWT . It has the same sampling time of the signal. It inherently leads to more accurate estimation of the QRS fiducial points. It detect R peaks over different types of arrhythmias and types of noise. Only one scale of SWT is applied in order to reduce the computational complexity.
Three dimensional reconstruction of lung and vessel tree has great significance to 3D observation and quantitative analysis for lung diseases . This paper presents non sheltered 3D models of lung and vessel tree based on a supervised semi 3D lung tissues segmentation method . A recursive strategy based on geometric active contour is proposed instead of the coarse to fine framework in existing literature to extract lung tissues from the volumetric CT slices . In this model the segmentation of the current slice is supervised by the result of the previous one slice due to the slight changes between adjacent slice of lung tissues . Through this mechanism lung tissues in all the slices are segmented fast and accurately . The serious problems of left and right lungs fusion caused by partial volume effects and segmentation of pleural nodules can be settled meanwhile during the semi 3D process . The proposed scheme is evaluated by fifteen scans from eight healthy participants and seven participants suffering from early stage lung tumors . The results validate the good performance of the proposed method compared with the coarse to fine framework . The segmented datasets are utilized to reconstruct the non sheltered 3D models of lung and vessel tree . 


Summary: This manuscript proposes a novel recursive strategy based on geometric active contour model to extract lung tissues from the volumetric CT slices accurately. The proposed method could settle the challenging task of separating left and right lungs. Non sheltered 3D models of lung and vessels tree are constructed based on the extracted datasets. The proposed method is validated by fifteen scans with good performance.
In this paper a new image segmentation method based on Particle Swarm Optimization and outlier rejection combined with level set is proposed . A traditional approach to the segmentation of Magnetic Resonance images is the Fuzzy C Means clustering algorithm . The membership function of this conventional algorithm is sensitive to the outlier and does not integrate the spatial information in the image . The algorithm is very sensitive to noise and in homogeneities in the image moreover it depends on cluster centers initialization . To improve the outlier rejection and to reduce the noise sensitivity of conventional FCM clustering algorithm a novel extended FCM algorithm for image segmentation is presented . In general in the FCM algorithm the initial cluster centers are chosen randomly with the help of PSO algorithm the clusters centers are chosen optimally . Our algorithm takes also into consideration the spatial neighborhood information . These a priori are used in the cost function to be optimized . For MR images the resulting fuzzy clustering is used to set the initial level set contour . The results confirm the effectiveness of the proposed algorithm . 


Summary: New image segmentation method based on PSO and outlier rejection with level set. The use of PSO algorithm allows an optimal choice of cluster centers. IKPCM lead to a fine segmentation using the level set method. IKPCM shows a significant improvement concerning the robustness to noise.