varunr14/newsummary huggingface dataset:
Pharmacokinetics can be a challenging topic to teach due to the complex relationships inherent between physiological parameters mathematical descriptors and equations and their combined impact on shaping the blood fluid concentration vs. time curves of drugs . A computer program was developed within Microsoft Excel for Windows designed to assist in the instruction of basic pharmacokinetics within an entry to practice pharmacy class environment . The program is composed of a series of spreadsheets linked by Visual Basic for Applications intended to illustrate the relationships between pharmacokinetic and in some cases physiological parameters doses and dose rates and the drug blood fluid concentration vs. time curves . Each module is accompanied by a simulation user s guide prompting the user to change specific independent parameters and then observe the impact of the change on the drug concentration vs. time curve and on other dependent parameters . Slider bars can be selected to readily see the effects of repeated changes on the dependencies . Topics covered include one compartment single dose administration intravenous infusion repeated doses renal and hepatic clearance nonlinear elimination two compartment model plasma protein binding and the relationship between pharmacokinetics and drug effect . The program has been used in various forms in the classroom over a number of years with positive ratings generally being received from students for its use in the classroom . 


Summary: A pharmacokinetics simulation teaching program is described. It is written in VBA in Excel for Windows. The program covers a wide variety of basic pharmacokinetic content. Students have responded favorably to its use.
Background and objectives The broad adoption of clinical decision support systems within clinical practice has been hampered mainly by the difficulty in expressing domain knowledge and patient data in a unified formalism . This paper presents a semantic based approach to the unified representation of healthcare domain knowledge and patient data for practical clinical decision making applications . Methods A four phase knowledge engineering cycle is implemented to develop a semantic healthcare knowledge base based on an HL7 reference information model including an ontology to model domain knowledge and patient data and an expression repository to encode clinical decision making rules and queries . A semantic clinical decision support system is designed to provide patient specific healthcare recommendations based on the knowledge base and patient data . Results The proposed solution is evaluated in the case study of type 2 diabetes mellitus inpatient management . The knowledge base is successfully instantiated with relevant domain knowledge and testing patient data . Ontology level evaluation confirms model validity . Application level evaluation of diagnostic accuracy reaches a sensitivity of 97.5 a specificity of 100 and a precision of 98 an acceptance rate of 97.3 is given by domain experts for the recommended care plan orders . Conclusions The proposed solution has been successfully validated in the case study as providing clinical decision support at a high accuracy and acceptance rate . The evaluation results demonstrate the technical feasibility and application prospect of our approach . 


Summary: We develop a semantic healthcare knowledge base to support the practical use of CDSS. We propose a unified representation of healthcare domain knowledge and patient data based on HL7 RIM and ontology. We encode semantic rules and queries for data driven and knowledge based inference. We design a semantic CDSS to enable data interoperability and knowledge sharing for patient specific clinical decision support.
Background and objective Sperm morphology analysis is an important factor in the diagnosis of human male infertility . This study presents an automatic algorithm for sperm morphology analysis using images of human sperm cells . Methods The SMA method was used to detect and analyze different parts of the human sperm . First of all SMA removes the image noises and enhances the contrast of the image to a great extent . Then it recognizes the different parts of sperm and analyzes the size and shape of each part . Finally the algorithm classifies each sperm as normal or abnormal . Malformations in the head midpiece and tail of a sperm can be detected by the SMA method . In contrast to other similar methods the SMA method can work with low resolution and non stained images . Furthermore an image collection created for the SMA has also been described in this study . This benchmark consists of 1457 sperm images from 235 patients and is known as human sperm morphology analysis dataset . Results The proposed algorithm was tested on HSMA DS . The experimental results show the high ability of SMA to detect morphological deformities from sperm images . In this study the SMA algorithm produced above 90 accuracy in sperm abnormality detection task . Another advantage of the proposed method is its low computation time as such the expert can quickly decide to choose the analyzed sperm or select another one . Conclusions Automatic and fast analysis of human sperm morphology can be useful during intracytoplasmic sperm injection for helping embryologists to select the best sperm in real time . 


Summary: Automatically selection of normal sperms. Working on unstained sperms. Ability to work on low resolution images. High accuracy and real time processing time. Preparing a freely available dataset.
This paper proposes an integrated modelling approach for location planning of radiotherapy treatment services based on cancer incidence and road network based accessibility . Previous research efforts have established travel distance time barriers as a key factor affecting access to cancer treatment services as well as epidemiological studies have shown that cancer incidence rates vary with population demography . Our study is built on the evidence that the travel distances to treatment centres and demographic profiles of the accessible regions greatly influence the uptake of cancer radiotherapy services . An integrated service planning approach that combines spatially explicit cancer incidence projections and the placement of new RT services based on road network based accessibility measures have never been attempted . This research presents a novel approach for the location planning of RT services and demonstrates its viability by modelling cancer incidence rates for different age sex groups in New South Wales Australia based on observed cancer incidence trends and estimations of the road network based access to current NSW treatment centres . Using three indices we show how the best location for a new RT centre may be chosen when there are multiple competing locations . 


Summary: Proposes a methodology for location planning of radiotherapy treatment services. The methodology is based on cancer incidence and road network based accessibility measures. Applied to the current radiotherapy treatment centres in New South Wales state of Australia. Current and future regional radiotherapy demand are estimated.
In recent years several computational methods have been developed to predict RNA binding sites in protein . Most of these methods do not consider interacting partners of a protein so they predict the same RNA binding sites for a given protein sequence even if the protein binds to different RNAs . Unlike the problem of predicting RNA binding sites in protein the problem of predicting protein binding sites in RNA has received little attention mainly because it is much more difficult and shows a lower accuracy on average . In our previous study we developed a method that predicts protein binding nucleotides from an RNA sequence . In an effort to improve the prediction accuracy and usefulness of the previous method we developed a new method that uses both RNA and protein sequence data . In this study we identified effective features of RNA and protein molecules and developed a new support vector machine model to predict protein binding nucleotides from RNA and protein sequence data . The new model that used both protein and RNA sequence data achieved a sensitivity of 86.5 a specificity of 86.2 a positive predictive value of 72.6 a negative predictive value of 93.8 and Matthews correlation coefficient of 0.69 in a 10 fold cross validation it achieved a sensitivity of 58.8 a specificity of 87.4 a PPV of 65.1 a NPV of 84.2 and MCC of 0.48 in independent testing . For comparative purpose we built another prediction model that used RNA sequence data alone and ran it on the same dataset . In a 10 fold cross validation it achieved a sensitivity of 85.7 a specificity of 80.5 a PPV of 67.7 a NPV of 92.2 and MCC of 0.63 in independent testing it achieved a sensitivity of 67.7 a specificity of 78.8 a PPV of 57.6 a NPV of 85.2 and MCC of 0.45 . In both cross validations and independent testing the new model that used both RNA and protein sequences showed a better performance than the model that used RNA sequence data alone in most performance measures . To the best of our knowledge this is the first sequence based prediction of protein binding nucleotides in RNA which considers the binding partner of RNA . The new model will provide valuable information for designing biochemical experiments to find putative protein binding sites in RNA with unknown structure . 


Summary: A method to predict protein binding sites in RNA from sequence data alone is proposed. Protein binding sites in RNA are predicted with consideration of protein in addition to RNA. This is the first sequence based prediction of protein binding sites in RNA at the nucleotide level. The prediction method achieved an accuracy of 79.2 and MCC of 0.48 in independent testing.
We propose a general scheme for detecting critical locations of piecewise polynomial multivariate equation systems . Our approach generalizes previously known methods for locating tangency events or self intersections in contexts such as surface surface intersection problems and the problem of tracing implicit plane curves . Given the algebraic constraints of the original problem we formulate additional constraints seeking locations where the differential matrix of the original problem has a non maximal rank . This makes the method independent of a specific geometric application as well as of dimensionality . Within the framework of subdivision based solvers test results are demonstrated for non linear systems with three and four unknowns . 


Summary: A method for detecting critical points of nonlinear algebraic systems. Detecting isolated critical points. Point cloud approximation for critical varieties of dimension greater than zero.
Telemedicine is the medical practice of information exchanged from one location to another through electronic communications to improve the delivery of health care services . This research article describes a telemedicine framework with knowledge engineering using taxonomic reasoning of ontology modeling and semantic similarity . In addition to being a precious support in the procedure of medical decision making this framework can be used to strengthen significant collaborations and traceability that are important for the development of official deployment of telemedicine applications . Adequate mechanisms for information management with traceability of the reasoning process are also essential in the fields of epidemiology and public health . In this paper we enrich the case based reasoning process by taking into account former evidence based knowledge . We use the regular four steps approach and implement an additional step establish diagnosis retrieve treatment apply evidence adaptation retain . Each step is performed using tools from knowledge engineering and information processing . The case representation is done by the taxonomy component of a medical ontology model . The proposed approach is illustrated with an example from the oncology domain . Medical ontology allows a good and efficient modeling of the patient and his treatment . We are pointing up the role of evidences and specialist s opinions in effectiveness and safety of care . 


Summary: Telemedicine framework with structured knowledge to support medical professionals. Enrichment of the case based reasoning process with evidences and expert opinions. Knowledge engineering using reasoning of ontology modeling and semantic similarity. Flexible collaborative model to provide a participative and extensible environment. Application in oncology to improve the reliability and safety of medical services.
Gene expression data analysis is based on the assumption that co expressed genes imply co regulated genes . This assumption is being reformulated because the co expression of a group of genes may be the result of an independent activation with respect to the same experimental condition and not due to the same regulatory regime . For this reason traditional techniques are recently being improved with the use of prior biological knowledge from open access repositories together with gene expression data . Biclustering is an unsupervised machine learning technique that searches patterns in gene expression data matrices . A scatter search based biclustering algorithm that integrates biological information is proposed in this paper . In addition to the gene expression data matrix the input of the algorithm is only a direct annotation file that relates each gene to a set of terms from a biological repository where genes are annotated . Two different biological measures FracGO and SimNTO are proposed to integrate this information by means of its addition to be optimized fitness function in the scatter search scheme . The measure FracGO is based on the biological enrichment and SimNTO is based on the overlapping among GO annotations of pairs of genes . Experimental results evaluate the proposed algorithm for two datasets and show the algorithm performs better when biological knowledge is integrated . Moreover the analysis and comparison between the two different biological measures is presented and it is concluded that the differences depend on both the data source and how the annotation file has been built in the case GO is used . It is also shown that the proposed algorithm obtains a greater number of enriched biclusters than other classical biclustering algorithms typically used as benchmark and an analysis of the overlapping among biclusters reveals that the biclusters obtained present a low overlapping . The proposed methodology is a general purpose algorithm which allows the integration of biological information from several sources and can be extended to other biclustering algorithms based on the optimization of a merit function . 


Summary: Biclustering of gene expression data. Integration of biological knowledge. Scatter search.
In this paper we propose a robust semi autonomous algorithm for 3D vessel segmentation and tracking based on an active contour model and a Kalman filter . For each computed tomography angiography slice we use the active contour model to segment the vessel boundary and the Kalman filter to track position and shape variations of the vessel boundary between slices . For successful segmentation via active contour we select an adequate number of initial points from the contour of the first slice . The points are set manually by user input for the first slice . For the remaining slices the initial contour position is estimated autonomously based on segmentation results of the previous slice . To obtain refined segmentation results an adaptive control spacing algorithm is introduced into the active contour model . Moreover a block search based initial contour estimation procedure is proposed to ensure that the initial contour of each slice can be near the vessel boundary . Experiments were performed on synthetic and real chest CTA images . Compared with the well known Chan Vese model the proposed algorithm exhibited better performance in segmentation and tracking . In particular receiver operating characteristic analysis on the synthetic and real CTA images demonstrated the time efficiency and tracking robustness of the proposed model . In terms of computational time redundancy processing time can be effectively reduced by approximately 20 . 


Summary: We propose 3D vessel segmentation and tracking algorithm based on an active contour model and a Kalman filter. To obtain refined segmentation results we model an adaptive control spacing algorithm for the active contour model. To obtain refined tracking results we model an initial contour estimation algorithm for the Kalman filter. ROC analyses demonstrated the time efficiency and tracking robustness of the proposed model. Processing time can be effectively reduced by approximately 20 .
Background Proteomics the study of proteomes has been increasingly utilized in a wide variety of biological problems . The Two Dimensional Gel Electrophoresis technique is a powerful proteomics technique aiming at separation of the complex protein mixtures . Spot detection and segmentation are fundamental components of 2D gel image analysis but remain arduous and difficult tasks . Several software packages and academic approaches are available for 2D gel image spot detection and segmentation . Each one has its respective advantages and disadvantages and achieves a different level of success in dealing with the challenges of 2D gel image analysis . A common characteristic of the available methods is their dependency on user intervention in order to achieve optimal results a process that can lead to subjective and non reproducible results . In this work the authors propose a novel spot detection and segmentation methodology for 2D gel images . Methods This work introduces a novel spot detection and spot segmentation methodology that is based on a multi thresholding scheme applied on overlapping regions of the image a custom grow cut algorithm a region growing scheme and morphological operators . The performance of the proposed methodology is evaluated on real as well as synthetic 2D gel images using well established statistical measures including precision sensitivity and their weighted measure F measure as well as volumetric overlap volumetric error and volumetric overlap error . Results Experimental results show that the proposed methodology outperforms state of the art software packages and methods proposed in the literature and results in more plausible spot boundaries and more accurate segmentation . The proposed method achieved the highest F measure for spot detection and the lowest volumetric overlap error for the segmentation process . Conclusions Evaluation against state of the art 2D gel image analysis software packages and techniques proposed in the literature including Melanie 7 Delta2D PDQuest and Scimo demonstrates that the proposed approach outperforms the other methods evaluated in this work and constitutes an advantageous and reliable solution for 2D gel image analysis . 


Summary: A novel approach for 2D gel image spot detection and segmentation. The segmentation process includes a grow cut approach with a custom update rule that takes into consideration the inherent characteristics of 2D gel images. Real and synthetic 2D gel images containing a total of more than 20 000 protein spots are used for qualitative and quantitative evaluation. Better detection and segmentation performance than state of the art methods.
In this paper we define Tchebycheffian spline spaces over planar T meshes and we address the problem of determining their dimension . We extend to the Tchebycheffian spline context the homological approach previously used to characterize polynomial spline spaces over T meshes and we exploit this characterization in the study of the dimension . In particular we give combinatorial lower and upper bounds for the dimension and we show that these bounds coincide if the dimensions of the underlying extended Tchebycheff section spaces are large enough with respect to the smoothness under some mild conditions on the T mesh . Finally we provide simple examples of Tchebycheffian spline spaces over T meshes with unstable dimension which means that their dimension depends on the exact geometry of the T mesh . These results are extensions of those known in the literature for polynomial spline spaces over T meshes . 


Summary: We define Tchebycheffian spline spaces over planar T meshes and we address the problem of determining their dimension. We give combinatorial lower and upper bounds for the dimension. We show that these bounds coincide under certain conditions on the T mesh and or the Tchebycheffian spline space. We provide simple examples of Tchebycheffian spline spaces over T meshes with unstable dimension. These results are extensions of known results in the literature for polynomial spline spaces over T meshes.
Although studied for decades effective face recognition remains difficult to accomplish on account of occlusions and pose and illumination variations. Pose variance is a particular challenge in face recognition. Effective local descriptors have been proposed for frontal face recognition. When these descriptors are directly applied to cross pose face recognition the performance significantly decreases. To improve the descriptor performance for cross pose face recognition we propose a face recognition algorithm based on multiple virtual views and alignment error. First warps between poses are learned using the Lucas Kanade algorithm. Based on these warps multiple virtual profile views are generated from a single frontal face which enables non frontal faces to be matched using the scale invariant feature transform SIFT algorithm. Furthermore warps indicate the correspondence between patches of two faces. A two phase alignment error is proposed to obtain accurate warps which contain pose alignment and individual alignment. Correlations between patches are considered to calculate the alignment error of two faces. Finally a hybrid similarity between two faces is calculated it combines the number of matched keypoints from SIFT and the alignment error. Experimental results show that our proposed method achieves better recognition accuracy than existing algorithms even when the pose difference angle was greater than 30 . 


Summary: Cross pose face recognition with only a single frontal gallery face is a challenging task. Multiple virtual views are generated from a single frontal gallery face in advance. Alignment error of two faces is calculated after a two phase pose and individual alignment. Correlation between patches is considered by overlapping and covariance. Cross pose face recognition accuracy is significantly improved than compared methods.
We present an improved nanofabrication method of high aspect ratio tungsten structures for use in high efficiency nanofocusing hard X ray zone plates. A ZEP 7000 electron beam resist layer used for patterning is cured by a second much larger electron dose after development. The curing step improves pattern transfer fidelity into a chromium hard mask by reactive ion etching using Cl2 O2 chemistry. The pattern can then be transferred into an underlying tungsten layer by another reactive ion etching step using SF6 O2. A 630 nm thick tungsten zone plate with smallest line width of 30 nm was fabricated using this method and characterized. At 8.2 keV photon energy the device showed an efficiency of 2.2 with a focal spot size at the diffraction limit measured at Diamond Light Source I 13 1 beamline. 


Summary: Tungsten structures with 30nm line width 630nm height 21 1 aspect ratio E beam resist curing provides increased pattern transfer fidelity. Diffraction limited zone plate optics performing at 2.2 efficiency at 8.2keV
This expository paper exhibits the power and versatility of the Bernstein B zier form of a polynomial and the role that it has played in the analysis of multivariate spline spaces . Several particular applications are discussed in some detail . The purpose of the paper is to provide the reader with a working facility with the Bernstein B zier form . 


Summary: This is a survey of the Bernstein B zier form of a multivariate polynomial.