varunr14/newsummary huggingface dataset:
Objectives To compare the risk of infection for rheumatoid arthritis patients who took etanercept or adalimumab medication in a nationwide population . Methods RA patients who took etanercept or adalimumab were identified in the Taiwan s National Health Insurance Research Database . The composite outcome of serious infections including hospitalization for infection reception of an antimicrobial injection and tuberculosis were followed for 365 days . A Kaplan Meier survival curve with a log rank test and Cox proportional hazards regression were used to compare risks of infection between the two cohorts of tumor necrosis factor antagonists users . Hazard ratios were obtained and adjusted with propensity scores and clinical factors . Sensitivity analyses and subgroup analyses were also performed . Results In total 1660 incident etanercept users and 484 incident adalimumab users were eligible for the analysis . The unadjusted HR for infection of the etanercept users was significantly higher than that of the adalimumab users 1.09 3.42 p 0.024 . The HRs were 2.04 and 2.02 after adjusting for propensity scores and for propensity scores in addition to clinical factors respectively . The subgroup analyses revealed that HRs for composite infection was significantly higher in patient subgroups of older age female as well as patients who did not have DM COPD and hospitalization history at the baseline . Conclusion In this head to head cohort study involving a nationwide population of patients with RA etanercept users demonstrated a higher risk of infection than adalimumab users . Results of this study suggest the possible existence of an intra class difference in infection risk among TNF antagonists . 


Summary: A head to head cohort study involving a nationwide population has been performed. Etanercept users have higher risk of infection than adalimumab users. Infection free survival is significantly lower in etanercept users. The difference of infection risk is significant in subgroup analysis.
Lab on a chip devices are broadly used for research in the life sciences and diagnostics and represent a very fast moving field . LOC devices are designed prototyped and assembled using numerous strategies and materials but some fundamental trends are that these devices typically need to be sealed supplied with liquids reagents and samples and often interconnected with electrical or microelectronic components . In general closing and connecting to the outside world these miniature labs remain a challenge irrespectively of the type of application pursued . Here we review methods for sealing and connecting LOC devices using standard approaches as well as recent state of the art methods . This review provides easy to understand examples and targets the microtechnology engineering community as well as researchers in the life sciences . 


Summary: We review two main challenges of microfluidics technology sealing and interfacing. These are crucial for commercialization and wide spread use of microfluidics. We review well established techniques as well as recent state of the art methods. We discuss both research and commercial activities. We provide up to date examples for each technology discussed.
Semen analysis is the first step in the evaluation of an infertile couple . Within this process an accurate and objective morphological analysis becomes more critical as it is based on the correct detection and segmentation of human sperm components . In this paper we present an improved two stage framework for detection and segmentation of human sperm head characteristics that uses three different color spaces . The first stage detects regions of interest that define sperm heads using k means then candidate heads are refined using mathematical morphology . In the second stage we work on each region of interest to segment accurately the sperm head as well as nucleus and acrosome using clustering and histogram statistical analysis techniques . Our proposal is also characterized by being fully automatic where a user intervention is not required . Our experimental evaluation shows that our proposed method outperforms the state of the art . This is supported by the results of different evaluation metrics . In addition we propose a gold standard built with the cooperation of a referent expert in the field aiming to compare methods for detecting and segmenting sperm cells . Our results achieve notable improvement getting above 98 in the sperm head detection process at the expense of having significantly fewer false positives obtained by the state of the art method . Our results also show an accurate head acrosome and nucleus segmentation achieving over 80 overlapping against hand segmented gold standard . Our method achieves higher Dice coefficient lower Hausdorff distance and less dispersion with respect to the results achieved by the state of the art method . 


Summary: We use three different color spaces for detection and segmentation of human sperm head acrosome and nucleus. We propose a gold standard built with the cooperation of a referent expert in the field aiming to create a benchmark set methods for detecting and segmenting sperm cells. We achieve notable improvement in sperm head detection and fewer false positives compared to the state of the art method. Our segmentation approach obtains over 80 overlapping against hand segmented gold standard. Our method achieves higher Dice coefficient lower Hausdorff distance and less dispersion with respect to the results achieved by the state of the art method.
In images with low contrast to noise ratio the information gain from the observed pixel values can be insufficient to distinguish foreground objects . A Bayesian approach to this problem is to incorporate prior information about the objects into a statistical model . A method for representing spatial prior information as an external field in a hidden Potts model is introduced . This prior distribution over the latent pixel labels is a mixture of Gaussian fields centred on the positions of the objects at a previous point in time . It is particularly applicable in longitudinal imaging studies where the manual segmentation of one image can be used as a prior for automatic segmentation of subsequent images . The method is demonstrated by application to cone beam computed tomography an imaging modality that exhibits distortions in pixel values due to X ray scatter . The external field prior results in a substantial improvement in segmentation accuracy reducing the mean pixel misclassification rate for an electron density phantom from 87 to 6 . The method is also applied to radiotherapy patient data demonstrating how to derive the external field prior in a clinical context . 


Summary: External field prior improves image segmentation accuracy. Manual segmentation of one image is used as a prior for subsequent images. Applicable to longitudinal imaging such as image guided radiation therapy.
In PET CT thoracic imaging respiratory motion reduces image quality . A solution consists in performing respiratory gated PET acquisitions . The aim of this study was to generate clinically realistic Monte Carlo respiratory PET data obtained using the 4D NCAT numerical phantom and the GATE simulation tool to assess the impact of respiratory motion and respiratory motion compensation in PET on lesion detection and volume measurement . To obtain reconstructed images as close as possible to those obtained in clinical conditions a particular attention was paid to apply to the simulated data the same correction and reconstruction processes as those applied to real clinical data . The simulations required 140 000h generating 1.5 To of data . Calibration phantom and patient reconstructed images from the simulated data were visually and quantitatively very similar to those obtained in clinical studies . The lesion detectability was higher when the better trade off between lesion movement limitation and image statistic preservation is considered . We then compared the lesion volumes measured on conventional PET acquisitions versus respiratory gated acquisitions using an automatic segmentation method and a 40 threshold approach . A time consuming initial manual exclusion of noisy structures needed with the 40 threshold was not necessary when the automatic method was used . The lesion detectability along with the accuracy of tumor volume estimates was largely improved with the gated compared to ungated PET images . 


Summary: We create a dataset of clinically realistic Monte Carlo respiratory PET data. We validated the realism of the simulated data. We evaluated the lesion detectability regarding their location volume and contrast. Respiratory gating of 18F FDG PET images improves the accuracy of volume estimates.
The asymmetry in the tail dependence between U.S. equity portfolios and the aggregate U.S. market is a well established property . Given the limited number of observations in the tails of a joint distribution standard non parametric measures of tail dependence have poor finite sample properties and generally reject the asymmetry in the tail dependence . A parametric model based on a multivariate noncentral t distribution is developed to measure and test asymmetry in tail dependence . This model allows different levels of tail dependence to be estimated depending on the distribution s parameters and accommodates situations in which the volatilities or the correlations across returns are time varying . For most of the size book to market and momentum portfolios the tail dependence with the market portfolio is significantly higher on the downside than on the upside . 


Summary: The tail dependence between US equity portfolios and the US market is asymmetric. We describe a multivariate t distribution with asymmetry in the lower and upper tail dependence. The lower and upper tail dependence parameters are estimated by maximum likelihood. The estimated tail dependence parameters are consistent with the data provided volatilities and correlations are allowed to vary over time.
Excess zeroes are often thought of as a cause of data over dispersion this claim is not entirely accurate . In actuality excess zeroes reduce the mean of a dataset thus inflating the dispersion index . While this results in an increased chance for data over dispersion the implication is not guaranteed . Thus one should consider a flexible distribution that not only can account for excess zeroes but can also address potential over or under dispersion . A zero inflated Conway Maxwell Poisson regression allows for modeling the relationship between explanatory and response variables while capturing the effects due to excess zeroes and dispersion . This work derives the ZICMP model and illustrates its flexibility extrapolates the corresponding likelihood ratio test for the presence of significant data dispersion and highlights various statistical properties and model fit through several examples . 


Summary: Zero inflated Conway Maxwell Poisson models dispersed datasets with excess zeroes. Hypothesis test detects statistically significant dispersion in light of excess zeroes. Data simulations and examples illustrate flexibility in model fit.
In mammographic imaging the presence of microcalcifications small deposits of calcium in the breast is a primary indicator of breast cancer . However not all microcalcifications are malignant and their distribution within the breast can be used to indicate whether clusters of microcalcifications are benign or malignant . Computer aided diagnosis systems can be employed to help classify such microcalcification clusters . In this paper a novel method for classifying microcalcification clusters is presented by representing discrete mereotopological relations between the individual microcalcifications over a range of scales in the form of a mereotopological barcode . This barcode based representation is able to model complex relations between multiple regions and the results on mammographic microcalcification data shows the effectiveness of this approach . Classification accuracies of 95 and 80 are achieved on the MIAS and DDSM datasets respectively . These results are comparable to existing state of the art methods . This work also demonstrates that mereotopological barcodes could be used to help trained clinicians in their diagnosis by providing a clinical interpretation of barcodes that represent both benign and malignant cases . 


Summary: A novel multi scale shape descriptor using persistent discrete mereotopology. Introduces the mereotopological barcode that brings together work from discrete mereotopology and computational topology. The method is applied to mammographic microcalcifications with results comparable to state of the art. Describes how mereotopological barcodes could be used in a clinical setting.
Several applications aim to identify rare events from very large data sets . Classification algorithms may present great limitations on large data sets and show a performance degradation due to class imbalance . Many solutions have been presented in literature to deal with the problem of huge amount of data or imbalancing separately . In this paper we assessed the performances of a novel method Parallel Selective Sampling able to select data from the majority class to reduce imbalance in large data sets . PSS was combined with the Support Vector Machine classification . PSS SVM showed excellent performances on synthetic data sets much better than SVM . Moreover we showed that on real data sets PSS SVM classifiers had performances slightly better than those of SVM and RUSBoost classifiers with reduced processing times . In fact the proposed strategy was conceived and designed for parallel and distributed computing . In conclusion PSS SVM is a valuable alternative to SVM and RUSBoost for the problem of classification by huge and imbalanced data due to its accurate statistical predictions and low computational complexity . 


Summary: We proposed a new algorithm to preprocess huge and imbalanced data. This algorithm based on distance calculations reduce both size and imbalance. The selective sampling method was conceived for parallel and distributed computing. It was combined with SVM obtaining optimized classification performances. Synthetic and real data sets were used to evaluate the classifiers performances.
To restrict pollen tube growth to a single focal plane is an important subject to enable their accurate growth analysis under microscopic observation . In the conventional method to assay pollen tube growth the pollen tubes grow in a disorderly manner on solid medium rendering it impossible to observe their growth in detail . Here we present a new method to assay pollen tube growth using poly dimethylsiloxane microchannel device to isolate individual pollen tubes . The growth of the pollen tube is confined to the microchannel and to the same focal plane allowing accurate microscopic observations . This methodology has the potential for analyses of pollen tube growth in microfluidic environments in response to chemical products and signaling molecules which paves the way for various experiments on plant reproduction . 


Summary: We describe a new pollen tube growth assay using a microchannel device. The microchannel restricts the growth of the pollen tube to a single focal plane. The growth of each pollen tube can be observed under a microscope and quantified. This method might enable exploring the effects of signaling molecules on pollen tube.
The prevalence of social media has greatly catalyzed the dissemination and proliferation of online memes . However this information abundance is exceeding the capability of online users to consume it . Ranking memes based on their popularities could promote online advertisement and content distribution . Despite such importance few existing work can solve this problem well . They are either daunted by unpractical assumptions or incapability of characterizing dynamic information . As such in this paper we elaborate a model free scheme to rank online memes in the context of social media . This scheme is capable to characterize the nonlinear interactions of online users which mark the process of meme diffusion . Empirical studies on two large scale real world datasets demonstrate the effectiveness and robustness of the proposed scheme . In addition due to its fine grained modeling of user dynamics this ranking scheme can also be utilized to explain meme popularity through the lens of social influence . 


Summary: We develop a model free scheme for meme ranking task. We have evaluated the proposed scheme on two large scale real world datasets. Our work presents fine grained modeling of dynamic information and can be readily generalized to other domains.
In this study we proposed a new adaptive method for fusing multiple emotional modalities to improve the performance of the emotion recognition system . Three channel forehead biosignals along with peripheral physiological measurements were utilized as emotional modalities . Six basic emotions i.e . anger sadness fear disgust happiness and surprise were elicited by displaying preselected video clips for each of the 25 participants in the experiment the physiological signals were collected simultaneously . In our multimodal emotion recognition system recorded signals with the formation of several classification units identified the emotions independently . Then the results were fused using the adaptive weighted linear model to produce the final result . Each classification unit is assigned a weight that is determined dynamically by considering the performance of the units during the testing phase and the training phase results . This dynamic weighting scheme enables the emotion recognition system to adapt itself to each new user . The results showed that the suggested method outperformed conventional fusion of the features and classification units using the majority voting method . In addition a considerable improvement compared to the systems that used the static weighting schemes for fusing classification units was also shown . Using support vector machine and k nearest neighbors classifiers the overall classification accuracies of 84.7 and 80 were obtained in identifying the emotions respectively . In addition applying the forehead or physiological signals in the proposed scheme indicates that designing a reliable emotion recognition system is feasible without the need for additional emotional modalities . 


Summary: A new dynamic fusion method for designing an emotion recognition system is proposed. A weight is assigned to each classifier based on its performance. The performance of the classifiers during the training and testing phases is considered. Static weights in varying contexts such as emotions do not produce acceptable results. Dynamic weighting strategy improves the performance of the system considerably.
An important problem in high dimensional data analysis is determining whether sample points are uniformly distributed over some compact support or rather possess some underlying structure . We propose two new graph theoretic tests of uniformity which utilize the minimum spanning tree and a snake . We compare the powers of statistics based on these graphs with other statistics from the literature on an array of non uniform alternatives in a variety of supports . For data in a hypercube we find that test statistics based on the minimum spanning tree have superior power when the data displays regularity . For arbitrarily shaped or unknown supports we use run length statistics of the sequence of segment lengths along the snake s path to test uniformity . The snake is particularly useful because no knowledge or estimation of the support is required to compute the test statistic it can be computed quickly for any dimension and it shows what kinds of non uniformities are present . These properties make the snake unique among multivariate tests of uniformity since others only function on specific and known supports have computational difficulties in high dimension or have inconsistent type I error rates . 


Summary: A test of uniformity determines if data is uniform or possesses underlying structure. Tests of uniformity should be the first step in any pattern or exploratory data analysis. We develop new tests of uniformity and compare with many in the literature. One test outperforms others when regularity minimum spacings exists in the data. Our other test is the only option for testing on arbitrary supports and dimensions.
Optical lithography technique has been applied to fabricate devices from atomically thin sheets exfoliated mechanically from kish graphite bulk MoS2 and WSe2. During the fabrication processes the exfoliated graphene few layer MoS2 and WSe2 sheets have been patterned into specific shapes as required and metal contacts have been deposited on these two dimensional sheets to make field effect devices with different structures. The key to the successful implementation of the technique is the appropriate alignment mark design which can solve the problems of aligning photomasks to the random location orientation and irregular shape exfoliated two dimensional sheets on the substrates. Raman characterization performed on the patterned two dimensional sheets after the fabrication processes shows that little defects have been introduced during fabrication. Field effect has been observed from I V characteristics with the highly doped silicon substrate as the back gate. The extracted field effect hole and electron mobilities of graphene are 1010 cm2 V 1 s 1 and 3550 cm2 V 1 s 1 respectively and the field effect carrier mobilities of MoS2 and WSe2 are 0.06 cm2 V 1 s 1 and 0.03 cm2 V 1 s 1 separately which are comparable with experimental results of other reports. 


Summary: Optical lithography has been applied for the fabrication of 2D electronic devices. The exfoliated graphene few layer MoS2 and WSe2 sheets have been patterned. The key to the successful implementation is the appropriate alignment mark design. The final electronic devices show little defects.
Multivariate adaptive regression splines provide a flexible statistical modeling method that employs forward and backward search algorithms to identify the combination of basis functions that best fits the data and simultaneously conduct variable selection . In optimization MARS has been used successfully to estimate the unknown functions in stochastic dynamic programming stochastic programming and a Markov decision process and MARS could be potentially useful in many real world optimization problems where objective functions need to be estimated from data such as in surrogate optimization . Many optimization methods depend on convexity but a non convex MARS approximation is inherently possible because interaction terms are products of univariate terms . In this paper a convex MARS modeling algorithm is described . In order to ensure MARS convexity two major modifications are made coefficients are constrained such that pairs of basis functions are guaranteed to jointly form convex functions and the form of interaction terms is altered to eliminate the inherent non convexity . Finally MARS convexity can be achieved by the fact that the sum of convex functions is convex . Convex MARS is applied to inventory forecasting SDP problems with four and nine dimensions and to an air quality ground level ozone problem . 


Summary: Convex MARS enables a convex approximation without degrading the quality of fit. Convex MARS is appropriate for approximations in convex optimization problems. The threshold version of Convex MARS provides stronger convexity and better accuracy.