Subsequent Matching Research Articles

Current Opportunities and Challenges of Next Generation Sequencing (NGS) of DNA; Determining Health and Diseases

Many publications have demonstrated the huge potential of NGS methods in terms of new species discovery, environment monitoring, ecological studies, etc. [24,35,92,97,103]. Undoubtedly, NGS will become one the major tools for species identification and for routine diagnostic use. While read lengths are still quite short for most existing systems ranging between 50 bp and 800 bp, they are likely to improve soon. This will enable easier, faster, and more reliable contig assembly and subsequent matching against reference databases. When data generation is no longer a bottleneck, the storage, speed of analysis, and interpretation of DNA sequence data are becoming the major challenges. Also, the integration or the use of data originating from diverse datasets and a variety of data providers are serious issues that need to be addressed. Poor sequence record annotations and species name assignments are known problems that should be instantly addressed and would allow the creation of reference databases used for routine diagnostics based on NGS. Samples with huge amounts of short DNA fragments need to be analyzed and compared against reference databases in an efficient and fast way. Although a number of solutions have been proposed by Industry; offering commercial software, there still remain hurdles to take. One of the challenges that we need to address is data upload from client’s computers to central or distributed data storage and analysis services. Another one is the efficient parallelization of analyses using cloud or grid solutions. The reliability and up-time of storage and analyses facilities is another important problem that need to be addressed if one wants to use it for routine diagnostics. Finally, the management, reporting and visualization of the analyses results are among the last issues, but not the least challenging ones. Considering the constant growth of computational power and storage capacity needed by different bioinformatics applications, working with single or a limited number of servers is no longer realistic. Using a cloud environment and grid computing is becoming a must. Even single cloud service provider can be restrictive for bioinformatics applications and working with more than one cloud can make the workflow more robust in the face of failures and always growing capacity needs. In this white paper we review the current state of the art in this field. We discuss the main limitations and challenges that we need to address such as; data upload from client’s computers to central or distributed data storage and analysis services; efficient parallelization of analyses using grid solutions; reliability and up-time of storage and analyses facilities for routine diagnostics; management, retrieving and visualization of the analyses results.

CUDA Optimization of Non-local Means Extended to Wrapped Gaussian Distributions for Interferometric Phase Denoising

Interferometric Synthetic Aperture Radar (InSAR) captures hundreds of millions of phase measurements with a single image, which can be differenced with a subsequent matching image to measure the Earth's physical properties such as atmosphere, topography, and ground instability. Each pixel in an InSAR image lies somewhere between perfect information and complete noise; deriving useful measurements from InSAR is therefore predicated upon estimating the quality (coherence) of each pixel, while also enhancing the information-bearing pixels through filtering. Rejecting noisy pixels at the outset and filtering the available information without introducing artifacts is crucial for generating accurate and spatially dense measurements. A capable filtering strategy must accommodate the diversity of manmade and natural ground cover exhibiting noise spawned by vegetation and water interwoven with useable signals echoed by infrastructure, rocks, and bare ground. Traditional filtering strategies assuming spatial homogeneity have lately been replaced by filters that honor discontinuities in ground cover, but two key improvements are needed: a) techniques must be adapted to enhance phase rather than amplitude, and b) runtime needs to be reduced to support deployment for operational land-information products. We present a new algorithm for wrapped phase filtering based on the non-local means algorithm (NLM) of Baudes et al. (2005) and the non-local InSAR (NL-InSAR) algorithm of Deledalle et al. (2011). The new filter, wrapped-NLM (WNLM), extends NLM to wrapped phase data that is inherently lossy due to an unknown integer number of phase ambiguities per pixel. The filter is similar to that of NL-InSAR in that we adopt their procedure of iteratively improving the filtered phase estimates by updating the Bayesian prior based on the previously filtered data (2009). Our filter differs from NL-INSAR in that it does not assume the Goodman model (1963) nor that of speckle noise (Goodman J. W., 2007) which were found to suffer in some areas due to having too many degrees of freedom; instead we use a more general assumption that the phase noise distribution is additive wrapped Gaussian, making the filter more robust to a larger variety of input data. This also simplifies the algorithm making it possible to implement an efficient parallel algorithm on the GPU using CUDA.

An efficient algorithm for attribute-based subsequence matching

Subsequence matching plays a fundamental role in the solutions to sequence-related problems such as sequence classification and similarity search. Subsequence matching is to find out expected event sequences from a database with a specified sequence composed of events. One cannot specify some or all of the events in the sequence but can give attribute values of these events in some applications. Therefore, a problem is how to find out expected event sequences from a database with an attribute-based sequence composed of attribute values. We propose an algorithm for the problem. The algorithm uses a list structure to store the temporal information about both sequences and events associated with values in a database. The structure can be compressed using a simple technique. Experimental data show that the proposed algorithm is one to two orders of magnitude faster than the state-of-the-art algorithm, especially for real-world databases, and that the technique is effective.

Pattern recognition in multivariate time series – A case study applied to fault detection in a gas turbine

Advances in information technology, together with the evolution of systems in control, automation and instrumentation have enabled the recovery, storage and manipulation of a large amount of data from industrial plants. This development has motivated the advancement of research in fault detection, especially based on process history data. Although a large amount of work has been conducted in recent years on the diagnostics of gas turbines, few of them present the use of clustering approaches applied to multivariate time series, adopting PCA similarity factor (SPCA) in order to detect and/or prevent failures. This paper presents a comprehensive method for pattern recognition associated to fault prediction in gas turbines using time series mining techniques. Algorithms comprising appropriate similarity metrics, subsequence matching and fuzzy clustering were applied on data extracted from a Plant Information Management System (PIMS) represented by multivariate time series. A real case study comprising the fault detection in a gas turbine was investigated. The results suggest the existence of a safe way to start the turbine that can be useful to support the development of a dynamic system for monitoring and predicting the probability of failure and for decision-making at operational level.

Soccer Injuries in Players Aged 7 to 12 Years

Background: As part of a risk-management approach, sound epidemiological data are needed to develop prevention programs. A recent review on soccer injuries of players younger than 19 years concluded that prospective data concerning children are lacking. Purpose: To analyze the incidence and characteristics of soccer injuries in children aged 7 to 12 years. Study Design: Descriptive epidemiological study. Methods: The present survey was a prospective descriptive epidemiological study on soccer injuries over 2 seasons in the Czech Republic and Switzerland. Exposure of players during training and match play (in hours) and injury data were reported by coaches via an Internet-based registration system. Location, type, and severity of injuries were classified according to an established consensus. Injury characteristics are presented as absolute numbers and injury incidence rates (injuries per 1000 hours of soccer exposure). An injury was defined as any physical complaint sustained during a scheduled training session or match play resulting in at least 1 of the following: (1) inability to complete the current match or training session, (2) absence from subsequent training sessions or matches, and (3) injury requiring medical attention. Results: In total, 6038 player-seasons with 395,295 hours of soccer exposure were recorded. The mean (±SD) age of the players was 9.5 ± 2.0 years, and 3.9% of the participants were girls. A total of 417 injuries were reported. Most (76.3%) injuries were located in the lower limbs, with 15.6% located in the upper limbs. Joint and ligament injuries comprised 30.5%, contusions 22.5%, muscle and tendon injuries 18.5%, and fractures and bone injuries 15.4% of all injuries; 23.7% of injuries led to more than 28 days of absence from sport participation. The overall injury incidence was 0.61 (95% CI, 0.53-0.69) injuries per 1000 hours of soccer exposure during training sessions and 4.57 (95% CI, 4.00-5.23) during match play. Injury incidence rates increased with increasing age. Conclusion: The observed injury incidences were lower compared with studies in youth players. Children showed a relatively high proportion of fractures and bone stress and of injuries to the upper limbs. Clinical Relevance: The study provides an evidence base for injury incidence rates and injury characteristics in children’s soccer. These data are the basis to develop an age-specific injury-prevention program.

Improving biomedical signal search results in big data case-based reasoning environments

Time series subsequence matching has importance in a variety of areas in healthcare informatics. These include case-based diagnosis and treatment as well as discovery of trends among patients. However, few medical systems employ subsequence matching due to high computational and memory complexities. This paper proposes a randomized Monte Carlo sampling method to broaden search criteria with minimal increases in computational and memory complexities over R-NN indexing. Information gain improves while producing result sets that approximate the theoretical result space, query results increase by several orders of magnitude, and recall is improved with no significant degradation to precision over R-NN matching.

Compressed Subsequence Matching and Packed Tree Coloring

We present a new algorithm for subsequence matching in grammar compressed strings. Given a grammar of size n compressing a string of size N and a pattern string of size m over an alphabet of size $$\sigma $$ , our algorithm uses $$O(n+\frac{n\sigma }{w})$$ space and $$O(n+\frac{n\sigma }{w}+m\log N\log w\cdot occ)$$ or $$O(n+\frac{n\sigma }{w}\log w+m\log N\cdot occ)$$ time. Here w is the word size and occ is the number of minimal occurrences of the pattern. Our algorithm uses less space than previous algorithms and is also faster for $$occ=o(\frac{n}{\log N})$$ occurrences. The algorithm uses a new data structure that allows us to efficiently find the next occurrence of a given character after a given position in a compressed string. This data structure in turn is based on a new data structure for the tree color problem, where the node colors are packed in bit strings.

NSPRING: the SPRING extension for subsequence matching of time series supporting normalization

Mining sequences and patterns in time series data streams is fast becoming a common practice in today's world. The rapid progress of data collection and web technologies yields tremendous growth of flowing data in various complex forms that need to be analyzed in real time. Traditional data mining methods that typically require the process data to be scanned repeatedly are not feasible for stream data applications. However, new techniques like SPRING attempt to address these challenges by identifying sequences of patterns on time series streams, thus reducing the complexity to be linear in both time and space. Unfortunately, SPRING does not support data normalization, which renders it to be not applicable for most data sets. In this paper, we are proposing an approach called NSPRING based on SPRING that extends the advantages of SPRING, e.g., low in time and space complexity, while it can support normalization. Furthermore, NSPRING retains similar mining accuracy to SPRING.

Graph Structure-Based Simultaneous Localization and Mapping Using a Hybrid Method of 2D Laser Scan and Monocular Camera Image in Environments with Laser Scan Ambiguity.

Localization is an essential issue for robot navigation, allowing the robot to perform tasks autonomously. However, in environments with laser scan ambiguity, such as long corridors, the conventional SLAM (simultaneous localization and mapping) algorithms exploiting a laser scanner may not estimate the robot pose robustly. To resolve this problem, we propose a novel localization approach based on a hybrid method incorporating a 2D laser scanner and a monocular camera in the framework of a graph structure-based SLAM. 3D coordinates of image feature points are acquired through the hybrid method, with the assumption that the wall is normal to the ground and vertically flat. However, this assumption can be relieved, because the subsequent feature matching process rejects the outliers on an inclined or non-flat wall. Through graph optimization with constraints generated by the hybrid method, the final robot pose is estimated. To verify the effectiveness of the proposed method, real experiments were conducted in an indoor environment with a long corridor. The experimental results were compared with those of the conventional GMappingapproach. The results demonstrate that it is possible to localize the robot in environments with laser scan ambiguity in real time, and the performance of the proposed method is superior to that of the conventional approach.

Causal inference with longitudinal outcomes and non-ignorable drop-out: Estimating the effect of living alone on cognitive decline.

In this paper we develop a model to estimate the causal effect of living arrangement (living alone versus living with someone) on cognitive decline based on a 15-year prospective cohort study, where episodic memory function is measured every five years. One key feature of the model is the combination of propensity score matching to balance confounding variables between the two living arrangement groups -in order to reduce bias due to unbalanced covariates at baseline, with a pattern mixture model for longitudinal data -in order to deal with non-ignorable drop-out. A fully Bayesian approach allows us to convey the uncertainty in the estimation of the propensity score and subsequent matching in the inference of the causal effect of interest. The analysis conducted here adds to previous studies in the literature concerning the protective effect of living with someone, by proposing a modeling approach treating living arrangement as an exposure.

Lasting First Impressions: The Influence of Initial Random Events on Long Term Decision Making

I examine the influence of early random outcomes on the choices and later success of players in a major online multiplayer game. Players misattribute early random shocks to the value of an important choice. I find that players are significantly less successful in subsequent matches when making the same choice that benefitted from positive shocks early on. This effect, which occurred in just a handful of initial matches, lasts over the year long career of a player, spanning hundreds of matches of the game. The setting of the game provides a rich dataset, and is also a rare empirical example of a multi-armed bandit problem.

Piecewise statistic approximation based similarity measure for time series

In the research field of time series analysis, dynamic time warping distance (DTW) is a prevalent similarity measure with high precision. However, the computational complexity of DTW is high, which makes it difficult to be applied to the high dimensional time series. An effective solution is to compute DTW on the piecewise representation (PR-DTW), which employs the features of the subsequences of time series for similarity measure. However, the features that most existing piecewise representations focus on are too simple, which capture only one aspect of the fluctuation information of time series, and thus influence the precision of PR-DTW. In order to solve this problem, we propose a novel piecewise representation model, named piecewise statistic approximation (PSA), for supporting the PR-DTW measure. Rather than focusing on a single type of features, PSA extracts multiple statistical features to capture the synthetic fluctuation information for similarity measure. Besides, by taking the weighted Euclidean distance for the subsequence matching in the subroutine, PSA based DTW (PSADTW) can discriminate the expressivities of the multiple features. Comprehensive experiments over 45 real-world datasets empirically demonstrate that, PSA is well suited to support both precise and efficient PR-DTW measure.

Real‐time markerless lung tumor tracking in fluoroscopic video: Handling overlapping of projected structures

Fluoroscopic imaging is a well-suited technique for online visualization of tumor motion in the thoracic region. Template-based approaches for tumor tracking in such images are commonly used. However, overlapping of different structures, mainly bones, can lead to limited visibility of the projected tumor shape, which in turn can negatively affect the performance of the tracking method. In this study, a method based on multiple-template matching was developed, providing fast and robust detection of tumor motion even under the influence of occurring tumor overlaps. A cohort of 14 patients with varying tumor sizes and locations was investigated. Image data from eight of these patients were used for evaluation. Based on the requirement of tumor visibility, the remaining datasets did not qualify for tracking. Generation of multiple templates was improved by implementation of an algorithm for automated selection of reference images containing the most characteristic tumor appearances. Various measures were taken to ensure real-time capability of the algorithm. A prematching step was introduced in order to reduce dispensable comparison operations by selecting the most appropriate template. Subsequent matching was further optimized by using prior knowledge about likely tumor motion to effectively limit necessary matching tasks. Tracking accuracy of the developed multiple-template method was compared with that of single-template. Mean errors of the multiple-template approach were 0.6 ± 0.6 mm in left-right and 0.9 ± 0.9 mm in superior-inferior direction in the isocenter plane. The single-template approach achieved mean errors of 0.7 ± 0.7 mm in left-right and 1.5 ± 1.3 mm in superior-inferior direction. These results derive from evaluation against manual tumor tracking performed by four expert observers. Computational times needed for tumor detection in a single fluoroscopic frame ranged between 1 and 29 ms depending on the tumor size and motion amplitude. This study shows that in case of tumor overlapping with dense structures, multiple-template tracking provides more accurate results than a single-template approach. The developed algorithm shows promising results in terms of suitability for real-time application and robustness against frequently changing overlapping.

Embedding-based subsequence matching with gaps–range–tolerances: a Query-By-Humming application

We present a subsequence matching framework that allows for gaps in both query and target sequences, employs variable matching tolerance efficiently tuned for each query and target sequence, and constrains the maximum matching range. Using this framework, a dynamic programming method is proposed, called SMBGT, that, given a short query sequence Q and a large database, identifies in quadratic time the subsequence of the database that best matches Q. SMBGT is highly applicable to music retrieval. However, in Query-By-Humming applications, runtime is critical. Hence, we propose a novel embedding-based approach, called ISMBGT, for speeding up search under SMBGT. Using a set of reference sequences, ISMBGT maps both Q and each position of each database sequence into vectors. The database vectors closest to the query vector are identified, and SMBGT is then applied between Q and the subsequences that correspond to those database vectors. The key novelties of ISMBGT are that it does not require training, it is query sensitive, and it exploits the flexibility of SMBGT. We present an extensive experimental evaluation using synthetic and hummed queries on a large music database. Our findings show that ISMBGT can achieve speedups of up to an order of magnitude against brute-force search and over an order of magnitude against cDTW, while maintaining a retrieval accuracy very close to that of brute-force search.

Designing highly reliable adverse-event detection systems to predict subsequent claims.

To respond proactively to patient safety events, many healthcare organizations have been enhancing and customizing their event reporting systems. Yet an indiscriminate expansion of the range and number of event reports may reduce, rather than raise, risk managers' ability to detect events that warrant a response. To avoid becoming overwhelmed by too many event reports that have little immediate operational value, risk managers therefore require a concurrent and complementary refinement of their data-processing capabilities. To examine the extent to which adverse event reports can predict subsequent claims. The study sample included all adverse event reports and all records of closed claims that related to patient care episodes between July 1, 2006, and May 31, 2009, at a large hospital system in northern Virginia. After matching closed claims to event reports, we fitted multivariate predictive models to identify event report entries that predict future claims. During the period under study, 20 151 event reports and 94 claims were filed across the health system. We were able to match 60 claims (63.8%) to at least 1 preceding event report, implying that only 0.3% of event reports preceded a subsequent matching claim. The superior prediction model identified 90% of eventual matched claims by retaining only 20% of all event reports. Simple prediction algorithms can supplement expert judgment by screening for reports that are likely to result in a claim, thereby enabling risk managers to evaluate adverse event reports more expeditiously and to identify, and ultimately prevent, serious safety lapses more reliably.

Relationship Between Midweek Training Measures of Testosterone and Cortisol Concentrations and Game Outcome in Professional Rugby Union Matches

The aim of this study was to assess the response of salivary-free testosterone and cortisol concentrations across selected midweek skill-based training sessions and their association with subsequent match outcome 3 days later. Twenty-two rugby union players were assessed for salivary-free testosterone and cortisol concentrations before and after a midweek training session over 6 consecutive weeks. The relative percentage change (response) in the testosterone and cortisol concentration and the testosterone to cortisol (T/C) ratio was also determined. Game-day analysis consisted of prematch testosterone concentrations and match outcome. Data were pooled across the winning (n = 3) and losing (n = 3) outcomes. The midweek pretraining T/C ratio was significantly lower (p < 0.01) before a win than a loss and the increase in the pre- to post-T/C ratio before a win was significant (p < 0.001). The increase in the pre- to post-testosterone concentration before a win was also shown to be significant (p < 0.01). However, the relative changes in testosterone before games that were won were not statistically different to that of games lost (p > 0.01). Significant relationships were also demonstrated between game-day pre-testosterone concentrations and the midweek cortisol response (r = -0.90, p = 0.01) and midweek T/C ratio response (r = 0.90, p = 0.01). In conclusion, a midweek measurement of the T/C ratio against a skill-based training session seems to show some potential as an early indicator of subsequent successfully executed performances in competitive rugby union. If this work is subsequently validated, further monitoring of midweek hormone concentrations in response to a mixed psychological-physical training session may assist with assessing competitive readiness leading up to competition.

Near-Duplicate Subsequence Matching Between the Continuous Stream and Large Video Dataset

In this paper, we study the subsequence matching problem of near-duplicate video detection. In particular, we address the application of monitoring a continuous stream with a large video dataset. To achieve real-time response and high accuracy, we propose a novel framework containing two characteristics . First, the subsequence matching is transformed to a 2-D Hough space projection of pairwise frame similarities between two subsequences. We present an approximate Hough transform that replaces the 2-D Hough space with a 1-D Hough space. The near-duplicate subsequence detection can be deemed to be the voting and searching in the 1-D Hough space with a lower time complexity. Second, a coarse-to-fine matching strategy is incorporated in the proposed framework. The coarse level matching selects the candidate videos based on the time-decay hit frequency between the query stream and dataset videos. The fine level matching applies the approximate Hough transform to detect the near-duplicate subsequences coexisting within the query stream and candidate videos. Several state-of-the-art methods are implemented for comparison. Experimental results show our framework outperforms in terms of accuracy and efficiency.

Identifying sea scallops from benthic camera images

The article presents an algorithmic framework for the automated analysis of benthic imagery data. The data are collected by an autonomous underwater vehicle for the purpose of population assessment of epibenthic organisms, such as scallops. The architecture consists of three layers of processing: visual attention, graph‐cut segmentation methods, and template matching. The visual attention layer filters the imagery input, focusing subsequent processing only on regions in the images that are likely to contain target objects. The segmentation layer prepares for subsequent template matching. Finally, template matching classifies filtered objects into targets and distractors. The significance of the proposed approach is in its modular nature and its ability to process imagery datasets of low resolution, brightness, and contrast.

Preprocedural CT evaluation of transcatheter aortic valve replacement: what the radiologist needs to know.

Aortic valve stenosis is the most common valvular heart disease in the Western world. When symptomatic, aortic valve stenosis is a debilitating disease with a dismal short-term prognosis, invariably leading to heart failure and death. Elective surgical valve replacement has traditionally been considered the standard of care for symptomatic aortic valve stenosis. However, several studies have identified various subgroups of patients with a significantly elevated risk for surgery-related complications and death. Thus, not every patient is a suitable candidate for surgery. Recent developments in transcatheter-based therapies have provided an alternative therapeutic strategy for the nonsurgical patient population known as transcatheter aortic valve replacement (TAVR) (also called transcatheter aortic valve implantation or percutaneous aortic valve replacement). In TAVR, the native aortic valve is replaced with a bioprosthetic valve via a nonsurgical endovascular, transaortic, or transapical pathway. Nevertheless, several anatomic and technical criteria must be met to safeguard patient eligibility and procedural success. Therefore, noninvasive imaging plays a crucial role in both patient selection and subsequent matching to a specific transcatheter valve size in an effort to ensure accurate prosthesis deployment and minimize peri- and postprocedural complications. The authors review the relevant anatomy of the aortic root, emphasizing the implications of anatomic pitfalls for correct reporting of imaging-derived measurements and important differences between findings obtained with different imaging modalities. They also discuss the evolving role of computed tomography and the role of the radiologist in patient triage in light of current viewpoints regarding patient selection, device size selection, and the preprocedural evaluation of possible access routes. Online supplemental material is available for this article.

Research and Improvement on the Algorithm of Face Gray Image Normalization

In face recognition system, the purpose of gray pretreatment is to denoise and enhance the image. The traditional linear or nonlinear denoising algorithm can bring edge loss, which make it difficulty for the subsequent image segmentation or matching. Although Alpha filter can bring the minimum loss of image edge, the size of filtering window cannot be adaptively changed according to the noise. The Alpha filter is improved on the basis that the information entropy can reflect the noise strength to some degrees. The single pixel entropy in neighborhood is compared with the information entropy average and then the noise infection of neighborhood pixel is determined. Moreover, according to the noise infection, the window size is adaptively adjusted to filter. The results show that the loss of image edge obviously reduces. Because the image size is fixed, we can calculate the integration of normalized image according to cumulative distribution function of the image. Therefore, the image histogram equalization is derived and the image gray is transformed to get the enhanced image. Finally, the results show that the face image after improved gray pretreatment can well ensure the image edge integrity and the face recognition effect is improved by edge feature.