Precise Transcript Reconstruction with End-Guided Assembly

Accurate annotation of transcript isoforms is crucial for functional genomics research, but automated methods for reconstructing full-length transcripts from RNA sequencing (RNA-seq) data are imprecise. We developed a generalized transcript assembly framework called Bookend that incorporates data from multiple modes of RNA-seq, with a focus on identifying, labeling, and deconvoluting RNA 5′ and 3′ ends. Through end-guided assembly with Bookend we demonstrate that correctly modeling transcript start and end sites is essential for precise transcript assembly. Furthermore, we discover that reads from full-length single-cell RNA-seq (scRNA-seq) methods are sparsely end-labeled, and that these ends are sufficient to dramatically improve precision of assembly in single cells. Finally, we show that hybrid assembly across short-read, long-read, and end-capture RNA-seq in the model plant Arabidopsis and meta-assembly of single mouse embryonic stem cells (mESCs) are both capable of producing tissue-specific end-to-end transcript annotations of comparable or superior quality to existing reference isoforms.

Automatic Mapping and Characterisation of Linear Depositional Bedforms: Theory and Application Using Bathymetry from the North West Shelf of Australia

Bedforms are key components of Earth surfaces and yet their evaluation typically relies on manual measurements that are challenging to reproduce. Several methods exist to automate their identification and calculate their metrics, but they often exhibit limitations where applied at large scales. This paper presents an innovative workflow for identifying and measuring individual depositional bedforms. The workflow relies on the identification of local minima and maxima that are grouped by neighbourhood analysis and calibrated using curvature. The method was trialed using a synthetic digital elevation model and two bathymetry surveys from Australia’s northwest marine region, resulting in the identification of nearly 2000 bedforms. The comparison of the metrics calculated for each individual feature with manual measurements show differences of less than 10%, indicating the robustness of the workflow. The cross-comparison of the metrics resulted in the definition of several sub-types of bedforms, including sandwaves and palaeoshorelines, that were then correlated with oceanic conditions, further corroborating the validity of the workflow. Results from this study support the idea that the use of automated methods to characterise bedforms should be further developed and that the integration of automated measurements at large scales will support the development of new classification charts that currently rely solely on manual measurements.

DEVELOPMENT AND RESEARCH OF THE AUTOMATED METHODS FOR ASSURANCE USER’S EFFECTIVE PERFORMANCE

In the work the automated methods for assurance user’s effective performance are investigated. The structure of the automated system of support of human productivity is developed. For practical implementation, the MEAN technology stack is used, the interaction of its component is described. Automated methods in the form of an application are implemented and its main advantages are determined.

Retrospective analysis of the emergence of antibiotic-resistant Salmonella enterica infections in a level IV hospital from Lima, Peru

This study retrospectively analysed the emergence of multidrug-resistant Salmonella enterica in a level IV hospital in Lima, Peru. A total of 64 S. enterica from January 2009 to June 2010 (Period 1, 24 isolates) and January 2012 to December 2014 (Period 2, 40 isolates) were included. Some 25 were from non-hospitalized and 39 from hospitalized patients. Antimicrobial susceptibility to 15 antimicrobial agents was established by automated methods. Most of the isolates were from blood (46.9%), urine (21.9%) and faeces (14.1%). There was a reduction in blood isolates in Period 2, while all the faecal isolates were from this period. In Period 1, only 3/24 (12.5%) isolates showed antibiotic resistance, whereas 25/39 isolates (64.1%) from Period 2 were antibiotic-resistant, with multidrug-resistant and extensively drug-resistant rates of 17.9% and 20.5%, respectively. Multidrug-resistant/extensively drug-resistant Salmonella isolates were introduced in the hospital in 2013, with Salmonella recovered from faeces from non-hospitalized patients suggested an increase in community-acquired multidrug-resistant/extensively drug-resistant Salmonella infections.

Assessment of automatic decision-support systems for detecting active T2 lesions in multiple sclerosis patients

Background: Active (new/enlarging) T2 lesion counts are routinely used in the clinical management of multiple sclerosis. Thus, automated tools able to accurately identify active T2 lesions would be of high interest to neuroradiologists for assisting in their clinical activity. Objective: To compare the accuracy in detecting active T2 lesions and of radiologically active patients based on different visual and automated methods. Methods: One hundred multiple sclerosis patients underwent two magnetic resonance imaging examinations within 12 months. Four approaches were assessed for detecting active T2 lesions: (1) conventional neuroradiological reports; (2) prospective visual analyses performed by an expert; (3) automated unsupervised tool; and (4) supervised convolutional neural network. As a gold standard, a reference outcome was created by the consensus of two observers. Results: The automated methods detected a higher number of active T2 lesions, and a higher number of active patients, but a higher number of false-positive active patients than visual methods. The convolutional neural network model was more sensitive in detecting active T2 lesions and active patients than the other automated method. Conclusion: Automated convolutional neural network models show potential as an aid to neuroradiological assessment in clinical practice, although visual supervision of the outcomes is still required.

Large Scaled Topographic Mapping and Issues in Depicting VGI and Open Data

Open data and geospatial data collected by volunteers are nowadays easy to obtain and available with worldwide coverage through projects like OpenStreetMap. However, the use of these datasets leads to new challenges in depiction especially in the field of large scale topographic cartography. In addition to quality research, new processing and depiction methods for integrating these data are emerging. In the course of this work, specific problems of maps based on OpenStreetMap and Open Data elevation models are pointed out and possible solutions are introduced. In addition, a method for the preprocessing of contour lines is presented and the process flow is described in more detail. The goal of this work is to give insight into a toolbox of specially adapted and (semi-)automated methods. In this way, the quality standard of the depiction of topographic maps based on Open Data is to be increased, but also limitations are being shown.

Target-based terrestrial laser scan registration extended by target orientation

In almost all projects, in which terrestrial laser scanning is used, the scans must be registered after the data acquisition. Despite more and more new and automated methods for registration, the classical target-based registration is still one of the standard procedures. The advantages are obvious: independence from the scan object, the geometric configuration can often be influenced and registration results are easy to interpret. When plane black-and-white targets are used, the algorithm for estimating the target center fits a plane through the scan of a target, anyway. This information about the plane orientation has remained unused so far. Hence, including this information in the registration does not require any additional effort in the scanning process. In this paper, we extend the target-based registration by the plane orientation. We describe the required methodology, analyze the benefits in terms of precision and reliability and discuss in which cases the extension is useful and brings a relevant advantage. Based on simulations and two case studies we find out that especially for registrations with bad geometric configurations the extension brings a big advantage. The extension enables registrations that are much more precise. These are also visible on the registered point clouds. Thus, only a methodological change in the target-based registration improves its results.

Empirical Assessment of Alternative Methods for Identifying Seasonality in Observational Healthcare Data

BackgroundSeasonality classification is a well-known and important part of time series analysis. Understanding the seasonality of a biological event can contribute to an improved understanding of its causes and help guide appropriate responses. Observational data, however, are not comprised of biological events, but timestamped diagnosis codes the combination of which (along with additional requirements) are used as proxies for biological events. As there exist different methods for determining the seasonality of a time series, it is necessary to know if these methods exhibit concordance. In this study we seek to determine the concordance of these methods by applying them to time series derived from diagnosis codes in observational data. Methods: We compared 8 methods for determining the seasonality of a time series at three levels of significance (0.01, 0.05, and 0.1), against 10 observational health databases. We evaluated 61,467 time series at each level of significance, totaling 184,401 evaluations. Results: Methods of binary seasonality classification when applied to time series derived from diagnosis codes in observational health data produce inconsistent results. Across all databases and levels of significance, concordance ranged from 20.2% to 40.2%.Conclusion: The results indicate that researchers relying on automated methods to assess the seasonality of time series derived from diagnosis codes in observational data should be aware that the methods are not interchangeable. Seasonality determination is highly dependent on the method chosen.

Assessing quality and agreement of structured data in automatic versus manual abstraction of the electronic health record for a clinical epidemiology study

Objective We evaluate data agreement between an electronic health record (EHR) sample abstracted by automated characterization with a standard abstracted by manual review. Study Design and Setting We obtain data for an epidemiology cohort study using standard manual abstraction of the EHR and automated identification of the same patients using a structured algorithm to query the EHR. Summary measures of agreement (e.g., Cohen’s kappa) are reported for 12 variables commonly used in epidemiological studies. Results Best agreement between abstraction methods is observed among demographic characteristics such as age, sex, and race, and for positive history of disease. Poor agreement is found in missing data and negative history, suggesting potential impact for researchers using automated EHR characterization. EHR data quality depends upon providers, who may be influenced by both institutional and federal government documentation guidelines. Conclusion Automated EHR abstraction discrepancies may decrease power and increase bias; therefore, caution is warranted when selecting variables from EHRs for epidemiological study using an automated characterization approach. Validation of automated methods must also continue to advance in sophistication with other technologies, such as machine learning and natural language processing, to extract non-structured data from the EHR, for application to EHR characterization for clinical epidemiology.