Concept: Manual transmission
To objectively measure the strength of the capsulotomy performed with a femtosecond laser-assisted technique or performed manually in a pig-eye laboratory study.
INTRODUCTION: Warfarin treatment with a high time in therapeutic range (TTR) is correlated to fewer complications. The TTR in Sweden is generally high but varies partly depending on local expertise and traditions. A dosing algorithm could minimize variations and increase treatment quality. Here we evaluate the performance of a computerized dosing algorithm. MATERIALS AND METHODS: 53.779 warfarin treated patients from 125 centers using the Swedish national quality registry AuriculA. If certain criteria are met, the algorithm gives one of seven possible dose suggestions, which can be unchanged, decreased or increased weekly dose by 5, 10 or 15%. The outcome evaluated by the resulting INR value was compared between dose suggestions arising from the algorithm that were accepted and those that were manually changed. There were no randomization, and outcomes were retrospectively analyzed. RESULTS: Both the algorithm-based and the manually changed doses had worse outcome if only two instead of three previous INR values were available. The algorithm suggestions were superior to manual dosing regarding percent samples within the target range 2-3 (hit-rate) or deviation from INR 2.5 (mean error). Of the seven possible outcomes from the algorithm, six were significantly superior and one equal to the manually changed doses when three previous INR:s were present. CONCLUSIONS: The algorithm-based dosing suggestions show better outcome in most cases. This can make dosing of warfarin easier and more efficient. There are however cases where manual dosing fares better. Here the algorithm will be improved to further enhance its dosing performance in the future.
Climbing or negative geotaxis is an innate behavior of the fruit fly Drosophila melanogaster. There has been considerable interest in using this simple behavior to gain insights into the changes in brain function associated with aging, influence of drugs, mutated genes, and human neurological disorders. At present, most climbing tests are conducted manually and there is a lack of a simple and automatic device for repeatable and quantitative analysis of fly climbing behavior. Here we present an automatic fly climbing system, named the Hillary Climber (after Sir Edmund Hillary), that can replace the human manual tapping of vials with a mechanical tapping mechanism to provide more consistent force and reduce variability between the users and trials. Following tapping the HC records fly climbing, tracks the fly climbing path, and analyzes the velocity of individual flies and the percentage of successful climbers. The system is relatively simple to build, easy to operate, and efficient and reliable for climbing tests.
House mice (Mus musculus) emit complex ultrasonic vocalizations (USVs) during social and sexual interactions, which have features similar to bird song (i.e., they are composed of several different types of syllables, uttered in succession over time to form a pattern of sequences). Manually processing complex vocalization data is time-consuming and potentially subjective, and therefore, we developed an algorithm that automatically detects mouse ultrasonic vocalizations (Automatic Mouse Ultrasound Detector or A-MUD). A-MUD is a script that runs on STx acoustic software (S_TOOLS-STx version 4.2.2), which is free for scientific use. This algorithm improved the efficiency of processing USV files, as it was 4-12 times faster than manual segmentation, depending upon the size of the file. We evaluated A-MUD error rates using manually segmented sound files as a ‘gold standard’ reference, and compared them to a commercially available program. A-MUD had lower error rates than the commercial software, as it detected significantly more correct positives, and fewer false positives and false negatives. The errors generated by A-MUD were mainly false negatives, rather than false positives. This study is the first to systematically compare error rates for automatic ultrasonic vocalization detection methods, and A-MUD and subsequent versions will be made available for the scientific community.
Autonomous vehicles are being viewed with scepticism in their ability to improve safety and the driving experience. A critical issue with automated driving at this stage of its development is that it is not yet reliable and safe. When automated driving fails, or is limited, the autonomous mode disengages and the drivers are expected to resume manual driving. For this transition to occur safely, it is imperative that drivers react in an appropriate and timely manner. Recent data released from the California trials provide compelling insights into the current factors influencing disengagements of autonomous mode. Here we show that the number of accidents observed has a significantly high correlation with the autonomous miles travelled. The reaction times to take control of the vehicle in the event of a disengagement was found to have a stable distribution across different companies at 0.83 seconds on average. However, there were differences observed in reaction times based on the type of disengagements, type of roadway and autonomous miles travelled. Lack of trust caused by the exposure to automated disengagements was found to increase the likelihood to take control of the vehicle manually. Further, with increased vehicle miles travelled the reaction times were found to increase, which suggests an increased level of trust with more vehicle miles travelled. We believe that this research would provide insurers, planners, traffic management officials and engineers fundamental insights into trust and reaction times that would help them design and engineer their systems.
We present a practical “how-to” guide to help determine whether single-subject fMRI independent components (ICs) characterise structured noise or not. Manual identification of signal and noise after ICA decomposition is required for efficient data denoising: to train supervised algorithms, to check the results of unsupervised ones or to manually clean the data. In this paper we describe the main spatial and temporal features of ICs and provide general guidelines on how to evaluate these. Examples of signal and noise components are provided from a wide range of datasets (3T data, including examples from the UK Biobank and the Human Connectome Project, and 7T data), together with practical guidelines for their identification. Finally, we discuss how the data quality, data type and preprocessing can influence the characteristics of the ICs and present examples of particularly challenging datasets.
Pavement cells (PCs) are the most frequently occurring cell type in the leaf epidermis and play important roles in leaf growth and function. In many plant species, PCs form highly complex jigsaw puzzle shaped cells with interlocking lobes. Understanding of their development is of high interest for plant science research because of their importance for leaf growth and hence for plant fitness and crop yield. Studies of PC development, however, are limited because robust methods are lacking that enable automatic segmentation and quantification of PC shape parameters suitable to reflect their cellular complexity. Here, we present our new ImageJ-based tool, PaCeQuant, which provides a fully automatic image analysis workflow for PC shape quantification. PaCeQuant automatically detects cell boundaries of PCs from confocal input images, and enables manual correction of automatic segmentation results or direct import of manually segmented cells. PaCeQuant simultaneously extracts 27 shape features that include global, contour-based, skeleton-based and PC-specific object descriptors. In addition, we included a method for classification and analysis of lobes at two-cell-junctions and three-cell-junctions, respectively. We provide an R script for graphical visualization and statistical analysis. We validated PaCeQuant by extensive comparative analysis to manual segmentation and existing quantification tools, and demonstrated its usability to analyze PC shape characteristics during development and between different genotypes. PaCeQuant thus provides a platform for robust, efficient and reproducible quantitative analysis of PC shape characteristics that can easily be applied to study PC development in large data sets.
Screening candidate studies for inclusion in a systematic review is time-consuming when conducted manually. Automation tools could reduce the human effort devoted to screening. Existing methods use supervised machine learning which train classifiers to identify relevant words in the abstracts of candidate articles that have previously been labelled by a human reviewer for inclusion or exclusion. Such classifiers typically reduce the number of abstracts requiring manual screening by about 50%.
The sequencing depth provided by high-throughput sequencing technologies has allowed a rise in the number of de novo sequenced genomes that could potentially be closed without further sequencing. However, genome scaffolding and closure require costly human supervision that often results in genomes being published as drafts. A number of automatic scaffolders were recently released, which improved the global quality of genomes published in the last few years. Yet, none of them reach the efficiency of manual scaffolding.
The Markyt visualisation, prediction and benchmark platform for chemical and gene entity recognition at BioCreative/CHEMDNER challenge
- Database : the journal of biological databases and curation
- Published over 4 years ago
Biomedical text mining methods and technologies have improved significantly in the last decade. Considerable efforts have been invested in understanding the main challenges of biomedical literature retrieval and extraction and proposing solutions to problems of practical interest. Most notably, community-oriented initiatives such as the BioCreative challenge have enabled controlled environments for the comparison of automatic systems while pursuing practical biomedical tasks. Under this scenario, the present work describes the Markyt Web-based document curation platform, which has been implemented to support the visualisation, prediction and benchmark of chemical and gene mention annotations at BioCreative/CHEMDNER challenge. Creating this platform is an important step for the systematic and public evaluation of automatic prediction systems and the reusability of the knowledge compiled for the challenge. Markyt was not only critical to support the manual annotation and annotation revision process but also facilitated the comparative visualisation of automated results against the manually generated Gold Standard annotations and comparative assessment of generated results. We expect that future biomedical text mining challenges and the text mining community may benefit from the Markyt platform to better explore and interpret annotations and improve automatic system predictions.Database URL: http://www.markyt.org, https://github.com/sing-group/Markyt.