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Search: authors:"Jos B. T. M. Roerdink"

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Visual Data Exploration for Balance Quantification in Real-Time During Exergaming

Unintentional injuries are among the ten leading causes of death in older adults; falls cause 60% of these deaths. Despite their effectiveness to improve balance and reduce the risk of falls, balance training programs have several drawbacks in practice, such as lack of engaging elements, boring exercises, and the effort and cost of travelling, ultimately resulting in low...

Efficient and Robust Path Openings Using the Scale-Invariant Rank Operator

, The Netherlands 2 Jos B. T. M. Roerdink studied biology and physics at the University of Nijmegen, the Netherlands, where he obtained his M.Sc. in theoretical physics in 1979. Following his Ph.D. (1983

Quantifying Postural Control during Exergaming Using Multivariate Whole-Body Movement Data: A Self-Organizing Maps Approach

Background Exergames are becoming an increasingly popular tool for training balance ability, thereby preventing falls in older adults. Automatic, real time, assessment of the user’s balance control offers opportunities in terms of providing targeted feedback and dynamically adjusting the gameplay to the individual user, yet algorithms for quantification of balance control remain...

A Memory and Computation Efficient Sparse Level-Set Method

Since its introduction, the level set method has become the favorite technique for capturing and tracking moving interfaces, and found applications in a wide variety of scientific fields. In this paper we present efficient data structures and algorithms for tracking dynamic interfaces through the level set method. Several approaches which address both computational and memory...

Time-Resolved Transcriptomics and Bioinformatic Analyses Reveal Intrinsic Stress Responses during Batch Culture of Bacillus subtilis

We have determined the time-resolved transcriptome of the model gram-positive organism B. subtilis during growth in a batch fermentor on rich medium. DNA microarrays were used to monitor gene transcription using 10-minute intervals at 40 consecutive time points. From the growth curve and analysis of all gene expression levels, we identified 4 distinct growth phases and one clear...

A Neurophysiologically Plausible Population Code Model for Feature Integration Explains Visual Crowding

An object in the peripheral visual field is more difficult to recognize when surrounded by other objects. This phenomenon is called “crowding”. Crowding places a fundamental constraint on human vision that limits performance on numerous tasks. It has been suggested that crowding results from spatial feature integration necessary for object recognition. However, in the absence of...

MOTIFATOR: detection and characterization of regulatory motifs using prokaryote transcriptome data

Summary: Unraveling regulatory mechanisms (e.g. identification of motifs in cis-regulatory regions) remains a major challenge in the analysis of transcriptome experiments. Existing applications identify putative motifs from gene lists obtained at rather arbitrary cutoff and require additional manual processing steps. Our standalone application MOTIFATOR identifies the most...

FIVA: Functional Information Viewer and Analyzer extracting biological knowledge from transcriptome data of prokaryotes

Summary: FIVA (Function Information Viewer and Analyzer) aids researchers in the prokaryotic community to quickly identify relevant biological processes following transcriptome analysis. Our software assists in functional profiling of large sets of genes and generates a comprehensive overview of affected biological processes. Availability: