Estimating 3D ground reaction forces in running using three inertial measurement units.

To understand the mechanisms causing running injuries, it is crucial to get insights into biomechanical loading in the runners' environment. Ground reaction forces (GRFs) describe the external forces on the body during running, however, measuring these forces is usually only possible in a gait laboratory. Previous studies show that it is possible to use inertial measurement units (IMUs) to estimate vertical forces, however, forces in anterior-posterior direction play an important role in the push-off. Furthermore, to perform an inverse dynamics approach, for modelling tissue specific loads, 3D GRFs are needed as input. Therefore, the goal of this work was to estimate 3D GRFs using three inertial measurement units. Twelve rear foot strike runners did nine trials at three different velocities (10, 12 and 14 km/h) and three stride frequencies (preferred and preferred ± 10%) on an instrumented treadmill. Then, data from IMUs placed on the pelvis and lower legs were used as input for artificial neural networks (ANNs) to estimate 3D GRFs. Additionally, estimated vertical GRF from a physical model was used as input to create a hybrid machine learning model. Using different splits in validation and training data, different ANNs were fitted and assembled into an ensemble model. Leave-one-subject-out cross-validation was used to validate the models. Performance of the machine learning, hybrid machine learning and a physical model were compared. The estimated vs. measured GRF for the hybrid model had a RMSE normalized over the full range of values of 10.8, 7.8 and 6.8% and a Pearson correlation coefficient of 0.58, 0.91, 0.97 for the mediolateral direction, posterior-anterior and vertical direction respectively. Performance for the three compared models was similar. The ensemble models showed higher model accuracy compared to the ensemble-members. This study is the first to estimate 3D GRF during continuous running from IMUs and shows that it is possible to estimate GRF in posterior-anterior and vertical direction, making it possible to estimate these forces in the outdoor setting. This step towards quantification of biomechanical load in the runners' environment is helpful to gain a better understanding of the development of running injuries.

An efficient algorithm for the extraction of HGVS variant descriptions from sequences.

MOTIVATION: Unambiguous sequence variant descriptions are important in reporting the outcome of clinical diagnostic DNA tests. The standard nomenclature of the Human Genome Variation Society (HGVS) describes the observed variant sequence relative to a given reference sequence. We propose an efficient algorithm for the extraction of HGVS descriptions from two sequences with three main requirements in mind: minimizing the length of the resulting descriptions, minimizing the computation time and keeping the unambiguous descriptions biologically meaningful. RESULTS: Our algorithm is able to compute the HGVS descriptions of complete chromosomes or other large DNA strings in a reasonable amount of computation time and its resulting descriptions are relatively small. Additional applications include updating of gene variant database contents and reference sequence liftovers. AVAILABILITY: The algorithm is accessible as an experimental service in the Mutalyzer program suite (https://mutalyzer.nl). The C++ source code and Python interface are accessible at: https://github.com/mutalyzer/description-extractor. CONTACT: j.k.vis@lumc.nl.

The Open Physiology workflow: modeling processes over physiology circuitboards of interoperable tissue units.

A key challenge for the physiology modeling community is to enable the searching, objective comparison and, ultimately, re-use of models and associated data that are interoperable in terms of their physiological meaning. In this work, we outline the development of a workflow to modularize the simulation of tissue-level processes in physiology. In particular, we show how, via this approach, we can systematically extract, parcellate and annotate tissue histology data to represent component units of tissue function. These functional units are semantically interoperable, in terms of their physiological meaning. In particular, they are interoperable with respect to [i] each other and with respect to [ii] a circuitboard representation of long-range advective routes of fluid flow over which to model long-range molecular exchange between these units. We exemplify this approach through the combination of models for physiology-based pharmacokinetics and pharmacodynamics to quantitatively depict biological mechanisms across multiple scales. Links to the data, models and software components that constitute this workflow are found at http://open-physiology.org/.

Meta-analysis on blood transcriptomic studies identifies consistently coexpressed protein-protein interaction modules as robust markers of human aging.

The bodily decline that occurs with advancing age strongly impacts on the prospects for future health and life expectancy. Despite the profound role of age in disease etiology, knowledge about the molecular mechanisms driving the process of aging in humans is limited. Here, we used an integrative network-based approach for combining multiple large-scale expression studies in blood (2539 individuals) with protein-protein Interaction (PPI) data for the detection of consistently coexpressed PPI modules that may reflect key processes that change throughout the course of normative aging. Module detection followed by a meta-analysis on chronological age identified fifteen consistently coexpressed PPI modules associated with chronological age, including a highly significant module (P = 3.5 × 10(-38)) enriched for 'T-cell activation' marking age-associated shifts in lymphocyte blood cell counts (R(2) = 0.603; P = 1.9 × 10(-10)). Adjusting the analysis in the compendium for the 'T-cell activation' module showed five consistently coexpressed PPI modules that robustly associated with chronological age and included modules enriched for 'Translational elongation', 'Cytolysis' and 'DNA metabolic process'. In an independent study of 3535 individuals, four of five modules consistently associated with chronological age, underpinning the robustness of the approach. We found three of five modules to be significantly enriched with aging-related genes, as defined by the GenAge database, and association with prospective survival at high ages for one of the modules including ASF1A. The hereby-detected age-associated and consistently coexpressed PPI modules therefore may provide a molecular basis for future research into mechanisms underlying human aging.

Integrating protein-protein interaction networks with gene-gene co-expression networks improves gene signatures for classifying breast cancer metastasis.

Multiple studies have illustrated that gene expression profiling of primary breast cancers throughout the final stages of tumor development can provide valuable markers for risk prediction of metastasis and disease sub typing. However, the identification of a biologically interpretable and universally shared set of markers proved to be difficult. Here, we propose a method for de novo grouping of genes by dissecting the protein-protein interaction network into disjoint sub networks using pair wise gene expression correlation measures. We show that the obtained sub networks are functionally coherent and are consistently identified when applied on a compendium composed of six different breast cancer studies. Application of the proposed method using different integration approaches underlines the robustness of the identified sub network related to cell cycle and identifies putative new sub network markers for metastasis related to cell-cell adhesion, the proteasome complex and JUN-FOS signalling. Although gene selection with the proposed method does not directly improve upon previously reported cross study classification performances, it shows great promises for applications in data integration and result interpretation.

Clinical subtypes of Parkinson's disease.

The clinical heterogeneity of Parkinson's disease (PD) may point at the existence of subtypes. Because subtypes likely reflect distinct underlying etiologies, their identification may facilitate future genetic and pharmacotherapeutic studies. Aim of this study was to identify subtypes by a data-driven approach applied to a broad spectrum of motor and nonmotor features of PD. Data of motor and nonmotor PD symptoms were collected in 802 patients in two different European prevalent cohorts. A model-based cluster analysis was conducted on baseline data of 344 patients of a Dutch cohort (PROPARK). Reproducibility of these results was tested in data of the second annual assessment of the same cohort and validated in an independent Spanish cohort (ELEP) of 357 patients. The subtypes were subsequently characterized on clinical and demographic variables. Four similar PD subtypes were identified in two different populations and are largely characterized by differences in the severity of nondopaminergic features and motor complications: Subtype 1 was mildly affected in all domains, Subtype 2 was predominantly characterized by severe motor complications, Subtype 3 was affected mainly on nondopaminergic domains without prominent motor complications, while Subtype 4 was severely affected on all domains. The subtypes had largely similar mean disease durations (nonsignificant differences between three clusters) but showed considerable differences with respect to their association with demographic and clinical variables. In prevalent disease, PD subtypes are largely characterized by the severity of nondopaminergic features and motor complications and likely reflect complex interactions between disease mechanisms, treatment, aging, and gender.

Finding discriminative subtypes of aggressive brain tumours using magnetic resonance spectroscopy.

Aggressive tumour types such as glioblastomas (gl) and metastases (me) are known to be difficult to discriminate on the basis of single-voxel proton magnetic resonance spectroscopy (SV 1H-MRS) information. Each of them is also heterogeneous in nature and a statistically robust subtyping analysis is likely to shed light on their structure and, possibly, on their differences. In this brief paper we carry out such analysis. From the original MRS frequencies and their first derivative approximation, the most discriminant variables are first selected by χ(2)-testing. Subtypes are then discovered in the distribution of gl and me by repeated model based cluster analysis. Then, the mean of each subtype is contrasted with the original distribution of MRS spectra by t-testing with tail probabilities for the proportion of false positive (TPPFP) control. Finally, the distribution of gl and me in each subtype is compared with random expectation by χ(2)-testing. The experimental results confirm the existence of consistent subtypes. They exhibit relative proportions of gl and me very unlikely to occur at random.

The identification of Parkinson's disease subtypes using cluster analysis: a systematic review.

The clinical variability between patients with Parkinson's disease (PD) may point at the existence of subtypes of the disease. Identification of subtypes is important, since a focus on homogeneous groups may enhance the chance of success of research on mechanisms of disease and may also lead to tailored treatment strategies. Cluster analysis (CA) is an objective method to classify patients into subtypes. We systematically reviewed the methodology and results of CA studies in PD to gain a better understanding of the robustness of identified subtypes. We found seven studies that fulfilled the inclusion criteria. Studies were limited by incomplete reporting and methodological limitations. Differences between studies rendered comparisons of the results difficult. However, it appeared that studies which applied a comparable design identified similar subtypes. The cluster profiles "old age-at-onset and rapid disease progression" and "young age-at-onset and slow disease progression" emerged from the majority of studies. Other cluster profiles were less consistent across studies. Future studies with a rigorous study design that is standardized with respect to the included variables, data processing, and CA technique may advance the knowledge on subtypes in PD.

Reliability of cluster results for different types of time adjustments in complex disease research.

We aim to identify subtypes of diseases like Osteoarthritis (OA) and Parkinson's Disease (PD) that present clinical heterogeneity. We do so by searching for homogeneous clusters in values of markers that reflect the severity of the disease. In the current paper we consider two important items for a cluster analysis. First, as time can contribute largely to the measured variability in the data, we search for the most appropriate way to adjust for it. Second, as we aim for reliable cluster analyses, cluster results should exhibit robustness to little change in the data. To investigate these issues, we transform the data by adding noise of different levels before cluster modeling and we rely on a chi(2)-based measure of association to compare cluster results for different types of time adjustment. The results of our experiments suggest to adjust data for a logarithmic age effect for OA and a square root effect of the disease duration for PD because these adjustments lead more reliable cluster results.

DNA computing of solutions to knapsack problems.

One line of DNA computing research focuses on parallel search algorithms, which can be used to solve many optimization problems. DNA in solution can provide an enormous molecular library, which can be searched by molecular biological techniques. We have implemented such a parallel search for solutions to knapsack problems, which ask for the best way to pack a knapsack of limited volume. Several instances of knapsack problems were solved using DNA. We demonstrate how the computations can be extended by in vivo translation of the DNA library into protein. This combination of DNA and protein allows for multi-criterion optimization. The knapsack computations performed can then be seen as protein optimizations, one of the most complex computations performed by natural systems.

TreeSOM: Cluster analysis in the self-organizing map.

Clustering problems arise in various domains of science and engineering. A large number of methods have been developed to date. The Kohonen self-organizing map (SOM) is a popular tool that maps a high-dimensional space onto a small number of dimensions by placing similar elements close together, forming clusters. Cluster analysis is often left to the user. In this paper we present the method TreeSOM and a set of tools to perform unsupervised SOM cluster analysis, determine cluster confidence and visualize the result as a tree facilitating comparison with existing hierarchical classifiers. We also introduce a distance measure for cluster trees that allows one to select a SOM with the most confident clusters.

The molecule evoluator. An interactive evolutionary algorithm for the design of drug-like molecules.

We developed a software tool to design drug-like molecules, the "Molecule Evoluator", which we introduce and describe here. An atom-based evolutionary approach was used allowing both several types of mutation and crossover to occur. The novelty, we claim, is the unprecedented interactive evolution, in which the user acts as a fitness function. This brings a human being's creativity, implicit knowledge, and imagination into the design process, next to the more standard chemical rules. Proof-of-concept was demonstrated in a number of ways, both computationally and in the lab. Thus, we synthesized a number of compounds designed with the aid of the Molecule Evoluator. One of these is described here, a new chemical entity with activity on alpha-adrenergic receptors.

Mining a chemical database for fragment co-occurrence: discovery of "chemical clichés".

Nowadays millions of different compounds are known, their structures stored in electronic databases. Analysis of these data could yield valuable insights into the laws of chemistry and the habits of chemists. We have therefore explored the public database of the National Cancer Institute (>250,000 compounds) by pattern searching. We split the molecules of this database into fragments to find out which fragments exist, how frequent they are, and whether the occurrence of one fragment in a molecule is related to the occurrence of another, nonoverlapping fragment. It turns out that some fragments and combinations of fragments are so frequent that they can be called "chemical clichés". We believe that the fragment data can give insight into the chemical space explored so far by synthesis. The lists of fragments and their (co-)occurrences can help create novel chemical compounds by (i) systematically listing the most popular and therefore most easily used substituents and ring systems for synthesizing new compounds, (ii) being an easily accessible repository for rarer fragments suitable for lead compound optimization, and (iii) pointing out some of the yet unexplored parts of chemical space.