Prediction of specific hand movements using electroencephalographic signals.

OBJECTIVE: To identify specific hand movements from electroencephalographic activity. DESIGN: Proof of concept study. SETTING: Rehabilitation hospital in Toronto, Canada. PARTICIPANTS: Fifteen healthy individuals with no neurological conditions. INTERVENTION: Each individual performed six different hand movements, including four grasps commonly targeted during rehabilitation. All of them used their dominant hand and four of them repeated the experiment with their non-dominant hand. EEG was acquired from 8 different locations (C1, C2, C3, C4, CZ, F3, F4 and Fz). Time-frequency distributions (spectrogram) of the pre-movement EEG activity for each electrode were generated and each of the time-resolved spectral components (1 Hz to 50 Hz) was correlated with a hyperbolic tangent function to detect power decreases. The spectral components and time ranges with the largest correlation values were identified using a threshold. The resulting features were then used to implement a distance-based classifier. OUTCOME MEASURES: Accuracy of classification. RESULTS: A minimum of three different dominant hand movements were classified correctly with average accuracies between 65-75% across all 15 participants. Average accuracies between 67-85% for the same three movements were achieved across four of the 15 participants who were tested with their non-dominant hand. CONCLUSION: The results suggest that it may be possible to predict specific hand movements from a small number of electroencephalographic electrodes. Further studies including members of the spinal cord injury community are necessary to verify the suitability of the proposed process.

How computational models contribute to our understanding of the germ line.

Computational models are an invaluable tool in modern biology. They provide a framework within which to summarize existing knowledge, enable competing hypotheses to be compared qualitatively and quantitatively, and to facilitate the interpretation of complex data. Moreover, models allow questions to be investigated that are difficult to approach experimentally. Theories can be tested in context, identifying the gaps in our understanding and potentially leading to new hypotheses. Models can be developed on a variety of scales and with different levels of mechanistic detail, depending on the available data, the biological questions of interest, and the available mathematical and computational tools. The goal of this review is to provide a broad picture of how modeling has been applied to reproductive biology. Specifically, we look at four uses of modeling: (i) comparing hypotheses; (ii) interpreting data; (iii) exploring experimentally challenging questions; and (iv) hypothesis evaluation and generation. We present examples of each of these applications in reproductive biology, drawing from a range of organisms-including Drosophila, Caenorhabditis elegans, mouse, and humans. We aim to describe the data and techniques used to construct each model, and to highlight the benefits of modeling to the field, as complementary to experimental work. Mol. Reprod. Dev. 83: 944-957, 2016 © 2016 Wiley Periodicals, Inc.

Mechano-logical model of C. elegans germ line suggests feedback on the cell cycle.

The Caenorhabditis elegans germ line is an outstanding model system in which to study the control of cell division and differentiation. Although many of the molecules that regulate germ cell proliferation and fate decisions have been identified, how these signals interact with cellular dynamics and physical forces within the gonad remains poorly understood. We therefore developed a dynamic, 3D in silico model of the C. elegans germ line, incorporating both the mechanical interactions between cells and the decision-making processes within cells. Our model successfully reproduces key features of the germ line during development and adulthood, including a reasonable ovulation rate, correct sperm count, and appropriate organization of the germ line into stably maintained zones. The model highlights a previously overlooked way in which germ cell pressure may influence gonadogenesis, and also predicts that adult germ cells might be subject to mechanical feedback on the cell cycle akin to contact inhibition. We provide experimental data consistent with the latter hypothesis. Finally, we present cell trajectories and ancestry recorded over the course of a simulation. The novel approaches and software described here link mechanics and cellular decision-making, and are applicable to modeling other developmental and stem cell systems.

Modelling osteomyelitis.

BACKGROUND: This work focuses on the computational modelling of osteomyelitis, a bone pathology caused by bacteria infection (mostly Staphylococcus aureus). The infection alters the RANK/RANKL/OPG signalling dynamics that regulates osteoblasts and osteoclasts behaviour in bone remodelling, i.e. the resorption and mineralization activity. The infection rapidly leads to severe bone loss, necrosis of the affected portion, and it may even spread to other parts of the body. On the other hand, osteoporosis is not a bacterial infection but similarly is a defective bone pathology arising due to imbalances in the RANK/RANKL/OPG molecular pathway, and due to the progressive weakening of bone structure. RESULTS: Since both osteoporosis and osteomyelitis cause loss of bone mass, we focused on comparing the dynamics of these diseases by means of computational models. Firstly, we performed meta-analysis on a gene expression data of normal, osteoporotic and osteomyelitis bone conditions. We mainly focused on RANKL/OPG signalling, the TNF and TNF receptor superfamilies and the NF-kB pathway. Using information from the gene expression data we estimated parameters for a novel model of osteoporosis and of osteomyelitis. Our models could be seen as a hybrid ODE and probabilistic verification modelling framework which aims at investigating the dynamics of the effects of the infection in bone remodelling. Finally we discuss different diagnostic estimators defined by formal verification techniques, in order to assess different bone pathologies (osteopenia, osteoporosis and osteomyelitis) in an effective way. CONCLUSIONS: We present a modeling framework able to reproduce aspects of the different bone remodeling defective dynamics of osteomyelitis and osteoporosis. We report that the verification-based estimators are meaningful in the light of a feed forward between computational medicine and clinical bioinformatics.