Prior Knowledge Improves Decoding of Finger Flexion from Electrocorticographic Signals

Brain–computer interfaces (BCIs) use brain signals to convey a user’s intent. Some BCI approaches begin by decoding kinematic parameters of movements from brain signals, and then proceed to using these signals, in absence of movements, to allow a user to control an output. Recent results have shown that electrocorticographic (ECoG) recordings from the surface of the brain in humans can give information about kinematic parameters (e.g., hand velocity or finger flexion). The decoding approaches in these studies usually employed classical classification/regression algorithms that derive a linear mapping between brain signals and outputs. However, they typically only incorporate little prior information about the target movement parameter. In this paper, we incorporate prior knowledge using a Bayesian decoding method, and use it to decode finger flexion from ECoG signals. Specifically, we exploit the constraints that govern finger flexion and incorporate these constraints in the construction, structure, and the probabilistic functions of the prior model of a switched non-parametric dynamic system (SNDS). Given a measurement model resulting from a traditional linear regression method, we decoded finger flexion using posterior estimation that combined the prior and measurement models. Our results show that the application of the Bayesian decoding model, which incorporates prior knowledge, improves decoding performance compared to the application of a linear regression model, which does not incorporate prior knowledge. Thus, the results presented in this paper may ultimately lead to neurally controlled hand prostheses with full fine-grained finger articulation

Hexamethylcyclopentadiene: time-resolved photoelectron spectroscopy and ab initio multiple spawning simulations

Progress in our understanding of ultrafast light-induced processes in molecules is best achieved through a close combination of experimental and theoretical approaches. Direct comparison is obtained if theory is able to directly reproduce experimental observables. Here, we present a joint approach comparing time-resolved photoelectron spectroscopy (TRPES) with ab initio multiple spawning (AIMS) simulations on the MS-MR-CASPT2 level of theory. We disentangle the relationship between two phenomena that dominate the immediate molecular response upon light absorption: a spectrally dependent delay of the photoelectron signal and an induction time prior to excited state depopulation in dynamics simulations. As a benchmark molecule, we have chosen hexamethylcyclopentadiene, which shows an unprecedentedly large spectral delay of (310 \ub1 20) fs in TRPES experiments. For the dynamics simulations, methyl groups were replaced by "hydrogen atoms" having mass 15 and TRPES spectra were calculated. These showed an induction time of (108 \ub1 10) fs which could directly be assigned to progress along a torsional mode leading to the intersection seam with the molecular ground state. In a stepladder-type approach, the close connection between the two phenomena could be elucidated, allowing for a comparison with other polyenes and supporting the general validity of this finding for their excited state dynamics. Thus, the combination of TRPES and AIMS proves to be a powerful tool for a thorough understanding of ultrafast excited state dynamics in polyenes.Peer reviewed: YesNRC publication: Ye

Discrete element simulation of mill charge in 3D using the BLAZE-DEM GPU framework

The Discrete Element Method (DEM) simulation of charge motion in ball, semi-autogenous (SAG) and autogenous mills has advanced to a stage where the effects of lifter design, power draft and product size can be evaluated with sufficient accuracy using either two-dimensional (2D) or three-dimensional (3D) codes. While 2D codes may provide a reasonable profile of charge distribution in the mill there is a difference in power estimations as the anisotropic nature within the mill cannot be neglected. Thus 3D codes are preferred as they can provide a more accurate estimation of power draw and charge distribution. While 2D codes complete a typical industrial simulation in the order of hours, 3D codes require computing times in the order of days to weeks on a typical multi-threaded desktop computer. A newly developed and recently introduced 3D DEM simulation environment is BLAZE-DEM that utilizes the Graphical Processor Unit (GPU) via the NVIDIA CUDA programming model. Utilizing the parallelism of the GPU a 3D simulation of an industrial mill with four million particles takes 1 h to simulate one second (20 FPS) on a GTX 880 laptop GPU. This new performance level may allow 3D simulations to become a routine task for mill designers and researchers. This paper makes two notable extensions to the BLAZE-DEM environment. Firstly, the sphere-face contact is extended to include a GPU efficient sphere-edge contact strategy. Secondly, the world representation is extended by an efficient representation of convex geometrical primitives that can be combined to form non-convex world boundaries that drastically enhances the efficiency of particle world contact. In addition to these extensions this paper verifies and validates our GPU code by comparing charge profiles and power draw obtained using the CPU based code Millsoft and pilot scale experiments. Finally, we conclude with plant scale mill simulations.University of Utah. NVIDIA Corporation.http://www.elsevier.com/locate/mineng2016-06-30hb201

The GTPase Rab26 links synaptic vesicles to the autophagy pathway.

Small GTPases of the Rab family not only regulate target recognition in membrane traffic but also control other cellular functions such as cytoskeletal transport and autophagy. Here we show that Rab26 is specifically associated with clusters of synaptic vesicles in neurites. Overexpression of active but not of GDP-preferring Rab26 enhances vesicle clustering, which is particularly conspicuous for the EGFP-tagged variant, resulting in a massive accumulation of synaptic vesicles in neuronal somata without altering the distribution of other organelles. Both endogenous and induced clusters co-localize with autophagy-related proteins such as Atg16L1, LC3B and Rab33B but not with other organelles. Furthermore, Atg16L1 appears to be a direct effector of Rab26 and binds Rab26 in its GTP-bound form, albeit only with low affinity. We propose that Rab26 selectively directs synaptic and secretory vesicles into preautophagosomal structures, suggesting the presence of a novel pathway for degradation of synaptic vesicles

Universal behavior of localization of residue fluctuations in globular proteins

Localization properties of residue fluctuations in globular proteins are studied theoretically by using the Gaussian network model. Participation ratio for each residue fluctuation mode is calculated. It is found that the relationship between participation ratio and frequency is similar for all globular proteins, indicating a universal behavior in spite of their different size, shape, and architecture.Comment: 4 pages, 3 figures. To appear in Phys. Rev.

Ensemble Simulation From Multiple Data Sources In A Spatially Distributed Hydrological Model Of The Rijnland Water System In The Netherlands

Data for water management is increasingly easy to access, it has finer spatial and temporal resolution, and it is available from various sources. Precipitation data can be obtained from meteorological stations, radar, satellites and weather models. Land use data is also available from different satellite products and different providers. The various sources of data may confirm each other or give very different values in space and time. However, from these various data sources, it can often not be judged beforehand that one data is correct and others are wrong. Each source has its own value for a particular purpose. The Rijnland area in the Netherlands is one of the areas for which various data sources are available. Data sources that are researched in this paper are precipitation from rain gauges and radar, and three different land use maps. Various sources of data are used as input to the hydrological model (SIMGRO) of the water system to produce different discharge model output. Each run provides a member of the ensemble simulation which are combined to improve prediction of discharge from the catchment. It is shown that even simple averaging allows for increasing the model accuracy. Acknowledgement: This research is part of the EU FP7 MyWater research project. http://www.mywater-fp7.e