1,643 research outputs found

    Isolation of EpH4 mammary epithelial cell subpopulations which differ in their morphogenetic properties

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    Summary: EpH4 is a nontumorigenic cell line derived from spontaneously immortalized mouse mammary gland epithelial cells (Fialka et al., 1996). When grown in collagen gels, EpH4 cells give rise to different types of structures, e.g., solid cords or branching tubes. By removing and subsequently dissociating single three-dimensional colonies of defined morphology, we have isolated six clonal subpopulations of EpH4 cells which display distinct morphogenetic properties in collagen gel cultures. Thus, cells from the H1B clone form branching cords devoid of a central lumen, K3A3 cells from cords enclosing small multifocal lumina, and J3B1 cells form large cavitary structures containing a wide lumen. I3G2 cells form either cords or tubes, depending on the type of serum added to the culture medium. Finally, when grown in serum-free medium, Be1a cells form spherical cysts, whereas Be4a cells form long, extensively branched tubes. In additional assays of morphogenesis, i.e., cell sandwiching between two collagen gels or culture on a thick layer of Matrigel (a laminin-rich extracellular matrix), all clones form epithelial-cell-lined cavitary structures, except H1B cells which are unable to generate lumina under these conditions. The EpH4 sublines we have isolated provide an in vitro system for studying the mechanisms responsible for lumen formation and branching morphogenesis, as well as for identifying the factors which subvert these developmental processes during mammary carcinogenesi

    Analyzing and Decoding Natural Reach-and-Grasp Actions Using Gel, Water and Dry EEG Systems

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    Reaching and grasping is an essential part of everybody’s life, it allows meaningful interaction with the environment and is key to independent lifestyle. Recent electroencephalogram (EEG)-based studies have already shown that neural correlates of natural reach-and-grasp actions can be identified in the EEG. However, it is still in question whether these results obtained in a laboratory environment can make the transition to mobile applicable EEG systems for home use. In the current study, we investigated whether EEG-based correlates of natural reach-and-grasp actions can be successfully identified and decoded using mobile EEG systems, namely the water-based EEG-VersatileTM system and the dry-electrodes EEG-HeroTM headset. In addition, we also analyzed gel-based recordings obtained in a laboratory environment (g.USBamp/g.Ladybird, gold standard), which followed the same experimental parameters. For each recording system, 15 study participants performed 80 self-initiated reach-and-grasp actions toward a glass (palmar grasp) and a spoon (lateral grasp). Our results confirmed that EEG-based correlates of reach-and-grasp actions can be successfully identified using these mobile systems. In a single-trial multiclass-based decoding approach, which incorporated both movement conditions and rest, we could show that the low frequency time domain (LFTD) correlates were also decodable. Grand average peak accuracy calculated on unseen test data yielded for the water-based electrode system 62.3% (9.2% STD), whereas for the dry-electrodes headset reached 56.4% (8% STD). For the gel-based electrode system 61.3% (8.6% STD) could be achieved. To foster and promote further investigations in the field of EEG-based movement decoding, as well as to allow the interested community to make their own conclusions, we provide all datasets publicly available in the BNCI Horizon 2020 database (http://bnci-horizon-2020.eu/database/data-sets)

    Stam: a framework for spatio-temporal affordance maps

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    A�ordances have been introduced in literature as action op- portunities that objects o�er, and used in robotics to semantically rep- resent their interconnection. However, when considering an environment instead of an object, the problem becomes more complex due to the dynamism of its state. To tackle this issue, we introduce the concept of Spatio-Temporal A�ordances (STA) and Spatio-Temporal A�ordance Map (STAM). Using this formalism, we encode action semantics re- lated to the environment to improve task execution capabilities of an autonomous robot. We experimentally validate our approach to support the execution of robot tasks by showing that a�ordances encode accurate semantics of the environment
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