The sense of touch is represented by neural activity patterns evoked by mechanosensory input forces. The rodent whisker system is exceptional for studying the neurophysiology of touch in part because these forces can be precisely computed from video of whisker deformation. We evaluate the accuracy of a standard model of whisker bending, which assumes quasi-static dynamics and a linearly tapered conical profile, using controlled whisker deflections. We find significant discrepancies between model and experiment: real whiskers bend more than predicted upon contact at locations in the middle of the whisker and less at distal locations. Thus whiskers behave as if their stiffness near the base and near the tip is larger than expected for a homogeneous cone. We assess whether contact direction, friction, inhomogeneous elasticity, whisker orientation, or nonconical shape could explain these deviations. We show that a thin-middle taper of mouse whisker shape accounts for the majority of this behavior. This taper is conserved across rows and columns of the whisker array. The taper has a large effect on the touch-evoked forces and the ease with which whiskers slip past objects, which are key drivers of neural activity in tactile object localization and identification. This holds for orientations with intrinsic whisker curvature pointed toward, away from, or down from objects, validating two-dimensional models of simple whisker-object interactions. The precision of computational models relating sensory input forces to neural activity patterns can be quantitatively enhanced by taking thin-middle taper into account with a simple corrective function that we provide.

quantitative features of the disease from the resulting massive amounts of information [1]. Here, I show that the entropies of cell-population distributions on specific multidimensional molecular and morphological landscapes provide a set of measures for the precise characterization of normal and pathological states, such as those corresponding to healthy individuals and acute myeloid leukemia (AML) patients [2]. I provide a systematic procedure to identify the specific landscapes and illustrate how, applied to cell samples from peripheral blood and bone marrow aspirates, this characterization accurately diagnoses AML from just flow cytometry data. The methodology can generally be applied to other types of cell populations and establishes a straightforward link between the traditional statistical thermodynamics methodology and biomedical applications. References:[1] N. Aghaeepour, G. Finak, The FlowCAP Consortium, The DREAM Consortium (*), H.

quantitative features of the disease from the resulting massive amounts of information [1]. Here, I show that the entropies of cell-population distributions on specific multidimensional molecular and morphological landscapes provide a set of measures for the precise characterization of normal and pathological states, such as those corresponding to healthy individuals and acute myeloid leukemia (AML) patients [2]. I provide a systematic procedure to identify the specific landscapes and illustrate how, applied to cell samples from peripheral blood and bone marrow aspirates, this characterization accurately diagnoses AML from just flow cytometry data. The methodology can generally be applied to other types of cell populations and establishes a straightforward link between the traditional statistical thermodynamics methodology and biomedical applications. References:[1] N. Aghaeepour, G. Finak, The FlowCAP Consortium, The DREAM Consortium (*), H.

quantitative features of the disease from the resulting massive amounts of information [1]. Here, I show that the entropies of cell-population distributions on specific multidimensional molecular and morphological landscapes provide a set of measures for the precise characterization of normal and pathological states, such as those corresponding to healthy individuals and acute myeloid leukemia (AML) patients [2]. I provide a systematic procedure to identify the specific landscapes and illustrate how, applied to cell samples from peripheral blood and bone marrow aspirates, this characterization accurately diagnoses AML from just flow cytometry data. The methodology can generally be applied to other types of cell populations and establishes a straightforward link between the traditional statistical thermodynamics methodology and biomedical applications. References: [1] N. Aghaeepour, G. Finak, The FlowCAP Consortium, The DREAM Consortium (*), H.