The aims of this study were to explore the hydrodynamic mechanism of Xenopus laevis swimming and to describe how hind limb kinematics shift to control swimming performance. Kinematics of the joints, feet and body were obtained from high speed video of X. laevis frogs (N=4) during swimming over a range of speeds. A blade element approach was used to estimate thrust produced by both translational and rotational components of foot velocity. Peak thrust from the feet ranged from 0.09 to 0.69 N across speeds ranging from 0.28 to 1.2 m s(-1). Among 23 swimming strokes, net thrust impulse from rotational foot motion was significantly higher than net translational thrust impulse, ranging from 6.1 to 29.3 N ms, compared with a range of -7.0 to 4.1 N ms from foot translation. Additionally, X. laevis kinematics were used as a basis for a forward dynamic anuran swimming model. Input joint kinematics were modulated to independently vary the magnitudes of foot translational and rotational velocity. Simulations predicted that maximum swimming velocity (among all of the kinematics patterns tested) requires that maximal translational and maximal rotational foot velocity act in phase. However, consistent with experimental kinematics, translational and rotational motion contributed unequally to total thrust. The simulation powered purely by foot translation reached a lower peak stroke velocity than the pure rotational case (0.38 vs 0.54 m s(-1)). In all simulations, thrust from the foot was positive for the first half of the power stroke, but negative for the second half. Pure translational foot motion caused greater negative thrust (70% of peak positive thrust) compared with pure rotational simulation (35% peak positive thrust) suggesting that translational motion is propulsive only in the early stages of joint extension. Later in the power stroke, thrust produced by foot rotation overcomes negative thrust (due to translation). Hydrodynamic analysis from X. laevis as well as forward dynamics give insight into the differential roles of translational and rotational foot motion in the aquatic propulsion of anurans, providing a mechanistic link between joint kinematics and swimming performance.
Optical label-free detectors, such as the venerable surface plasmon resonance (SPR) sensor, are generally favored for their ability to obtain quantitative data on intermolecular binding. However, before the recent introduction of resonant microcavities that use whispering gallery mode (WGM) recirculation, sensitivity to single binding events had not materialized. Here we describe the enhancement mechanisms responsible for the extreme sensitivity of the WGM biosensor, review its current implementations and applications, and discuss its future possibilities.
Pectin methyl esterases (PMEs) and their endogenous inhibitors are involved in the regulation of many processes in plant physiology, ranging from tissue growth and fruit ripening to parasitic plant haustorial formation and host invasion. Thus, control of PME activity is critical for enhancing our understanding of plant physiological processes and regulation. Here, we report on the identification of epigallocatechin gallate (EGCG), a green tea component, as a natural inhibitor for pectin methyl esterases. In a gel assay for PME activity, EGCG blocked esterase activity of pure PME as well as PME extracts from citrus and from parasitic plants. Fluorometric tests were used to determine the IC50 for a synthetic substrate. Molecular docking analysis of PME and EGCG suggests close interaction of EGCG with the catalytic cleft of PME. Inhibition of PME by the green tea compound, EGCG, provides the means to study the diverse roles of PMEs in cell wall metabolism and plant development. In addition, this study introduces the use of EGCG as natural product to be used in the food industry and agriculture.
High spatial resolution imaging of material properties is an important task for the continued development of nanomaterials and studies of biological systems. Time-varying interaction forces between the vibrating tip and the sample in a tapping-mode atomic force microscope contain detailed information about the elastic, adhesive, and dissipative response of the sample. We report real-time measurement and analysis of the time-varying tip-sample interaction forces with recently introduced torsional harmonic cantilevers. With these measurements, high-resolution maps of elastic modulus, adhesion force, energy dissipation, and topography are generated simultaneously in a single scan. With peak tapping forces as low as 0.6 nN, we demonstrate measurements on blended polymers and self-assembled molecular architectures with feature sizes at 1, 10, and 500 nm. We also observed an elastic modulus measurement range of four orders of magnitude (1 MPa to 10 GPa) for a single cantilever under identical feedback conditions, which can be particularly useful for analyzing heterogeneous samples with largely different material components.
Cells actively produce contractile forces for a variety of processes including cytokinesis and motility. Contractility is known to rely on myosin II motors which convert chemical energy from ATP hydrolysis into forces on actin filaments. However, the basic physical principles of cell contractility remain poorly understood. We reconstitute contractility in a simplified model system of purified F-actin, muscle myosin II motors, and alpha-actinin cross-linkers. We show that contractility occurs above a threshold motor concentration and within a window of cross-linker concentrations. We also quantify the pore size of the bundled networks and find contractility to occur at a critical distance between the bundles. We propose a simple mechanism of contraction based on myosin filaments pulling neighboring bundles together into an aggregated structure. Observations of this reconstituted system in both bulk and low-dimensional geometries show that the contracting gels pull on and deform their surface with a contractile force of approximately 1 microN, or approximately 100 pN per F-actin bundle. Cytoplasmic extracts contracting in identical environments show a similar behavior and dependence on myosin as the reconstituted system. Our results suggest that cellular contractility can be sensitively regulated by tuning the (local) activity of molecular motors and the cross-linker density and binding affinity.
The quantitative study of the near-equilibrium structural behavior of individual biomolecules requires high-resolution experimental approaches with longtime stability. We present a new technique to explore the dynamics of weak intramolecular interactions. It is based on the analysis of the 3D Brownian fluctuations of a laser-confined glass bead that is tethered to a flat surface by the biomolecule of interest. A continuous autofocusing mechanism allows us to maintain or adjust the height of the optical trap with nanometer accuracy over long periods of time. The resulting remarkably stable trapping potential adds a well-defined femto-to-piconewton force bias to the energy landscape of molecular configurations. A combination of optical interferometry and advanced pattern-tracking algorithms provides the 3D bead positions with nanometer spatial and >120 Hz temporal resolution. The analysis of accumulated 3D positions has allowed us not only to identify small single biomolecules but also to characterize their nanomechanical behavior, for example, the force-extension relations of short oligonucleotides and the unfolding/refolding transitions of small protein tethers.
Much of our knowledge of brain function has been gleaned from studies using microelectrodes to characterize the response properties of individual neurons in vivo. However, because it is difficult to accurately determine the location of a microelectrode tip within the brain, it is impossible to systematically map the fine three-dimensional spatial organization of many brain areas, especially in deep structures. Here, we present a practical method based on digital stereo microfocal X-ray imaging that makes it possible to estimate the three-dimensional position of each and every microelectrode recording site in "real time" during experimental sessions. We determined the system's ex vivo localization accuracy to be better than 50 microm, and we show how we have used this method to coregister hundreds of deep-brain microelectrode recordings in monkeys to a common frame of reference with median error of <150 microm. We further show how we can coregister those sites with magnetic resonance images (MRIs), allowing for comparison with anatomy, and laying the groundwork for more detailed electrophysiology/functional MRI comparison. Minimally, this method allows one to marry the single-cell specificity of microelectrode recording with the spatial mapping abilities of imaging techniques; furthermore, it has the potential of yielding fundamentally new kinds of high-resolution maps of brain function.
Biological visual systems have the remarkable ability to recognize objects despite confounding factors such as object position, size, pose, and lighting. In primates, this ability likely results from neuronal responses at the highest stage of the ventral visual stream [inferior temporal cortex (IT)] that signal object identity while tolerating these factors. However, for even the apparently simplest IT tolerance ("invariance"), tolerance to object position on the retina, little is known about how this feat is achieved. One possibility is that IT position tolerance is innate in that discriminatory power for newly learned objects automatically generalizes across position. Alternatively, visual experience plays a role in developing position tolerance. To test these ideas, we trained adult monkeys in a difficult object discrimination task in which their visual experience with novel objects was restricted to a single retinal position. After training, we recorded the spiking activity of an unbiased population of IT neurons and found that it contained significantly greater selectivity among the newly learned objects at the experienced position compared with a carefully matched, non-experienced position. Interleaved testing with other objects shows that this difference cannot be attributed to a bias in spatial attention or neuronal sampling. We conclude from these results that, at least under some conditions, full transfer of IT neuronal selectivity across retinal position is not automatic. This finding raises the possibility that visual experience plays a role in building neuronal tolerance in the ventral visual stream and the recognition abilities it supports.
Progress in understanding the brain mechanisms underlying vision requires the construction of computational models that not only emulate the brain's anatomy and physiology, but ultimately match its performance on visual tasks. In recent years, "natural" images have become popular in the study of vision and have been used to show apparently impressive progress in building such models. Here, we challenge the use of uncontrolled "natural" images in guiding that progress. In particular, we show that a simple V1-like model--a neuroscientist's "null" model, which should perform poorly at real-world visual object recognition tasks--outperforms state-of-the-art object recognition systems (biologically inspired and otherwise) on a standard, ostensibly natural image recognition test. As a counterpoint, we designed a "simpler" recognition test to better span the real-world variation in object pose, position, and scale, and we show that this test correctly exposes the inadequacy of the V1-like model. Taken together, these results demonstrate that tests based on uncontrolled natural images can be seriously misleading, potentially guiding progress in the wrong direction. Instead, we reexamine what it means for images to be natural and argue for a renewed focus on the core problem of object recognition--real-world image variation.