The dimerized pentraxin-like domain of the adhesion G protein-coupled receptor 112 (ADGRG4) suggests function in sensing mechanical forces

Adhesion G protein-coupled receptors (aGPCRs) feature large extracellular regions with modular domains that often resemble protein classes of various function. The pentraxin (PTX) domain, which is predicted by sequence homology within the extracellular region of four different aGPCR members, is well known to form pentamers and other oligomers. Oligomerization of GPCRs is frequently reported and mainly driven by interactions of the seven-transmembrane region and N or C termini. While the functional importance of dimers is well-established for some class C GPCRs, relatively little is known about aGPCR multimerization. Here, we showcase the example of ADGRG4, an orphan aGPCR that possesses a PTX-like domain at its very N-terminal tip, followed by an extremely long stalk containing serine-threonine repeats. Using X-ray crystallography and biophysical methods, we determined the structure of this unusual PTX-like domain and provide experimental evidence for a homodimer equilibrium of this domain which is Ca2+-independent and driven by intermolecular contacts that differ vastly from the known soluble PTXs. The formation of this dimer seems to be conserved in mammalian ADGRG4 indicating functional relevance. Our data alongside of theoretical considerations lead to the hypothesis that ADGRG4 acts as an in vivo sensor for shear forces in enterochromaffin and Paneth cells of the small intestine.

Bridging two insect flight modes in evolution, physiology and robophysics

Since taking flight, insects have undergone repeated evolutionary transitions between two seemingly distinct flight modes1-3. Some insects neurally activate their muscles synchronously with each wingstroke. However, many insects have achieved wingbeat frequencies beyond the speed limit of typical neuromuscular systems by evolving flight muscles that are asynchronous with neural activation and activate in response to mechanical stretch2-8. These modes reflect the two fundamental ways of generating rhythmic movement: time-periodic forcing versus emergent oscillations from self-excitation8-10. How repeated evolutionary transitions have occurred and what governs the switching between these distinct modes remain unknown. Here we find that, despite widespread asynchronous actuation in insects across the phylogeny3,6, asynchrony probably evolved only once at the order level, with many reversions to the ancestral, synchronous mode. A synchronous moth species, evolved from an asynchronous ancestor, still preserves the stretch-activated muscle physiology. Numerical and robophysical analyses of a unified biophysical framework reveal that rather than a dichotomy, these two modes are two regimes of the same dynamics. Insects can transition between flight modes across a bridge in physiological parameter space. Finally, we integrate these two actuation modes into an insect-scale robot11-13 that enables transitions between modes and unlocks a new self-excited wingstroke strategy for engineered flight. Together, this framework accounts for repeated transitions in insect flight evolution and shows how flight modes can flip with changes in physiological parameters.

Understanding cytoskeletal avalanches using mechanical stability analysis

Eukaryotic cells are mechanically supported by a polymer network called the cytoskeleton, which consumes chemical energy to dynamically remodel its structure. Recent experiments in vivo have revealed that this remodeling occasionally happens through anomalously large displacements, reminiscent of earthquakes or avalanches. These cytoskeletal avalanches might indicate that the cytoskeleton's structural response to a changing cellular environment is highly sensitive, and they are therefore of significant biological interest. However, the physics underlying "cytoquakes" is poorly understood. Here, we use agent-based simulations of cytoskeletal self-organization to study fluctuations in the network's mechanical energy. We robustly observe non-Gaussian statistics and asymmetrically large rates of energy release compared to accumulation in a minimal cytoskeletal model. The large events of energy release are found to correlate with large, collective displacements of the cytoskeletal filaments. We also find that the changes in the localization of tension and the projections of the network motion onto the vibrational normal modes are asymmetrically distributed for energy release and accumulation. These results imply an avalanche-like process of slow energy storage punctuated by fast, large events of energy release involving a collective network rearrangement. We further show that mechanical instability precedes cytoquake occurrence through a machine-learning model that dynamically forecasts cytoquakes using the vibrational spectrum as input. Our results provide a connection between the cytoquake phenomenon and the network's mechanical energy and can help guide future investigations of the cytoskeleton's structural susceptibility.

Recent Advances in Understanding the Role of Cartilage Lubrication in Osteoarthritis

The remarkable lubrication properties of normal articular cartilage play an essential role in daily life, providing almost frictionless movements of joints. Alterations of cartilage surface or degradation of biomacromolecules within synovial fluid increase the wear and tear of the cartilage and hence determining the onset of the most common joint disease, osteoarthritis (OA). The irreversible and progressive degradation of articular cartilage is the hallmark of OA. Considering the absence of effective options to treat OA, the mechanosensitivity of chondrocytes has captured attention. As the only embedded cells in cartilage, the metabolism of chondrocytes is essential in maintaining homeostasis of cartilage, which triggers motivations to understand what is behind the low friction of cartilage and develop biolubrication-based strategies to postpone or even possibly heal OA. This review firstly focuses on the mechanism of cartilage lubrication, particularly on boundary lubrication. Then the mechanotransduction (especially shear stress) of chondrocytes is discussed. The following summarizes the recent development of cartilage-inspired biolubricants to highlight the correlation between cartilage lubrication and OA. One might expect that the restoration of cartilage lubrication at the early stage of OA could potentially promote the regeneration of cartilage and reverse its pathology to cure OA.

Mass Spectrometric and Bio-Computational Binding Strength Analysis of Multiply Charged RNAse S Gas-Phase Complexes Obtained by Electrospray Ionization from Varying In-Solution Equilibrium Conditions

We investigated the influence of a solvent’s composition on the stability of desorbed and multiply charged RNAse S ions by analyzing the non-covalent complex’s gas-phase dissociation processes. RNAse S was dissolved in electrospray ionization-compatible buffers with either increasing organic co-solvent content or different pHs. The direct transition of all the ions and the evaporation of the solvent from all the in-solution components of RNAse S under the respective in-solution conditions by electrospray ionization was followed by a collision-induced dissociation of the surviving non-covalent RNAse S complex ions. Both types of changes of solvent conditions yielded in mass spectrometrically observable differences of the in-solution complexation equilibria. Through quantitative analysis of the dissociation products, i.e., from normalized ion abundances of RNAse S, S-protein, and S-peptide, the apparent kinetic and apparent thermodynamic gas-phase complex properties were deduced. From the experimental data, it is concluded that the stability of RNAse S in the gas phase is independent of its in-solution equilibrium but is sensitive to the complexes’ gas-phase charge states. Bio-computational in-silico studies showed that after desolvation and ionization by electrospray, the remaining binding forces kept the S-peptide and S-protein together in the gas phase predominantly by polar interactions, which indirectly stabilized the in-bulk solution predominating non-polar intermolecular interactions. As polar interactions are sensitive to in-solution protonation, bio-computational results provide an explanation of quantitative experimental data with single amino acid residue resolution.

Visualizing bleb mass dynamics in single cells using quantitative phase microscopy

Understanding biological responses to directed energy (DE) is critical to ensure the safety of personnel within the Department of Defense. At the Air Force Research Laboratory, we have developed or adapted advanced optical imaging systems that quantify biophysical responses to DE. One notable cellular response to DE exposure is the formation of blebs, or semi-spherical protrusions of the plasma membrane in living cells. In this work, we demonstrate the capacity of quantitative phase imaging (QPI) to both visualize and quantify the formation of membrane blebs following DE exposure. QPI is an interferometric imaging tool that uses optical path length as a label-free contrast mechanism and is sensitive to the non-aqueous mass density, or dry mass, of living cells. Blebs from both CHO-K1 and U937 cells were generated after exposure to a series of 600 ns, 21.2 kV/cm electric pulses. These blebs were visualized in real time, and their dry mass relative to the rest of the cell body was quantified as a function of time. It is our hope that this system will lead to an improved understanding of both DE-induced and apoptotic blebbing.

The bioenergetics of COVID-19 immunopathology and the therapeutic potential of biophysical radiances

In developing an effective clinical tool against COVID-19, we need to consider why SARS-CoV-2 infections develop along remarkably different trajectories: from completely asymptomatic to a severe course of disease. In this paper we hypothesize that the progressive exhaustion and loss of lymphocytes associated with severe stages of COVID-19 result from an intracellular energy deficit in an organism which has already been depleted by preexisting chronic diseases, acute psychological stress and the aging process. A bioenergetics view of COVID-19 immunopathology opens a new biophysical opportunity to enhance impaired immune function via proposed pathways of photomagnetic catalysis of ATP synthesis, regenerative photobiomodulation and the ultrasonic acceleration of cell restructuring. Moreover, we suggest that a coherent application of multiple biophysical radiances (coMra) may synergistically enhance energy-matter-information kinetics of basal self-regeneration of cells and thus improve immune function and accelerate recovery.

•

Bioenergetics offers a unifying framework of COVID-19 immunopathology.

•

Functional reserve of immune cells depends on the kinetics of basal housekeeping.

•

Various biophysical stimuli enhance the kinetics of cellular self-regeneration.

•

A coherent application of multiple radiances has potential to treat COVID-19.

Biophysically detailed mathematical models of multiscale cardiac active mechanics

We propose four novel mathematical models, describing the microscopic mechanisms of force generation in the cardiac muscle tissue, which are suitable for multiscale numerical simulations of cardiac electromechanics. Such models are based on a biophysically accurate representation of the regulatory and contractile proteins in the sarcomeres. Our models, unlike most of the sarcomere dynamics models that are available in the literature and that feature a comparable richness of detail, do not require the time-consuming Monte Carlo method for their numerical approximation. Conversely, the models that we propose only require the solution of a system of PDEs and/or ODEs (the most reduced of the four only involving 20 ODEs), thus entailing a significant computational efficiency. By focusing on the two models that feature the best trade-off between detail of description and identifiability of parameters, we propose a pipeline to calibrate such parameters starting from experimental measurements available in literature. Thanks to this pipeline, we calibrate these models for room-temperature rat and for body-temperature human cells. We show, by means of numerical simulations, that the proposed models correctly predict the main features of force generation, including the steady-state force-calcium and force-length relationships, the length-dependent prolongation of twitches and increase of peak force, the force-velocity relationship. Moreover, they correctly reproduce the Frank-Starling effect, when employed in multiscale 3D numerical simulation of cardiac electromechanics.

Implications of back-and-forth motion and powerful propulsion for spirochetal invasion

The spirochete Leptospira spp. can move in liquid and on a solid surface using two periplasmic flagella (PFs), and its motility is an essential virulence factor for the pathogenic species. Mammals are infected with the spirochete through the wounded dermis, which implies the importance of behaviors on the boundary with such viscoelastic milieu; however, the leptospiral pathogenicity involving motility remains unclear. We used a glass chamber containing a gel area adjoining the leptospiral suspension to resemble host dermis exposed to contaminated water and analyzed the motility of individual cells at the liquid-gel border. Insertion of one end of the cell body to the gel increased switching of the swimming direction. Moreover, the swimming force of Leptospira was also measured by trapping single cells using an optical tweezer. It was found that they can generate [Formula: see text] 17 pN of force, which is [Formula: see text] 30 times of the swimming force of Escherichia coli. The force-speed relationship suggested the load-dependent force enhancement and showed that the power (the work per unit time) for the propulsion is [Formula: see text] 3.1 × 10-16 W, which is two-order of magnitudes larger than the propulsive power of E. coli. The powerful and efficient propulsion of Leptospira using back-and-forth movements could facilitate their invasion.

Performance Limitations in Sensorimotor Control: Trade-Offs Between Neural Computation and Accuracy in Tracking Fast Movements

The ability to move fast and accurately track moving objects is fundamentally constrained by the biophysics of neurons and dynamics of the muscles involved. Yet the corresponding trade-offs between these factors and tracking motor commands have not been rigorously quantified. We use feedback control principles to quantify performance limitations of the sensorimotor control system (SCS) to track fast periodic movements. We show that (1) linear models of the SCS fail to predict known undesirable phenomena, including skipped cycles, overshoot and undershoot, produced when tracking signals in the "fast regime," while nonlinear pulsatile control models can predict such undesirable phenomena, and (2) tools from nonlinear control theory allow us to characterize fundamental limitations in this fast regime. Using a validated and tractable nonlinear model of the SCS, we derive an analytical upper bound on frequencies that the SCS model can reliably track before producing such undesirable phenomena as a function of the neurons' biophysical constraints and muscle dynamics. The performance limitations derived here have important implications in sensorimotor control. For example, if the primary motor cortex is compromised due to disease or damage, the theory suggests ways to manipulate muscle dynamics by adding the necessary compensatory forces using an assistive neuroprosthetic device to restore motor performance and, more important, fast and agile movements. Just how one should compensate can be informed by our SCS model and the theory developed here.

Gradients in the mammalian cerebellar cortex enable Fourier-like transformation and improve storing capacity

Cerebellar granule cells (GCs) make up the majority of all neurons in the vertebrate brain, but heterogeneities among GCs and potential functional consequences are poorly understood. Here, we identified unexpected gradients in the biophysical properties of GCs in mice. GCs closer to the white matter (inner-zone GCs) had higher firing thresholds and could sustain firing with larger current inputs than GCs closer to the Purkinje cell layer (outer-zone GCs). Dynamic Clamp experiments showed that inner- and outer-zone GCs preferentially respond to high- and low-frequency mossy fiber inputs, respectively, enabling dispersion of the mossy fiber input into its frequency components as performed by a Fourier transformation. Furthermore, inner-zone GCs have faster axonal conduction velocity and elicit faster synaptic potentials in Purkinje cells. Neuronal network modeling revealed that these gradients improve spike-timing precision of Purkinje cells and decrease the number of GCs required to learn spike-sequences. Thus, our study uncovers biophysical gradients in the cerebellar cortex enabling a Fourier-like transformation of mossy fiber inputs.

Functional protein dynamics on uncharted time scales detected by nanoparticle-assisted NMR spin relaxation

Protein function depends critically on intrinsic internal dynamics, which is manifested in distinct ways, such as loop motions that regulate protein recognition and catalysis. Under physiological conditions, dynamic processes occur on a wide range of time scales from subpicoseconds to seconds. Commonly used NMR spin relaxation in solution provides valuable information on very fast and slow motions but is insensitive to the intermediate nanosecond to microsecond range that exceeds the protein tumbling correlation time. Presently, very little is known about the nature and functional role of these motions. It is demonstrated here how transverse spin relaxation becomes exquisitely sensitive to these motions at atomic resolution when studying proteins in the presence of nanoparticles. Application of this novel cross-disciplinary approach reveals large-scale dynamics of loops involved in functionally critical protein-protein interactions and protein-calcium ion recognition that were previously unobservable.

Electric field dynamics in the brain during multi-electrode transcranial electric stimulation

Neural oscillations play a crucial role in communication between remote brain areas. Transcranial electric stimulation with alternating currents (TACS) can manipulate these brain oscillations in a non-invasive manner. Recently, TACS using multiple electrodes with phase shifted stimulation currents were developed to alter long-range connectivity. Typically, an increase in coordination between two areas is assumed when they experience an in-phase stimulation and a disorganization through an anti-phase stimulation. However, the underlying biophysics of multi-electrode TACS has not been studied in detail. Here, we leverage direct invasive recordings from two non-human primates during multi-electrode TACS to characterize electric field magnitude and phase as a function of the phase of stimulation currents. Further, we report a novel "traveling wave" stimulation where the location of the electric field maximum changes over the stimulation cycle. Our results provide a mechanistic understanding of the biophysics of multi-electrode TACS and enable future developments of novel stimulation protocols.

Fear Level Classification Based on Emotional Dimensions and Machine Learning Techniques

There has been steady progress in the field of affective computing over the last two decades that has integrated artificial intelligence techniques in the construction of computational models of emotion. Having, as a purpose, the development of a system for treating phobias that would automatically determine fear levels and adapt exposure intensity based on the user’s current affective state, we propose a comparative study between various machine and deep learning techniques (four deep neural network models, a stochastic configuration network, Support Vector Machine, Linear Discriminant Analysis, Random Forest and k-Nearest Neighbors), with and without feature selection, for recognizing and classifying fear levels based on the electroencephalogram (EEG) and peripheral data from the DEAP (Database for Emotion Analysis using Physiological signals) database. Fear was considered an emotion eliciting low valence, high arousal and low dominance. By dividing the ratings of valence/arousal/dominance emotion dimensions, we propose two paradigms for fear level estimation—the two-level (0—no fear and 1—fear) and the four-level (0—no fear, 1—low fear, 2—medium fear, 3—high fear) paradigms. Although all the methods provide good classification accuracies, the highest F scores have been obtained using the Random Forest Classifier—89.96% and 85.33% for the two-level and four-level fear evaluation modality.

Domes and cones: Adhesion-induced fission of membranes by ESCRT proteins

ESCRT proteins participate in the fission step of exocytic membrane budding, by assisting in the closure and scission of the membrane neck that connects the nascent bud to the plasma membrane. However, the precise mechanism by which the proteins achieve this so-called reverse-topology membrane scission remains to be elucidated. One mechanism is described by the dome model, which postulates that ESCRT-III proteins assemble in the shape of a hemispherical dome at the location of the neck, and guide the closure of this neck via membrane–protein adhesion. A different mechanism is described by the flattening cone model, in which the ESCRT-III complex first assembles at the neck in the shape of a cone, which then flattens leading to neck closure. Here, we use the theoretical framework of curvature elasticity and membrane–protein adhesion to quantitatively describe and compare both mechanisms. This comparison shows that the minimal adhesive strength of the membrane–protein interactions required for scission is much lower for cones than for domes, and that the geometric constraints on the shape of the assembly required to induce scission are more stringent for domes than for cones. Finally, we compute for the first time the adhesion-induced constriction forces exerted by the ESCRT assemblies onto the membrane necks. These forces are higher for cones and of the order of 100 pN.

Membrane fission is a crucial step in many biological processes ranging from cell division to viral budding. During fission, the membrane forms a narrow neck that is subsequently cleaved by proteins. ESCRT proteins initiate this process by forming supramolecular assemblies that adhere to the cytosolic face of the membranes. However, how these assemblies achieve the closure and scission of membrane necks is still a matter of controversy. Here, we elucidate and compare the two most prominent mechanisms that have been proposed for ESCRT-induced membrane fission. In both mechanisms, the ESCRT molecules form assemblies that adhere to the open neck of the membrane bud. In the first mechanism, these assemblies grow in a dome-like shape, whereas in the second mechanism the assemblies take a cone-like shape and flatten as they grow. Our computational study shows that the minimal strength of membrane–protein adhesion necessary for cone-induced fission is lower than for dome-induced fission, and that cones generate higher constriction forces of the order of 100 pN onto the membrane necks.

Computational motility models of neurogastroenterology and neuromodulation

The success of neuromodulation therapies, particularly in the brain, spinal cord, and peripheral nerves, has been greatly aided by computational, biophysical models. However, treating gastrointestinal disorders with electrical stimulation has been much less explored, partly because the mode of action of such treatments is unclear, and selection of stimulation parameters is often empirical. Progress in gut neuromodulation is limited by the comparative lack of biophysical models capable of simulating neuromodulation of gastrointestinal function. Here, we review the recently developed biophysical models of electrically-active cells in the gastrointestinal system that contribute to motility. Biophysical models are replacing phenomenologically-defined models due to advancements in electrophysiological characterization of key players in the gut: enteric neurons, smooth muscle fibers, and interstitial cells of Cajal. In this review, we explore existing biophysically-defined cellular and network models that contribute to gastrointestinal motility. We focus on recent models that are laying the groundwork for modeling electrical stimulation of the gastrointestinal system. Developing models of gut neuromodulation will improve our mechanistic understanding of these treatments, leading to better parameterization, selectivity, and efficacy of neuromodulation to treat gastrointestinal disorders. Such models may have direct clinical translation to current neuromodulation therapies, such as sacral nerve stimulation.

A mechanism for bistability in glycosylation

Glycosyltransferases are a class of enzymes that catalyse the posttranslational modification of proteins to produce a large number of glycoconjugate acceptors from a limited number of nucleotide-sugar donors. The products of one glycosyltransferase can be the substrates of several other enzymes, causing a combinatorial explosion in the number of possible glycan products. The kinetic behaviour of systems where multiple acceptor substrates compete for a single enzyme is presented, and the case in which high concentrations of an acceptor substrate are inhibitory as a result of abortive complex formation, is shown to result in non-Michaelian kinetics that can lead to bistability in an open system. A kinetic mechanism is proposed that is consistent with the available experimental evidence and provides a possible explanation for conflicting observations on the β-1,4-galactosyltransferases. Abrupt switching between steady states in networks of glycosyltransferase-catalysed reactions may account for the observed changes in glycosyl-epitopes in cancer cells.

Network-level allosteric effects are elucidated by detailing how ligand-binding events modulate utilization of catalytic potentials

Allosteric regulation has traditionally been described by mathematically-complex allosteric rate laws in the form of ratios of polynomials derived from the application of simplifying kinetic assumptions. Alternatively, an approach that explicitly describes all known ligand-binding events requires no simplifying assumptions while allowing for the computation of enzymatic states. Here, we employ such a modeling approach to examine the "catalytic potential" of an enzyme-an enzyme's capacity to catalyze a biochemical reaction. The catalytic potential is the fundamental result of multiple ligand-binding events that represents a "tug of war" among the various regulators and substrates within the network. This formalism allows for the assessment of interacting allosteric enzymes and development of a network-level understanding of regulation. We first define the catalytic potential and use it to characterize the response of three key kinases (hexokinase, phosphofructokinase, and pyruvate kinase) in human red blood cell glycolysis to perturbations in ATP utilization. Next, we examine the sensitivity of the catalytic potential by using existing personalized models, finding that the catalytic potential allows for the identification of subtle but important differences in how individuals respond to such perturbations. Finally, we explore how the catalytic potential can help to elucidate how enzymes work in tandem to maintain a homeostatic state. Taken together, this work provides an interpretation and visualization of the dynamic interactions and network-level effects of interacting allosteric enzymes.

Reconstruction of continuous brachial artery pressure wave from continuous finger arterial pressure in humans

Generalized transfer functions (GTFs) are available to compute the more relevant proximal blood pressure (BP) waveform from a more easily measured peripheral BP waveform. However, GTFs are based on the black box model. This paper presents a practical approach to reconstruct brachial artery pressure (BAP) distally from finger artery pressure (FAP). We assume that continuous BAP can be simply approximated by summing two halves of the continuous FAP shifted by the time delay. We firstly showed that the pressure wave in the finger artery can be considered twice as much as the forward/backward wave in the finger. A simplified individualized transfer function was then derived so as to estimate BAP from FAP. The effectiveness of the method was examined by experiment involving 26 healthy volunteers (26.7 ± 3.8 years old) in a resting state. By comparing with a reference BAP, we found that the proposed method can correct the FAP. The errors of the proposed method in estimating systolic and diastolic pressures are - 0.6 ± 6.0 and - 0.6 ± 3.7 mmHg, respectively. These results agree with the standard of Association for the Advancement of Medical Instrumentation (AAMI). We also found that the reconstructed BAP from FAP by terminal arterial occlusion technology (TAOT) is comparable to that of the artery occlusion technology (AOT). Our method or TAOT is promising in estimating continuous proximal blood pressure from peripheral blood pressure in practice.

A biophysical mechanism for preferred direction enhancement in fly motion vision

Seeing the direction of motion is essential for survival of all sighted animals. Consequently, nerve cells that respond to visual stimuli moving in one but not in the opposite direction, so-called 'direction-selective' neurons, are found abundantly. In general, direction selectivity can arise by either signal amplification for stimuli moving in the cell's preferred direction ('preferred direction enhancement'), signal suppression for stimuli moving along the opposite direction ('null direction suppression'), or a combination of both. While signal suppression can be readily implemented in biophysical terms by a hyperpolarization followed by a rectification corresponding to the nonlinear voltage-dependence of the Calcium channel, the biophysical mechanism for signal amplification has remained unclear so far. Taking inspiration from the fly, I analyze a neural circuit where a direction-selective ON-cell receives inhibitory input from an OFF cell on the preferred side of the dendrite, while excitatory ON-cells contact the dendrite centrally. This way, an ON edge moving along the cell's preferred direction suppresses the inhibitory input, leading to a release from inhibition in the postsynaptic cell. The benefit of such a two-fold signal inversion lies in the resulting increase of the postsynaptic cell's input resistance, amplifying its response to a subsequent excitatory input signal even with a passive dendrite, i.e. without voltage-gated ion channels. A motion detector implementing this mechanism together with null direction suppression shows a high degree of direction selectivity over a large range of temporal frequency, narrow directional tuning, and a large signal-to-noise ratio.

Evaluation of the Perceptual Characteristics of a Force Induced by Asymmetric Vibrations

This paper describes the perceptual characteristics of a sense of a force induced by asymmetric vibration using a vibration speaker-type non-grounded haptic interface. We confirm that the vibration speaker generates a perceived force that pulls or pushes a user's hand in a particular direction when an asymmetric amplitude signal that is generated by inverting a part of a sine wave is input. In this paper, to verify the system with respect to various factors of force perception caused by asymmetric vibration, we conducted six experiments and the following results were obtained. (1) The force vector can be controlled by reversing the asymmetric waves. (2) By investigating the physical characteristics of the vibration, asymmetric vibration was confirmed. (3) The presentation of vibration in the shear direction on the finger pad is effective. (4) The point of subjective equality of the perceived force can be controlled by up to 0.43 N by changing the amplitude voltage of the input signals. (5) The minimum stimulation time required for force perception is 66.7 ms. (6) When the vibration is continuously presented for 40 to 50 s, the perceived force decreases because of adaptation. Hence, we confirmed that we can control both the direction and magnitude of the reaction force by changing the input signal of the vibration speaker.

Large-scale patterning of single cells and cell clusters in hydrogels

Biophysical properties of the extracellular matrix (ECM) are known to play a significant role in cell behavior. To gain a better understanding of the effects of the biophysical microenvironment on cell behavior, the practical challenge is longitudinally monitoring behavioral variations within a population to make statistically powerful assessments. Population-level measurements mask heterogeneity in cell responses, and large-scale individual cell measurements are often performed in a one-time, snapshot manner after removing cells from their matrix. Here we present an easy and low-cost method for large-scale, longitudinal studies of heterogeneous cell behavior in 3D hydrogel matrices. Using a platform we term "the drop-patterning chip", thousands of cells were simultaneously transferred from microwell arrays and fully embedded, only using the force of gravity, in precise patterns in 3D collagen I or Matrigel. This method allows for throughputs approaching 2D patterning methods that lack phenotypic information on cell-matrix interactions, and does not rely on special equipment and cell treatments that may result in a proximal stiff surface. With a large and yet well-organized group of cells captured in 3D matrices, we demonstrated the capability of locating selected individual cells and monitoring cell division, migration, and proliferation for multiple days.

Dissipation of transmembrane potassium gradient is the main cause of cerebral ischemia-induced depolarization in astrocytes and neurons

Membrane potential (V_M) depolarization occurs immediately following cerebral ischemia and is devastating for the astrocyte homeostasis and neuronal signaling. Previously, an excessive release of extracellular K⁺ and glutamate has been shown to underlie an ischemia-induced V_M depolarization. Ischemic insults should impair membrane ion channels and disrupt the physiological ion gradients. However, their respective contribution to ischemia-induced neuronal and glial depolarization and loss of neuronal excitability are unanswered questions. A short-term oxygen-glucose deprivation (OGD) was used for the purpose of examining the acute effect of ischemic conditions on ion channel activity and physiological K⁺ gradient in neurons and glial cells. We show that a 30 min OGD treatment exerted no measurable damage to the function of membrane ion channels in neurons, astrocytes, and NG2 glia. As a result of the resilience of membrane ion channels, neuronal spikes last twice as long as our previously reported 15 min time window. In the electrophysiological analysis, a 30 min OGD-induced dissipation of transmembrane K⁺ gradient contributed differently in brain cell depolarization: severe in astrocytes and neurons, and undetectable in NG2 glia. The discrete cellular responses to OGD corresponded to a total loss of 69% of the intracellular K⁺ contents in hippocampal slices as measured by Inductively Coupled Plasma Mass Spectrometry (ICP-MS). A major brain cell depolarization mechanism identified here is important for our understanding of cerebral ischemia pathology. Additionally, further understanding of the resilient response of NG2 glia to ischemia-induced intracellular K⁺ loss and depolarization should facilitate the development of future stroke therapy.

Systems biology of cellular membranes: a convergence with biophysics

Systems biology and systems medicine have played an important role in the last two decades in shaping our understanding of biological processes. While systems biology is synonymous with network maps and '-omics' approaches, it is not often associated with mechanical processes. Here, we make the case for considering the mechanical and geometrical aspects of biological membranes as a key step in pushing the frontiers of systems biology of cellular membranes forward. We begin by introducing the basic components of cellular membranes, and highlight their dynamical aspects. We then survey the functions of the plasma membrane and the endomembrane system in signaling, and discuss the role and origin of membrane curvature in these diverse cellular processes. We further give an overview of the experimental and modeling approaches to study membrane phenomena. We close with a perspective on the converging futures of systems biology and membrane biophysics, invoking the need to include physical variables such as location and geometry in the study of cellular membranes. WIREs Syst Biol Med 2017, 9:e1386. doi: 10.1002/wsbm.1386 For further resources related to this article, please visit the WIREs website.

Changes in glucosylceramide structure affect virulence and membrane biophysical properties of Cryptococcus neoformans

Fungal glucosylceramide (GlcCer) is a plasma membrane sphingolipid in which the sphingosine backbone is unsaturated in carbon position 8 (C8) and methylated in carbon position 9 (C9). Studies in the fungal pathogen, Cryptococcus neoformans, have shown that loss of GlcCer synthase activity results in complete loss of virulence in the mouse model. However, whether the loss of virulence is due to the lack of the enzyme or to the loss of the sphingolipid is not known. In this study, we used genetic engineering to alter the chemical structure of fungal GlcCer and studied its effect on fungal growth and pathogenicity. Here we show that unsaturation in C8 and methylation in C9 is required for virulence in the mouse model without affecting fungal growth in vitro or common virulence factors. However, changes in GlcCer structure led to a dramatic susceptibility to membrane stressors resulting in increased cell membrane permeability and rendering the fungal mutant unable to grow within host macrophages. Biophysical studies using synthetic vesicles containing GlcCer revealed that the saturated and unmethylated sphingolipid formed vesicles with higher lipid order that were more likely to phase separate into ordered domains. Taken together, these studies show for the first time that a specific structure of GlcCer is a major regulator of membrane permeability required for fungal pathogenicity.

eShiver: Lateral Force Feedback on Fingertips through Oscillatory Motion of an Electroadhesive Surface

We describe a new haptic force feedback device capable of creating lateral shear force on a bare fingertip-the eShiver. The eShiver creates a net lateral force from in-plane oscillatory motion of a surface synchronized with a "friction switch" based on Johnsen-Rahbek electroadhesion. Using an artificial finger, a maximum net lateral force of ±300 mN is achieved at 55 Hz lateral oscillation frequency, and net force is shown to be a function of velocity and applied voltage, as well as the phase between them. A second set of experiments is carried out on a human finger, and a lateral force of up to ±450 mN is achieved at a lateral oscillation frequency of 1,000 Hz. This force is reached at a peak lateral surface velocity of 400 mm/s and a peak applied voltage of 400 V. We develop a simple lumped parameter model of the eShiver, and a time domain simulation of the artificial finger is shown to agree with the experimental results. Three distinct zones of operation are found, which predict the limitations of force generation and which may be used for optimization. The human finger is found to be similar to the artificial finger in its dependence on actuation parameters, suggesting that the same lumped parameter model may be applied, albeit with different parameters. Curiously, the friction force due to Johnsen-Rahbek electroadhesion is found to increase substantially over time as the finger remains in contact with the surface. Considerations for optimizing the performance of the eShiver are discussed.

KiloHertz Bandwidth, Dual-Stage Haptic Device Lets You Touch Brownian Motion

This paper describes a haptic interface that has a uniform response over the entire human tactile frequency range. Structural mechanics makes it very difficult to implement articulated mechanical systems that can transmit high frequency signals. Here, we separated the frequency range into two frequency bands. The lower band is within the first structural mode of the corresponding haptic device while the higher one can be transmitted accurately by a fast actuator operating from conservation of momentum, that is, without reaction forces to the ground. To couple the two systems, we adopted a channel separation approach akin to that employed in the design of acoustic reproduction systems. The two channels are recombined at the tip of the device to give a uniform frequency response from DC to one kHz. In terms of mechanical design, the high-frequency transducer was embedded inside the tip of the main stage so that during operation, the human operator has only to interact with a single finger interface. In order to exemplify the type of application that would benefit from this kind of interface, we applied it to the haptic exploration with microscopic scales objects which are known to behave with very fast dynamics. The novel haptic interface was bilaterally coupled with a micromanipulation platform to demonstrate its capabilities. Operators could feel interaction forces arising from contact as well as those resulting from Brownian motion and could manoeuvre a micro bead in the absence of vision.

FRET-FLIM for Visualizing and Quantifying Protein Interactions in Live Plant Cells

Proteins are the workhorses that control most biological processes in living cells. Although proteins can accomplish their functions independently, the vast majority of functions require proteins to interact with other proteins or biomacromolecules. Protein interactions can be investigated through biochemical assays such as co-immunoprecipitation (co-IP) or Western blot analysis, but such assays lack spatial information. Here we describe a well-developed imaging method, Förster resonance energy transfer (FRET) analyzed by fluorescence lifetime imaging microscopy (FLIM), that can be used to visualize protein interactions with both spatial and temporal resolution in live cells. We demonstrate its use in plant developmental research by visualizing in vivo dimerization of AUXIN RESPONSE FACTOR (ARF) proteins, mediating auxin responses.

Evaluation and Minimization of Uncertainty in ITC Binding Measurements: Heat Error, Concentration Error, Saturation, and Stoichiometry

BACKGROUND

Isothermal titration calorimetry (ITC) is uniquely useful for characterizing binding thermodynamics, because it straightforwardly provides both the binding enthalpy and free energy. However, the precision of the results depends on the experimental setup and how thermodynamic results are obtained from the raw data.

METHODS

Experiments and Monte Carlo analysis are used to study how uncertainties in injection heat and concentration propagate to binding enthalpies in various scenarios. We identify regimes in which it is preferable to fix the stoichiometry parameter, N, and evaluate the reliability of uncertainties provided by the least squares method.

RESULTS

The noise in the injection heat is mainly proportional in character, with ~1% and ~3% uncertainty at 27C and 65C, respectively; concentration errors are ~1%. Simulations of experiments based on these uncertainties delineate how experimental design and curve fitting methods influence the uncertainty in the final results.

CONCLUSIONS

In most cases, experimental uncertainty is minimized by using more injections and by fixing N at its known value. With appropriate technique, the uncertainty in measured binding enthalpies can be kept below ~2% under many conditions, including low C values.

GENERAL SIGNIFICANCE

We quantify uncertainties in ITC data due to heat and concentration error, and identify practices to minimize these uncertainties. The resulting guidelines are important when ITC data are used quantitatively, such as to test computer simulations of binding. Reproducibility and further study are supported by free distribution of the new software developed here.

Shaping Neural Circuits by High Order Synaptic Interactions

Spike timing dependent plasticity (STDP) is believed to play an important role in shaping the structure of neural circuits. Here we show that STDP generates effective interactions between synapses of different neurons, which were neglected in previous theoretical treatments, and can be described as a sum over contributions from structural motifs. These interactions can have a pivotal influence on the connectivity patterns that emerge under the influence of STDP. In particular, we consider two highly ordered forms of structure: wide synfire chains, in which groups of neurons project to each other sequentially, and self connected assemblies. We show that high order synaptic interactions can enable the formation of both structures, depending on the form of the STDP function and the time course of synaptic currents. Furthermore, within a certain regime of biophysical parameters, emergence of the ordered connectivity occurs robustly and autonomously in a stochastic network of spiking neurons, without a need to expose the neural network to structured inputs during learning.

Plasticity between neural connections plays a key role in our ability to process and store information. One of the fundamental questions on plasticity, is the extent to which local processes, affecting individual synapses, are responsible for large scale structures of neural connectivity. Here we focus on two types of structures: synfire chains and self connected assemblies. These structures are often proposed as forms of neural connectivity that can support brain functions such as memory and generation of motor activity. We show that an important plasticity mechanism, spike timing dependent plasticity, can lead to autonomous emergence of these large scale structures in the brain: in contrast to previous theoretical proposals, we show that the emergence can occur autonomously even if instructive signals are not fed into the neural network while its form is shaped by synaptic plasticity.

Large Scale Chromosome Folding Is Stable against Local Changes in Chromatin Structure

Characterizing the link between small-scale chromatin structure and large-scale chromosome folding during interphase is a prerequisite for understanding transcription. Yet, this link remains poorly investigated. Here, we introduce a simple biophysical model where interphase chromosomes are described in terms of the folding of chromatin sequences composed of alternating blocks of fibers with different thicknesses and flexibilities, and we use it to study the influence of sequence disorder on chromosome behaviors in space and time. By employing extensive computer simulations, we thus demonstrate that chromosomes undergo noticeable conformational changes only on length-scales smaller than 10⁵ basepairs and time-scales shorter than a few seconds, and we suggest there might exist effective upper bounds to the detection of chromosome reorganization in eukaryotes. We prove the relevance of our framework by modeling recent experimental FISH data on murine chromosomes.

A key determining factor in many important cellular processes as DNA transcription, for instance, the specific composition of the chromatin fiber sequence has a major influence on chromosome folding during interphase. Yet, how this is achieved in detail remains largely elusive. In this work, we explore this link by means of a novel quantitative computational polymer model for interphase chromosomes where the associated chromatin filaments are composed of mixtures of fibers with heterogeneous physical properties. Our work suggests a scenario where chromosomes undergo only limited reorganization, namely on length-scales below 10⁵ basepairs and time-scales shorter than a few seconds. Our conclusions are supported by recent FISH data on murine chromosomes.

Computational implications of biophysical diversity and multiple timescales in neurons and synapses for circuit performance

Despite advances in experimental and theoretical neuroscience, we are still trying to identify key biophysical details that are important for characterizing the operation of brain circuits. Biological mechanisms at the level of single neurons and synapses can be combined as 'building blocks' to generate circuit function. We focus on the importance of capturing multiple timescales when describing these intrinsic and synaptic components. Whether inherent in the ionic currents, the neuron's complex morphology, or the neurotransmitter composition of synapses, these multiple timescales prove crucial for capturing the variability and richness of circuit output and enhancing the information-carrying capacity observed across nervous systems.

Iatrogenic cerebral gas embolism: analysis of the presentation, management and outcomes of patients referred to The Alfred Hospital Hyperbaric Unit

INTRODUCTION

The aim of this study was to review patients with iatrogenic cerebral gas embolism (CGE) referred to The Alfred Hospital hyperbaric unit to determine whether hyperbaric oxygen treatment (HBOT) reduced morbidity and mortality.

METHODS

This is a retrospective cohort study with a contemporaneous comparison group of patients referred between January 1998 and December 2014. The primary end point was good neurological outcome at the time of discharge from hospital or rehabilitation facility as assessed by the Glasgow Outcome Scale (GOS-E).

RESULTS

Thirty-six patients were treated with HBOT for CGE and nine patients were diagnosed with CGE but did not receive HBOT. Thirty-two patients developed CGE from an arterial source and 13 from a venous source. The mean time from recognition of the event to institution of HBOT was 15 hours. Four of 45 patients (8.9%) died. Good neurological outcomes (defined as GOS-E 7 or 8) occurred in 27 patients and moderate disability in 13. The only independent factor that was associated with good neurological outcome was time to first HBOT (OR 0.94, 0.89-0.99; P = 0.05). Hemiplegia as the first presenting sign, however, was associated with poor outcome (OR 0.27, 0.06-1.08; P = 0.05). The source of embolus (arterial versus venous), hyperbaric treatment table used and patient age did not affect outcome.

CONCLUSION

Appropriate treatment of CGE with hyperbaric oxygen was found to be impeded by delays in diagnosis and subsequent transfer of patients. Better neurological outcome was associated with HBOT within eight hours of CGE.

On the Use of DSC-MRI for Measuring Vascular Permeability

BACKGROUND AND PURPOSE

Contrast agent extravasation has been shown to confound brain tumor perfusion measurements with DSC-MR imaging, necessitating the use of correction techniques (eg, Weisskoff, Bjornerud). Leakage parameters (K2 and K(a)) postulated to reflect vessel permeability can be extracted from these correction methods; however, the biophysical interpretation of these parameters and their relationship to commonly used MR imaging measures of vascular permeability (eg, contrast agent volume transfer constant, [K(trans)]) remain unclear. Given that vascular density, as assessed by blood volume, and vascular permeability, as reflected by K(trans) (and potentially K2 or K(a)), report on unique and clinically informative vascular characteristics, there is a compelling interest to simultaneously assess these features.

MATERIALS AND METHODS

We acquired multiecho DSC-MR imaging data, allowing the simultaneous computation and voxelwise comparison of single- and dual-echo derived measures of K2, K(a) and K(trans) in patients with glioma. This acquisition enabled the investigation of competing T1 and T2* leakage effects and TE dependency on these parameters.

RESULTS

K2 and K(a) displayed nonsignificant (P = .150 and P = .060, respectively) voxelwise linear correlations with K(trans), while a significant (P < .001) inverse relationship was observed between K2 and Ka (coefficient of determination [r(2)] = 0.466-0.984). Significantly different (P < .005) mean estimates were found between voxels exhibiting predominately T1 and T2* effects for K2 and K(a). K(trans), however, was observed to be similar between these voxels (0.109 versus 0.092 minutes(-1)). Significant differences (P < .001) in extracellular-extravascular volume fraction (v(e)) (0.285 versus 0.167) were also observed between cohorts. Additionally, K2 and K(a) were found to have a significant quadratic relationship (P = .031 and P = .005, respectively) with v(e).

CONCLUSIONS

Estimates of vascular permeability in brain tumors may be simultaneously acquired from multiple-echo DSC-MR imaging via K(trans); however, caution should be used in assuming a similar relationship for K2 and K(a).

The influence of striking object characteristics on the impact energy

A common form of violence investigated in legal medicine is blunt trauma caused by striking with different objects. The injuries and medical consequences have been widely examined, whereas the forces and especially the energies acting on impact have rarely been analyzed. This study focuses on how the impact energy of different striking objects depends on their characteristics. A total of 1170 measurements of horizontal strikes against a static and relatively heavy pendulum have been acquired with 13 volunteers. The main focus was laid on how the weight, the length, and the center of mass of the different striking objects influenced the striking energy. The results show average impact energies in the range of 67.3 up to 311.5 J for men with an optimum weight of about 1.3 kg with its center of mass in the far end quarter for a 1-m-long striking object. The average values for women range from 30 to 202.6 J, with an optimum weight between 1.65 and 2.2 kg and similar settings for the center of mass as the men. Also, the impact energies are getting higher with shorter object lengths and reach a maximum at a length of about 0.3 to 0.4 m. The male volunteers' impact energy was on average by 84.2% higher than the values of the female volunteers, where the impact masses were very similar and the impact velocities played the key role.

Cooperative binding: a multiple personality

Cooperative binding has been described in many publications and has been related to or defined by several different properties of the binding behavior of the ligand to the target molecule. In addition to the commonly used Hill coefficient, other characteristics such as a sigmoidal shape of the overall titration curve in a linear plot, a change of ligand affinity of the other binding sites when a site of the target molecule becomes occupied, or complex roots of the binding polynomial have been used to define or to quantify cooperative binding. In this work, we analyze how the different properties are related in the most general model for binding curves based on the grand canonical partition function and present several examples which highlight differences between the cooperativity characterizing properties which are discussed. Our results mainly show that among the presented definitions there are not two which fully coincide. Moreover, this work poses the question whether it can make sense to distinguish between positive and negative cooperativity based on the macroscopic binding isotherm only. This article shall emphasize that scientists who investigate cooperative effects in biological systems could help avoiding misunderstandings by stating clearly which kind of cooperativity they discuss.

Biophysical methods for the characterization of PTEN/lipid bilayer interactions

PTEN, a tumor suppressor protein that dephosphorylates phosphoinositides at the 3-position of the inositol ring, is a cytosolic protein that needs to associate with the plasma membrane or other subcellular membranes to exert its lipid phosphatase function. Upon membrane association PTEN interacts with at least three different lipid entities: An anionic lipid that is present in sufficiently high concentration to create a negative potential that allows PTEN to interact electrostatically with the membrane, phosphatidylinositol-4,5-bisphosphate, which interacts with PTEN's N-terminal end and the substrate, usually phosphatidylinositol-3,4,5-trisphosphate. Many parameters influence PTEN's interaction with the lipid bilayer, for example, the lateral organization of the lipids or the presence of other chemical species like cholesterol or other lipids. To investigate systematically the different steps of PTEN's complex binding mechanism and to explore its dynamic behavior in the membrane bound state, in vitro methods need to be employed that allow for a systematic variation of the experimental conditions. In this review we survey a variety of methods that can be used to assess PTEN lipid binding affinity, the dynamics of its membrane association as well as its dynamic behavior in the membrane bound state.

Biophysical induction of vascular smooth muscle cell podosomes

Vascular smooth muscle cell (VSMC) migration and matrix degradation occurs with intimal hyperplasia associated with atherosclerosis, vascular injury, and restenosis. One proposed mechanism by which VSMCs degrade matrix is through the use of podosomes, transient actin-based structures that are thought to play a role in extracellular matrix degradation by creating localized sites of matrix metalloproteinase (MMP) secretion. To date, podosomes in VSMCs have largely been studied by stimulating cells with phorbol esters, such as phorbol 12,13-dibutyrate (PDBu), however little is known about the physiological cues that drive podosome formation. We present the first evidence that physiological, physical stimuli mimicking cues present within the microenvironment of diseased arteries can induce podosome formation in VSMCs. Both microtopographical cues and imposed pressure mimicking stage II hypertension induce podosome formation in A7R5 rat aortic smooth muscle cells. Moreover, wounding using a scratch assay induces podosomes at the leading edge of VSMCs. Notably the effect of each of these biophysical stimuli on podosome stimulation can be inhibited using a Src inhibitor. Together, these data indicate that physical cues can induce podosome formation in VSMCs.

Membrane-mediated interaction between strongly anisotropic protein scaffolds

Specialized proteins serve as scaffolds sculpting strongly curved membranes of intracellular organelles. Effective membrane shaping requires segregation of these proteins into domains and is, therefore, critically dependent on the protein-protein interaction. Interactions mediated by membrane elastic deformations have been extensively analyzed within approximations of large inter-protein distances, small extents of the protein-mediated membrane bending and small deviations of the protein shapes from isotropic spherical segments. At the same time, important classes of the realistic membrane-shaping proteins have strongly elongated shapes with large and highly anisotropic curvature. Here we investigated, computationally, the membrane mediated interaction between proteins or protein oligomers representing membrane scaffolds with strongly anisotropic curvature, and addressed, quantitatively, a specific case of the scaffold geometrical parameters characterizing BAR domains, which are crucial for membrane shaping in endocytosis. In addition to the previously analyzed contributions to the interaction, we considered a repulsive force stemming from the entropy of the scaffold orientation. We computed this interaction to be of the same order of magnitude as the well-known attractive force related to the entropy of membrane undulations. We demonstrated the scaffold shape anisotropy to cause a mutual aligning of the scaffolds and to generate a strong attractive interaction bringing the scaffolds close to each other to equilibrium distances much smaller than the scaffold size. We computed the energy of interaction between scaffolds of a realistic geometry to constitute tens of kBT, which guarantees a robust segregation of the scaffolds into domains.

An adaptive displacement estimation algorithm for improved reconstruction of thermal strain

Thermal strain imaging (TSI) can be used to differentiate between lipid and water-based tissues in atherosclerotic arteries. However, detecting small lipid pools in vivo requires accurate and robust displacement estimation over a wide range of displacement magnitudes. Phase-shift estimators such as Loupas' estimator and time-shift estimators such as normalized cross-correlation (NXcorr) are commonly used to track tissue displacements. However, Loupas' estimator is limited by phase-wrapping and NXcorr performs poorly when the SNR is low. In this paper, we present an adaptive displacement estimation algorithm that combines both Loupas' estimator and NXcorr. We evaluated this algorithm using computer simulations and an ex vivo human tissue sample. Using 1-D simulation studies, we showed that when the displacement magnitude induced by thermal strain was >λ/8 and the electronic system SNR was >25.5 dB, the NXcorr displacement estimate was less biased than the estimate found using Loupas' estimator. On the other hand, when the displacement magnitude was ≤λ/4 and the electronic system SNR was ≤25.5 dB, Loupas' estimator had less variance than NXcorr. We used these findings to design an adaptive displacement estimation algorithm. Computer simulations of TSI showed that the adaptive displacement estimator was less biased than either Loupas' estimator or NXcorr. Strain reconstructed from the adaptive displacement estimates improved the strain SNR by 43.7 to 350% and the spatial accuracy by 1.2 to 23.0% (P < 0.001). An ex vivo human tissue study provided results that were comparable to computer simulations. The results of this study showed that a novel displacement estimation algorithm, which combines two different displacement estimators, yielded improved displacement estimation and resulted in improved strain reconstruction.

A syntactic two-component encoding model for the trajectories of human actions

Human actions have been widely studied for their potential application in various areas such as sports, pervasive patient monitoring, and rehabilitation. However, challenges still persist pertaining to determining the most useful ways to describe human actions at the sensor, then limb and complete action levels of representation and deriving important relations between these levels each involving their own atomic components. In this paper, we report on a motion encoder developed for the sensor level based on the need to distinguish between the shape of the sensor's trajectory and its temporal characteristics during execution. This distinction is critical as it provides a different encoding scheme than the usual velocity and acceleration measures which confound these two attributes of any motion. At the same time, we eliminate noise from sensors by comparing temporal and spatial indexing schemes and a number of optimal filtering models for robust encoding. Results demonstrate the benefits of spatial indexing and separating the shape and dynamics of a motion, as well as its ability to decompose complex motions into several atomic ones. Finally, we discuss how this specific type of sensor encoder bears on the derivation of limb and complete action descriptions.

Bistable forespore engulfment in Bacillus subtilis by a zipper mechanism in absence of the cell wall

To survive starvation, the bacterium Bacillus subtilis forms durable spores. The initial step of sporulation is asymmetric cell division, leading to a large mother-cell and a small forespore compartment. After division is completed and the dividing septum is thinned, the mother cell engulfs the forespore in a slow process based on cell-wall degradation and synthesis. However, recently a new cell-wall independent mechanism was shown to significantly contribute, which can even lead to fast engulfment in 60 of the cases when the cell wall is completely removed. In this backup mechanism, strong ligand-receptor binding between mother-cell protein SpoIIIAH and forespore-protein SpoIIQ leads to zipper-like engulfment, but quantitative understanding is missing. In our work, we combined fluorescence image analysis and stochastic Langevin simulations of the fluctuating membrane to investigate the origin of fast bistable engulfment in absence of the cell wall. Our cell morphologies compare favorably with experimental time-lapse microscopy, with engulfment sensitive to the number of SpoIIQ-SpoIIIAH bonds in a threshold-like manner. By systematic exploration of model parameters, we predict regions of osmotic pressure and membrane-surface tension that produce successful engulfment. Indeed, decreasing the medium osmolarity in experiments prevents engulfment in line with our predictions. Forespore engulfment may thus not only be an ideal model system to study decision-making in single cells, but its biophysical principles are likely applicable to engulfment in other cell types, e.g. during phagocytosis in eukaryotes.

When the bacterium B. subtilis runs out of food, it undergoes a fundamental development process by which it forms durable spores. Sporulation is initiated by asymmetric cell division after which the larger mother cell engulfs the smaller forespore, followed by spore maturation and release. This survival strategy is so robust that engulfment even proceeds when cells are deprived of their protective cell wall. Under these severe perturbations, 60 of the mother cells still engulf their forespores in only 10 of the normal engulfment time, while the remaining 40 of mother cells withdraw from engulfment. This all-or-none outcome of engulfment suggests decision-making, which was recently also identified in other types of engulfment, e.g. during phagocytosis when immune cells engulf and destroy pathogens. Here, we developed a biophysical model to explain fast bistable forespore engulfment in absence of the cell wall and energy sources. Our discovered principles may prove very general, thus predicting key ingredients of successful engulfment across all kingdoms of life.

Splash dispersal of Phyllosticta citricarpa conidia from infected citrus fruit

Rain-splash dispersal of Phyllosticta citricarpa (syn. Guignardia citricarpa) conidia (pycnidiospores) from infected oranges was studied in still air and combined with wind. High power microscopy demonstrated the presence of conidia in splash droplets from diseased oranges, which exuded conidia for over one hour during repeated wetting. The largest (5 mm) incident drops produced the highest splashes (up to 41.0 cm). A linear-by-quadratic surface model predicted highest splashes to be 41.91 cm at a horizontal distance of 25.97 cm from the target orange. Large splash droplets contained most conidia (4-5.5 mm splashes averaged 308 conidia), but were splashed <30 cm horizontal distance. Most (80-90%) splashes were <1 mm diameter but carried only 0-4 conidia per droplet. In multiple splash experiments, splashes combined to reach higher maxima (up to 61.7 cm; linear-by-quadratic surface model prediction, 62.1 cm) than in the single splash experiments. In combination with wind, higher wind speeds carried an increasing proportion of splashes downwind travelling horizontally at least 8 m at the highest wind speed tested (7 m/s), due to a small proportion of droplets (<1 mm) being aerosolised. These experiments suggest that P. citricarpa conidia can be dispersed from infected oranges by splashes of water in rainfall events.

Estimating parameters and predicting membrane voltages with conductance-based neuron models

Recent results demonstrate techniques for fully quantitative, statistical inference of the dynamics of individual neurons under the Hodgkin-Huxley framework of voltage-gated conductances. Using a variational approximation, this approach has been successfully applied to simulated data from model neurons. Here, we use this method to analyze a population of real neurons recorded in a slice preparation of the zebra finch forebrain nucleus HVC. Our results demonstrate that using only 1,500 ms of voltage recorded while injecting a complex current waveform, we can estimate the values of 12 state variables and 72 parameters in a dynamical model, such that the model accurately predicts the responses of the neuron to novel injected currents. A less complex model produced consistently worse predictions, indicating that the additional currents contribute significantly to the dynamics of these neurons. Preliminary results indicate some differences in the channel complement of the models for different classes of HVC neurons, which accords with expectations from the biology. Whereas the model for each cell is incomplete (representing only the somatic compartment, and likely to be missing classes of channels that the real neurons possess), our approach opens the possibility to investigate in modeling the plausibility of additional classes of channels the cell might possess, thus improving the models over time. These results provide an important foundational basis for building biologically realistic network models, such as the one in HVC that contributes to the process of song production and developmental vocal learning in songbirds.

Dendritic nonlinearities reduce network size requirements and mediate ON and OFF states of persistent activity in a PFC microcircuit model

Technological advances have unraveled the existence of small clusters of co-active neurons in the neocortex. The functional implications of these microcircuits are in large part unexplored. Using a heavily constrained biophysical model of a L5 PFC microcircuit, we recently showed that these structures act as tunable modules of persistent activity, the cellular correlate of working memory. Here, we investigate the mechanisms that underlie persistent activity emergence (ON) and termination (OFF) and search for the minimum network size required for expressing these states within physiological regimes. We show that (a) NMDA-mediated dendritic spikes gate the induction of persistent firing in the microcircuit. (b) The minimum network size required for persistent activity induction is inversely proportional to the synaptic drive of each excitatory neuron. (c) Relaxation of connectivity and synaptic delay constraints eliminates the gating effect of NMDA spikes, albeit at a cost of much larger networks. (d) Persistent activity termination by increased inhibition depends on the strength of the synaptic input and is negatively modulated by dADP. (e) Slow synaptic mechanisms and network activity contain predictive information regarding the ability of a given stimulus to turn ON and/or OFF persistent firing in the microcircuit model. Overall, this study zooms out from dendrites to cell assemblies and suggests a tight interaction between dendritic non-linearities and network properties (size/connectivity) that may facilitate the short-memory function of the PFC.

Working memory, the ability to retain information for a short period of time, is a fundamental cognitive function that shapes behavior. The cellular correlate of working memory is the prolonged spiking (persistent) activity of neurons in the prefrontal cortex. Impairments of prefrontal cortex functionalities and working memory have been associated with a variety of cognitive disorders, such as schizophrenia, the attention deficit hyperactivity disorder, and drug addiction. Hence, understanding how neurons embedded in the local circuitry support and maintain persistent activity is of outmost importance. Our work uses a multi-level integrative approach spanning from the dendritic, to the neuronal and network levels to identify the key biophysical and anatomical mechanisms contributing to persistent activity, leading to a number of high impact findings: it predicts a tradeoff between dendritic regenerative events and the size of a network expressing persistent activity. It also proposes when and how the persistent state can be stabilized, opening new avenues for pharmacological interventions. Finally, it describes decoding mechanisms for upcoming ON/OFF state transitions, furthering our understanding of information processing in the PFC and shedding new light on the emergence of anticipatory behaviors.

Long-range connectivity defines behavioral specificity of amygdala neurons

Memories are acquired and encoded within large-scale neuronal networks spanning different brain areas. The anatomical and functional specificity of such long-range interactions and their role in learning is poorly understood. The amygdala and the medial prefrontal cortex (mPFC) are interconnected brain structures involved in the extinction of conditioned fear. Here, we show that a defined subpopulation of basal amygdala (BA) projection neurons targeting the prelimbic (PL) subdivision of mPFC is active during states of high fear, whereas BA neurons targeting the infralimbic (IL) subdivision are recruited, and exhibit cell-type-specific plasticity, during fear extinction. Pathway-specific optogenetic manipulations demonstrate that the activity balance between pathways is causally involved in fear extinction. Together, our findings demonstrate that, although intermingled locally, long-range connectivity defines distinct subpopulations of amygdala projection neurons and indicate that the formation of long-term extinction memories depends on the balance of activity between two defined amygdala-prefrontal pathways.

Biophysical controls on light response of net CO2 exchange in a winter wheat field in the North China Plain

To investigate the impacts of biophysical factors on light response of net ecosystem exchange (NEE), CO2 flux was measured using the eddy covariance technique in a winter wheat field in the North China Plain from 2003 to 2006. A rectangular hyperbolic function was used to describe NEE light response. Maximum photosynthetic capacity (P max) was 46.6 ± 4.0 µmol CO2 m(-2) s(-1) and initial light use efficiency (α) 0.059 ± 0.006 µmol µmol(-1) in April-May, two or three times as high as those in March. Stepwise multiple linear regressions showed that P max increased with the increase in leaf area index (LAI), canopy conductance (g c) and air temperature (T a) but declined with increasing vapor pressure deficit (VPD) (P<0.001). The factors influencing P max were sorted as LAI, g c, T a and VPD. α was proportional to ln(LAI), g c, T a and VPD (P<0.001). The effects of LAI, g c and T a on α were larger than that of VPD. When T a>25°C or VPD>1.1-1.3 kPa, NEE residual increased with the increase in T a and VPD (P<0.001), indicating that temperature and water stress occurred. When g c was more than 14 mm s(-1) in March and May and 26 mm s(-1) in April, the NEE residuals decline disappeared, or even turned into an increase in g c (P<0.01), implying shifts from stomatal limitation to non-stomatal limitation on NEE. Although the differences between sunny and cloudy sky conditions were unremarkable for light response parameters, simulated net CO2 uptake under the same radiation intensity averaged 18% higher in cloudy days than in sunny days during the year 2003-2006. It is necessary to include these effects in relevant carbon cycle models to improve our estimation of carbon balance at regional and global scales.

Biophysics of cell membrane lipids in cancer drug resistance: Implications for drug transport and drug delivery with nanoparticles

In this review, we focus on the biophysics of cell membrane lipids, particularly when cancers develop acquired drug resistance, and how biophysical changes in resistant cell membrane influence drug transport and nanoparticle-mediated drug delivery. Recent advances in membrane lipid research show the varied roles of lipids in regulating membrane P-glycoprotein function, membrane trafficking, apoptotic pathways, drug transport, and endocytic functions, particularly endocytosis, the primary mechanism of cellular uptake of nanoparticle-based drug delivery systems. Since acquired drug resistance alters lipid biosynthesis, understanding the role of lipids in cell membrane biophysics and its effect on drug transport is critical for developing effective therapeutic and drug delivery approaches to overcome drug resistance. Here we discuss novel strategies for (a) modulating the biophysical properties of membrane lipids of resistant cells to facilitate drug transport and regain endocytic function and (b) developing effective nanoparticles based on their biophysical interactions with membrane lipids to enhance drug delivery and overcome drug resistance.