A positively tuned voltage indicator for extended electrical recordings in the brain.

Genetically encoded voltage indicators (GEVIs) enable optical recording of electrical signals in the brain, providing subthreshold sensitivity and temporal resolution not possible with calcium indicators. However, one- and two-photon voltage imaging over prolonged periods with the same GEVI has not yet been demonstrated. Here, we report engineering of ASAP family GEVIs to enhance photostability by inversion of the fluorescence-voltage relationship. Two of the resulting GEVIs, ASAP4b and ASAP4e, respond to 100-mV depolarizations with ≥180% fluorescence increases, compared with the 50% fluorescence decrease of the parental ASAP3. With standard microscopy equipment, ASAP4e enables single-trial detection of spikes in mice over the course of minutes. Unlike GEVIs previously used for one-photon voltage recordings, ASAP4b and ASAP4e also perform well under two-photon illumination. By imaging voltage and calcium simultaneously, we show that ASAP4b and ASAP4e can identify place cells and detect voltage spikes with better temporal resolution than commonly used calcium indicators. Thus, ASAP4b and ASAP4e extend the capabilities of voltage imaging to standard one- and two-photon microscopes while improving the duration of voltage recordings.

Helical nanofiber yarn enabling highly stretchable engineered microtissue.

Development of microtissues that possess mechanical properties mimicking those of native stretchable tissues, such as muscle and tendon, is in high demand for tissue engineering and regenerative medicine. However, regardless of the significant advances in synthetic biomaterials, it remains challenging to fabricate living microtissue with high stretchability because application of large strains to microtissues can damage the cells by rupturing their structures. Inspired by the hierarchical helical structure of native fibrous tissues and its behavior of nonaffine deformation, we develop a highly stretchable and tough microtissue fiber made up of a hierarchical helix yarn scaffold, scaling from nanometers to millimeters, that can overcome this limitation. This microtissue can be stretched up to 15 times its initial length and has a toughness of 57 GJ m-3 More importantly, cells grown on this scaffold maintain high viability, even under severe cyclic strains (up to 600%) that can be attributed to the nonaffine deformation under large strains, mimicking native biopolymer scaffolds. Furthermore, as proof of principle, we demonstrate that the nanotopography of the helical nanofiber yarn is able to induce cytoskeletal alignment and nuclear elongation, which promote myogenic differentiation of mesenchymal stem cells by triggering nuclear translocation of transcriptional coactivator with PDZ-binding motif (TAZ). The highly stretchable microtissues we develop here will facilitate a variety of tissue engineering applications and the development of engineered living systems.

High stretchability, strength, and toughness of living cells enabled by hyperelastic vimentin intermediate filaments.

In many developmental and pathological processes, including cellular migration during normal development and invasion in cancer metastasis, cells are required to withstand severe deformations. The structural integrity of eukaryotic cells under small deformations has been known to depend on the cytoskeleton including actin filaments (F-actin), microtubules (MT), and intermediate filaments (IFs). However, it remains unclear how cells resist severe deformations since both F-actin and microtubules yield or disassemble under moderate strains. Using vimentin containing IFs (VIFs) as a model for studying the large family of IF proteins, we demonstrate that they dominate cytoplasmic mechanics and maintain cell viability at large deformations. Our results show that cytoskeletal VIFs form a stretchable, hyperelastic network in living cells. This network works synergistically with other cytoplasmic components, substantially enhancing the strength, stretchability, resilience, and toughness of cells. Moreover, we find the hyperelastic VIF network, together with other quickly recoverable cytoskeletal components, forms a mechanically robust structure which can mechanically recover after damage.

Optimal Underwater Acoustic Warfare Strategy Based on a Three-Layer GA-BP Neural Network.

A defense platform is usually based on two methods to make underwater acoustic warfare strategy decisions. One is through Monte-Carlo method online simulation, which is slow. The other is by typical empirical (database) and typical back-propagation (BP) neural network algorithms based on genetic algorithm (GA) optimization, which is less accurate and less robust. Therefore, this paper proposes a method to build an optimal underwater acoustic warfare feedback system using a three-layer GA-BP neural network and dropout processing of the neural network to prevent overfitting, so that the three-layer GA-BP neural network has adequate memory capability while still having suitable generalization capability. This method improves the accuracy and stability of the defense platform in making underwater acoustic warfare strategy decisions, thus increasing the survival probability of the defense platform in the face of incoming torpedoes. This paper uses the optimal underwater acoustic warfare strategies corresponding to incoming torpedoes with different postures as the sample set. Additionally, it uses a three-layer GA-BP neural network with an overfitting treatment for training. The prediction results have less error than the typical single-layer GA-BP neural network, and the survival probability of the defense platform improves by 6.15%. This defense platform underwater acoustic warfare strategy prediction method addresses the impact on the survival probability of the defense platform due to the decision speed and accuracy.

Elastic Properties and Fracture Behaviors of Biaxially Deformed, Polymorphic MoTe2.

Biaxial deformation of suspended membranes widely exists and is used in nanoindentation to probe elastic properties of structurally isotropic two-dimensional (2D) materials. However, the elastic properties and, in particular, the fracture behaviors of anisotropic 2D materials remain largely unclarified in the case of biaxial deformation. MoTe2 is a polymorphic 2D material with both isotropic (2H) and anisotropic (1T' and Td) phases and, therefore, an ideal system of single-stoichiometric materials with which to study these critical issues. Here, we report the elastic properties and fracture behaviors of biaxially deformed, polymorphic MoTe2 by combining temperature-variant nanoindentation and first-principles calculations. It is found that due to similar atomic bonding, the effective moduli of the three phases deviate by less than 15%. However, the breaking strengths of distorted 1T' and Td phases are only half the value of 2H phase due to their uneven distribution of bonding strengths. Fractures of both isotropic 2H and anisotropic 1T' phases obey the theorem of minimum energy, forming triangular and linear fracture patterns, respectively, along the orientations parallel to Mo-Mo zigzag chains. Our findings not only provide a reference database for the elastic behaviors of versatile MoTe2 phases but also illuminate a general strategy for the mechanical investigation of any isotropic and anisotropic 2D materials.

Wrinkling patterns in soft shells.

Curvature plays an important role in the morphological evolution of soft shells under stretch. Here, through a combination of experiment, theory and simulation, we investigate the behavior of a hemispherical soft shell subject to an increasing outward point force at its pole. In contrast to an inward point force inducing a polygonal pattern of buckling in the shell, we observe a four-stage morphological transition and symmetry breaking under an increasing outward point force. The shell undergoes axisymmetric deformation around its pole and then buckles into a non-axisymmetric shape with a number of shallow wrinkles emanating from the pole, followed by the emergence of crater-like deep crumples and ultimately a transformation into a wrinkled pseudocone. Our theoretical analysis and numerical simulations yield the critical conditions for the morphological transitions at each stage of deformation and reveal the underlying interplays between elastic bending and stretching energies and the curvature of the shell.

Mapping the neural dynamics of locomotion across the Drosophila brain.

Locomotion engages widely distributed networks of neurons. However, our understanding of the spatial architecture and temporal dynamics of the networks that underpin walking remains incomplete. We use volumetric two-photon imaging to map neural activity associated with walking across the entire brain of Drosophila. We define spatially clustered neural signals selectively associated with changes in either forward or angular velocity, demonstrating that neurons with similar behavioral selectivity are clustered. These signals reveal distinct topographic maps in diverse brain regions involved in navigation, memory, sensory processing, and motor control, as well as regions not previously linked to locomotion. We identify temporal trajectories of neural activity that sweep across these maps, including signals that anticipate future movement, representing the sequential engagement of clusters with different behavioral specificities. Finally, we register these maps to a connectome and identify neural networks that we propose underlie the observed signals, setting a foundation for subsequent circuit dissection. Overall, our work suggests a spatiotemporal framework for the emergence and execution of complex walking maneuvers and links this brain-wide neural activity to single neurons and local circuits.

BIFROST: a method for registering diverse imaging datasets.

Quantitative comparison of brain-wide neural dynamics across different experimental conditions often requires precise alignment to a common set of anatomical coordinates. While such approaches are routinely applied in functional magnetic resonance imaging (fMRI), registering in vivo fluorescence imaging data to ex vivo-derived reference atlases is challenging, given the many differences in imaging modality, microscope specification, and sample preparation. Moreover, in many systems, animal to animal variation in brain structure limits registration precision. Using the highly stereotyped architecture of the fruit fly brain as a model, we overcome these challenges by building a reference atlas based directly on in vivo multiphoton-imaged brains, called the Functional Drosophila Atlas (FDA). We then develop a novel two-step pipeline, BrIdge For Registering Over Statistical Templates (BIFROST), for transforming neural imaging data into this common space, and for importing ex vivo resources, such as connectomes. Using genetically labeled cell types to provide ground truth, we demonstrate that this method allows voxel registration with micron precision. Thus, this method provides a generalizable pipeline for registering neural activity datasets to one another, allowing quantitative comparisons across experiments, microscopes, genotypes, and anatomical atlases, including connectomes.

Optimal Control of Temporal Networks With Variable Input and Node-Source Connection.

Many networked systems built upon real-life physical or social interactions have time-varying connections among individual units, where the temporal changes in connectivity and/or interaction strength lead to complicated dynamics. The temporal network model was proposed in the form of controlled linear dynamical systems acting in an ordered sequence of time intervals. One of the core challenges in network science is the control of networks and the optimization of the control strategy. However, most canonical frameworks for solving optimal control problems were established for static networks featuring constant topology. New theories and techniques are yet to be developed for the temporal networks, with an important case being that the input and the source-node connection are both variables. In this work, by formulating a quadratic energy cost without solving the Riccati differential equation, we show that the control effort can be reduced substantially by improving either the system trajectories or the input matrices. The two approaches are further combined in a coordinate descent framework, integrating linearly constrained quadratic programming, and a projected gradient descent method. Taken together, the results underline the potential of temporal networks as energy-efficient control systems and present strategies to improve the control input. Moreover, the proposed algorithms can serve as a starting point for future engineering of real-world temporal networks.

A red fluorescent protein with improved monomericity enables ratiometric voltage imaging with ASAP3.

A ratiometric genetically encoded voltage indicator (GEVI) would be desirable for tracking transmembrane voltage changes in the presence of sample motion. We performed combinatorial multi-site mutagenesis on a cyan-excitable red fluorescent protein to create the bright and monomeric mCyRFP3, which proved to be uniquely non-perturbing when fused to the GEVI ASAP3. The green/red ratio from ASAP3-mCyRFP3 (ASAP3-R3) reported voltage while correcting for motion artifacts, allowing the visualization of membrane voltage changes in contracting cardiomyocytes and throughout the cell cycle of motile cells.

Linear Quadratic Optimal Control of Time-Invariant Linear Networks With Selectable Input Matrix.

Optimal control of networks is to minimize the cost function of a network in a dynamical process with an optimal control strategy. For the time-invariant linear systems, · x(t)=A x(t)+B u(t) , and the traditional linear quadratic regulator (LQR), which minimizes a quadratic cost function, has been well established given both the adjacency matrix A and the control input matrix B . However, this conventional approach is not applicable when we have the freedom to design B . In this article, we investigate the situation when the input matrix B is a variable to be designed to reduce the control cost. First, the problem is formulated and we establish an equivalent expression of the quadratic cost function with respect to B , which is difficult to obtain within the traditional theoretical framework as it requires obtaining an explicit solution of a Riccati differential equation (RDE). Next, we derive the gradient of the quadratic cost function with respect to the matrix variable B analytically. Further, we obtain three inequalities of the cost functions, after which several possible design (optimization) problems are discussed, and algorithms based on gradient information are proposed. It is shown that the cost of controlling the LTI systems can be significantly reduced when the input matrix becomes "designable." We find that the nodes connected to input sources can be sparsely identified and they are distributed as evenly as possible in the LTI networks if one wants to control the networks with the lowest cost. Our findings help us better understand how the LTI systems should be controlled through designing the input matrix.

Universal Statistical Laws for the Velocities of Collective Migrating Cells.

Migratory dynamics of collective cells is central to the morphogenesis of biological tissues. The statistical distribution of cell velocities in 2D confluent monolayers is measured through large-scale and long-term experiments of various cell types lying on different substrates. A linear relation is discovered between the variability and the mean of cell speeds during the jamming process of confluent cell monolayers, suggesting time-invariant distribution profile of cell velocities. It is further found that the probability density function of cell velocities obeys the non-canonical q-Gaussian statistics, regardless of cell types and substrate stiffness. It is the Tsallis entropy, instead of the classical Boltzmann-Gibbs entropy, that dictates the universal statistical laws of collective cell migration. The universal statistical law stems from cell-cell interactions, as demonstrated by the wound healing experiments. This previously unappreciated finding provides a linkage between cell-level heterogeneity and tissue-level ensembles in embryonic development and tumor growth.

Cell swelling, softening and invasion in a three-dimensional breast cancer model.

Sculpting of structure and function of three-dimensional multicellular tissues depend critically on the spatial and temporal coordination of cellular physical properties, yet the organizational principles that govern these events, and their disruption in disease, remain poorly understood. Using a multicellular mammary cancer organoid model, here we map in three dimensions the spatial and temporal evolution of positions, motions, and physical characteristics of individual cells. Compared with cells in the organoid core, cells at the organoid periphery and the invasive front are found to be systematically softer, larger and more dynamic. These mechanical changes are shown to arise from supracellular fluid flow through gap junctions, suppression of which delays transition to an invasive phenotype. Together, these findings highlight the role of spatiotemporal coordination of cellular physical properties in tissue organization and disease progression.