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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.

Despite remarkable progress in single-image super-resolution based on neural networks, the results are not ideal when applied to real-world images, because the real-world degradation process is unknown and complex. The emergence of some optical zoom datasets shows that neural networks still achieve good results on real-world images as long as the low-resolution images used for training have similar features and distributions with the real-world images. However, obtaining such optical zoom datasets is complicated and the datasets are only applicable to specific cameras and shooting conditions. By studying the optical zoom datasets, we propose a super-resolution image degradation model consisting of blurring, frequency domain processing, adding noise and downsampling. Specifically, blurring uses a blur kernel with a wave-like shape inferred from the point spread function, which produces the artifacts like real-world images. Frequency domain processing simulates the frequency domain aliasing of real-world images, such as jagged edges and background stripes. Experiments demonstrate that the new degradation model achieves visual effects comparable to optical zoom datasets. Existing high-resolution datasets can be converted to “optical zoom datasets” by the degradation model, where the synthetic low-resolution images have real-world image features, thereby extending super-resolution methods to real-world images.

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.

Simulating the microstructure and mechanical properties of natural tissues to achieve rapid tissue regeneration and protect cells under strain during body movement is a giant challenge in locomotive tissue engineering. Here, we built a hierarchical helical scaffold with high stretchability, which not only structurally resembles natural tissues but also matches their non-affine deformation mechanical properties. The cells on this scaffold can maintain high viability under cyclic strains, because the non-planar motion of coil structures mitigates the local maximum strain during stretching. Moreover, the in vivoexperiments demonstrated that the scaffold affords a stable structural and mechanical microenvironment for cell growth and tissue morphogenesis. This helical scaffold breaks the inherent mindset of fixation repairing and makes it possible for reconstruction of the defective tissue even in moderate motion. The study proposes a new thinking about construction of stretchable scaffolds with superior mechanical properties for repairing locomotive tissue injuries.

Control of the structure and function of three-dimensional multicellular tissues depends 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, we map here the spatial and temporal evolution of positions, motions and physical characteristics of individual cells in three dimensions. 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, the suppression of which delays the transition to an invasive phenotype. These findings highlight the role of spatiotemporal coordination of cellular physical properties in tissue organization and disease progression.

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. MoTe2is 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 MoTe2by 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 Tdphases 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 MoTe2phases but also illuminate a general strategy for the mechanical investigation of any isotropic and anisotropic 2D materials.