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Calcium imaging with protein-based indicators 1 , 2 is widely used to follow neural activity in intact nervous systems, but current protein sensors report neural activity at timescales much slower than electrical signalling and are limited by trade-offs between sensitivity and kinetics. Here we used large-scale screening and structure-guided mutagenesis to develop and optimize several fast and sensitive GCaMP-type indicators 3 – 8 . The resulting ‘jGCaMP8’ sensors, based on the calcium-binding protein calmodulin and a fragment of endothelial nitric oxide synthase, have ultra-fast kinetics (half-rise times of 2 ms) and the highest sensitivity for neural activity reported for a protein-based calcium sensor. jGCaMP8 sensors will allow tracking of large populations of neurons on timescales relevant to neural computation. Using large-scale screening and structure-guided mutagenesis, fast and sensitive GCaMP sensors are developed and optimized with improved kinetics without compromising sensitivity or brightness.

Animals are not simple input-output machines. Their responses to even very similar stimuli are variable. A key, long-standing question in neuroscience is to understand the neural correlates of such behavioral variability. To reveal these correlates, behavior and neural population activity must be related to one another on single trials. Such analysis is challenging due to the dynamical nature of brain function (e.g., in decision making), heterogeneity across neurons and limited sampling of the relevant neural population. By analyzing population recordings from mouse frontal cortex in perceptual decision-making tasks, we show that an analysis approach tailored to the coarse grain features of the dynamics is able to reveal previously unrecognized structure in the organization of population activity. This structure is similar on error and correct trials, suggesting dynamics that may be constrained by the underlying circuitry, is able to predict multiple aspects of behavioral variability and reveals long time-scale modulation of population activity. To explain the neural correlates of behavior and its variability, one must analyze single-trial population dynamics. Here, the authors develop a statistical method that extracts low-dimensional dynamics that explain behavior better than high-dimensional neural activity revealing unexpected structure.

Calcium imaging with fluorescent protein sensors is widely used to record activity in neuronal populations. The transform between neural activity and calcium-related fluorescence involves nonlinearities and low-pass filtering, but the effects of the transformation on analyses of neural populations are not well understood. We compared neuronal spikes and fluorescence in matched neural populations in behaving mice. We report multiple discrepancies between analyses performed on the two types of data, including changes in single-neuron selectivity and population decoding. These were only partially resolved by spike inference algorithms applied to fluorescence. To model the relation between spiking and fluorescence we simultaneously recorded spikes and fluorescence from individual neurons. Using these recordings we developed a model transforming spike trains to synthetic-imaging data. The model recapitulated the differences in analyses. Our analysis highlights challenges in relating electrophysiology and imaging data, and suggests forward modeling as an effective way to understand differences between these data.

This paper studies a model predictive hybrid tracking control scheme under a multiple harmonics time‐varying disturbance observer for a discrete‐time dynamics nonholonomic autonomous mobile robot (AMR) with disturbance. To solve the robust tracking control problem of the AMR and unmanned aerial vehicle (UAV) air–ground cooperative, a hybrid tracking control strategy combined with improved model predictive control (MPC) method is presented. First, a time‐varying air‐ground cooperative tracking control model based on the nonholonomic constraints AMR and UAV is established by polar coordinate transformation. Second, to estimate disturbances of the time‐varying model, a multiple harmonics disturbance observer with time‐varying gains is designed. A hybrid tracking control scheme for the AMR based on the estimated states and MPC method with relaxing factor and kinematics constraints is proposed. Finally, experimental results show the effectiveness of the proposed control strategy. A time‐varying air‐ground cooperative tracking control model had been presented. A multiple harmonics disturbance observer with time‐varying gains had been designed for cooperative tracking. A hybrid tracking control scheme had been solved by improved MPC method with relaxing factor.

The refined microstructure and enhanced mechanical properties of wire-arc directed energy deposition (WA-DED) Al-Cu alloys have attracted a great deal of attention in various industries. Despite numerous strengthening strategies developed to enhance the performance of Al-Cu alloys, the effect of scandium (Sc) in their as-deposited state has received limited attention. In this work, Al-Cu-Sc alloy samples with different Sc contents were designed and prepared by WA-DED technology with interlayer powder coating. The microstructural characteristics and mechanical properties of Al-Cu alloys with varying Sc contents were systematically compared by applying an alcohol-based solution with different Sc concentrations. The experimental results demonstrate that the addition of Sc promotes the columnar-to-equiaxed transition (CET). Moreover, compared to the Al-Cu-Sc alloy with lower Sc content (0.15%, average grain size: 128.35 μm), the alloy with higher Sc content (0.32%) exhibited a finer average grain size of 95.81 μm. The increased Sc content was also beneficial in suppressing the formation of solidification shrinkage pores. As the Sc content increases, the interconnected θ′-Al2Cu phase breaks up, leading to its more uniform dispersion in the aluminum matrix. In terms of mechanical properties, the sample with higher Sc content demonstrated superior tensile properties, exhibiting an ultimate tensile strength (UTS) and elongation (EL) of 265.89 MPa and 12.29%, respectively, compared to 240.67 MPa and 9.05% for the Sc-L sample. In contrast, the yield strength (YS) and microhardness showed no significant variation with the change in Sc content.

In hydrogen recirculation side channel pumps, the motor rotor is exposed to a high-pressure mixture of steam and hydrogen, which makes hydrogen embrittlement occur in permanent magnets (PMs). A protective coating is necessary for the PMs in high-pressure hydrogen. However, in the process of sleeve interference installation, the protective coating of the PMs is easily damaged. This paper proposes two surface-mounted insert permanent magnet (SIPM) synchronous motor topologies, SIPM1 and SIPM2, in which the retaining sleeves can be eliminated and the PM protective coating is safe in the assembling process. A dovetail PM and rotor core structure is used to protect the PM with higher rotor strength without retaining the sleeve. The electromagnetic performance of the motors with different rotors, including airgap flux density, output torque, torque ripple, and energy efficiency is compared and optimized. It is concluded that the output torque of the SIPM motor can be promoted by 22.4% and torque ripple can be reduced by 2.9%, while the PM volume remains the same as that of the conventional SPM motor. At the same time, the SIPM motor can have lower harmonic contents of back electromotive force (EMF) and rotor loss compared to the SPM motor with a retaining sleeve. Furthermore, the stress of the PM is analyzed under conditions of PM glue action and failure. The proposed SIPM2 has the ability to operate safely at high-speed and high-temperature operating conditions when the PM glue fails.

Medium Mn lightweight steels with a relatively higher Mn content of 9–12 wt% have been actively developed recently to meet the demands of crashworthiness and lightweight vehicles. In this study, a combined intensive quenching (IQ) and deep cryogenic treatment (DCT) was first proposed to achieve the microstructural homogeneity as well as the final strength–ductility synergy of medium Mn lightweight steels with Mn segregation bands, together with a comparison with the conventional intercritical annealing. The proposed IQ and DCT process induced the formation of finer large fractioned plate-like martensite in the austenite matrix and thereby contributed to finer and uniform austenite grains after subsequent intercritical annealing. The martensitic transformation rate (dVγ/dε) and transformation kinetics (k value) were used to evaluate the mechanical stability of retained austenite, showing that the D700&750 sample exhibited a similar dVγ/dε value and extremely low k value when compared to the conventional IA650–850 samples, implying that the former had the higher mechanical stability of austenite. The higher mechanical stability of austenite enabled the TRIP effect to occur in a larger strain range, leading to continuous strain hardening behavior. Thus, the highest yield strength (728 MPa) and the largest total elongation of 61.6% were achieved in the D700&750 sample, where the ductility was more than three times higher than that of the conventional IA samples. The grain size and morphologies of retained austenite were believed to be the main factors influencing the strain-hardening behavior of this type of ultrafine lamellar and equiaxed ferrite and austenite duplex structure.

Despite advances in experimental techniques and accumulation of large datasets concerning the composition and properties of the cortex, quantitative modeling of cortical circuits under in-vivo-like conditions remains challenging. Here we report and publicly release a biophysically detailed circuit model of layer 4 in the mouse primary visual cortex, receiving thalamo-cortical visual inputs. The 45,000-neuron model was subjected to a battery of visual stimuli, and results were compared to published work and new in vivo experiments. Simulations reproduced a variety of observations, including effects of optogenetic perturbations. Critical to the agreement between responses in silico and in vivo were the rules of functional synaptic connectivity between neurons. Interestingly, after extreme simplification the model still performed satisfactorily on many measurements, although quantitative agreement with experiments suffered. These results emphasize the importance of functional rules of cortical wiring and enable a next generation of data-driven models of in vivo neural activity and computations.

As a disease with high global incidence, hypertension is known to cause systemic vasculopathy. Ophthalmic vessels are the only vascular structures that can be directly observed in a non-invasive manner. We aim to investigate the changes in ocular microvessels in hypertension using deep learning on optical coherence tomography angiography (OCTA) images. The convolutional neural network architecture Xception and multi-Swin transformer were used to screen 422 OCTA images (252 from 136 hypertension subjects; 170 from 85 healthy subjects) for hypertension. Moreover, the separability of the OCTA images based on high-dimensional feature angles was analyzed to better understand how deep learning models distinguish such images with class activation mapping. Under Xception, the overall average accuracy of 5-fold cross-validation was 76.05% and sensitivity was 85.52%. In contrast, the Swin transformer showed single-model (macular), single-model (optic disk), and multimodel average accuracies of 82.25%, 74.936%, and 85.06%, respectively, for predicting hypertension. The changes caused by hypertension on the fundus vessels can be observed more accurately and efficiently using OCTA image features through deep learning. These results are expected to assist with screening of hypertension and reducing the risk of its severe complications. ChiCTR, ChiCTR2000041330. Registered 23 December 2020, https://www.chictr.org.cn/ChiCTR2000041330.

Taking raw coal and briquette coal samples with preset center holes as research objects, this paper makes a systematic analysis and research of crack evolution laws of the two different coal samples under the local load. The results show that the raw coal and briquette coal samples are different mainly in number, dimension, and complexity of the internal microstructures, so it is not right to replace raw coal with briquette coal when performing observational study of the crack evolution of microstructures; under the effect of local load, local property, randomness of crack initiation position, and crack initiation stress of raw coal samples are greater than those of briquette coal samples; law of instantaneous maximum effective cut-through rate of raw coal samples is more complex than that of briquette coals; under the effect of uniformly distributed load, end effect factor Fe, sample microstructure influencing factor Fs, and preset center hole factor Fh are the major factors influencing crack growth, among which the amplified end effect factor Fe and sample microstructure influencing factor Fs are dominant factors; under the effect of local load, local load influencing factor Fp, end effect factor Fe, sample microstructure influencing factor Fs, and preset center hole factor Fs are the major factors influencing crack growth, among which the local load influencing factor Fp, end effect factor Fe, and sample microstructure influencing factor Fs are dominant factors. Compared with briquette coal samples, raw coal samples are more sensitive to influencing factors, such as local load influencing factor Fp, end effect factor Fe, sample microstructure influencing factor Fs, and preset center hole factor Fh, and can aggravate the influence of these factors on the crack growth; the paper also puts forward a method for describing local load based on a coupling mechanical model of uniaxial compression and local shear.