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The rapid development of scientific CMOS (sCMOS) technology has greatly advanced optical microscopy for biomedical research with superior sensitivity, resolution, field-of-view, and frame rates. However, for sCMOS sensors, the parallel charge-voltage conversion and different responsivity at each pixel induces extra readout and pattern noise compared to charge-coupled devices (CCD) and electron-multiplying CCD (EM-CCD) sensors. This can produce artifacts, deteriorate imaging capability, and hinder quantification of fluorescent signals, thereby compromising strategies to reduce photo-damage to live samples. Here, we propose a content-adaptive algorithm for the automatic correction of sCMOS-related noise (ACsN) for fluorescence microscopy. ACsN combines camera physics and layered sparse filtering to significantly reduce the most relevant noise sources in a sCMOS sensor while preserving the fine details of the signal. The method improves the camera performance, enabling fast, low-light and quantitative optical microscopy with video-rate denoising for a broad range of imaging conditions and modalities. Scientific complementary metal-oxide semiconductor (sCMOS) cameras have advanced the imaging field, but they often suffer from additional noise compared to CCD sensors. Here the authors present a content-adaptive algorithm for the automatic correction of sCMOS-related noise for fluorescence microscopy.

High-throughput super-resolution (SR) imaging is attractive for rapid and high-precision profiling in a wide range of biomedical applications. However, current SR methods require sophisticated acquisition optics and long integration times to acquire a single field of view. By exploiting the natural photophysics of fluorescence, fluctuation-based microscopy techniques can routinely break the diffraction limit without requiring additional optical components. However, their long acquisition time still poses a challenge for high-throughput imaging and the visualization of transient cellular dynamics. Here we propose super-resolution imaging based on autocorrelation with two-step deconvolution (SACD). Our method notably reduces the number of frames required by maximizing the detectable fluorescence fluctuation behaviour in each measurement. SACD requires only 20 frames to achieve a twofold improvement in lateral and axial resolution, whereas current SR optical fluctuation imaging (SOFI) needs more than 1,000 frames. With an acquisition time of ~10 min, we record SR images with 128-nm resolution over a field of view of 2.0 mm × 1.4 mm, which includes more than 2,000 cells. By applying the continuity and sparsity joint constraint, sparse deconvolution-assisted SACD enables four-dimensional imaging of live cells and events such as mitochondrial fission and fusion. Overall, as an open-sourced module, we anticipate that SACD will improve accessibility to SR imaging, thus facilitating biological studies of cells and organisms with high throughput and low cost.Super-resolution imaging based on autocorrelation with two-step deconvolution (SACD) enables recording super-resolution images with 128-nm spatial resolution over a field of view of 2.0 mm × 1.4 mm within a 10-min acquisition time.

Despite the prevalence of superresolution (SR) microscopy, quantitative live-cell SR imaging that maintains the completeness of delicate structures and the linearity of fluorescence signals remains an uncharted territory. Structured illumination microscopy (SIM) is the ideal tool for live-cell SR imaging. However, it suffers from an out-of-focus background that leads to reconstruction artifacts. Previous post hoc background suppression methods are prone to human bias, fail at densely labeled structures, and are nonlinear. Here, we propose a physical model-based Background Filtering method for living cell SR imaging combined with the 2D-SIM reconstruction procedure (BF-SIM). BF-SIM helps preserve intricate and weak structures down to sub-70 nm resolution while maintaining signal linearity, which allows for the discovery of dynamic actin structures that, to the best of our knowledge, have not been previously monitored. Quantitative live-cell superresolution imaging that maintains the linearity of fluorescence signals remains difficult. Here, the authors propose a physical model-based background filtering method for 2D-SIM, which allows for quantitative imaging and high signal completeness.

Aversive stimuli can cause hippocampal place cells to remap their firing fields, but it is not known whether remapping plays a role in storing memories of aversive experiences. Here, we addressed this question by performing in vivo calcium imaging of CA1 place cells in freely behaving rats (n = 14). Rats were first trained to prefer a short path over a long path for obtaining food reward, then trained to avoid the short path by delivering a mild footshock. Remapping was assessed by comparing place cell population vector similarity before acquisition versus after extinction of avoidance. Some rats received shock after systemic injections of the amnestic drug scopolamine at a dose (1 mg/kg) that impaired avoidance learning but spared spatial tuning and shock-evoked responses of CA1 neurons. Place cells remapped significantly more following remembered than forgotten shocks (drug-free versus scopolamine conditions); shock-induced remapping did not cause place fields to migrate toward or away from the shocked location and was similarly prevalent in cells that were responsive versus non-responsive to shocks. When rats were exposed to a neutral barrier rather than aversive shock, place cells remapped significantly less in response to the barrier. We conclude that place cell remapping occurs in response to events that are remembered rather than merely perceived and forgotten, suggesting that reorganization of hippocampal population codes may play a role in storing memories for aversive events. The human brain is able to remember experiences that occurred at specific places and times, such as a birthday party held at a particular restaurant. A part of the brain known as the hippocampus helps to store these episodic memories, but how exactly is not fully understood. Within the hippocampus are specialized neurons known as place cells which ‘label’ locations with unique patterns of brain activity. When we revisit a place, such as the restaurant, place cells recall the stored pattern of brain activity allowing us to recognize the familiar location. It has been shown that a new negative experience at a familiar place – for example, if we went back to the restaurant and had a terrible meal – triggers place cells to update the brain activity label associated with the location. However, it remains uncertain whether this re-labelling assists in storing the memory of the unpleasant experience. To investigate, Blair et al. used a technique known as calcium imaging to monitor place cells in the hippocampus of freely moving rats. The rats were given a new experience – a mild foot shock – at a previously explored location. Tiny cameras attached to their heads were then used to record the activity of hundreds of place cells before and after the shock. Initially, the rats remembered the aversive experience and avoided the location where they had been shocked. Over time, the rats began to return to the location; however, their place cells displayed different patterns of activity compared to their previous visits before the shock. To test whether this change in place cell activity corresponded with new memories, another group of rats were administered a mild amnesia-inducing drug before the shock, causing them to forget the experience. These rats did not avoid the shock site or show any changes in place cell activity when they revisited it. These findings imply that new events cause place cells to alter their ‘label’ for a location only if the event is remembered, not if it is forgotten. This indicates that alterations in place cell activity patterns may play a role in storing memories of unpleasant experiences. Having a better understanding of how episodic memories are stored could lead to better treatments for diseases that impair memory, such as Alzheimer’s disease and age-related dementia.

Epifluorescence miniature microscopes (‘miniscopes’) are widely used for in vivo calcium imaging of neural population activity. Imaging data are typically collected during a behavioral task and stored for later offline analysis, but emerging techniques for online imaging can support novel closed-loop experiments in which neural population activity is decoded in real time to trigger neurostimulation or sensory feedback. To achieve short feedback latencies, online imaging systems must be optimally designed to maximize computational speed and efficiency while minimizing errors in population decoding. Here we introduce DeCalciOn , an open-source device for real-time imaging and population decoding of in vivo calcium signals that is hardware compatible with all miniscopes that use the UCLA Data Acquisition (DAQ) interface. DeCalciOn performs online motion stabilization, neural enhancement, calcium trace extraction, and decoding of up to 1024 traces per frame at latencies of <50 ms after fluorescence photons arrive at the miniscope image sensor. We show that DeCalciOn can accurately decode the position of rats ( n = 12) running on a linear track from calcium fluorescence in the hippocampal CA1 layer, and can categorically classify behaviors performed by rats ( n = 2) during an instrumental task from calcium fluorescence in orbitofrontal cortex. DeCalciOn achieves high decoding accuracy at short latencies using innovations such as field-programmable gate array hardware for real-time image processing and contour-free methods to efficiently extract calcium traces from sensor images. In summary, our system offers an affordable plug-and-play solution for real-time calcium imaging experiments in behaving animals.

Quantitative descriptions of animal behavior are essential to study the neural substrates of cognitive and emotional processes. Analyses of naturalistic behaviors are often performed by hand or with expensive, inflexible commercial software. Recently, machine learning methods for markerless pose estimation enabled automated tracking of freely moving animals, including in labs with limited coding expertise. However, classifying specific behaviors based on pose data requires additional computational analyses and remains a significant challenge for many groups. We developed BehaviorDEPOT (DEcoding behavior based on POsitional Tracking), a simple, flexible software program that can detect behavior from video timeseries and can analyze the results of experimental assays. BehaviorDEPOT calculates kinematic and postural statistics from keypoint tracking data and creates heuristics that reliably detect behaviors. It requires no programming experience and is applicable to a wide range of behaviors and experimental designs. We provide several hard-coded heuristics. Our freezing detection heuristic achieves above 90% accuracy in videos of mice and rats, including those wearing tethered head-mounts. BehaviorDEPOT also helps researchers develop their own heuristics and incorporate them into the software’s graphical interface. Behavioral data is stored framewise for easy alignment with neural data. We demonstrate the immediate utility and flexibility of BehaviorDEPOT using popular assays including fear conditioning, decision-making in a T-maze, open field, elevated plus maze, and novel object exploration.

The lack of corresponding learning service platform will lead to the decline of the teaching quality of art education. Therefore, based on the principle of multi semantic fusion, this paper establishes a multi semantic art education exchange platform and discusses in detail the function mechanism in the process of establishing the platform. Then, the platform is applied to the art education and learning of students of different grades in colleges and universities. By comparing the art education and learning of students of different grades, the platform is designed and improved, which greatly improves the teaching quality of art education. At the same time, in view of the problems existing in the process of art education, the art education exchange platform based on multi semantic fusion can still put forward corresponding rectification measures. The experimental results show that the multi-semantic art education exchange platform is conducive to students of different grades learning art education courses and improving the quality of art education teaching.

Every collected photon is precious in live-cell super-resolution (SR) microscopy. Here, we describe a data-efficient, deep learning-based denoising solution to improve diverse SR imaging modalities. The method, SN2N, is a Self-inspired Noise2Noise module with self-supervised data generation and self-constrained learning process. SN2N is fully competitive with supervised learning methods and circumvents the need for large training set and clean ground truth, requiring only a single noisy frame for training. We show that SN2N improves photon efficiency by one-to-two orders of magnitude and is compatible with multiple imaging modalities for volumetric, multicolor, time-lapse SR microscopy. We further integrated SN2N into different SR reconstruction algorithms to effectively mitigate image artifacts. We anticipate SN2N will enable improved live-SR imaging and inspire further advances. SN2N, a Self-inspired Noise2Noise module, offers a versatile solution for volumetric time-lapse super-resolution imaging of live cells. SN2N uses self-supervised data generation and self-constrained learning for training with a single noisy frame.

Hydrogen peroxide is an environment-friendly reactive oxygen species and a significant green oxidant. However, the highly efficient and sustainable production of H 2 O 2 remains a challenge. Herein, we demonstrated a state-of-the-art photocatalytic method for H 2 O 2 production using an optimal tubular graphic carbon nitride (TCN)/ZnIn 2 S 4 (ZIS) heterojunction. Within 3 h, a H 2 O 2 production rate of 2.77 mmol g −1 h −1 was achieved by the TCN/ZIS heterojunction, which is 3.4- and 23.1-fold higher than that obtained by its individual TCN and ZIS components, respectively. Experimental results showed that the excellent photoactivity of TCN/ZIS is mainly due to the formation of heterojunction, which improves the charge separation ability and thereby promotes the proton-coupled electron transfer by initially reducing O 2 to ·O 2 − and subsequently generating H 2 O 2 . This work has developed an efficient and green strategy for H 2 O 2 production with scientific and practical value for environmental remediation.

The defects formed in the manufacture of the vermicular graphite cast iron engine cylinder head seriously affect the operation of the engine, which is necessary to detect. Ultrasonic testing is a non-destructive testing method that has the advantages of quick response, high resolution, and high security. In this paper, various types of specimens are prepared corresponding to different types of actual defects in the vermicular iron cylinder head. An ultrasonic A-scan system was built to test the specimens. The short-time Fourier transform, the continuous wavelet transform, the empirical wavelet transform, and the empirical modal decomposition were adopted to transform the signals into spectrograms which were further analyzed to reveal the inherent features of defects. The results show that the short-time Fourier transform can be used to distinguish all the common defects comparing to other methods. Comparing to the time-domain waveforms, the transformed spectrograms provide clear time-frequency distribution and highlight the inherent characteristics of the signal.