Effects of Wuxi CDC WeChat official account article features on user engagement in health promotion.

OBJECTIVE: To identify the characteristics of subscribers to assess users' needs and analyze the features of articles published on Wuxi CDC WeChat official account (WOA) to evaluate the effectiveness of health education dissemination and guide future communication strategies. METHODS: Collect data from the WeChat official account (WOA) of the Wuxi Center for Disease Control and Prevention (CDC) to identify factors affecting the effectiveness of health education dissemination as measured by shares and 100% reading completion rate between January 1, 2022, and December 31, 2022. Multivariate logistic regression analysis was utilized to identify influencing features of articles associated with health education dissemination. RESULTS: By the end of 2022, our account had accumulated 891,170 subscribers, of which, 523,576 were females (58.75%), 349,856 were males (39.3%), mainly located in third-tier cities (82.59%). Age distribution peaked in the 26-35 and 36-45 age groups (43.63% and 30.6%, respectively). A total of 170 articles were included in the analysis. Multivariate logistic regression analysis revealed that articles with a lower word count (OR = 0.999, 95% CI = 0.998 ~ 1), lower picture count (OR = 0.892, 95% CI = 0.828 ~ 0.962), dominated headlines (OR = 2.454, 95% CI = 1.234 ~ 4.879) and thematically focused on Nutrition and food-borne diseases (OR = 5.728, 95% CI = 1.778 ~ 18.458) demonstrated higher engagement, as measured by shares and 100% completion rates. CONCLUSIONS: Our findings suggest that future content should prioritize conciseness, optimize images, and align with subscriber interests, particularly in nutrition and food hygiene. Additionally, maintaining informative yet engaging content formats remains crucial for maximizing reach and impact.

NeuroSeg-II: A deep learning approach for generalized neuron segmentation in two-photon Ca2+ imaging.

The development of two-photon microscopy and Ca2+ indicators has enabled the recording of multiscale neuronal activities in vivo and thus advanced the understanding of brain functions. However, it is challenging to perform automatic, accurate, and generalized neuron segmentation when processing a large amount of imaging data. Here, we propose a novel deep-learning-based neural network, termed as NeuroSeg-II, to conduct automatic neuron segmentation for in vivo two-photon Ca2+ imaging data. This network architecture is based on Mask region-based convolutional neural network (R-CNN) but has enhancements of an attention mechanism and modified feature hierarchy modules. We added an attention mechanism module to focus the computation on neuron regions in imaging data. We also enhanced the feature hierarchy to extract feature information at diverse levels. To incorporate both spatial and temporal information in our data processing, we fused the images from average projection and correlation map extracting the temporal information of active neurons, and the integrated information was expressed as two-dimensional (2D) images. To achieve a generalized neuron segmentation, we conducted a hybrid learning strategy by training our model with imaging data from different labs, including multiscale data with different Ca2+ indicators. The results showed that our approach achieved promising segmentation performance across different imaging scales and Ca2+ indicators, even including the challenging data of large field-of-view mesoscopic images. By comparing state-of-the-art neuron segmentation methods for two-photon Ca2+ imaging data, we showed that our approach achieved the highest accuracy with a publicly available dataset. Thus, NeuroSeg-II enables good segmentation accuracy and a convenient training and testing process.

Fast and Accurate Motion Correction for Two-Photon Ca2+ Imaging in Behaving Mice.

Two-photon Ca2+ imaging is a widely used technique for investigating brain functions across multiple spatial scales. However, the recording of neuronal activities is affected by movement of the brain during tasks in which the animal is behaving normally. Although post-hoc image registration is the commonly used approach, the recent developments of online neuroscience experiments require real-time image processing with efficient motion correction performance, posing new challenges in neuroinformatics. We propose a fast and accurate image density feature-based motion correction method to address the problem of imaging animal during behaviors. This method is implemented by first robustly estimating and clustering the density features from two-photon images. Then, it takes advantage of the temporal correlation in imaging data to update features of consecutive imaging frames with efficient calculations. Thus, motion artifacts can be quickly and accurately corrected by matching the features and obtaining the transformation parameters for the raw images. Based on this efficient motion correction strategy, our algorithm yields promising computational efficiency on imaging datasets with scales ranging from dendritic spines to neuronal populations. Furthermore, we show that the proposed motion correction method outperforms other methods by evaluating not only computational speed but also the quality of the correction performance. Specifically, we provide a powerful tool to perform motion correction for two-photon Ca2+ imaging data, which may facilitate online imaging experiments in the future.

Brain-wide projection reconstruction of single functionally defined neurons.

Reconstructing axonal projections of single neurons at the whole-brain level is currently a converging goal of the neuroscience community that is fundamental for understanding the logic of information flow in the brain. Thousands of single neurons from different brain regions have recently been morphologically reconstructed, but the corresponding physiological functional features of these reconstructed neurons are unclear. By combining two-photon Ca2+ imaging with targeted single-cell plasmid electroporation, we reconstruct the brain-wide morphologies of single neurons that are defined by a sound-evoked response map in the auditory cortices (AUDs) of awake mice. Long-range interhemispheric projections can be reliably labelled via co-injection with an adeno-associated virus, which enables enhanced expression of indicator protein in the targeted neurons. Here we show that this method avoids the randomness and ambiguity of conventional methods of neuronal morphological reconstruction, offering an avenue for developing a precise one-to-one map of neuronal projection patterns and physiological functional features.

Restoration of Two-Photon Ca2+ Imaging Data Through Model Blind Spatiotemporal Filtering.

Two-photon Ca2+ imaging is a leading technique for recording neuronal activities in vivo with cellular or subcellular resolution. However, during experiments, the images often suffer from corruption due to complex noises. Therefore, the analysis of Ca2+ imaging data requires preprocessing steps, such as denoising, to extract biologically relevant information. We present an approach that facilitates imaging data restoration through image denoising performed by a neural network combining spatiotemporal filtering and model blind learning. Tests with synthetic and real two-photon Ca2+ imaging datasets demonstrate that the proposed approach enables efficient restoration of imaging data. In addition, we demonstrate that the proposed approach outperforms the current state-of-the-art methods by evaluating the qualities of the denoising performance of the models quantitatively. Therefore, our method provides an invaluable tool for denoising two-photon Ca2+ imaging data by model blind spatiotemporal processing.

Single-neuron representation of learned complex sounds in the auditory cortex.

The sensory responses of cortical neuronal populations following training have been extensively studied. However, the spike firing properties of individual cortical neurons following training remain unknown. Here, we have combined two-photon Ca2+ imaging and single-cell electrophysiology in awake behaving mice following auditory associative training. We find a sparse set (~5%) of layer 2/3 neurons in the primary auditory cortex, each of which reliably exhibits high-rate prolonged burst firing responses to the trained sound. Such bursts are largely absent in the auditory cortex of untrained mice. Strikingly, in mice trained with different multitone chords, we discover distinct subsets of neurons that exhibit bursting responses specifically to a chord but neither to any constituent tone nor to the other chord. Thus, our results demonstrate an integrated representation of learned complex sounds in a small subset of cortical neurons.

NeuroSeg: automated cell detection and segmentation for in vivo two-photon Ca2+ imaging data.

Two-photon Ca2+ imaging has become a popular approach for monitoring neuronal population activity with cellular or subcellular resolution in vivo. This approach allows for the recording of hundreds to thousands of neurons per animal and thus leads to a large amount of data to be processed. In particular, manually drawing regions of interest is the most time-consuming aspect of data analysis. However, the development of automated image analysis pipelines, which will be essential for dealing with the likely future deluge of imaging data, remains a major challenge. To address this issue, we developed NeuroSeg, an open-source MATLAB program that can facilitate the accurate and efficient segmentation of neurons in two-photon Ca2+ imaging data. We proposed an approach using a generalized Laplacian of Gaussian filter to detect cells and weighting-based segmentation to separate individual cells from the background. We tested this approach on an in vivo two-photon Ca2+ imaging dataset obtained from mouse cortical neurons with differently sized view fields. We show that this approach exhibits superior performance for cell detection and segmentation compared with the existing published tools. In addition, we integrated the previously reported, activity-based segmentation into our approach and found that this combined method was even more promising. The NeuroSeg software, including source code and graphical user interface, is freely available and will be a useful tool for in vivo brain activity mapping.

Functional imaging of neuronal activity of auditory cortex by using Cal-520 in anesthetized and awake mice.

The organization in the primary auditory cortex (Au1) is critical to the basic function of auditory information processing and integration. However, recent mapping experiments using in vivo two-photon imaging with different Ca2+ indicators have reached controversial conclusions on this topic, possibly because of the different sensitivities and properties of the indicators used. Therefore, it is essential to identify a reliable Ca2+ indicator for use in in vivo functional imaging of the Au1, to understand its functional organization. Here, we demonstrate that a previously reported indicator, Cal-520, performs well in both anesthetized and awake conditions. Cal-520 shows a sufficient sensitivity for the detection of single action potentials, and a high signal-to-noise ratio. Cal-520 reliably reported on both spontaneous and sound-evoked neuronal activity in anesthetized and awake mice. After testing with pure tones at a range of frequencies, we confirmed the local heterogeneity of the functional organization of the mouse Au1. Therefore, Cal-520 is a reliable and useful Ca2+ indicator for in vivo functional imaging of the Au1.

Locomotion-Related Population Cortical Ca2+ Transients in Freely Behaving Mice.

Locomotion involves complex neural activity throughout different cortical and subcortical networks. The primary motor cortex (M1) receives a variety of projections from different brain regions and is responsible for executing movements. The primary visual cortex (V1) receives external visual stimuli and plays an important role in guiding locomotion. Understanding how exactly the M1 and the V1 are involved in locomotion requires recording the neural activities in these areas in freely moving animals. Here, we used an optical fiber-based method for the real-time monitoring of neuronal population activities in freely moving mice. We combined the bulk loading of a synthetic Ca2+ indicator and the optical fiber-based Ca2+ recordings of neuronal activities. An optical fiber 200 µm in diameter can detect the coherent activity of a subpopulation of neurons. In layer 5 of the M1 and V1, we showed that population Ca2+ transients reliably occurred preceding the impending locomotion. Interestingly, the M1 Ca2+ transients started ~100 ms earlier than that in V1. Furthermore, the population Ca2+ transients were robustly correlated with head movements. Thus, our work provides a simple but efficient approach for monitoring the cortical Ca2+ activity of a local cluster of neurons during locomotion in freely moving animals.

[Exploration of the food cluster automation in the China Total Diet Study].

OBJECTIVE: To achieve the food cluster automation of China Total Diet Study and improve the quality and efficiency of the food cluster calculation. METHOD: The food coding features were studied in the Chinese Food Composition Table. After analyzing the principles of food clustering on Total Diet Study, constructing an algorithm allows the computer language to identify these characteristics and principles to achieve automatic food cluster. RESULTS: The instance was selected from the data of a province in the fifth Chinese Total Diet Study. 292 food items were clustered into 53 kinds of cluster food by computer program, and the results are corresponded to the cluster requirements of total diet study. CONCLUSION: The automatic calculation of the food cluster can be achieved by computer program more reliable and effectively in the Total Diet Study.