Statistically unbiased prediction enables accurate denoising of voltage imaging data.

Here we report SUPPORT (statistically unbiased prediction utilizing spatiotemporal information in imaging data), a self-supervised learning method for removing Poisson-Gaussian noise in voltage imaging data. SUPPORT is based on the insight that a pixel value in voltage imaging data is highly dependent on its spatiotemporal neighboring pixels, even when its temporally adjacent frames alone do not provide useful information for statistical prediction. Such dependency is captured and used by a convolutional neural network with a spatiotemporal blind spot to accurately denoise voltage imaging data in which the existence of the action potential in a time frame cannot be inferred by the information in other frames. Through simulations and experiments, we show that SUPPORT enables precise denoising of voltage imaging data and other types of microscopy image while preserving the underlying dynamics within the scene.

In vivo whole-brain imaging of zebrafish larvae using three-dimensional fluorescence microscopy.

As a vertebrate model animal, larval zebrafish are widely used in neuroscience and provide a unique opportunity to monitor whole-brain activity at the cellular resolution. Here, we provide an optimized protocol for performing whole-brain imaging of larval zebrafish using three-dimensional fluorescence microscopy, including sample preparation and immobilization, sample embedding, image acquisition, and visualization after imaging. The current protocol enables in vivo imaging of the structure and neuronal activity of a larval zebrafish brain at a cellular resolution for over 1 h using confocal microscopy and custom-designed fluorescence microscopy. The critical steps in the protocol are also discussed, including sample mounting and positioning, preventing bubble formation and dust in the agarose gel, and avoiding motion in images caused by incomplete solidification of the agarose gel and paralyzation of the fish. The protocol has been validated and confirmed in multiple settings. This protocol can be easily adapted for imaging other organs of a larval zebrafish.

Three-dimensional fluorescence microscopy through virtual refocusing using a recursive light propagation network.

Three-dimensional fluorescence microscopy has an intrinsic performance limit set by the number of photons that can be collected from the sample in a given time interval. Here, we extend our earlier work - a recursive light propagation network (RLP-Net) - which is a computational microscopy technique that overcomes such limitations through virtual refocusing that enables volume reconstruction from two adjacent 2-D wide-field fluorescence images. RLP-Net employs a recursive inference scheme in which the network progressively predicts the subsequent planes along the axial direction. This recursive inference scheme reflects that the law of physics for the light propagation remains spatially invariant and therefore a fixed function (i.e., a neural network) for a short distance light propagation can be recursively applied for a longer distance light propagation. In addition, we employ a self-supervised denoising method to enable accurate virtual light propagation over a long distance. We demonstrate the capability of our method through high-speed volumetric imaging of neuronal activity of a live zebrafish brain. The source code used in the paper is available at https://github.com/NICALab/rlpnet.

3DM: deep decomposition and deconvolution microscopy for rapid neural activity imaging.

We report the development of deep decomposition and deconvolution microscopy (3DM), a computational microscopy method for the volumetric imaging of neural activity. 3DM overcomes the major challenge of deconvolution microscopy, the ill-posed inverse problem. We take advantage of the temporal sparsity of neural activity to reformulate and solve the inverse problem using two neural networks which perform sparse decomposition and deconvolution. We demonstrate the capability of 3DM via in vivo imaging of the neural activity of a whole larval zebrafish brain with a field of view of 1040 µm × 400 µm × 235 µm and with estimated lateral and axial resolutions of 1.7 µm and 5.4 µm, respectively, at imaging rates of up to 4.2 volumes per second.

Effects of dietary supplementation of lipid-encapsulated zinc oxide on colibacillosis, growth and intestinal morphology in weaned piglets challenged with enterotoxigenic Escherichia coli.

This study was aimed to evaluate the effect of dietary supplementation of lipid-encapsulated (coated) zinc oxide ZnO on post-weaning diarrhea (colibacillosis) in weaned piglets challenged with enterotoxigenic Escherichia coli (ETEC). Thirty-two 35-day-old weaned piglets were orally challenged with 3 × 10(10) colony forming units of ETEC K88 while eight piglets received no challenge (control). Each eight challenged piglets received a diet containing 100 ppm ZnO (low ZnO), 2500 ppm ZnO (high ZnO) or 100 ppm of lipid (10%)-coated ZnO (coated ZnO) for 7 days; control pigs received the low ZnO diet. Daily gain, goblet cell density in the villi of the duodenum, jejunum and ileum, and villus height in the jejunum and ileum, which decreased due to the challenge, were equally greater in the coated ZnO and high ZnO groups versus low ZnO group. Fecal consistency score, serum interleukin-8 concentration, subjective score of fecal E. coli shedding, and digesta pH in the stomach, jejunum and ileum, which increased due to the challenge, were equally low in the coated ZnO and high ZnO groups versus low ZnO. Results suggest that a low level of coated ZnO might well substitute for a pharmacological level of native ZnO in dietary supplementation to alleviate colibacillosis of weaned piglets.

Remote sensing imageries for land cover and water quality dynamics on the west coast of Korea.

As human activities influence land cover changes, the environment on human life such as water quality, has been impacted. In particular, huge constructions or reclamation projects are responsible for dramatic land cover changes. The Saemangeum area in South Korea has been one of the largest reclamation projects to progress nearly in two decades. In this study, Landsat-5 Thematic Mapper and Landsat-7 Enhanced Thematic Mapper Plus images were used to classify land cover types in the Saemangeum area. A change detection method was utilized to determine the impacts of the reclamation project. While wetland, grassland, and urban areas were increased, forest, water, and agricultural areas were decreased during the reclamation progress. Water quality analysis related to the land cover changes was conducted to determine the influence of reclamation construction on the environment. Chemical oxygen demand and suspended sediment variability were significantly impacted by the sea current changes after the dyke construction. On the contrary, water temperature and dissolved oxygen were affected by the seasonal influences rather than the reclamation construction. Total nitrogen and total phosphorus were influenced by the fertilizers and pesticides as a result of agricultural activity. The trends of suspended sediment from Landsat images were similar with those from the ground observation sites and also impacted by the dyke construction.