High-speed large-scale 4D activities mapping of moving C. elegans by deep-learning-enabled light-field microscopy on a chip

We report a novel fusion of microfluidics and light-field microscopy, to achieve high-speed 4D (space + time) imaging of moving C. elegans on a chip. Our approach combines automatic chip-based worm loading / compartmentalization / flushing / reloading with instantaneous deep-learning light-field imaging of moving worm. Taken together, we realized intoto image-based screening of wild-type and uncoordinated-type worms at a volume rate of 33 Hz, with sustained observation of 1 minute per worm, and overall throughput of 42 worms per hour. With quickly yielding over 80000 image volumes that four-dimensionally visualize the dynamics of all the worms, we can quantitatively analyse their behaviours as well as the neural activities, and correlate the phenotypes with the neuron functions. The different types of worms can be readily identified as a result of the high-throughput activity mapping. Our approach shows great potential for various lab-on-a-chip biological studies, such as embryo sorting and cell growth assays.

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High-speed large-scale 4D activities mapping of moving C. elegans by deep-learning-enabled light-field microscopy on a chip

Sensors and Actuators B Chemical ◽

10.1016/j.snb.2021.130638 ◽

2021 ◽

pp. 130638

Author(s):

Tingting Zhu ◽

Lanxin Zhu ◽

Yi Li ◽

Xiaopeng Chen ◽

Mingyang He ◽

...

Keyword(s):

Deep Learning ◽

High Speed ◽

Large Scale ◽

Light Field ◽

C Elegans

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Studies of the Precessing Vortex Core in Swirling Flows

Journal of Applied Research and Technology ◽

10.22201/icat.16656423.2012.10.5.368 ◽

2012 ◽

Vol 10 (5) ◽

Cited By ~ 9

Author(s):

M.O. Vigueras-Zuñiga ◽

A. Valera-Medina ◽

N. Syred

Keyword(s):

Vortex Core ◽

High Speed ◽

Large Scale ◽

Combustion Regime ◽

Complex Nature ◽

High Speed Photography ◽

Acoustic Oscillations ◽

Lean Premixed Combustion ◽

Precessing Vortex Core ◽

Different Types

Large scale coherent structures play an important role in the behavior of the combustion regime inside any type ofcombustor stabilized by swirl, with special impact on factors such as flame stability, blow off, emissions and theoccurrence of thermo-acoustic oscillations. Lean premixed combustion is widely used and is known to impact many ofthese factors, causing complex interrelationships with any coherent structure formed. Despite the extensiveexperimentation in this matter, the above phenomena are poorly understood. Numerical simulations have been usedto try to explain the development of different regimes, but their extremely complex nature and lack of time dependentvalidation show varied and debatable results. The precessing vortex core (PVC) is a well-known coherent structurewhose development, intensity and occurrence has not been well documented. This paper thus adopts an experimentalapproach to characterize the PVC in a simple swirl burner under combustion conditions so as to reveal the effects ofswirl and other variables on the latter. Aided by a high speed photography (HSP) system, the recognition and extentof several different types of PVCs were observed and discussed.

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3D localization of point source based on light field imaging and deep learning

2019 International Conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology ◽

10.1117/12.2541587 ◽

2020 ◽

Author(s):

Shizhu Yuan ◽

Yao Hu ◽

Rui Cao ◽

Chengqiang Xu ◽

Qun Hao ◽

...

Keyword(s):

Deep Learning ◽

Point Source ◽

Light Field ◽

3D Localization ◽

Light Field Imaging ◽

Field Imaging

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Combined Color Semantics and Deep Learning for the Automatic Detection of Dolphin Dorsal Fins

Electronics ◽

10.3390/electronics9050758 ◽

2020 ◽

Vol 9 (5) ◽

pp. 758 ◽

Cited By ~ 2

Author(s):

Vito Renò ◽

Gianvito Losapio ◽

Flavio Forenza ◽

Tiziano Politi ◽

Ettore Stella ◽

...

Keyword(s):

Deep Learning ◽

Domain Knowledge ◽

Large Scale ◽

Binary Classification ◽

Invasive Technique ◽

Hybrid Technique ◽

Domain Experts ◽

Photo Identification ◽

Non Invasive ◽

Biological Studies

Photo-identification is a widely used non-invasive technique in biological studies for understanding if a specimen has been seen multiple times only relying on specific unique visual characteristics. This information is essential to infer knowledge about the spatial distribution, site fidelity, abundance or habitat use of a species. Today there is a large demand for algorithms that can help domain experts in the analysis of large image datasets. For this reason, it is straightforward that the problem of identify and crop the relevant portion of an image is not negligible in any photo-identification pipeline. This paper approaches the problem of automatically cropping cetaceans images with a hybrid technique based on domain analysis and deep learning. Domain knowledge is applied for proposing relevant regions with the aim of highlighting the dorsal fins, then a binary classification of fin vs. no-fin is performed by a convolutional neural network. Results obtained on real images demonstrate the feasibility of the proposed approach in the automated process of large datasets of Risso’s dolphins photos, enabling its use on more complex large scale studies. Moreover, the results of this study suggest to extend this methodology to biological investigations of different species.

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Research Progress of Rotating Machinery Fault Diagnosis Based on Deep Learning

E3S Web of Conferences ◽

10.1051/e3sconf/202125702030 ◽

2021 ◽

Vol 257 ◽

pp. 02030

Author(s):

Zhehua Du ◽

Xin Lin

Keyword(s):

Deep Learning ◽

Fault Diagnosis ◽

High Speed ◽

Large Scale ◽

Rolling Bearing ◽

Rotating Machinery ◽

Research Progress ◽

Engineering Practice ◽

Advantages And Disadvantages ◽

Deep Learning Model

In modern production, the precision and the importance of rotating machinery is higher and higher in the direction of large-scale, high speed and automation development, so that the traditional fault diagnosis methods are insufficient to deal with massive, multi-source and high-dimensional data, cannot meet the requirements of security and reliability. Therefore, several typical deep learning models are briefly introduced at first and the application of deep learning in fault diagnosis of rotor system, gear box and rolling bearing in recent years is studied and analyzed based on its strong feature extraction ability and advantages of clustering analysis. Finally, the advantages and disadvantages of deep learning model are summarized and the fault diagnosis methods of rotating machinery are summarized and prospected based on engineering practice.

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Deep learning-enhanced light-field imaging with continuous validation

10.1101/2020.07.30.228924 ◽

2020 ◽

Author(s):

Nils Wagner ◽

Fynn Beuttenmueller ◽

Nils Norlin ◽

Jakob Gierten ◽

Juan Carlos Boffi ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Neural Activity ◽

Light Field ◽

Light Sheet ◽

Training Data ◽

Volume Reconstruction ◽

High Quality ◽

Reconstruction Process ◽

Field Imaging

Light-field microscopy (LFM) has emerged as a powerful tool for fast volumetric image acquisition in biology, but its effective throughput and widespread use has been hampered by a computationally demanding and artefact-prone image reconstruction process. Here, we present a novel framework consisting of a hybrid light-field light-sheet microscope and deep learning-based volume reconstruction, where single light-sheet acquisitions continuously serve as training data and validation for the convolutional neural network reconstructing the LFM volume. Our network delivers high-quality reconstructions at video-rate throughput and we demonstrate the capabilities of our approach by imaging medaka heart dynamics and zebrafish neural activity.

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Lifespan Regulation by Insulin Signaling Through Phosphorylation of Proteins Beyond FOXO

10.21203/rs.3.rs-72001/v1 ◽

2020 ◽

Author(s):

Wen-jun Li ◽

Li Tao ◽

Chenwei wang ◽

Yong-Hong Yan ◽

Mei-Jun Zhang ◽

...

Keyword(s):

Machine Learning ◽

Large Scale ◽

Candidate Selection ◽

Wild Type ◽

Data Resource ◽

Kinase Substrate ◽

C Elegans ◽

Benchmark Data ◽

Selection For

Abstract Insulin/IGF-1 Signaling (IIS) constrains longevity by inhibiting the transcription factor FOXO. Beyond FOXO, little is known about how phosphorylation—as mediated by IIS kinases— regulates lifespan. Here, we profiled IIS-dependent phosphorylation changes in a large-scale quantitative phosphoproteomic analysis of wild-type and three IIS mutant C. elegans strains. Our state-of-the-art analysis experimentally identified more than 15,000 phosphosites, among which 448 were differentially phosphorylated in the long-lived daf-2/insulin receptor mutant. We developed a machine-learning-based tool for systematically ranking the likely functional importance of phosphosites to guide candidate selection for follow-up validation. We show that AKT-1 pT492 inhibits DAF-16/FOXO and compensates the loss of daf-2 function, that EIF-2α pS49 potently regulates protein synthesis and daf-2 longevity, and that reduced phosphorylation of multiple germline proteins (e.g., CDK-1) apparently transmits a signal representing reduced DAF-2 signaling to the soma. Finally, kinase-substrate analysis and subsequent experimental validation confirm that casein kinase 2 negatively regulates lifespan. Our new benchmark data resource and machine-learning tool enables unprecedented access to detailed functional insights for studies of longevity.

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View-channel-depth light-field microscopy: real-time volumetric reconstruction of biological dynamics by deep learning

10.1101/432807 ◽

2018 ◽

Cited By ~ 4

Author(s):

Zhaoqiang Wang ◽

Lanxin Zhu ◽

Hao Zhang ◽

Guo Li ◽

Chengqiang Yi ◽

...

Keyword(s):

High Speed ◽

Light Field ◽

Three Dimensional ◽

Image Sequences ◽

Biological Processes ◽

Channel Depth ◽

Volumetric Imaging ◽

C Elegans ◽

Volumetric Reconstruction ◽

Zebrafish Heart

AbstractLight-field microscopy has emerged as a technique of choice for high-speed volumetric imaging of fast biological processes. However, artefacts, non-uniform resolution, and a slow reconstruction speed have limited its full capabilities for in toto extraction of the dynamic spatiotemporal patterns in samples. Here, we combined a view-channel-depth (VCD) neural network with light-field microscopy to mitigate these limitations, yielding artefact-free three-dimensional image sequences with uniform spatial resolution and three-order-higher video-rate reconstruction throughput. We imaged neuronal activities across moving C. elegans and blood flow in a beating zebrafish heart at single-cell resolution with volume rates up to 200 Hz.

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