We report a deep learning-enabled field-portable and cost-effective imaging flow cytometer that automatically captures phase-contrast color images of the contents of a continuously flowing water sample at a throughput of 100 mL/h. The device is based on partially coherent lens-free holographic microscopy and acquires the diffraction patterns of flowing micro-objects inside a microfluidic channel. These holographic diffraction patterns are reconstructed in real time using a deep learning-based phase-recovery and image-reconstruction method to produce a color image of each micro-object without the use of external labeling. Motion blur is eliminated by simultaneously illuminating the sample with red, green, and blue light-emitting diodes that are pulsed. Operated by a laptop computer, this portable device measures 15.5 cm × 15 cm × 12.5 cm, weighs 1 kg, and compared to standard imaging flow cytometers, it provides extreme reductions of cost, size and weight while also providing a high volumetric throughput over a large object size range. We demonstrated the capabilities of this device by measuring ocean samples at the Los Angeles coastline and obtaining images of its micro- and nanoplankton composition. Furthermore, we measured the concentration of a potentially toxic alga (Pseudo-nitzschia) in six public beaches in Los Angeles and achieved good agreement with measurements conducted by the California Department of Public Health. The cost-effectiveness, compactness, and simplicity of this computational platform might lead to the creation of a network of imaging flow cytometers for large-scale and continuous monitoring of the ocean microbiome, including its plankton composition.

To map the aquatic vegetation of Bavarian (Germany) freshwater lakes in a large-scaled and quick way, remote sensing is a helpful tool. For interpretation of the data, a spectral library of different macrophyte and sediment reflectances is under development. Therefore, multi-temporal in situ remote sensing reflectances were sampled from May to October 2011 with hyperspectral RAMSES spectroradiometers. Occurring spectral variations during the growing season could be linked to biometric and phenological data of the particulate species. Principal component analyses showed that, by applying the presented method, differentiation of the macrophytes from sediment and among each other is possible and can be improved by multi-temporal data.

No abstract

No abstract

No abstract