Automated, high-throughput quantification of EGFP-expressing neutrophils in zebrafish by machine learning and a highly-parallelized microscope.

Normal development of the immune system is essential for overall health and disease resistance. Bony fish, such as the zebrafish (Danio rerio), possess all the major immune cell lineages as mammals and can be employed to model human host response to immune challenge. Zebrafish neutrophils, for example, are present in the transparent larvae as early as 48 hours post fertilization and have been examined in numerous infection and immunotoxicology reports. One significant advantage of the zebrafish model is the ability to affordably generate high numbers of individual larvae that can be arrayed in multi-well plates for high throughput genetic and chemical exposure screens. However, traditional workflows for imaging individual larvae have been limited to low-throughput studies using traditional microscopes and manual analyses. Using a newly developed, parallelized microscope, the Multi-Camera Array Microscope (MCAM™), we have optimized a rapid, high-resolution algorithmic method to count fluorescently labeled cells in zebrafish larvae in vivo. Using transgenic zebrafish larvae, in which neutrophils express EGFP, we captured 18 gigapixels of images across a full 96-well plate, in 75 seconds, and processed the resulting datastream, counting individual fluorescent neutrophils in all individual larvae in 5 minutes. This automation is facilitated by a machine learning segmentation algorithm that defines the most in-focus view of each larva in each well after which pixel intensity thresholding and blob detection are employed to locate and count fluorescent cells. We validated this method by comparing algorithmic neutrophil counts to manual counts in larvae subjected to changes in neutrophil numbers, demonstrating the utility of this approach for high-throughput genetic and chemical screens where a change in neutrophil number is an endpoint metric. Using the MCAM™ we have been able to, within minutes, acquire both enough data to create an automated algorithm and execute a biological experiment with statistical significance. Finally, we present this open-source software package which allows the user to train and evaluate a custom machine learning segmentation model and use it to localize zebrafish and analyze cell counts within the segmented region of interest. This software can be modified as needed for studies involving other zebrafish cell lineages using different transgenic reporter lines and can also be adapted for studies using other amenable model species.

Parallelized computational 3D video microscopy of freely moving organisms at multiple gigapixels per second.

Wide field of view microscopy that can resolve 3D information at high speed and spatial resolution is highly desirable for studying the behaviour of freely moving model organisms. However, it is challenging to design an optical instrument that optimises all these properties simultaneously. Existing techniques typically require the acquisition of sequential image snapshots to observe large areas or measure 3D information, thus compromising on speed and throughput. Here, we present 3D-RAPID, a computational microscope based on a synchronized array of 54 cameras that can capture high-speed 3D topographic videos over an area of 135 cm2, achieving up to 230 frames per second at spatiotemporal throughputs exceeding 5 gigapixels per second. 3D-RAPID employs a 3D reconstruction algorithm that, for each synchronized snapshot, fuses all 54 images into a composite that includes a co-registered 3D height map. The self-supervised 3D reconstruction algorithm trains a neural network to map raw photometric images to 3D topography using stereo overlap redundancy and ray-propagation physics as the only supervision mechanism. The resulting reconstruction process is thus robust to generalization errors and scales to arbitrarily long videos from arbitrarily sized camera arrays. We demonstrate the broad applicability of 3D-RAPID with collections of several freely behaving organisms, including ants, fruit flies, and zebrafish larvae.

Automated, high-throughput quantification of EGFP-expressing neutrophils in zebrafish by machine learning and a highly-parallelized microscope.

Normal development of the immune system is essential for overall health and disease resistance. Bony fish, such as the zebrafish (Danio rerio), possess all the major immune cell lineages as mammals and can be employed to model human host response to immune challenge. Zebrafish neutrophils, for example, are present in the transparent larvae as early as 48 hours post fertilization and have been examined in numerous infection and immunotoxicology reports. One significant advantage of the zebrafish model is the ability to affordably generate high numbers of individual larvae that can be arrayed in multi-well plates for high throughput genetic and chemical exposure screens. However, traditional workflows for imaging individual larvae have been limited to low-throughput studies using traditional microscopes and manual analyses. Using a newly developed, parallelized microscope, the Multi-Camera Array Microscope (MCAM™), we have optimized a rapid, high-resolution algorithmic method to count fluorescently labeled cells in zebrafish larvae in vivo. Using transgenic zebrafish larvae, in which neutrophils express EGFP, we captured 18 gigapixels of images across a full 96-well plate, in 75 seconds, and processed the resulting datastream, counting individual fluorescent neutrophils in all individual larvae in 5 minutes. This automation is facilitated by a machine learning segmentation algorithm that defines the most in-focus view of each larva in each well after which pixel intensity thresholding and blob detection are employed to locate and count fluorescent cells. We validated this method by comparing algorithmic neutrophil counts to manual counts in larvae subjected to changes in neutrophil numbers, demonstrating the utility of this approach for high-throughput genetic and chemical screens where a change in neutrophil number is an endpoint metric. Using the MCAM™ we have been able to, within minutes, acquire both enough data to create an automated algorithm and execute a biological experiment with statistical significance. Finally, we present this open-source software package which allows the user to train and evaluate a custom machine learning segmentation model and use it to localize zebrafish and analyze cell counts within the segmented region of interest. This software can be modified as needed for studies involving other zebrafish cell lineages using different transgenic reporter lines and can also be adapted for studies using other amenable model species.

Multi-modal imaging using a cascaded microscope design.

We present a multi-modal fiber array snapshot technique (M-FAST) based on an array of 96 compact cameras placed behind a primary objective lens and a fiber bundle array. Our technique is capable of large-area, high-resolution, multi-channel video acquisition. The proposed design provides two key improvements to prior cascaded imaging system approaches: a novel optical arrangement that accommodates the use of planar camera arrays, and a new ability to acquire multi-modal image data acquisition. M-FAST is a multi-modal, scalable imaging system that can acquire snapshot dual-channel fluorescence images as well as differential phase contrast measurements over a large 6.59 mm × 9.74 mm field-of-view at 2.2-µm center full-pitch resolution.

Parallelized computational 3D video microscopy of freely moving organisms at multiple gigapixels per second.

To study the behavior of freely moving model organisms such as zebrafish (Danio rerio) and fruit flies (Drosophila) across multiple spatial scales, it would be ideal to use a light microscope that can resolve 3D information over a wide field of view (FOV) at high speed and high spatial resolution. However, it is challenging to design an optical instrument to achieve all of these properties simultaneously. Existing techniques for large-FOV microscopic imaging and for 3D image measurement typically require many sequential image snapshots, thus compromising speed and throughput. Here, we present 3D-RAPID, a computational microscope based on a synchronized array of 54 cameras that can capture high-speed 3D topographic videos over a 135-cm^2 area, achieving up to 230 frames per second at throughputs exceeding 5 gigapixels (GPs) per second. 3D-RAPID features a 3D reconstruction algorithm that, for each synchronized temporal snapshot, simultaneously fuses all 54 images seamlessly into a globally-consistent composite that includes a coregistered 3D height map. The self-supervised 3D reconstruction algorithm itself trains a spatiotemporally-compressed convolutional neural network (CNN) that maps raw photometric images to 3D topography, using stereo overlap redundancy and ray-propagation physics as the only supervision mechanism. As a result, our end-to-end 3D reconstruction algorithm is robust to generalization errors and scales to arbitrarily long videos from arbitrarily sized camera arrays. The scalable hardware and software design of 3D-RAPID addresses a longstanding problem in the field of behavioral imaging, enabling parallelized 3D observation of large collections of freely moving organisms at high spatiotemporal throughputs, which we demonstrate in ants (Pogonomyrmex barbatus), fruit flies, and zebrafish larvae.

Gigapixel imaging with a novel multi-camera array microscope.

The dynamics of living organisms are organized across many spatial scales. However, current cost-effective imaging systems can measure only a subset of these scales at once. We have created a scalable multi-camera array microscope (MCAM) that enables comprehensive high-resolution recording from multiple spatial scales simultaneously, ranging from structures that approach the cellular scale to large-group behavioral dynamics. By collecting data from up to 96 cameras, we computationally generate gigapixel-scale images and movies with a field of view over hundreds of square centimeters at an optical resolution of 18 µm. This allows us to observe the behavior and fine anatomical features of numerous freely moving model organisms on multiple spatial scales, including larval zebrafish, fruit flies, nematodes, carpenter ants, and slime mold. Further, the MCAM architecture allows stereoscopic tracking of the z-position of organisms using the overlapping field of view from adjacent cameras. Overall, by removing the bottlenecks imposed by single-camera image acquisition systems, the MCAM provides a powerful platform for investigating detailed biological features and behavioral processes of small model organisms across a wide range of spatial scales.

Consequences of quantum noise control for the relaxation resonance frequency and phase noise in heterogeneous Silicon/III-V lasers.

We have recently introduced a new semiconductor laser design which is based on an extreme, 99%, reduction of the laser mode absorption losses. In previous reports, we showed that this was achieved by a laser mode design which confines the great majority of the modal energy (> 99%) in a low-loss Silicon guiding layer rather than in highly-doped, thus lossy, III-V p[Formula: see text] and n[Formula: see text] layers, which is the case with traditional III-V lasers. The resulting reduced electron-field interaction was shown to lead to a commensurate reduction of the spontaneous emission rate by the excited conduction band electrons into the laser mode and thus to a reduction of the frequency noise spectral density of the laser field often characterized by the Schawlow-Townes linewidth. In this paper, we demonstrate theoretically and present experimental evidence of yet another major beneficial consequence of the new laser design: a near total elimination of the contribution of amplitude-phase coupling (the Henry [Formula: see text] parameter) to the frequency noise at "high" frequencies. This is due to an order of magnitude lowering of the relaxation resonance frequency of the laser. Here, we show that the practical elimination of this coupling enables yet another order of magnitude reduction of the frequency noise at high frequencies, resulting in a quantum-limited frequency noise spectral density of 130 Hz[Formula: see text]/Hz (linewidth of 0.4 kHz) for frequencies beyond the relaxation resonance frequency 680 MHz. This development is of key importance in the development of semiconductor lasers with higher coherence, particularly in the context of integrated photonics with a small laser footprint without requiring any sort of external cavity.

Kicking the habit/semiconductor lasers without isolators.

In this paper, we propose and demonstrate a solution to the problem of coherence degradation and collapse caused by the back reflection of laser power into the laser resonator. The problem is most onerous in semiconductor lasers (SCLs), which are normally coupled to optical fibers, and results in the fact that practically every commercial SCL has appended to it a Faraday-effect isolator that blocks most of the reflected optical power preventing it from entering the laser resonator. The isolator assembly is many times greater in volume and cost than the SCL itself. This problem has resisted a practical and economic solution despite decades of effort and remains the main obstacle to the emergence of a CMOS-compatible photonic integrated circuit technology. A simple solution to the problem is thus of major economic and technological importance. We propose a strategy aimed at weaning semiconductor lasers from their dependence on external isolators. Lasers with large internal Q-factors can tolerate large reflections, limited only by the achievable Q values, without coherence collapse. A laser design is demonstrated on the heterogeneous Si/III-V platform that can withstand 25 dB higher reflected power compared to commercial DFB lasers. Larger values of internal Qs, achievable by employing resonator material of lower losses and improved optical design, should further increase the isolation margin and thus obviate the need for isolators altogether.

Determining the Quality Factor of Dielectric Ceramic Mixtures with Dielectric Constants in the Microwave Frequency Range.

Microwave dielectric ceramic materials are extensively utilized in microwave applications because of their high dielectric constants and quality factors. These applications also require ceramics of zero temperature coefficients at the resonant frequency (τ f ), which can be realized through mixing a ceramic that one is interested in with another ceramic with -τ f , or by performing the ionic substitution. With the mixing/ionic substitution, it is indispensable to compute the quality factors precisely. Previous study indicates that the quality factor depends on the grain size, porosity, internal strain, structure, phase evolution, and conductivity etc. Here we derive a quality factor formula based on the definition, which works very well for multiphase composites, single phase solid solutions, and equivalent ionic substituted single phase materials. Our formula calculation and fits to the previous experimental results demonstrate that the quality factor of the ceramic mixtures strongly depend on the dielectric constants and the dielectric constant variation index. Our results suggest that the impacts from grain size, porosity, and internal strain etc. can be summarized to the dielectric constant or dielectric constant variation index, which is of great importance for future design of high performance microwave dielectric ceramics.

1.6 kW Yb fiber amplifier using chirped seed amplification for stimulated Brillouin scattering suppression.

In a high power fiber amplifier, a frequency-chirped seed interrupts the coherent interaction between the laser and Stokes waves, raising the threshold for stimulated Brillouin scattering (SBS). Moving the external mirror of a vertical cavity surface-emitting diode laser 0.2 µm in 10 µs can yield a frequency chirp of 5×1017 Hz/s at a nearly constant output power. Opto-electronic feedback loops can linearize the chirp, and stabilize the output power. The linear variation of phase with time allows multiple amplifiers to be coherently combined using a frequency shifter to compensate for static and dynamic path length differences. The seed bandwidth, as seen by the counter-propagating SBS, also increases linearly with fiber length, resulting in a nearly-length-independent SBS threshold. Experimental results at the 1.6 kW level with a 19 m delivery fiber are presented. A numerical simulation is also presented.

On-chip integrated differential optical microring refractive index sensing platform based on a laminar flow scheme.

We propose an on-chip integrated differential optical microring refractive index sensing platform which leverages laminar flow conditions. Close spacing between a sensing and a reference resonator, and sharing the same microfluidic channel allows the two resonators to experience similar environmental disturbances, such as temperature fluctuations and fluidic-induced transients, achieving reliable and sensitive sensing performance. We obtain a noise floor of 80.0 MHz (0.3 pm) and a bulk refractive index sensitivity of 17.0 THz per refractive index unit (RIU) (64.2 nm/RIU), achieving a limit of detection of 1.4×10(-5) RIU in a 30 min and an 8°C window.