A deepwater photobioreactor system including a vertical stack extending between an ocean surface and an ocean floor. The vertical stack includes an inlet conduit and an outlet conduit where the inlet conduit is arranged to transport at least seawater and the outlet conduit is arranged to transport at least a biomass. The system includes a first photobioreactor in fluid communication with the inlet conduit and the outlet conduit that is connected to the vertical stack via the inlet and outlet conduits at a first position along the vertical stack below the ocean surface. The first bioreactor is arranged to cultivate the biomass. The system also includes a mooring system arranged to anchor the vertical stack to the ocean floor and arranged to receive the biomass via the outlet conduit and output the biomass to a harvest pipeline.

Disclosed herein are aspects of a system for coupling electrochemical marine carbon capture with photosynthesis. In some aspects of the present disclosure, the system comprises an electrochemical cell that converts saline water into (i) a base stream, (ii) an at least partially deionized water stream, and (iii) an acid stream. In some aspects, the system further comprises a biomass cultivation unit in fluid communication with the acid stream, the biomass cultivation unit comprising a photosynthetic organism. The acid stream catalyzes release of CO.sub.2 in a growth medium for the photosynthetic organism to accelerate growth of the photosynthetic organism relative to growth without the acid stream and facilitates CO.sub.2 storage by the photosynthetic organism. Also disclosed herein are aspects of a method for coupling electrochemical marine carbon capture with photosynthesis.

A cell observation system has: a part for a culturing container to be placed, the culturing container having a surface where cells are culturable and an optically transparent side face forming a peripheral wall of the surface; a light emission unit configured to irradiate the surface with irradiation light from the side face; and an image capturing unit configured to capture images of the surface and has at least any of features (a) controlling variation in an amount of light of the irradiation light, the surface being irradiated with the irradiation light, and the variation being due to absorption of light by a medium in the culturing container and (b) correcting an effect caused by variation in an amount of light of the irradiation light, the surface being irradiated with the irradiation light, and the variation being due to absorption of light by a medium in the culturing container.

A method for automated, artificial-intelligence-based COC retrieval includes using an artificial intelligence/machine learning system (AI/ML system) to optically scan a follicular fluid sample to produce an image object using an imaging system that includes a microscopy system, a camera system, and a lighting system. The method includes comparing the image object to a predetermined threshold using an AI/ML system, wherein the predetermined threshold is at least in part an optical pattern with a probability of corresponding to a cumulus-oocyte-complex (COC). The method includes identifying a COC within the follicular fluid sample based at least in part on the image object satisfying the predetermined threshold. The method includes determining a COC location within the follicular fluid sample based at least in part on identifying a region of the image object corresponding to an optical pattern within the image object that satisfies the predetermined threshold.

A method for automated, artificial-intelligence-based egg identification using optical coherence tomography (OCT) includes positioning an OCT imaging system head in proximity to a biological sample containing an oocyte, wherein the OCT is operatively coupled to an artificial intelligence/machine learning system (AI/ML system) and an imaging system, wherein the imaging system includes a camera system, and a lighting system. The method includes creating at least one three-dimensional image of the oocyte using the OCT, AI/ML system, and imaging system. The method includes using the AI/ML system to analyze the three-dimensional image, wherein an analysis includes detection of a polar body's presence or absence based at least in part on planar views of the oocyte.

Disclosed is a method of artificial intelligence-based robotic pipetting for cell preparation. The method includes training an artificial intelligence/machine learning system (AI/ML system) to classify images of biological specimens. The method includes storing the classified images. The method includes receiving an image object from an imaging system that includes a microscopy system, a camera system configured to receive imaging from the microscopy system, and a lighting system configured to illuminate a biological material. The method includes processing the image object, using the AJ/ML system to determine a presence of a retrievable target cell. The method includes positioning a robotic pipettor at a target physical orientation relative to the retrievable target cell. The method includes confirming the robotic pipettor's location at the target physical orientation, using AI/ML system. The method includes instructing the robotic pipettor to obtain the retrievable target cell from the target physical orientation.

Provided are methods, devices, and systems for measuring aquatic biomass in an aqueous liquid, optionally for the cultivation, growth optimization, and harvest of an aquatic biomass for plant protein production. In particular, measurements of aquatic biomass density are based on light absorption of the aquatic biomass. The aquatic biomass includes an aquatic organism such as Lemna, including Lemna minor. The plant protein isolates include a RuBisCO protein.

A device and method for detecting the level of foam in a reactor vessel monitors the intensity of the light of the movement of the light detected by a camera. The camera monitors at least one light source positioned inside the reactor vessel or viewable through the reactor vessel.

Disclosed is a system for cultivating and harvesting a cyanobacterial biomass. The system comprises at least one vessel that, when in operation, grows the cyanobacterial biomass in a nutrient growth media under regulated growth conditions and a base unit configured to receive the at least one vessel. The system also comprises at least one light source and a cultivation air pump that, when in operation, supplies light, heat and air to the at least one vessel. Notably, the cultivation air pump supplied air via a one-way air valve in the at least one vessel. The system also comprises at least one photodiode that, when in operation, measure a cyanobacterial biomass concentration in the at least one vessel. Disclosed also is a method for cultivating and harvesting a cyanobacterial biomass.

A method for automated ICSI includes receiving at least one droplet containing an egg in a dish placed on a stage. The method includes using an artificial intelligence/machine learning system (AI/ML system) and an imaging system to detect a zona pellucida. The imaging system includes a microscopy system, a camera system, and a lighting system. The method includes holding the egg using a robotic microtool and lowering a robotic pipettor into the droplet. The method includes using the AI/ML system and imaging system to determine an area at which to hold the egg and positioning the robotic microtool to that area. The method includes using the AI/ML system and imaging system to instruct the robotic microtool to apply negative pressure to hold the egg to the robotic pipettor. The method includes using the AI/ML system and imaging system to determine a target location where zona ablation should be performed.