Abstract: The present embodiments relate to a particle separation device. The particle separation device can include an inlet, a main channel comprising a series of angled portions to separate particles of varying sizes and varying flow rates. The particle separation device can also include a number of outlets to obtain particles of varying sizes. In some instances, the main channel can include multiple stages to allow for multi-stage sorting to increase sorting performance.

Abstract: Metastasis is a major cause of fatality and expense in cancer. Many metastatic patients express calcified, fibrotic, or deep-seated (lymph/Jung/brain) lesions that are not easily profiled. For such patients, decision-making regarding treatment selection is population-based and heavily reliant on standards of care and are not tailored to the individual's tumor. The present disclosure provides methods of identifying treatment protocols for cancer patients using released tumor cells (RTCs). Method described herein include isolating/enriching RTCs, profiling RTCs and comparing RTCs to reference RTC profile libraries.

Abstract: Provided herein is a microfluidic chip for sorting cells with an actuator assembly that relies on one or more feedback-controlled piezoelectric actuators to divert sample fluid flowing along a sample fluid path in a sample fluid line into one or more desired outlets. Also provided are methods of use.

Abstract: An aircraft noise monitoring system uses a set of geographically distributed noise sensors to receive data corresponding to events captured by the noise sensors. Each event corresponds to noise that exceeds a threshold level. For each event, the system will receive a classification of the event as an aircraft noise event or a non-aircraft noise event. It will then use the data corresponding to the events and the received classifications to train a convolutional neural network (CNN) in a classification process. After training, when the system receives a new noise event, it will use the CNN to classify the new noise event as an aircraft noise event or a non-aircraft noise event, and it will generate an output indicating whether the new noise event is an aircraft noise event or a non-aircraft noise event.

Abstract: An aircraft noise monitoring system uses a set of geographically distributed noise sensors to receive data corresponding to events captured by the noise sensors. Each event corresponds to noise that exceeds a threshold level. For each event, the system will receive a classification of the event as an aircraft noise event or a non-aircraft noise event. It will then use the data corresponding to the events and the received classifications to train a convolutional neural network (CNN) in a classification process. After training, when the system receives a new noise event, it will use the CNN to classify the new noise event as an aircraft noise event or a non-aircraft noise event, and it will generate an output indicating whether the new noise event is an aircraft noise event or a non-aircraft noise event.