The field testing of a vortex sewage overflow.

A full scale prototype of a vortex storm sewage overflow with peripheral spill has been build in Sheffield, its design being based on the results of model tests. The project described has been involved in monitoring this prototype with the aims of i) Assessing its hydraulic performance, ii) Assessing its ability to retain polluting material, particularly large 'gross solids' in the sewer, iii) To compare its performance with predictions made by the model tests. A review of previous work concerning storm overflows, the development of vortex overflows and sewer monitoring techniques was undertaken. The overflow was monitored with flow measurement equipment, bottle samplers and equipment designed to count the numbers of gross solids in the sewage entering and spilling from the chamber. The latter worked by pumping large volumes of sewage through a transparent cell, where it was filmed by a video camera. Objects passing were counted by eye when the film was examined later. The hydraulic monitoring showed that the overflow was effective at controlling flows in the sewage, and that mathematical and physical models predicted its performance. Analysis of discrete samples collected using bottle samplers showed little difference between the fine suspended solids and the dissolved material in inlet or spill. The results from measuring gross solids appeared to show that their concentration in the spill was less than that in the inflow by 20-40%. However insufficient storms were recorded to be sure to what extent the method of sampling affected the results. The results from the gross solid monitoring bore some resemblance to the predictions made by the model tests using estimates of the nature of particles in the storm sewage. This suggested that model tests using synthetic gross solid particles could give a good indication of the performance of full scale overflows

Estimation of Cell Cycle States of Human Melanoma Cells with Quantitative Phase Imaging and Deep Learning

Visualization and classification of cell cycle stages in live cells requires the introduction of transient or stably expressing fluorescent markers. This is not feasible for all cell types, and can be time consuming to implement. Labelling of living cells also has the potential to perturb normal cellular function. Here we describe a computational strategy to estimate core cell cycle stages without markers by taking advantage of features extracted from information-rich ptychographic time-lapse movies. We show that a deep-learning approach can estimate the cell cycle trajectories of individual human melanoma cells from short 3-frame (~23 minute) snapshots, and can identify cell cycle arrest induced by chemotherapeutic agents targeting melanoma driver mutations