We present a wearable microfluidic impedance cytometer implemented on a flexible circuit wristband with on-line smartphone readout for portable biomarker counting and analysis. The platform contains a standard polydimethylsiloxane (PDMS) microfluidic channel integrated on a wristband, and the circuitry on the wristband is composed  of a custom analog lock-in amplification system, a microcontroller with an 8-bit analog-to-digital converter (ADC), and a Bluetooth module wirelessly paired with a smartphone. The lock-in amplification (LIA) system is implemented with a novel architecture which consists of the lock-in amplifier followed by a high-pass filter stage with DC offset subtraction, and a post-subtraction high gain stage enabling detection of particles as small as 2.8  μm using the 8-bit ADC. The Android smartphone application was used to initiate the system and for offline data-plotting and peak counting, and supports online data readout, analysis, and file management. The data is exportable to researchers and medical professionals for in-depth analysis and remote health monitoring. The system, including the microfluidic sensor, microcontroller, and Bluetooth module all fit on the wristband with a footprint of less than 80 cm 2 . We demonstrate the ability of the system to obtain generalized blood cell counts; however the system can be applied to a wide variety of biomarkers by interchanging the standard microfluidic channel with microfluidic channels designed for biomarker isolation.

We present a portable system for personalized blood cell counting consisting of a microfluidic impedance cytometer and portable analog readout electronics, feeding into an analog-to-digital converter (ADC), and being transmitted via Bluetooth to a user-accessible mobile application. We fabricated a microfluidic impedance cytometer with a novel portable analog readout. The novel design of the analog readout, which consists of a lock-in-amplifier followed by a high-pass filter stage for subtraction of drift and DC offset, and a post-subtraction high gain stage, enables detection of particles and cells as small as 1 μm in diameter, despite using a low-end 8-bit ADC. The lock-in-amplifier and the ADC were set up to receive and transmit data from a Bluetooth module. In order to initiate the system, as well as to transmit all of the data, a user friendly mobile application was developed, and a proof-of-concept trial was run on a blood sample. Applications such as personalized health monitoring require robust device operation and resilience to clogging. It is desirable to avoid using channels comparable in size to the particles being detected thus requiring high levels of sensitivity. Despite using low-end off-the-shelf hardware, our sensing platform was capable of detecting changes in impedance as small as 0.032%, allowing detection of 3 μm diameter particles in a 300 μm wide channel. The sensitivity of our system is comparable to that of a high-end bench-top impedance spectrometer when tested using the same sensors. The novel analog design allowed for an instrument with a footprint of less than 80 cm. The aim of this work is to demonstrate the potential of using microfluidic impedance spectroscopy for low cost health monitoring. We demonstrated the utility of the platform technology towards cell counting, however, our platform is broadly applicable to assaying wide panels of biomarkers including proteins, nucleic acids, and various cell types.

We introduce Deep-CEE (Deep Learning for Galaxy Cluster Extraction and Evaluation), a proof of concept for a novel deep learning technique, applied directly to wide-field colour imaging to search for galaxy clusters, without the need for photometric catalogues. This technique is complementary to traditional methods and could also be used in combination with them to confirm galaxy cluster candidates. We use a state-of-the-art probabilistic algorithm, adapted to localise and classify galaxy clusters from other astronomical objects in SDSS imaging. As there is an abundance of labelled data for galaxy clusters from previous classifications in publicly available catalogues, we do not need to rely on simulated data. This means we keep our training data as realistic as possible, which is advantageous when training a deep learning algorithm. Ultimately, we will apply our model to surveys such as LSST and Euclid to probe wider and deeper into unexplored regions of the Universe. This will produce large samples of both high redshift and low mass clusters, which can be utilised to constrain both environment-driven galaxy evolution and cosmology.

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We introduce Z-Sequence, a novel empirical model that utilises photometric measurements of observed galaxies within a specified search radius to estimate the photometric redshift of galaxy clusters. Z-Sequence itself is composed of a machine learning ensemble based on the k-nearest neighbours algorithm. We implement an automated feature selection strategy that iteratively determines appropriate combinations of filters and colours to minimize photometric redshift prediction error. We intend for Z-Sequence to be a standalone technique but it can be combined with cluster finders that do not intrinsically predict redshift, such as our own DEEP-CEE. In this proof-of-concept study we train, fine-tune and test Z-Sequence on publicly available cluster catalogues derived from the Sloan Digital Sky Survey. We determine the photometric redshift prediction error of Z-Sequence via the median value of |Δz|/(1 + z) (across a photometric redshift range of 0.05 ≤ z ≤ 0.6) to be ∼0.01 when applying a small search radius. The photometric redshift prediction error for test samples increases by 30-50 per cent when the search radius is enlarged, likely due to line-of-sight interloping galaxies. Eventually, we aim to apply Z-Sequence to upcoming imaging surveys such as the Legacy Survey of Space and Time to provide photometric redshift estimates for large samples of as yet undiscovered and distant clusters.