Stripe sensor tomography

We introduce a general concept of tomographic imaging for the case of an imaging sensor that has a stripelike shape. We first show that there is no difference, in principle, between two-dimensional tomography using conventional electromagnetic or particle radiation and tomography where a stripe sensor is mechanically scanned over a sample at a sequence of different angles. For a single stripe detector imaging, linear motion and angular rotation are required. We experimentally demonstrate single stripe sensor imaging principle using an elongated inductive coil detector. By utilizing an array of parallel stripe sensors that can be individually addressed, two-dimensional imaging can be performed with rotation only, eliminating the requirement for linear motion, as we also experimentally demonstrate with parallel coil array. We conclude that imaging with a stripe-type sensor of particular width and thickness (where the width is much larger than the thickness) is resolution limited only by the thickness (smaller parameter) of the sensor. We give examples of multiple sensor families where this imaging technique may be beneficial such as magnetoresistive, inductive, superconducting quantum interference device, and Hall effect sensors, and, in particular, discuss the possibilities of the technique in the field of magnetic resonance imaging

Triceps Surae Short Latency Stretch Reflexes Contribute to Ankle Stiffness Regulation during Human Running

During human running, short latency stretch reflexes (SLRs) are elicited in the triceps surae muscles, but the function of these responses is still a matter of controversy. As the SLR is primarily mediated by Ia afferent nerve fibres, various methods have been used to examine SLR function by selectively blocking the Ia pathway in seated, standing and walking paradigms, but stretch reflex function has not been examined in detail during running. The purpose of this study was to examine triceps surae SLR function at different running speeds using Achilles tendon vibration to modify SLR size. Ten healthy participants ran on an instrumented treadmill at speeds between 7 and 15 km/h under 2 Achilles tendon vibration conditions: no vibration and 90 Hz vibration. Surface EMG from the triceps surae and tibialis anterior muscles, and 3D lower limb kinematics and ground reaction forces were simultaneously collected. In response to vibration, the SLR was depressed in the triceps surae muscles at all speeds. This coincided with short-lasting yielding at the ankle joint at speeds between 7 and 12 km/h, suggesting that the SLR contributes to muscle stiffness regulation by minimising ankle yielding during the early contact phase of running. Furthermore, at the fastest speed of 15 km/h, the SLR was still depressed by vibration in all muscles but yielding was no longer evident. This finding suggests that the SLR has greater functional importance at slow to intermediate running speeds than at faster speeds

An integrated circuit for chip-based analysis of enzyme kinetics and metabolite quantification

We have created a novel chip-based diagnostic tools based upon quantification of metabolites using enzymes specific for their chemical conversion. Using this device we show for the first time that a solid-state circuit can be used to measure enzyme kinetics and calculate the Michaelis-Menten constant. Substrate concentration dependency of enzyme reaction rates is central to this aim. Ion-sensitive field effect transistors (ISFET) are excellent transducers for biosensing applications that are reliant upon enzyme assays, especially since they can be fabricated using mainstream microelectronics technology to ensure low unit cost, mass-manufacture, scaling to make many sensors and straightforward miniaturisation for use in point-of-care devices. Here, we describe an integrated ISFET array comprising 216 sensors. The device was fabricated with a complementary metal oxide semiconductor (CMOS) process. Unlike traditional CMOS ISFET sensors that use the Si3N4 passivation of the foundry for ion detection, the device reported here was processed with a layer of Ta2O5 that increased the detection sensitivity to 45 mV/pH unit at the sensor readout. The drift was reduced to 0.8 mV/hour with a linear pH response between pH 2 – 12. A high-speed instrumentation system capable of acquiring nearly 500 fps was developed to stream out the data. The device was then used to measure glucose concentration through the activity of hexokinase in the range of 0.05 mM – 231 mM, encompassing glucose’s physiological range in blood. Localised and temporal enzyme kinetics of hexokinase was studied in detail. These results present a roadmap towards a viable personal metabolome machine

Accuracy of inference on the physics of binary evolution from gravitational-wave observations

The properties of the population of merging binary black holes encode some of the uncertain physics of the evolution of massive stars in binaries. The binary black hole merger rate and chirp mass distribution are being measured by ground-based gravitational-wave detectors. We consider isolated binary evolution and explore how accurately the physical model can be constrained with such observations by applying the Fisher information matrix to the merging black hole population simulated with the rapid binary population synthesis code COMPAS. We investigate variations in four COMPAS parameters: common envelope efficiency, kick velocity dispersion, and mass loss rates during the luminous blue variable and Wolf--Rayet stellar evolutionary phases. We find that 1000 observations would constrain these model parameters to a fractional accuracy of a few percent. Given the empirically determined binary black hole merger rate, we can expect gravitational-wave observations alone to place strong constraints on the physics of stellar and binary evolution within a few years.Comment: 12 pages, 9 figures; version accepted by Monthly Notices of the Royal Astronomical Societ

It’s worth the wait: optimizing questioning methods for effective intraoperative teaching

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138426/1/ans14046_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138426/2/ans14046.pd

A colorimetric CMOS-based platform for rapid total serum cholesterol quantification

Elevated cholesterol levels are associated with a greater risk of developing cardiovascular disease and other illnesses, making it a prime candidate for detection on a disposable biosensor for rapid point of care diagnostics. One of the methods to quantify cholesterol levels in human blood serum uses an optically mediated enzyme assay and a bench top spectrophotometer. The bulkiness and power hungry nature of the equipment limits its usage to laboratories. Here, we present a new disposable sensing platform that is based on a complementary metal oxide semiconductor process for total cholesterol quantification in pure blood serum. The platform that we implemented comprises readily mass-manufacturable components that exploit colorimetric changes of cholesterol oxidase and cholesterol esterase reactions. We have shown that our quantification results are comparable to that obtained by a bench top spectrophotometer. Using the implemented device, we have measured cholesterol concentration in human blood serum as low as 29 μM with a limit of detection at 13 μM, which is approximately 400 times lower than average physiological range, implying that our device also has the potential to be used for applications that require greater sensitivity

Learning large-scale dynamic discrete choice models of spatio-temporal preferences with application to migratory pastoralism in East Africa

Understanding spatio-temporal resource preferences is paramount in the design of policies for sustainable development. Unfortunately, resource preferences are often unknown to policy-makers and have to be inferred from data. In this paper we consider the problem of inferring agents' preferences from observed movement trajectories, and formulate it as an Inverse Reinforcement Learning (IRL) problem. With the goal of informing policy-making, we take a probabilistic approach and consider generative models that can be used to simulate behavior under new circumstances such as changes in resource availability, access policies, or climate. We study the Dynamic Discrete Choice (DDC) models from econometrics and prove that they generalize the Max-Entropy IRL model, a widely used probabilistic approach from the machine learning literature. Furthermore, we develop SPL-GD, a new learning algorithm for DDC models that is considerably faster than the state of the art and scales to very large datasets. We consider an application in the context of pastoralism in the arid and semi-arid regions of Africa, where migratory pastoralists face regular risks due to resource availability, droughts, and resource degradation from climate change and development. We show how our approach based on satellite and survey data can accurately model migratory pastoralism in East Africa and that it considerably outperforms other approaches on a large-scale real-world dataset of pastoralists' movements in Ethiopia collected over 3 years