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It is critical to quantitatively analyse the developing human fetal brain in order to fully understand neurodevelopment in both normal fetuses and those with congenital disorders. To facilitate this analysis, automatic multi-tissue fetal brain segmentation algorithms are needed, which in turn requires open datasets of segmented fetal brains. Here we introduce a publicly available dataset of 50 manually segmented pathological and non-pathological fetal magnetic resonance brain volume reconstructions across a range of gestational ages (20 to 33 weeks) into 7 different tissue categories (external cerebrospinal fluid, grey matter, white matter, ventricles, cerebellum, deep grey matter, brainstem/spinal cord). In addition, we quantitatively evaluate the accuracy of several automatic multi-tissue segmentation algorithms of the developing human fetal brain. Four research groups participated, submitting a total of 10 algorithms, demonstrating the benefits the dataset for the development of automatic algorithms.

We present a microfluidic device for specific extraction and thermally activated release of analytes using nucleic acid aptamers. The device primarily consists of a microchamber that is packed with aptamer-functionalized microbeads as a stationary phase, and integrated with a micro heater and temperature sensor. We demonstrate the device operation by performing the extraction of a metabolic analyte, adenosine monophosphate coupled with thiazole orange (TO-AMP), with high selectivity to an RNA aptamer. Controlled release of TO-AMP from the aptamer surface is then conducted at low temperatures using on-chip thermal activation. This allows isocratic analyte elution, which eliminates the use of potentially harsh reagents, and enables efficient regeneration of the aptamer surfaces when device reusability is desired.

This paper studies the problem of option replication in general stochastic volatility markets with transaction costs, using a new specification for the volatility adjustment in Leland's algorithm [23]. We prove several limit theorems for the normalized replication error of Leland's strategy, as well as that of the strategy suggested by Lépinette [27]. The asymptotic results obtained not only generalize the existing results, but also enable us to fix the under-hedging property pointed out by Kabanov and Safarian in [18]. We also discuss possible methods to improve the convergence rate and to reduce the option price inclusive of transaction costs.

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Micro-and nanofabrication has allowed the production of ultra-sensitive, portable, and inexpensive biosensors. These devices generally rely on chemical or biological receptors which recognize a particular compound of interest and relay this recognition event effectively by transduction. Recent advances in RNA and DNA synthesis have enabled the use of aptamers, in vitro generated oligonucleotides, which offer high affinity biomolecular recognition to a theoretically limitless variety of analytes. DNA and RNA aptamers have gained so much attention in the biosensor community, that they have begun competing with more established affinity ligands including enzymes, lectins, and most notably, immunoreceptors such as antibodies. This article reviews the current state-of-the-art of aptasensors, or biosensors that use aptamers as molecular recognition elements, emphasizing the synergy between aptamer-based biosensing and micro-and nanotechnology. Aptasensors developed on micro-and nanoscale platforms based on mass changes, electroanalytical techniques, optical transduction, and purification and separation methods will be covered.