We present a novel approach to solving the trajectory plan ning problem (TPP) in time-varying environments. The es sence of our approach lies in a heuristic but natural decom position of TPP into two subproblems: (1) planning a path to avoid collision with static obstacles and (2) planning the velocity along the path to avoid collision with moving obsta cles. We call thefirst subproblem the path planning problem (PPP) and the second the velocity planning problem (VPP). Thus, our decomposition is summarized by the equation TPP => PPP + VPP. The symbol => indicates that the de composition holds under certain assumptions, e.g., when obstacles are moving independently of ( i.e., not tracking ) the robot. Furthermore, we pose the VPP in path-time space, where time is explicitly represented as an extra dimension, and reduce it to a graph search in this space. In fact, VPP is transformed to a two-dimensional PPP in path-time space with some additional constraints. Algorithms are then pre sented to solve the VPP with different optimality criteria: minimum length in path-time space, and minimum time.

The volume of point of care (POC) testing continues to grow steadily due to the increased availability of easy-to-use devices, thus making it possible to deliver less costly care closer to the patient site in a shorter time relative to the central laboratory services. A novel class of molecules called microRNAs have recently gained attention in healthcare management for their potential as biomarkers for human diseases. The increasing interest of miRNAs in clinical practice has led to an unmet need for assays that can rapidly and accurately measure miRNAs at the POC. However, the most widely used methods for analyzing miRNAs, including Northern blot-based platforms, in situ hybridization, reverse transcription qPCR, microarray, and next-generation sequencing, are still far from being used as ideal POC diagnostic tools, due to considerable time, expertize required for sample preparation, and in terms of miniaturizations making them suitable platforms for centralized labs. In this review, we highlight various existing and upcoming technologies for miRNA amplification and detection with a particular emphasis on the POC testing industries. The review summarizes different miRNA targets and signals amplification-based assays, from conventional methods to alternative technologies, such as isothermal amplification, paper-based, oligonucleotide-templated reaction, nanobead-based, electrochemical signalingbased, and microfluidic chip-based strategies. Based on critical analysis of these technologies, the possibilities and feasibilities for further development of POC testing for miRNA diagnostics are addressed and discussed.

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Molecularly imprinted polymers (MIPs) are biomimetics which can selectively bind to analytes of interest. One of the most interesting areas where MIPs have shown the biggest potential is food analysis. MIPs have found use as sorbents in sample preparation attributed to the high selectivity and high loading capacity. MIPs have been intensively employed in classical solid-phase extraction and solid-phase microextraction. More recently, MIPs have been combined with magnetic bead extraction, which greatly simplifies sample handling procedures. Studies have consistently shown that MIPs can effectively minimize complex food matrix effects, and improve recoveries and detection limits. In addition to sample preparation, MIPs have also been viewed as promising alternatives to bio-receptors due to the inherent molecular recognition abilities and the high stability in harsh chemical and physical conditions. MIPs have been utilized as receptors in biosensing platforms such as electrochemical, optical and mass biosensors to detect various analytes in food. In this review, we will discuss the current state-of-the-art of MIP synthesis and applications in the context of food analysis. We will highlight the imprinting methods which are applicable for imprinting food templates, summarize the recent progress in using MIPs for preparing and analysing food samples, and discuss the current limitations in the commercialisation of MIPs technology. Finally, future perspectives will be given.