An Efficient Timer and Sizer of Biomacromolecular Motions

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An iterative learning strategy for the auto-tuning of the feedforward and feedback controller in type-1 diabetes

This paper proposes a scheme for the control of the blood glucose in subjects with type-1 diabetes mellitus based on the subcutaneous (s.c.) glucose measurement and s.c. insulin administration. The tuning of the controller is based on an iterative learning strategy that exploits the repetitiveness of the daily feeding habit of a patient. The control consists of a mixed feedback and feedforward contribution whose parameters are tuned through an iterative learning process that is based on the day-by-day automated analysis of the glucose response to the infusion of exogenous insulin. The scheme does not require any a priori information on the patient insulin/glucose response, on the meal times and on the amount of ingested carbohydrates (CHOs). Thanks to the learning mechanism the scheme is able to improve its performance over time. A specific logic is also introduced for the detection and prevention of possible hypoglycaemia events. The effectiveness of the methodology has been validated using long-term simulation studies applied to a set of nine in silico patients considering realistic uncertainties on the meal times and on the quantities of ingested CHOs.     

Maximum Likelihood Neural Network Prediction Models

Neural networks have received much attention in recent years mostly by non-statisticians. The purpose of this paper is to incorporate neural networks in a non-linear regression model and obtain maximum likelihood estimates of the network parameters using a standard Newton-Raphson algorithm. We use maximum likelihood estimators instead of the usual back-propagation technique and compare the neural network predictions with predictions of quadratic regression models and with non-parametric nearest neighbor predictions. These comparisons are made using data generated from a variety of functions. Because of the number of parameters involved, neural network models can easily over-fit the data, hence validation of results is crucial.     

Bacterial Artificial Chromosomes: A Functional Genomics Tool for the Study of Positive-strand RNA Viruses

Reverse genetics, an approach to rescue infectious virus entirely from a cloned cDNA, has revolutionized the field of positive-strand RNA viruses, whose genomes have the same polarity as cellular mRNA. The cDNA-based reverse genetics system is a seminal method that enables direct manipulation of the viral genomic RNA, thereby generating recombinant viruses for molecular and genetic studies of both viral RNA elements and gene products in viral replication and pathogenesis. It also provides a valuable platform that allows the development of genetically defined vaccines and viral vectors for the delivery of foreign genes. For many positive-strand RNA viruses such as Japanese encephalitis virus (JEV), however, the cloned cDNAs are unstable, posing a major obstacle to the construction and propagation of the functional cDNA. Here, the present report describes the strategic considerations in creating and amplifying a genetically stable full-length infectious JEV cDNA as a bacterial artificial chromosome (BAC) using the following general experimental procedures: viral RNA isolation, cDNA synthesis, cDNA subcloning and modification, assembly of a full-length cDNA, cDNA linearization, in vitro RNA synthesis, and virus recovery. This protocol provides a general methodology applicable to cloning full-length cDNA for a range of positive-strand RNA viruses, particularly those with a genome of >10 kb in length, into a BAC vector, from which infectious RNAs can be transcribed in vitro with a bacteriophage RNA polymerase.     

Dorsal and Ventral Hippocampal Sharp-Wave Ripples Activate Distinct Nucleus Accumbens Networks

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Mechanobiology of neural development

As the brain develops, proliferating cells organize into structures, differentiate, migrate, extrude long processes, and connect with other cells. These biological processes produce mechanical forces that further shape cellular dynamics and organ patterning. A major unanswered question in developmental biology is how the mechanical forces produced during development are detected and transduced by cells to impact biochemical and genetic programs of development. This gap in knowledge stems from a lack of understanding of the molecular players of cellular mechanics and an absence of techniques for measuring and manipulating mechanical forces in tissue. In this review article, we examine recent advances that are beginning to clear these bottlenecks and highlight results from new approaches that reveal the role of mechanical forces in neurodevelopmental processes.     

Stem cell competition driven by the Axin2-p53 axis controls brain size during murine development

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Amygdala Reward Neurons Form and Store Fear Extinction Memory

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Synaptic Connectivity between the Cortex and Claustrum Is Organized into Functional Modules

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