Metabolic scavenging by cancer cells: when the going gets tough, the tough keep eating

Cancer is fundamentally a disease of uncontrolled cell proliferation. Tumour metabolism has emerged as an exciting new discipline studying how cancer cells obtain the necessary energy and cellular ‘building blocks’ to sustain growth. Glucose and glutamine have long been regarded as the key nutrients fuelling tumour growth. However, the inhospitable tumour microenvironment of certain cancers, like pancreatic cancer, causes the supply of these nutrients to be chronically insufficient for the demands of proliferating cancer cells. Recent work has shown that cancer cells are able to overcome this nutrient insufficiency by scavenging alternative substrates, particularly proteins and lipids. Here, we review recent work identifying the endocytic process of macropinocytosis and subsequent lysosomal processing as an important substrate-acquisition route. In addition, we discuss the impact of hypoxia on fatty acid metabolism and the relevance of exogenous lipids for supporting tumour growth as well as the routes by which tumour cells can access these lipids. Together, these cancer-specific scavenging pathways provide a promising opportunity for therapeutic intervention

A Zador-Like Formula for Quantizers Based on Periodic Tilings

We consider Zador's asymptotic formula for the distortion-rate function for a variable-rate vector quantizer in the high-rate case. This formula involves the differential entropy of the source, the rate of the quantizer in bits per sample, and a coefficient G which depends on the geometry of the quantizer but is independent of the source. We give an explicit formula for G in the case when the quantizing regions form a periodic tiling of n-dimensional space, in terms of the volumes and second moments of the Voronoi cells. As an application we show, extending earlier work of Kashyap and Neuhoff, that even a variable-rate three-dimensional quantizer based on the ``A15'' structure is still inferior to a quantizer based on the body-centered cubic lattice. We also determine the smallest covering radius of such a structure.Comment: 8 page

Multiple Description Vector Quantization with Lattice Codebooks: Design and Analysis

The problem of designing a multiple description vector quantizer with lattice codebook Lambda is considered. A general solution is given to a labeling problem which plays a crucial role in the design of such quantizers. Numerical performance results are obtained for quantizers based on the lattices A_2 and Z^i, i=1,2,4,8, that make use of this labeling algorithm. The high-rate squared-error distortions for this family of L-dimensional vector quantizers are then analyzed for a memoryless source with probability density function p and differential entropy h(p) < infty. For any a in (0,1) and rate pair (R,R), it is shown that the two-channel distortion d_0 and the channel 1 (or channel 2) distortions d_s satisfy lim_{R -> infty} d_0 2^(2R(1+a)) = (1/4) G(Lambda) 2^{2h(p)} and lim_{R -> infty} d_s 2^(2R(1-a)) = G(S_L) 2^2h(p), where G(Lambda) is the normalized second moment of a Voronoi cell of the lattice Lambda and G(S_L) is the normalized second moment of a sphere in L dimensions.Comment: 46 pages, 14 figure

Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks

Recognizing arbitrary multi-character text in unconstrained natural photographs is a hard problem. In this paper, we address an equally hard sub-problem in this domain viz. recognizing arbitrary multi-digit numbers from Street View imagery. Traditional approaches to solve this problem typically separate out the localization, segmentation, and recognition steps. In this paper we propose a unified approach that integrates these three steps via the use of a deep convolutional neural network that operates directly on the image pixels. We employ the DistBelief implementation of deep neural networks in order to train large, distributed neural networks on high quality images. We find that the performance of this approach increases with the depth of the convolutional network, with the best performance occurring in the deepest architecture we trained, with eleven hidden layers. We evaluate this approach on the publicly available SVHN dataset and achieve over $96\%$ accuracy in recognizing complete street numbers. We show that on a per-digit recognition task, we improve upon the state-of-the-art, achieving $97.84\%$ accuracy. We also evaluate this approach on an even more challenging dataset generated from Street View imagery containing several tens of millions of street number annotations and achieve over $90\%$ accuracy. To further explore the applicability of the proposed system to broader text recognition tasks, we apply it to synthetic distorted text from reCAPTCHA. reCAPTCHA is one of the most secure reverse turing tests that uses distorted text to distinguish humans from bots. We report a $99.8\%$ accuracy on the hardest category of reCAPTCHA. Our evaluations on both tasks indicate that at specific operating thresholds, the performance of the proposed system is comparable to, and in some cases exceeds, that of human operators

Novel concept of a single-mass adaptively controlled triaxial angular rate sensor

This paper presents a novel concept for an adaptively controlled triaxial angular rate (AR) sensor device that is able to detect rotation in three orthogonal axes, using a single vibrating mass. Pedestrian navigation is presented as an example demonstrating the suitability of the proposed device to the requirements of emerging applications. The adaptive controller performs various functions. It updates estimates of all stiffness error, damping and input rotation parameters in real time, removing the need for any offline calibration stages. The parameter estimates are used in feedforward control to cancel out their otherwise erroneous effects, including zero-rate output: The controller also drives the mass along a controlled oscillation trajectory, removing the need for additional drive control. Finally, the output of the device is simply an estimate of input rotation, removing the need for additional demodulation normally used for vibratory AR sensors. To enable all unknown parameter estimates to converge to their true values, the necessary. model trajectory is shown to be a three-dimensional Lissajous pattern. A modified trajectory algorithm is presented that aims to reduce errors due to discretization of the continuous time system. Simulation results are presented to verify the operation of the adaptive controller. A finite-element modal analysis of a preliminary structural design is presented. It shows a micro electro mechanical systems realizable design having modal shapes and frequencies suitable for implementing the presented adaptive controller