Single-molecule correlated chemical probing of RNA

RNA molecules function as the central conduit of information transfer in biology. To do this, they encode information both in their sequences and in their higher-order structures. Understanding the higher-order structure of RNA remains challenging. In this work we devise a simple, experimentally concise, and accurate approach for examining higher-order RNA structure by converting widely used massively parallel sequencing into an easily implemented single-molecule experiment for detecting through-space interactions and multiple conformations. We then use this experiment to analyze higher-order RNA structure, detect biologically important hidden states, and refine accurate three-dimensional structure models

Neocortical layer 4 as a pluripotent function linearizer

Favorov OV, Kursun O. Neocortical layer 4 as a pluripotent function linearizer. J Neurophysiol 105: 1342-1360, 2011. First published January 19, 2011; doi:10.1152/jn.00708.2010.-A highly effective kernel-based strategy used in machine learning is to transform the input space into a new "feature" space where nonlinear problems become linear and more readily solvable with efficient linear techniques. We propose that a similar "problem-linearization" strategy is used by the neocortical input layer 4 to reduce the difficulty of learning nonlinear relations between the afferent inputs to a cortical column and its to-be-learned upper layer outputs. The key to this strategy is the presence of broadly tuned feed-forward inhibition in layer 4: it turns local layer 4 domains into functional analogs of radial basis function networks, which are known for their universal function approximation capabilities. With the use of a computational model of layer 4 with feed-forward inhibition and Hebbian afferent connections, self-organized on natural images to closely match structural and functional properties of layer 4 of the cat primary visual cortex, we show that such layer-4-like networks have a strong intrinsic tendency to perform input transforms that automatically linearize a broad repertoire of potential nonlinear functions over the afferent inputs. This capacity for pluripotent function linearization, which is highly robust to variations in network parameters, suggests that layer 4 might contribute importantly to sensory information processing as a pluripotent function linearizer, performing such a transform of afferent inputs to a cortical column that makes it possible for neurons in the upper layers of the column to learn and perform their complex functions using primarily linear operations

FEATURE SELECTION AND EXTRACTION USING AN UNSUPERVISED BIOLOGICALLY-SUGGESTED APPROXIMATION TO GEBELEIN'S MAXIMAL CORRELATION

Feature selection and extraction are critical steps in many areas where pattern recognition techniques are applied. Feature selection and extraction are based on identifying and maximizing dependency relations. Gebelein's Maximal Correlation (GMC) is the most general form of dependence in that it does not make any statistical assumptions concerning the nature of the dependencies. Unfortunately, benefiting from such a useful measure in practice is generally impossible as there are only a few cases for which explicit formulae are available to calculate it. In this paper, we point out a parallel between GMC and the SINBAD algorithms, developed originally as a model of feature extraction for neurons in the cerebral cortex. We use SINBAD as a robust approximation to GMC to perform feature selection and extraction on a number of artificial and real datasets. We show that SINBAD estimates of GMC compare favorably to other well known feature selection and extraction methods based on mutual information, kernel canonical correlation analysis and principal component analysis

A novel robust and low-leakage SRAM cell with nine carbon nanotube transistors

A novel static random-access memory (SRAM) cell with nine carbon nanotube MOSFETs (9-CN-MOSFETs) is proposed in this paper. With the new 9-CN-MOSFET SRAM cell, the read data stability is enhanced by 99.09%, while providing similar read speed as compared with the conventional six-transistor (6T) SRAM cell in a 16-nm carbon nanotube transistor technology. The worst-case write voltage margin is increased by (4.57\times ) and (3.90\times ) with the proposed 9-CN-MOSFET SRAM cell as compared with the conventional 6T SRAM cell and a previously published eight-transistor (8T) SRAM cell, respectively. A 1 Kibit SRAM array with the new memory cells consumes 34.18% and 12.27% lower leakage power as compared with the memory arrays with 6T and 8T SRAM cells, respectively, in idle mode. The overall electrical quality is enhanced by up to (13.63\times ) with the proposed 9-CN-MOSFET memory circuit as compared with the other memory cells that are evaluated in this paper

Variations-tolerant 9T SRAM circuit with robust and low leakage SLEEP mode

Design of static random access memory (SRAM) circuits is challenging due to the degradation of data stability, weakening of write ability, increase of leakage power consumption, and exacerbation of process parameter variations with CMOS technology scaling. An asymmetrically ground-gated nine-transistor (9T) MTCMOS SRAM circuit is proposed in this paper for providing a low-leakage SLEEP mode with data retention capability. The worst-case static noise margin and write voltage margin are increased by up to 2.52x and 21.84%, respectively, with the asymmetrical 9T SRAM cells as compared to conventional six-transistor (6T) and eight-transistor (8T) SRAM cells under die-to-die process parameter variations in a 65nm CMOS technology. Furthermore, the mean values of static noise margin and write voltage margin are enhanced by up to 2.58x and 21.78% with the new 9T SRAM cells as compared with the conventional 6T and 8T SRAM cells under within-die process parameter fluctuations