Temperature 1 Self-Assembly: Deterministic Assembly in 3D and Probabilistic Assembly in 2D

We investigate the power of the Wang tile self-assembly model at temperature 1, a threshold value that permits attachment between any two tiles that share even a single bond. When restricted to deterministic assembly in the plane, no temperature 1 assembly system has been shown to build a shape with a tile complexity smaller than the diameter of the shape. In contrast, we show that temperature 1 self-assembly in 3 dimensions, even when growth is restricted to at most 1 step into the third dimension, is capable of simulating a large class of temperature 2 systems, in turn permitting the simulation of arbitrary Turing machines and the assembly of $n\times n$ squares in near optimal $O(\log n)$ tile complexity. Further, we consider temperature 1 probabilistic assembly in 2D, and show that with a logarithmic scale up of tile complexity and shape scale, the same general class of temperature $\tau=2$ systems can be simulated with high probability, yielding Turing machine simulation and $O(\log^2 n)$ assembly of $n\times n$ squares with high probability. Our results show a sharp contrast in achievable tile complexity at temperature 1 if either growth into the third dimension or a small probability of error are permitted. Motivated by applications in nanotechnology and molecular computing, and the plausibility of implementing 3 dimensional self-assembly systems, our techniques may provide the needed power of temperature 2 systems, while at the same time avoiding the experimental challenges faced by those systems

Statistical inference of transmission fidelity of DNA methylation patterns over somatic cell divisions in mammals

We develop Bayesian inference methods for a recently-emerging type of epigenetic data to study the transmission fidelity of DNA methylation patterns over cell divisions. The data consist of parent-daughter double-stranded DNA methylation patterns with each pattern coming from a single cell and represented as an unordered pair of binary strings. The data are technically difficult and time-consuming to collect, putting a premium on an efficient inference method. Our aim is to estimate rates for the maintenance and de novo methylation events that gave rise to the observed patterns, while accounting for measurement error. We model data at multiple sites jointly, thus using whole-strand information, and considerably reduce confounding between parameters. We also adopt a hierarchical structure that allows for variation in rates across sites without an explosion in the effective number of parameters. Our context-specific priors capture the expected stationarity, or near-stationarity, of the stochastic process that generated the data analyzed here. This expected stationarity is shown to greatly increase the precision of the estimation. Applying our model to a data set collected at the human FMR1 locus, we find that measurement errors, generally ignored in similar studies, occur at a nontrivial rate (inappropriate bisulfite conversion error: 1.6$$ with 80$$ CI: 0.9--2.3$$). Accounting for these errors has a substantial impact on estimates of key biological parameters. The estimated average failure of maintenance rate and daughter de novo rate decline from 0.04 to 0.024 and from 0.14 to 0.07, respectively, when errors are accounted for. Our results also provide evidence that de novo events may occur on both parent and daughter strands: the median parent and daughter de novo rates are 0.08 (80$$ CI: 0.04--0.13) and 0.07 (80$$ CI: 0.04--0.11), respectively.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS297 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Initial Investigation on the Impact of In Situ Hydrogen Plasma Exposure to the Interface Between Molecular Beam Epitaxially Grown P-Ga_0.7In_0.3Sb (100) and Thermal Atomic Layer Deposited (ALD) Al₂O₃

This work presents, to the best of the authors knowledge, the first experimental findings on the impact of in situ H<sub>2</sub> plasma exposure to the electrical properties of the interface between p-type Ga<sub>0.7</sub>In<sub>0.3</sub>Sb and atomic layer deposited Al<sub>2</sub>O<sub>3</sub>. The effects of trimethyl aluminium (TMA) exposure prior to Al<sub>2</sub>O<sub>3</sub> deposition, and of a post gate metal forming gas anneal (FGA) are also investigated. The control sample, which was subjected to an ex situ HCl clean prior to ALD only, demonstrated a capacitance modulation of 36.29 % before FGA. This degraded for samples exposed to the H<sub>2</sub> plasma for all plasma powers investigated. TMA exposure offered no improvement, and significantly increased the frequency dispersion in accumulation for all samples. A post gate metal FGA at 350 °C for 15 minutes was found to substantially improve the interface quality, with the capacitance modulation, frequency dispersion in accumulation and dC/dV improving by as much as 190 %, 91 %, and 170 % respectively

IT Based Knowledge Sharing and Organizational Trust: The Development and Initial Test of a Comprehensive Model

Knowledge has been recognized as an important asset for organizations to gain competitive advantage. Increasingly capable Information and Communication Technologies (ICT) and Information Systems (IS) have been developed and employed by organizations to facilitate Knowledge Management (KM). Beside outcomes, organizations are concerned with how to motivate employees to share their knowledge in order to obtain valuable inputs (i.e. knowledge), facilitate KM processes and get the greatest benefits from the investments. This paper aims to: (1) develop a comprehensive research model for studying the behavior of using KM systems to share knowledge in a socio-technical context, and (2) study the effect of Organizational Trust (OT) within this KM context. Literature review and survey were conducted to provide supportive results

Explaining IT-Based Knowledge Sharing Behavior with IS Continuance Model and Social Factors

Knowledge is an important asset in determining the success and survival of an organization in today’s competitive markets. It becomes so important that many advanced Information and Communication Technologies (ICT) and Information Systems (IS) have been developed and employed by organizations specifically for Knowledge Management (KM). However, KM is not just a technical issue. Human is one of the important elements in KM. Human and technology must cooperate well so that KM can be facilitated. Therefore, how to motivate employees to share their knowledge becomes one of the most important KM issues. This paper aims to: (1) extend IS continuance model to study the behavior of using KMS to share knowledge within an organization and (2) integrate social factors in the model to study their relative importance to the use of KMS to share knowledge. It studied the impacts of perceived usefulness, satisfaction, social factors and task interdependence on the behavior of using KMS to share knowledge within an organization. Literature review and survey were conducted to provide supportive results. In the data analysis, the four factors were found to be significantly related to the behavior being studied and explained a significant proportion of the variance