Anti-DNA:RNA antibodies and silicon photonic microring resonators: increased sensitivity for multiplexed microRNA detection

In this paper, we present a method for the sensitive detection of microRNAs (miRNAs) utilizing an antibody that specifically recognizes DNA:RNA heteroduplexes and a silicon photonic microring resonator array transduction platform. Microring resonator arrays are covalently functionalized with DNA capture probes that are complementary to solution phase miRNA targets. Following hybridization on the sensor, the anti-DNA:RNA antibody is introduced and binds selectively to the heteroduplexes, giving a larger signal than the original miRNA hybridization due to the increased mass of the antibody, as compared to the 22-mer oligoribonucleotide. Furthermore, the secondary recognition step is performed in neat buffer solution and at relatively higher antibody concentrations, facilitating the detection of miRNAs of interest. The intrinsic sensitivity of the microring resonator platform coupled with the amplification provided by the anti-DNA:RNA antibodies allows for the detection of microRNAs at concentrations as low as 10 pM (350 amol). The simplicity and sequence generality of this amplification method position it as a promising tool for high-throughput, multiplexed miRNA analysis as well as a range of other RNA based detection applications.     

Facile synthesis and growth mechanism of Ni-catalyzed GaAs nanowires on non-crystalline substrates

GaAs nanowires (NWs) have been extensively explored for next generation electronics, photonics and photovoltaics due to their direct bandgap and excellent carrier mobility. Typically, these NWs are grown epitaxially on crystalline substrates, which could limit potential applications requiring high growth yield to be printable or transferable on amorphous and flexible substrates. Here, utilizing Ni as a catalytic seed, we successfully demonstrate the synthesis of highly crystalline, stoichiometric and dense GaAs NWs on amorphous SiO(2) substrates. Notably, the NWs are found to grow via the vapor-solid-solid (VSS) mechanism with non-spherical NiGa catalytic tips and low defect densities while exhibiting a narrow distribution of diameter (21.0 ± 3.9 nm) uniformly along the entire length of the NW (>10 μm). The NWs are then configured into field-effect transistors showing impressive electrical characteristics with I(ON)/I(OFF) > 10(3), which further demonstrates the purity and crystal quality of NWs obtained with this simple synthesis technique, compared to the conventional MBE or MOCVD grown GaAs NWs.     

Impaired Higher Order Implicit Sequence Learning on the Verbal Version of the Serial Reaction Time Task in Patients With Parkinson's Disease.

Although neuroimaging studies have strongly implicated basal ganglia involvement in implicit sequence learning, serial reaction time (SRT) studies with Parkinson's disease (PD) patients have yielded mixed results. The present research sought to examine the ability of people with PD to implicitly learn sequences with different sequential structures and to objectively assess explicit knowledge. A version of the SRT task that reduces motor demands was used to compare 19 patients with PD but not dementia and 37 matched controls. PD patients showed less implicit sequence-specific learning for both sequences and reduced response time improvement over sequential trials for the more complex sequence. A closer examination revealed that the deficit involved higher order sequential associations as well as the learning of pairwise information. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Unsupervised Learning for Efficient Texture Estimation From Limited Discrete Orientation Data

The estimation of orientation distribution functions (ODFs) from discrete orientation data, as produced by electron backscatter diffraction or crystal plasticity micromechanical simulations, is typically achieved via techniques such as the Williams–Imhof–Matthies–Vinel (WIMV) algorithm or generalized spherical harmonic expansions, which were originally developed for computing an ODF from pole figures measured by X-ray or neutron diffraction. These techniques rely on ad-hoc methods for choosing parameters, such as smoothing half-width and bandwidth, and for enforcing positivity constraints and appropriate normalization. In general, such approaches provide little or no information-theoretic guarantees as to their optimality in describing the given dataset. In the current study, an unsupervised learning algorithm is proposed which uses a finite mixture of Bingham distributions for the estimation of ODFs from discrete orientation data. The Bingham distribution is an antipodally-symmetric, max-entropy distribution on the unit quaternion hypersphere. The proposed algorithm also introduces a minimum message length criterion, a common tool in information theory for balancing data likelihood with model complexity, to determine the number of components in the Bingham mixture. This criterion leads to ODFs which are less likely to overfit (or underfit) the data, eliminating the need for a priori parameter choices.     

Social skill deficit interventions for substance abusers.

Because substance abusers demonstrate significant cognitive and behavioral skill deficits, 2 social competency models of habilitation are proposed to increase abusers' life management skills and coping abilities. Social problem-solving training programs focus on generic cognitive problem-solving deficits, while social skills training programs address specific behavioral deficiencies. Social problem solving is a cognitive-behavioral approach that teaches people how to think, emphasizing thinking that is alternative, consequential, means-end, perspective taking, and social-causal. Social skills training may be taught using modeling, role playing, rehearsal, coaching, and feedback. Both approaches have been successful for increasing social adjustment and diminishing psychopathology among high-risk substance abusers. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Excerpt from Designed for the Future — 80 Practical Ideas for a Sustainable World

In Designed for the Future^①, author Jared Green, who writes about cities and design for numerous publications, asks eighty of today’s most innovative architects, urban planners, landscape architects, journalists, artists, and environmental leaders the same question: What gives you the hope that a sustainable future is possible? Their imaginative answers show the way to our future success on earth and begin a much-needed dialogue about what we can realistically accomplish in the decades ahead. As an excerpt, this article introduces the author’s conception and intent of this book, and selects some inspiring ideas contributed by three leading landscape architects.     

Submixture model to predict nepheline precipitation in waste glasses

High-alumina high-level waste (HLW) glasses are prone to nepheline precipitation during canister-centerline cooling (CCC). If sufficient nepheline forms, the chemical durability of the glass will be significantly impacted. Overly conservative constraints have been developed and used to avoid the deleterious effects of nepheline formation in U.S. HLW glasses. The constraints used have been shown to significantly limit the loading of waste in glass at Hanford and therefore the cost and schedule of cleanup. A 90-glass study was performed to develop an improved understanding of the impacts of glass composition on the formation of nepheline during CCC. The CCC crystallinity data from these glasses were combined with 657 glasses found in the literature. The trends showed significant effects of Na₂O, Al₂O₃, SiO₂, B₂O₃, CaO, Li₂O, and potentially K₂O on the propensity for nepheline formation. A pseudo-ternary submixture model was proposed to identify the glass composition region prone to nepheline precipitation. This pseudo-ternary with axes of SiO₂ + 1.98B₂O₃, Na₂O + 0.653Li₂O + 0.158CaO, and Al₂O₃ was found to divide glasses that precipitate nepheline during CCC from those that do not. Application of this constraint is anticipated to increase the loading of Hanford high-alumina HLWs in glass by roughly one-third.     

The effect of solidification direction with respect to gravity on ice-templated TiO2 microstructures

Unidirectional ice-templating produces materials with aligned, elongated pores via: (i) directional solidification of particle suspensions wherein suspended particles are rejected and incorporated between aligned dendrites, (ii) sublimation of the solidified fluid, and (iii) sintering of the particles into elongated walls which are templated by the ice dendrites. Most ice-templating studies utilize upward solidification techniques, where solid ice is located at the bottom of the solidification mold (closest to the cold source), the liquid suspension is above the ice, and the solidification front advances upward, against gravity. Liquid water reaches its maximum density at 4 °C; thus, liquid nearest the solid/liquid interface, at 0ºC, is less dense than warmer liquid above (up to 4 °C, above which, a density inversion occurs, and liquid density decreases with increasing temperature). The lower density liquid nearest the solidification front is thus expected to rise due to buoyancy, promoting convective fluid motion in the liquid during solidification. Here, we investigate the effect of solidification direction with respect to the direction of gravity on ice-templated microstructures to study the role of buoyancy-driven fluid motion during solidification. We hypothesize that, for upward solidification, the convective fluid motion that results from the liquid density gradient occurs near the solidification front. For downward solidification, we expect that this fluid motion occurs farther away from the solidification front. Aqueous suspensions of TiO₂ nanoparticles (10–30 nm in size, 10, 15, and 21 vol.%) are solidified upward (against gravity, with ice on bottom and water on top), downward (water on bottom, ice on top), and horizontally (perpendicular to gravity). Microstructural investigation of sintered samples shows evidence of buoyancy-driven, convective fluid flow during solidification for samples solidified upwards (against gravity), including (i) tilting of the wall (and pore) orientation with respect to the induced temperature gradient, (ii) ice lens defects (cracks oriented perpendicular to the freezing direction), and (iii) radial macrosegregation. These features are not observed for downward nor horizontal solidification configurations, consistent with the hypothesis that convective fluid motion does not interact directly with the solidification front for downward solidification.