Additional file 2: of Epigenetics of amphetamine-induced sensitization: HDAC5 expression and microRNA in neural remodeling

Expression of HDAC5 antigens in the nucleus accumbens (NAc) of naĂŻve mice mice. We compared total (cy3-ab1439, Abcam) or phosphorylated HDAC5 (cy3-ab192339) in the nucleus accumbens (NAc). (PDF 1 kb

Additional file 3: of Epigenetics of amphetamine-induced sensitization: HDAC5 expression and microRNA in neural remodeling

Expression of HDAC5 antigens in mice of acute amphetamine exposure groups. We compared total (file 2) or phosphorylated (file 3) HDAC5 in the NAc. (PDF 3461 kb

Additional file 4: of Epigenetics of amphetamine-induced sensitization: HDAC5 expression and microRNA in neural remodeling

Expression of HDAC5 antigens in mice in the chronic amphetamine exposure groups. We compared total (file 4) or phosphorylated (file 5) HDAC5 in the NAc. (PDF 2600 kb

In-plane Thermal Conductivity Measurement with Nanosecond Grating Imaging Technique

<p>We develop a nanosecond grating imaging (NGI) technique to measure in-plane thermal transport properties in bulk and thin-film samples. Based on nanosecond time-domain thermoreflectance (ns-TDTR), NGI incorporates a photomask with periodic metal strips patterned on a transparent dielectric substrate to generate grating images of pump and probe lasers on the sample surface, which induces heat conduction along both cross- and in-plane directions. Analytical and numerical models have been developed to extract thermal conductivities in both bulk and thin-film samples from NGI measurements. This newly developed technique is used to determine thickness-dependent in-plane thermal conductivities (<i>κ_x</i>) in Cu nano-films, which agree well with the electron thermal conductivity values converted from four-point electrical conductivity measurements using the Wiedemamn–Franz law, as well as previously reported experimental values. The <i>κ_x</i> measured with NGI in an 8 nm x 8 nm GaAs/AlAs superlattice (SL) is about 10.2 W/m⋅K, larger than the cross-plane thermal conductivity (8.8 W/m⋅K), indicating the anisotropic thermal transport in the SL structure. The uncertainty of the measured <i>κ_x</i> is about 25% in the Cu film and less than 5% in SL. Sensitivity analysis suggests that, with the careful selection of proper substrate and interface resistance, the uncertainty of <i>κ_x</i> in Cu nano-films can be as low as 5%, showing the potential of the NGI technique to determine <i>κ_x</i> in thin films with improved accuracy. By simply installing a photomask into ns-TDTR, NGI provides a convenient, fast, and cost-effective method to measure the in-plane thermal conductivities in a wide range of structures and materials.</p

Accurate, Fully-Automated NMR Spectral Profiling for Metabolomics

<div><p>Many diseases cause significant changes to the concentrations of small molecules (a.k.a. metabolites) that appear in a person’s biofluids, which means such diseases can often be readily detected from a person’s “metabolic profile"—i.e., the list of concentrations of those metabolites. This information can be extracted from a biofluids Nuclear Magnetic Resonance (NMR) spectrum. However, due to its complexity, NMR spectral profiling has remained manual, resulting in slow, expensive and error-prone procedures that have hindered clinical and industrial adoption of metabolomics via NMR. This paper presents a system, BAYESIL, which can quickly, accurately, and autonomously produce a person’s metabolic profile. Given a 1D ¹HNMR spectrum of a complex biofluid (specifically serum or cerebrospinal fluid), BAYESIL can automatically determine the metabolic profile. This requires first performing several spectral processing steps, then matching the resulting spectrum against a reference compound library, which contains the “signatures” of each relevant metabolite. BAYESIL views spectral matching as an inference problem within a probabilistic graphical model that rapidly approximates the most probable metabolic profile. Our extensive studies on a diverse set of complex mixtures including real biological samples (serum and CSF), defined mixtures and realistic computer generated spectra; involving > 50 compounds, show that BAYESIL can autonomously find the concentration of NMR-detectable metabolites accurately (~ 90% correct identification and ~ 10% quantification error), in less than 5 minutes on a single CPU. These results demonstrate that BAYESIL is the first fully-automatic publicly-accessible system that provides quantitative NMR spectral profiling effectively—with an accuracy on these biofluids that meets or exceeds the performance of trained experts. We anticipate this tool will usher in high-throughput metabolomics and enable a wealth of new applications of NMR in clinical settings. BAYESIL is accessible at <a href="http://www.bayesil.ca" target="_blank">http://www.bayesil.ca</a>.</p></div

Bayesil’s quantification and identification.

<p>(left) Bayesil’s identification of individual compounds in 50 biological serum samples. (right) The average concentration for correctly identified compounds in the same samples. The error bars show the average difference between Bayesil and expert values for each compound and the red dots show the average detection threshold for the same compound.</p

Evolution of Bayesil’s distributions for a small region of human serum spectrum.

<p>The plots above horizontal axis show the original spectrum (solid black), individual clusters as well as overall fit (dashed red). The curves below horizontal axis show the Bayesil’s distribution over chemical shift variables for each cluster (), over 6 iterations of spectral deconvolution. The distributions become more peaked towards the correct center in each iteration. Distributions below the horizon have the color of their associated cluster.</p

Spectral processing steps in Bayesil.

<p>Reference deconvolution and smoothing are optional. After baseline correction, Bayesil may go back to phase correction to re-adjust the phasing. For this, the imaginary part of the spectrum is reconstructed using Hilbert transformation (not shown).</p

Historic data on metabolite characterization of human urine by different platforms.

<p>Historic data on metabolite characterization of human urine by different platforms.</p

Concentrations of isoflavones and thiols as determined by HPLC.

a<p>Isoflavone metabolites measured by HPLC/UV.</p>b<p>Thiol metabolites measured by HPLC/FD.</p><p>Oc: occurrence;</p><p>NA: not available.</p