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This paper reports a facile method for fabricating a new nanocable structure consisting of reduced graphene oxide (RGO) wrapped anatase TiO 2 nanofibers by UV-light photocatalytic reduction. The nanocable was prepared by injecting 0.8 g L À1 aqueous graphene oxide colloid into 0.8 g L À1 electrospun TiO 2 nanofibers in ethanol, followed by a mild UV light irradiation for 20 h. The conformal wrapping of RGO is a result of noncovalent interactions (van der Waals and electrostatics). The RGO walls of the nanocables exhibited a uniform thickness of $5 nm. Half of the oxygen groups were removed and the partially reduced RGO was favored to be well dispersed in water. The invariability of the I D /I G ratio of the D to G bands in Raman spectra proposed the presence of a ''defect-repair'' process in the reduction. We further demonstrated the enhancement of photoinduced electron transfer and depression of electron combination by integrating the RGO/TiO 2 nanocables into TiO 2 photoanodes, proving our nanocables are good candidates for photovoltaic devices.

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We previously developed a noninvasive technique for the quantification of fluorodeoxyglucose (FDG) positron emission tomography (PET) images using an image-derived input function obtained from a manually drawn carotid artery region. Here, we investigate the use of independent component analysis (ICA) for more objective identification of the carotid artery and surrounding tissue regions. Using FDG PET data from 22 subjects, ICA was applied to an easily defined cubical region including the carotid artery and neighboring tissue. Carotid artery and tissue time activity curves and three venous samples were used to generate spillover and partial volume-corrected input functions and to calculate the parametric images of the cerebral metabolic rate for glucose (CMRgl). Different from a blood-sampling-free ICA approach, the results from our ICA approach are numerically well matched to those based on the arterial blood sampled input function. In fact, the ICA-derived input functions and CMRgl measurements were not only highly correlated (correlation coefficients >0.99) to, but also highly comparable (regression slopes between 0.92 and 1.09), with those generated using arterial blood sampling. Moreover, the reliability of the ICA-derived input function remained high despite variations in the location and size of the cubical region. The ICA procedure makes it possible to quantify FDG PET images in an objective and reproducible manner.

Common oat (Avena sativa) is an important cereal crop serving as a valuable source of forage and human food. Although reference genomes of many important crops have been generated, such work in oat has lagged behind, primarily owing to its large, repeat-rich polyploid genome. Here, using Oxford Nanopore ultralong sequencing and Hi-C technologies, we have generated a reference-quality genome assembly of hulless common oat, comprising 21 pseudomolecules with a total length of 10.76 Gb and contig N50 of 75.27 Mb. We also produced genome assemblies for diploid and tetraploid Avena ancestors, which enabled the identification of oat subgenomes and provided insights into oat chromosomal evolution. The origin of hexaploid oat is inferred from whole-genome sequencing, chloroplast genomes and transcriptome assemblies of different Avena species. These findings and the high-quality reference genomes presented here will facilitate the full use of crop genetic resources to accelerate oat improvement.

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Abstract. Error-contaminated systems Ax ≈ b, for which A is ill-conditioned, are considered. Such systems may be solved using Tikhonov-like regularized total least squares (RTLS) methods. Golub, Hansen, and O'Leary [SIAM J. Matrix Anal. Appl., 21 (1999), pp. 185-194] eigenproblem for the RTLS which can be solved using the iterative inverse power method. Here we present an alternative derivation of the eigenproblem for constrained TLS through the augmented Lagrangian for the constrained normalized residual. This extends the analysis of the eigenproblem and leads to derivation of more efficient algorithms compared to the original formulation. Additional algorithms based on bisection search and a standard L-curve approach are presented. These algorithms vary with respect to the parameters that need to be prescribed. Numerical and convergence results supporting the different versions and contrasting with RTLSQEP are presented.

Introduction: A new model for an input function for human [ 18 F]-2-Deoxy-2-fluoro-D-glucose fluoro (FDG) positron emission tomography (PET) brain studies with bolus injection is presented. Methods:Input data for early time, roughly up to 0.6 minutes, are obtained non-invasively from the time activity curve measured from a carotid artery region of interest (CA-ROI). Representative tissue time activity curves are obtained by clustering the output curves to a limited number of dominant clusters. Three venous plasma samples at later time are used to fit the functional form of the input function in conjunction with obtaining kinetic rate parameters of the dominant clusters,K 1 , k 2 and k 3 using the compartmental model for FDG-PET. Experiments to test the approach use data from 18 healthy subjects. Results:The model provides an effective means to recover the input function in FDG-PET studies. Weighted nonlinear least squares parameter estimation using the recovered input function, as contrasted with use of plasma samples, yields highly correlated values of K =K 1 k 3 /(k 2 + k 3 ) for simulated data, correlation coefficient .99780, slope 1.019 and intercept almost zero. The estimates of K for real data by graphical Patlak analysis using the recovered input function are almost identical to those obtained using arterial plasma samples with correlation coefficients greater than 0.9976, regression slopes between .958 and 1.091 and intercepts that are virtually zero. Conclusions:A reliable semi-automated alternative for input function estimation which uses image-derived data augmented with 3 plasma samples is presented and evaluated for FDG-PET human brain studies.