12 research outputs found

    Phenotype correlations among the asymmetry measures.

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    <p>The correlation estimate is shown in the upper right triangle of the matrix and the p-value based on permutation testing is shown in the lower left triangle of the matrix.</p

    Deformation phenotypes.

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    <p>During the affine registration process, native space images are skewed (sheared) to ‘correct’ hemispheric asymmetry and align the images to the symmetric template. The magnitude of the skewing is a quantitative measure of hemispheric asymmetry. The arrows in each panel indicate the direction volume is shifted during image registration. The asymmetric distribution of volume in the native space (non-deformed) image is therefore opposite to the direction of the arrows. The skews have been exaggerated to emphasize the otherwise subtle distortions introduced by the registration process. Panel A: A positive skew in the transverse plane corresponds to an anterior shift of voxels in the left hemisphere and a posterior shift of voxels in the right hemisphere during registration to the symmetric template. Panel B: A positive skew in the coronal plane leads to a ventral shift of voxels in the left hemisphere and a dorsal shift of voxels in the right hemisphere during registration to the symmetric template. Panel C shows the distributions of the normalized phenotypes.</p

    Cingulate sulcus asymmetry.

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    <p>Transverse Slices showing the relative position of the left and right ascending ramus of the cingulate sulcus. The left panel shows an image that was consistently scored as +2, the middle panel shows an image scored as symmetric (score = 0), and the right panel shows an image scored as −2.</p

    Cerebral Widths and Asymmetry Quotient Distributions.

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    <p>Left panel shows an example of a traverse slice dorsal to the corpus callosum with cerebral widths indicated on the right hemisphere. Right panel shows the distributions of the asymmetry quotients for hemisphere volume and each cerebral width.</p

    Correlation coefficients of cerebral widths for each pair of regions.

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    <p>The Pearson's correlation is shown in the upper right triangle of the matrix and the p-value determined from permutation testing is shown in the lower left triangle of the matrix.</p

    Genetic (<b>ρ<sub>G</sub></b>) and environmental (<b>ρ<sub>E</sub></b>) correlations for cerebral width phenotypes.

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    <p>The estimate of the proportion of common genetic sources contributing to the phenotypic covariance of each pair of cerebral width is shown in the upper triangle of the table and the proportion of common environmental sources contributing to the covariance between traits is shown in the lower triangle of the table. The standard error of the estimate is in parentheses.</p

    Additional file 1: of The lysosomal protein cathepsin L is a progranulin protease

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    Figure S1. (a) Isolated lysosomes and total lysate from HEK293 cells treated with non-targeting (Con) or GRN transcript targeting siRNA were analyzed for PGRN-specific signal by western blot using the goat polyclonal PGRN antibody. (b) The same samples were also analyzed for GAPDH and lysosomal markers, Lamp2 and Cat D. Figure S2. Cat L (5 ng) efficiently processed PGRN (50 ng) into poly-granulin fragments in 1 h reaction time under pH 4.5. Under the same reaction condition, co-incubation with Cat L inhibitor, Z-FF-FMK blocked proteolytic processing of PGRN by Cat L in a dose-dependent manner. Figure S3. Annotated MS/MS spectra of the identified granulin peptides shown in Fig. 1d and in Tables 1 and 2. The Proteome Discoverer result files (.msf) were imported into the Scaffold 4.3 (Proteome Software) for sequence annotation. The peptides that were identified by both SEQUEST and Mascot above the cutoff values (SEQUEST: 1.3 for singly and doubly charged peptides, 2.5 for triply charged peptides; and Mascot Ion Score: 20) were manually evaluated PGRN peptides generated by Cat L activity were designated as CL-1 to CL-10; whereas the PGRN peptides generated by elastase activity were designated as EL-1 to EL-19. The fact that all the measured precursor masses of the identified peptides were within or around 1 ppm of the theoretical masses and that the tandem mass spectra (MS/MS) exhibit a continuous stretch of b- or y- ion series, or clear peak assignments, indicating confident identifications. Sequence assignments were further supported by multiple spectra of successive cleavages of the same sequence. (DOCX 6702 kb

    Superlinear Composition-Dependent Photocurrent in CVD-Grown Monolayer MoS<sub>2(1–<i>x</i>)</sub>Se<sub>2<i>x</i></sub> Alloy Devices

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    Transition metal dichalcogenides (TMDs) have emerged as a new class of two-dimensional materials that are promising for electronics and photonics. To date, optoelectronic measurements in these materials have shown the conventional behavior expected from photoconductors such as a linear or sublinear dependence of the photocurrent on light intensity. Here, we report the observation of a new regime of operation where the photocurrent depends superlinearly on light intensity. We use spatially resolved photocurrent measurements on devices consisting of CVD-grown monolayers of TMD alloys spanning MoS<sub>2</sub> to MoSe<sub>2</sub> to show the photoconductive nature of the photoresponse, with the photocurrent dominated by recombination and field-induced carrier separation in the channel. Time-dependent photoconductivity measurements show the presence of persistent photoconductivity for the S-rich alloys, while photocurrent measurements at fixed wavelength for devices of different alloy compositions show a systematic decrease of the responsivity with increasing Se content associated with increased linearity of the current–voltage characteristics. A model based on the presence of different types of recombination centers is presented to explain the origin of the superlinear dependence on light intensity, which emerges when the nonequilibrium occupancy of initially empty fast recombination centers becomes comparable to that of slow recombination centers

    Discovery-Based Science Education: Functional Genomic Dissection in Drosophila by Undergraduate Researchers

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    Discovery-Based Science Education: Functional Genomic Dissection in Drosophila by Undergraduate Researcher
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