Квазиклассическое описание излучения электрона в графене

Supplementary Item 12: Tracer isotope data

Canonical solution for demographics predicting attitudes toward welfare for canonical functions 1–5.

<p>The attitude variate and demographic variate of each function are “latent variables”. The canonical correlation R_c is the correlation between the two variates, and the percentage of variance is presented as R²_c in the middle row of the table. The structure coefficients (r_s, 3rd column within each function) represent the Pearson correlation between an item and it associated canonical variate as determined by factorization of the correlation matrix (e.g., the correlation of <i>Disagree</i> cuts damage lives with the attitudinal variate in the first function is -0.700). Structure coefficients greater than |.35| are underlined, and those greater than |.70| are double underlined. Each individual’s variate scores are calculated by summing the product of the structure coefficient and observed value for each item in the variate (i.e., for attitudes, the items from “<i>Disagree</i> cuts damage lives” to “<i>Disagree</i> single parents deserve”; for demographics, the items from “Bachelor degree plus” to “exposure to benefits”). The correlation of each item with the alternative variate (e.g., “<i>Disagree</i> cuts damage lives” with the demographic variate) is represented in the column of canonical cross loadings (cros., 2^nd column within each function). Also presented are the standardized canonical function coefficients (coef., 1^st column within each function). These are the standardized β coefficients from simultaneously regressing each item in the variate on the variate itself, and as such can be thought of as adjusted structure coefficients. This adjustment process accounts for their typically smaller size relative to the structure coefficients and their sometimes divergent directions. As the reader moves through the table, the function changes. Each function has its own attitude and demographic variate, and across functions the variates are orthogonal. It is thus useful to know how much of the original items variance is represented by the reported canonical functions, that is, each variables communality (<i>h</i>^<i>2</i> reported as a %, final column of table). The communality is calculated by taking the sum of squared structure coefficients (r_s) across the 5 reported functions. Communality coefficients greater than |35.00| are underlined, and those greater than |70.00| are double underlined. The bottom section of the table presents the correlation of the two extracted attitude components from the principle component analysis with the demographic variate of the canonical function.</p><p>Canonical solution for demographics predicting attitudes toward welfare for canonical functions 1–5.</p

Results of Principal components analysis.

<p>Unrotated component loadings from principal component analysis of attitudes.</p><p><i>Note</i>: Loadings < |.2| are not reported.</p><p>Results of Principal components analysis.</p

Tests of attitude valence.

<p>Outcomes of sample-weighted one sample t-tests against a value of 3 (indicating a neutral response). Cohen’s d is reported as a measure of effect size. Comparison confidence intervals are presented for an imputed data set (assumption of missing at random) using sample weighted multiple imputation. Imputation was performed in SPSS v22 using MCMC with seed set to 3319607 and a maximum of 10 iterations and 5 imputed datasets.</p><p>*** denotes that the effect is significant at p < .001</p><p>** denotes that the effect is significant at p < .01.</p><p>Tests of attitude valence.</p

Demographic characteristics of the samples.

<p>The characteristics used in each analysis are presented as percent non-missing data.</p><p>Note</p><p>* indicates that the variable was included in the CCA model</p><p>Demographic characteristics of the samples.</p

Attitudinal items collected in AuSSA 2009.

<p>Welfare items were rated from 1 (strongly agree) to 5 (strongly disagree), and subsequently recoded for analyses such that higher scores indicate more negative attitudes toward welfare, and lower scores indicate less negative attitudes.</p><p>Attitudinal items collected in AuSSA 2009.</p

Prevalence of negative welfare attitude responses.

<p>Five levels of response to each statement were given to respondents for each statement; strongly agree, agree, neither agree nor disagree, disagree, strongly disagree. Darker bars indicate the percentage of strongly negative responses, light bars indicate the percentage of negative responses.</p

Age and sex differences in the annual and seasonal variation of Australia’s suicide rate, 2000–2020

Suicide is a major public health concern both globally and in Australia. But in Australia the extent of substantive annual and seasonality trends since 2000 through the first two decades of the 21st Century, by age and sex, has not been formally reported. The current paper sought to identify annual and within-year (seasonality) trajectories in age-sex standardized suicide rates between 2000 and 2020. The annual and within-year (seasonality) trajectories of suicide were estimated from generalised regression analyses of Australia’s mortality database. No systematic variation in Australia’s suicide rate since 2000 was reported and was consistent between sex and age cohorts. Seasonal variation in rates were identified, with peaks in the new year (January), declines in late Summer/Autumn, stability in Winter, increases in Spring, but with a notable decline in early summer (November–December). These trends were driven men only. Interpretation of current suicide rates need to consider systematic long-term historical context. Despite a historical focus on youth suicide especially, working-aged and very old men have consistently reported higher standardized suicide rates over the first two decades of the 21st Century. Seasonal variation was reported but only reported by men, potentially because across the lifespan, suicide rates for females were a comparatively low incidence event. Particularly after recent successive national and international crises, we emphasise that surveillance and interpretation of current suicide rate requires careful consideration as to the extent any immediate variation may otherwise fall within otherwise normal historical norms.</p

Small-diameter white myotomal muscle fibres associated with growth hyperplasia in the carp (Cyprinus carpio) express a distinct myosin heavy chain gene

A carp myosin heavy chain gene isoform was isolated from a genomic clone, restriction mapped and partially sequenced to reveal the location of various exons. The clone contains a complete gene of approximately 12.0 kb which is half the size of the corresponding mammalian and avian myosin heavy chain genes. The mRNA transcript of this gene, however, is the same size as mammalian and avian striated muscle myosin heavy chain genes (about 6000 nucleotides), illustrating that the difference in size at the genomic level is due to shorter introns. A 169 bp NsiI restriction fragment containing only the 39 untranslated region of this gene was subcloned and used as an isoform-specific probe to study the expression of this particular isoform. Hybridisation analysis could only detect expression of this myosin heavy chain gene in the white muscle of adult carp that had been subjected to an increased environmental temperature. No expression of this gene was detected in carp under 1 year of age. In situ hybridisation demonstrated that expression of this gene is limited to small-diameter white muscle fibres of adult carp, which are thought to be responsible for muscle growth by fibre hyperplasia

Results from a series of longitudinal negative binomial regression models investigating the association between tertiles of prudent and western diet intake at baseline and subsequent number of depressive symptoms reported at waves 2 and 3 for study respondents in the oldest cohort.

*<p>controlling for sex, wave.</p