LEP protein, human

Complete coverage of the lepidopteran families and superfamilies, many of which contain just a few, difficult-to-collect species, is beyond the reach of this initial study. Our more modest aim here was simply to represent a majority of the probable major lineages of Ditrysia. The distribution of our exemplars across the major clades of Minet [6 ] is shown in Additional file 1, which also lists families/superfamilies not sampled, to illustrate the extent of our coverage. Our sampling, which builds on a preliminary study of macrolepidopteran (especially bombycoid) relationships [14 (link)], is most dense in Macrolepidoptera (66 exemplars; 11 of 11 superfamilies) and non-macrolepidopteran Obtectomera (17 exemplars; 4 of 6 superfamilies), which together contain about 70% of ditrysian species diversity. Thirty species of non-obtectomeran Apoditrysia are included, representing eight of eleven superfamilies, and seven species of non-apoditrysian Ditrysia representing four of five superfamilies. One of the latter, Tineoidea (two species included), was used to root the tree, as tineoids are generally agreed to be the oldest superfamily of Ditrysia [1 ,6 ,15 ]. We sampled relatively extensively within a few larger superfamilies, both to get an adequate estimate of ancestral character states, and to further test the resolving power of our genes within superfamilies; our main focus, however, is among-superfamily relationships. Altogether our sample includes 27 of 33 superfamilies and 55 of 100 families of Ditrysia. The six superfamilies not represented each contain fewer than 100 species. The missing families are likewise mostly species poor, the main exceptions being Lycaenidae and several large families of Gelechioidea. Thus, the families represented in our study contain the great majority (>85%) of all species of Ditrysia. The classification system used (Additional file 1) follows the authorities in Kristensen [5 ], with exceptions as noted, including the following: in Pyraloidea we follow the more recent classification of Solis and Maes [16 ]; in Geometridae we update the classification following Hausmann [17 ], Holloway [18 ], Scoble [19 ] and Young [20 ].
Specimens for this study, obtained with the kind help of collectors around the world (see Acknowledgements) are stored in 100% ethanol at -85°C as part of the ATOLep collection at the University of Maryland (details at http://www.leptree.net/collection). DNA extraction used only the head and thorax for most species, leaving the rest of the body including the genitalia as a voucher (see Additional file 1). Wing voucher images for all adult exemplars are posted at http://www.leptree.net/voucher_image_list, and DNA 'barcodes' for nearly all specimens have been kindly generated by the All-Leps Barcode of Life project http://www.lepbarcoding.org/, allowing check of our identifications against the BOLD (Barcode of Life Data system) [21 (link)] reference library and facilitating future identification of specimens whose identity is still pending (i.e., species listed as 'sp.' or 'unidentified' in this report).

The culprits of diabetes may vary for different subgroups of diabetic patients, which implies the distinction of possible interference factors to the glucose regulation system. Nevertheless, the underlying mechanisms through which the factors lead to dysglycemia are common. Numerous studies indicate that glycemia is primarily attributed to excess hepatic glucose output and abnormal insulin secretion and utilization [35 (link)]. Of note, beta-cell function is regulated by various mechanisms, not limited to glucose utilization [12 (link)]. Thus, confining the model for beta-cell function only with the variables of glucose and insulin may impede the study of beta-cell dysfunction. We aim to test through an in-silico approach how the T2D progression is affected by certain pathological factors. Here we propose a general form of diabetes progression model with a pathological factor X that is to be specified:
$$\begin{array}{c}{\displaystyle \frac{\mathrm{d}G}{\mathrm{d}t}}=G_{in}+p_1\left(X\right)-f_2\left(G\right)-C\left(I\right)GI,\hfill \end{array}$$
$$\begin{array}{c}{\displaystyle \frac{\mathrm{d}I}{\mathrm{d}t}}=f_1\left(G\right)p_2\left(X\right)\beta -kI,\hfill \end{array}$$
$$\begin{array}{c}{\displaystyle \frac{\mathrm{d}\beta }{\mathrm{d}t}}=(f_3\left(I\right)+p_3\left(X\right))\beta ,\hfill \end{array}$$
where X is a bounded variable with a real value; all the variables in the system are in the time scale of days: p₁(X) is incorporated into Eq (1) to stand for the increased hepatic glucose production caused by the pathological factor; p₂(X) integrated into Eq (2) symbolizes the impact of the factor on the insulin secretion rate; p₃(X) is incorporated to Eq (3) to describe the abnormal response of beta-cells to a hostile environment that develops in a slow time scale. The exact forms of the influence functions p_i(X) (i = 1, 2, 3) will be determined with X being an obesity-related factor in Section. We assume that p₁(X) = 0, p₂(X) = 1, and p₃(X) = 0 when X = 0 so the model is in accordance with the undisturbed glucose-insulin regulatory model when no diabetogenic factors exist in normal subjects.

Only children with complete cytokine data were included in the clustering analysis. Normality of features was evaluated by Shapiro–Wilk tests. Differences in demographic and clinical features were assessed using t tests for normally distributed continuous variables, Kruskal–Wallis for non-normally distributed continuous variables, and chi-square or Fisher’s exact test for categorical variables depending on the number of subjects per category. There were 59 children with BMI percentiles available for analysis. We defined obesity as having a BMI ≥ 95th %-ile. Time to exacerbation was modeled using a Cox proportional hazard model with cluster identity as the independent exposure variable with and without adjusting for obesity as a confounding factor using the R package survival²³ . The proportional hazards assumption was checked by visual inspection of log–log plots and analyzing the Schoenfeld residuals. Differences in cytokines were assessed using Mann–Whitney U tests to account for non-normal distributions. Differences in gene expression were assessed using t tests. The Benjamini–Hochberg method was used to correct for multiple comparisons²⁴ . A p-value of less than 0.05 and a q-value of less than 0.1 was considered significant.