Box-and-whisker plot of all the decoys.

<p>The maximum, the minimum, the 1st quartile, the 3rd quartile, the mean (in symbol +), and the average (in symbol ×) of Ca_rmsd (Y axis in Å) of each decoy set are rendered as box-and-whisker plot for each test instance (X axis).</p

Single ant colony AC searches the lowest with shared .

<p>A standard MMAS algorithm with perturbation from and to Rosetta predictor each other. The blue parts depict the original MMAS components (0, 1, 2, 3, and 5), and the pink parts depict the interaction between AC and Rosetta predictor (component 4 and 6).</p

pacBackbone+ schematic flowchart.

<p>Two different heuristics are introduced, 8 ant colonies and 1 Rosetta predictor are running in parallel threads. The colonies share one pheromone matrix , and Rosetta predictor sends accepted fragments to AC colonies for updating . Also AC colonies send the iteration best solutions to Rosetta to perturb the conformation at every beginning of the prediction stage. The information exchanged between AC colonies and Rosetta predictor is colored by red feedback lines, and the information exchanged among AC colonies is colored by the blue line.</p

The average and standard deviation comparison of 10-percentiles of each pair of decoys.

<p>Each symbol stands for a single test instance. The standard deviation is marked as error bar, and the average is at the cross point of two error bars determined by compared decoys represented in X and Y axis respectively. For instances colored by green no significant difference has been observed.</p

Different scores used as the minimizing objective functions.

<p>Different scores used as the minimizing objective functions.</p

General framework of solving PSP by parallel metaheuristic.

<p>The OP stands for all kinds of application problems, which can be computationally modeled as an optimization problem. Three such models are possible for solving OP, namely , and , where and are derived from how to solve OP numerically and non-numerically respectively. Three parallel solutions can be applied to solve the modeled optimization problems, and the current parallel platform at both hardware and software level can easily support the above three solutions.</p

Changes in Birth Weight between 2002 and 2012 in Guangzhou, China

<div><p>Background</p><p>Recent surveillance data suggest that mean birth weight has begun to decline in several developed countries. The aim of this study is to examine the changes in birth weight among singleton live births from 2002 to 2012 in Guangzhou, one of the most rapidly developed cities in China.</p><p>Methods</p><p>We used data from the Guangzhou Perinatal Health Care and Delivery Surveillance System for 34108 and 54575 singleton live births with 28–41 weeks of gestation, who were born to local mothers, in 2002 and 2012, respectively. The trends in birth weight, small (SGA) and large (LGA) for gestational age and gestational length were explored in the overall population and gestational age subgroups.</p><p>Results</p><p>The mean birth weight decreased from 3162 g in 2002 to 3137 g in 2012 (crude mean difference, −25 g; 95% CI, −30 to −19). The adjusted change in mean birth weight appeared to be slight (−6 g from 2002 to 2012) after controlling for maternal age, gestational age, educational level, parity, newborn's gender and delivery mode. The percentages of SGA and LGA in 2012 were 0.6% and 1.5% lower than those in 2002, respectively. The mean gestational age dropped from 39.2 weeks in 2002 to 38.9 weeks in 2012. In the stratified analysis, we observed the changes in birth weight differed among gestational age groups. The mean birth weight decreased among very preterm births (28–31 weeks), while remained relatively stable among other gestational age subcategories.</p><p>Conclusions</p><p>Among local population in Guangzhou from 2002 to 2012, birth weight appeared to slightly decrease. The percentage of SGA and LGA also simultaneously dropped, indicating that newborns might gain a healthier weight for gestational age.</p></div

Maternal and newborn characteristics among singleton live births in 2002 and 2012.

<p>Data are expressed as mean ± standard deviation or n(%).</p><p>Maternal and newborn characteristics among singleton live births in 2002 and 2012.</p

Changes in birth weight among singleton live births between 2002 and 2012.

<p>Data are expressed as mean ± standard deviation.</p><p>^* Adjusted for maternal age, gestational age, educational level, parity, newborn's gender and delivery mode.</p><p>^** Adjusted for maternal age, educational level, parity, newborn's gender and delivery mode.</p><p>Changes in birth weight among singleton live births between 2002 and 2012.</p

Percentage distribution of singleton live births by birthweight, born in 2002 and 2012.

<p>(A, 28–41 completed weeks of gestation; B, 28–31 completed weeks of gestation; C, 32–36 completed weeks of gestation; D, 37–38 completed weeks of gestation; E, 39–40 completed weeks of gestation; F, 41 completed weeks of gestation).</p