Background: The combination of experimental evolution with whole-genome resequencing of pooled individuals, also called evolve and resequence (E&R) is a powerful approach to study the selection processes and to infer the architecture of adaptive variation. Given the large potential of this method, a range of software tools were developed to identify selected SNPs and to measure their selection coefficients. Results: In this benchmarking study, we compare 15 test statistics implemented in 10 software tools using three different scenarios. We demonstrate that the power of the methods differs among the scenarios, but some consistently outperform others. LRT-1, CLEAR, and the CMH test perform best despite LRT-1 and the CMH test not requiring time series data. CLEAR provides the most accurate estimates of selection coefficients. Conclusion: This benchmark study will not only facilitate the analysis of already existing data, but also affect the design of future data collections.

Evolve and Resequencing (E&R) studies allow us to monitor adaptation at the genomic level. By sequencing evolving populations at regular time intervals, E&R studies promise to shed light on some of the major open questions in evolutionary biology such as the repeatability of evolution and the molecular basis of adaptation. However, data interpretation, statistical analysis and the experimental design of E&R studies increasingly require simulations of evolving populations, a task that is difficult to accomplish with existing tools, which may i) be too slow, ii) require substantial reformatting of data, iii) not support an adaptive scenario of interest or iv) not sufficiently capture the biology of the used model organism. Therefore we developed MimicrEE2, a multi-threaded Java program for genome-wide forward simulations of evolving populations. MimicrEE2 enables the convenient usage of available genomic resources, supports biological particulars of model organism frequently used in E&R studies and offers a wide range of different adaptive models (selective sweeps, polygenic adaptation, epistasis). Due to its user-friendly and efficient design MimicrEE2 will facilitate simulations of E&R studies even for small labs with limited bioinformatics expertise or computational resources. Additionally, the scripts provided for executing MimicrEE2 on a computer cluster permit the coverage even of a large parameter space. MimicrEE2 runs on any computer with Java installed. It is distributed under the GPLv3 license at https://sourceforge.net/projects/mimicree2/.

Average faces were created from 3D photographs, and the facial morphological differences between populations and genders were compared. African-American males had a more prominent upper forehead and periocular region, wider alar base and more protrusive lips. Caucasian-American males showed a more prominent nasal tip and malar area. African-American females had broader face, wider alar base and more protrusive lips. Caucasian-American females showed a more prominent chin point, malar region and lower forehead.

The combination of experimental evolution with whole genome re-sequencing of pooled individuals, also called Evolve and Resequence (E&R) is a powerful approach to study selection processes and to infer the architecture of adaptive variation. Given the large potential of this method, a range of software tools were developed to identify selected SNPs and to measure their selection coefficients. In this benchmarking study, we are comparing 15 test statistics implemented in 10 software tools using three different scenarios. We demonstrate that the power of the methods differs among the scenarios, but some consistently outperform others. LRT-1, which takes advantage of time series data consistently performed best for all three scenarios. Nevertheless, the CMH test, which requires only two time points had almost the same performance. This benchmark study will not only facilitate the analysis of already existing data, but also affect the design of future data collections.

Select xxx random SNPs (min freq>0.05 & max freq<0.95 ) Assign e ect sizes to the selected alleles (random pollarization of the allele) Prepare additional input les: recombination map, selection regime Run simulations with MimicrEE2 Run CMH test from Popoolation2 Make labels and predictions for the ROC curves Produce ROC curves with ROCR and compare e ciency estimating the AUC of each curve

Hamate fractures are exceedingly rare clinical entities. However, the diagnosis and treatment of these injuries are often delayed and can severely handicap the performance of affected laborers or athletes. This review focuses on fractures of the hamate and provides an update on the current consensus as to mechanism, diagnosis, management, and complications after such injuries.

Perennial weeds constitute a serious problem in Greek cotton-growing areas, as they strongly competing against the crop and downgrade the final product. Monitoring weeds at a regional scale and relating their occurrence with abiotic factors will assist in the control of these species. Purple nutsedge, field bindweed, bermudagrass, and johnsongrass were studied in cotton crops for three consecutive growing seasons (2007 through 2009) in a large area of central Greece. Weed densities and uniformities per sampling site were assessed in relation to soil and climatic data. Abundance index (AI), which is highly dependent on abiotic factors, was also estimated, and revealed purple nutsedge to the most persistent and damaging species among the recorded weeds. Field bindweed showed the highest correlation with soil properties and especially with clay content. Furthermore, correlation analysis was used over the sampling years in order to assess the stability of weed occurrence in the sampling sites. Purple nutsedge, field bindweed, and bermudagrass proved to be stable in location and intensity. The weed density spatial distribution was evaluated by using local indicators of spatial autocorrelation (LISA) statistics, and was mapped by ordinary kriging and co-kriging interpolation methods. Only 1 to 3 spatial outliers were identified in each 1 of the 3 yr. Between the two interpolation methods co-kriging delivered better results for field bindweed and purple nutsedge, indicating that soil data could improve the estimation of weed occurrence. These co-kriging interpolated weed maps would be a very useful tool for decision makers in taking appropriate weed control measures.

SUMMARYSunflower (Helianthus annuus L.) and rapeseed (Brassica napus L.) are considered as the most suitable crops for biodiesel production in the Mediterranean basin. Soybean (Glycine max L.) could also be used, under certain conditions. In Greece, the farming practice adopted in each region varies significantly, leading to significant differences in the levels of emitted greenhouse gases (GHG). Greenhouse gas emissions were estimated during the cultivation phase as grams of carbon dioxide equivalents (g CO2e) per megajoule (MJ), followed by emission savings (%) estimation when fossil fuels are replaced by biodiesel. Crop and region comparisons provided important information towards promoting sustainability. Overall, sunflower demonstrated the lowest average emissions, 53·8 g CO2e/MJ, followed by rapeseed and soybean. Furthermore, rapeseed achieved the lowest emission saving level required by European legislation in most cases studied, with an average value of 37%. Irrigation and nitrogen fertilization were the operations mostly contributing to the total quantity of GHG emissions. More specifically, the highest GHG emissions were found for soybean irrigation (34%) and rapeseed nitrogen fertilization (68%).