Inter- and intrafield distribution of cereal leaf beetle species (coleoptera : chrysomelidae) in Belgian winter wheat

Cereal leaf beetles (CLBs), a group of chrysomelid beetles of the genus Oulema (Coleoptera: Chrysomelidae), are well-known pest insects of small-grain cereals in many countries of the Northern hemisphere. Due to the small differences in morphology of species within this genus, classification up to species level remains a challenging task. Since an accurate view of species composition is important for developing targeted control strategies, the goal of this study was to unravel the Oulema species composition in Flanders' wheat fields. During three subsequent years at a series of different fields, Oulema species were collected and classified up to species level (2016: 28 fields, 2017: 30 fields, and 2018: 23 fields). This study reveals that the population consists of four different species: Oulema melanopus, Oulema duftschmidi, and Oulema obscura were most frequently encountered, while Oulema rufocyanea was only marginally present. Furthermore, the population was highly dynamic, as the population share of each species varied between different growing seasons and between the various sampling events within each season. The distance from the field edge had a minor influence on the species composition, but the abundance of beetles increased with the distance to the field edge. A discriminant analysis revealed that based on the measurements of various body parts, an accurate classification up to species level is possible. In conclusion, we observed that the population densities fluctuated within and between years, resulting in variable incidence of CLB in winter wheat fields in the Flanders region

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Taxonomic structure in early to middle childhood: A longitudinal study with Zimbabwean schoolchildren

Children's classification reasoning was examined with longitudinal data for 103 Zimbabwean Black (47) and White (56) children attending a randomly selected sample of public schools. The children varied by gender, social class membership (lower, middle, upper) and race (black, white). The children attempted a set of classification tasks at ages 7, 9, and 11. Responses to the classification tasks were scored in terms of interpretive strategy used to engage the tasks (taxonomic vs. instrumental). Repeated measures MANOVA and post-hoc orthogonal contrasts yielded significant differences in interpretive strategies by age or level of schooling, and social class. Higher social class membership was significantly related to more frequent use of taxonomic rather than functional classification strategies. Results support age/schooling-related effects in the development of taxonomic structure in a non-Western society

Weed population in relation to crop rotation and nitrogen fertilisation

In order to assess the impact of crop rotation and nitrogen fertilisation in an agrosystem, a long-term field experiment has been established in 2006 at the experimental farm of Ghent University and University College Ghent (Bottelare - Belgium). The trial comprises 11 different crop rotations in combination with four nitrogen fertilizer regimes. The different crop rotations are monoculture grain- and silage maize, whether or not followed by Italian ryegrass, permanent and temporary grass-clover and six other rotations with potatoes, wheat, fodder beet and peas. Normal crop husbandry measures were taken for each crop. The experiment was set up on a sandy loam soil, according to a strip plot design with 3 replicates. In the course of the experiment, crop rotation was the horizontal factor and fertilizer nitrogen (N) the vertical factor. The effect of crop rotation on yield, disease pressure, soil structure and earthworm abundance were evaluated yearly. In autumn 2013 the weed seed bank was analysed for each plot using the seedling emergence method. The obtained results indicated differences between the different crop rotations

Potentials and limitations of a growing degree day approach to predict the phenology of cereal leaf beetles

Cereal leaf beetles (CLBs) are described as an invasive pest of small grain cereals in many regions worldwide. Prediction models aimed to prevent yield losses caused by these feeding insects have been developed by researchers all over the world. As a foundation for many of these prediction models, it is known that a specific number of heat units, or growing degree days (GDDs), is required for an insect to complete a certain physiological process. In this paper, we overview the existing GDD models for CLBs. Furthermore, we used our Belgian input data to compare model predictions with our own observations.Though, the existing models were not able to predict the seasonal trends present in our data: the occurrence of various life stages were monitored earlier then the model predicted. Hence, a weighted GDD model was tested on the data as well: the accumulated GDDs during certain periods were balanced according to the significance of this period for the insect. Rainfall and/or relative humidity were included as well. Based on these selected variables, multiple linear regression models, ridge regression models, and regression trees were fitted. This approach performed considerably better compared to the simple accumulation of GDD. However, based on cross-year cross-location validation method, to gain insight in the future performance of the models, the accuracy was still too low to serve as an accurate warning tool