Bone microarchitecture at muscle attachment sites: The relationship between macroscopic scores of entheses and their cortical and trabecular microstructural design

The studies of entheses in bioarchaeology attempted to reconstruct the habitual physical activities of past populations. However, the studies of microarchitecture of the underlying bone are still lacking despite well‐known potential of bone internal microarchitecture to reflect mechanical loading. It is unknown whether different morphological expressions of entheseal changes (ECs) correlate with the microstructural characteristics of the underlining bone. This study analyzed bone microstructural characteristics at the entheses. Our focus was on examining the possible successive nature of the three‐stage scale of entheseal macroscopic changes by comparing EC scores with the microarchitectural features at the attachment sites. The study was based on the hypothesis that mechanical loading influences the microarchitecture of the bone at the attachment site. The bone samples were taken from 24 adult male skeletons from medieval cemeteries in Serbia, with different macroscopic expression score of EC. We evaluated the macroscopic and microscopic appearance of four entheses of the lower limbs (origin of the soleus muscle and the insertions of the adductor magnus, gluteus maximus, and iliopsoas muscles). The specimens were scanned using microcomputed tomography (Scanco µCT 40). Our data showed a lack of consistent correlation between stages of the macroscopic scoring systems with microarchitecture at the entheses, only cortical thickness was significantly different between EC stages. Analyzing relationship between trabecular and cortical bone microstructure we found correlations between cortical and trabecular variables only in Stage C. Results of our study suggest that macroscopic EC might not represent distinct successive phases in bone adaptation to mechanical loading. Am J Phys Anthropol 157:81–93, 2015. © 2014 Wiley Periodicals, Inc.     

Soft Shoulders Ahead: Spurious Signatures of Soft and Partial Selective Sweeps Result from Linked Hard Sweeps

Characterizing the nature of the adaptive process at the genetic level is a central goal for population genetics. In particular, we know little about the sources of adaptive substitution or about the number of adaptive variants currently segregating in nature. Historically, population geneticists have focused attention on the hard-sweep model of adaptation in which a de novo beneficial mutation arises and rapidly fixes in a population. Recently more attention has been given to soft-sweep models, in which alleles that were previously neutral, or nearly so, drift until such a time as the environment shifts and their selection coefficient changes to become beneficial. It remains an active and difficult problem, however, to tease apart the telltale signatures of hard vs. soft sweeps in genomic polymorphism data. Through extensive simulations of hard- and soft-sweep models, here we show that indeed the two might not be separable through the use of simple summary statistics. In particular, it seems that recombination in regions linked to, but distant from, sites of hard sweeps can create patterns of polymorphism that closely mirror what is expected to be found near soft sweeps. We find that a very similar situation arises when using haplotype-based statistics that are aimed at detecting partial or ongoing selective sweeps, such that it is difficult to distinguish the shoulder of a hard sweep from the center of a partial sweep. While knowing the location of the selected site mitigates this problem slightly, we show that stochasticity in signatures of natural selection will frequently cause the signal to reach its zenith far from this site and that this effect is more severe for soft sweeps; thus inferences of the target as well as the mode of positive selection may be inaccurate. In addition, both the time since a sweep ends and biologically realistic levels of allelic gene conversion lead to errors in the classification and identification of selective sweeps. This general problem of “soft shoulders” underscores the difficulty in differentiating soft and partial sweeps from hard-sweep scenarios in molecular population genomics data. The soft-shoulder effect also implies that the more common hard sweeps have been in recent evolutionary history, the more prevalent spurious signatures of soft or partial sweeps may appear in some genome-wide scans.     

Botrytis pseudocinerea Is a Significant Pathogen of Several Crop Plants but Susceptible to Displacement by Fungicide-Resistant B. cinerea Strains

Botrytis cinerea is one of the most important pathogens worldwide, causing gray mold on a large variety of crops. Botrytis pseudocinerea has been found previously to occur together with B. cinerea in low abundance in vineyards and strawberry fields. Here, we report B. pseudocinerea to be common and sometimes dominant over B. cinerea on several fruit and vegetable crops in Germany. On apples with calyx end rot and on oilseed rape, it was the major gray mold species. Abundance of B. pseudocinerea was often negatively correlated with fungicide treatments. On cultivated strawberries, it was frequently found in spring but was largely displaced by B. cinerea following fungicide applications. Whereas B. cinerea strains with multiple-fungicide resistance were common in these fields, B. pseudocinerea almost never developed resistance to any fungicide even though resistance mutations occurred at similar frequencies in both species under laboratory conditions. The absence of resistance to quinone outside inhibitors in B. pseudocinerea was correlated with an intron in cytB preventing the major G143A resistance mutation. Our work indicates that B. pseudocinerea has a wide host range similar to that of B. cinerea and that it can become an important gray mold pathogen on cultivated plants.     

DNA from pre-erythrocytic stage malaria parasites is detectable by PCR in the faeces and blood of hosts

Following the bite of an infective mosquito, malaria parasites first invade the liver where they develop and replicate for a number of days before being released into the bloodstream where they invade red blood cells and cause disease. The biology of the liver stages of malaria parasites is relatively poorly understood due to the inaccessibility of the parasites to sampling during this phase of their life cycle. Here we report the detection in blood and faecal samples of malaria parasite DNA throughout their development in the livers of mice and before the parasites begin their growth in the blood circulation. It is shown that parasite DNA derived from pre-erythrocytic stage parasites reaches the faeces via the bile. We then show that different primate malaria species can be detected by PCR in blood and faecal samples from naturally infected captive macaque monkeys. These results demonstrate that pre-erythrocytic parasites can be detected and quantified in experimentally infected animals. Furthermore, these results have important implications for both molecular epidemiology and phylogenetics of malaria parasites. In the former case, individuals who are malaria parasite negative by microscopy, but PCR positive for parasite DNA in their blood, are considered to be “sub-microscopic” blood stage parasite carriers. We now propose that PCR positivity is not necessarily an indicator of the presence of blood stage parasites, as the DNA could derive from pre-erythrocytic parasites. Similarly, in the case of molecular phylogenetics based on DNA sequences alone, we argue that DNA amplified from blood or faeces does not necessarily come from a parasite species that infects the red blood cells of that particular host.     

Automated Identification of Subcellular Organelles by Coherent Anti-Stokes Raman Scattering

Coherent anti-Stokes Raman scattering (CARS) is an emerging tool for label-free characterization of living cells. Here, unsupervised multivariate analysis of CARS datasets was used to visualize the subcellular compartments. In addition, a supervised learning algorithm based on the “random forest” ensemble learning method as a classifier, was trained with CARS spectra using immunofluorescence images as a reference. The supervised classifier was then used, to our knowledge for the first time, to automatically identify lipid droplets, nucleus, nucleoli, and endoplasmic reticulum in datasets that are not used for training. These four subcellular components were simultaneously and label-free monitored instead of using several fluorescent labels. These results open new avenues for label-free time-resolved investigation of subcellular components in different cells, especially cancer cells.     

CellGeo: A computational platform for the analysis of shape changes in cells with complex geometries

Cell biologists increasingly rely on computer-aided image analysis, allowing them to collect precise, unbiased quantitative results. However, despite great progress in image processing and computer vision, current computational approaches fail to address many key aspects of cell behavior, including the cell protrusions that guide cell migration and drive morphogenesis. We developed the open source MATLAB application CellGeo, a user-friendly computational platform to allow simultaneous, automated tracking and analysis of dynamic changes in cell shape, including protrusions ranging from filopodia to lamellipodia. Our method maps an arbitrary cell shape onto a tree graph that, unlike traditional skeletonization algorithms, preserves complex boundary features. CellGeo allows rigorous but flexible definition and accurate automated detection and tracking of geometric features of interest. We demonstrate CellGeo’s utility by deriving new insights into (a) the roles of Diaphanous, Enabled, and Capping protein in regulating filopodia and lamellipodia dynamics in Drosophila melanogaster cells and (b) the dynamic properties of growth cones in catecholaminergic a–differentiated neuroblastoma cells.