A finite element based method to determine the properties of planar soft tissue.

A finite element based method to determine the incremental elastic material properties of planar membranes was developed and evaluated. The method is applicable to tissues that exhibit inhomogeneity, geometric and material nonlinearity, and anisotropy. Markers are placed on the tissue to form a four-node quadrilateral element. The specimen is loaded to an initial reference state, then three incremental loading sets are applied and the nodal displacements recorded. One of these loadings must include shear. These data are used to solve an over-determined system of equations for the tangent stiffness matrix. The method was first verified using analytical data. Next, data obtained from a latex rubber sheet were used to evaluate experimental procedures. Finally, experiments conducted on preconditioned rat skin revealed nonlinear orthotropic behavior. The vector norm comparing the applied and calculated nodal force vectors was used to evaluate the accuracy of the solutions.     

A MONTE CARLO SIMULATION OF CLONE GROWTH

A Monte Carlo simulation of clone growth is discussed from the point of view of clonal volume. It is shown that clone volume is a good representation of the number of cells per clone for a wide range of single cell growth equations. However, the rate at which the coefficient of variation in clonal volume approaches that of cell number per clone is strongly dependent upon the particular growth equation.     

High levels of expression of full length human pro-alpha 2(V) collagen cDNA in pro-alpha 2(V)-deficient hamster cells

A full length cDNA encoding human pro-alpha 2(V) collagen was constructed. Partial sequencing of the cDNA and primer extension analysis of mRNA from fibroblasts found that pro-alpha 2(V) mRNA differs from the mRNAs of other fibrillar collagens in the increased length of its 5'-untranslated region. The pro-alpha 2(V) cDNA was placed downstream of the human cytomegalovirus immediate early promoter/regulatory sequences for expression studies in cultured Chinese hamster lung cells. These cells have been shown previously to synthesize large quantities of pro-alpha 1(V) homotrimers as their only collagenous product. Transfection resulted in a number of clonal cell lines that express human alpha 2(V) RNA at levels comparable to, and in some cases greater than, levels found in normal human skin fibroblasts. Pro-alpha 2(V) chains produced in the majority of clonal lines were of sufficient quantity to complex all available endogenous pro-alpha 1(V) chains. Chimeric heterotrimers, composed of hamster alpha 1(V) and human alpha 2(V) chains in a 2:1 ratio, were stable to pepsin digestion and were found predominantly associated with the cell layer. Surprisingly, pro-alpha 2(V) chains, in excess to pro-alpha 1(V) chains, were found in the extracellular matrix and, in much greater abundance, in media. These chains were pepsin sensitive, indicating that pro-alpha 2(V) chains can be secreted as nonstable homotrimers or as free chains.     

Composition and stability of the vervet monkey milk microbiome

The human milk microbiome is vertically transmitted to offspring during the postnatal period and has emerged as a critical driver of infant immune and metabolic development. Despite this importance in humans, the milk microbiome of nonhuman primates remains largely unexplored. This dearth of comparative work precludes our ability to understand how species‐specific differences in the milk microbiome may differentially drive maternal effects and limits how translational models can be used to understand the role of vertically transmitted milk microbes in human development. Here, we present the first culture‐independent data on the milk microbiome of a nonhuman primate. We collected milk and matched fecal microbiome samples at early and late lactation from a cohort of captive lactating vervet monkeys (N = 15). We found that, similar to humans, the vervet monkey milk microbiome comprises a shared community of taxa that are universally present across individuals. However, unlike in humans, this shared community is dominated by the genera Lactobacillus, Bacteroides, and Prevotella. We also found that, in contrast to previous culture‐dependent studies in humans, the vervet milk microbiome exhibits greater alpha‐diversity than the gut microbiome across lactation. Finally, we did not find support for the translocation of microbes from the gut to the mammary gland within females (i.e., “entero‐mammary pathway”). Taken together, our results show that the vervet monkey milk microbiome is taxonomically diverse, distinct from the gut microbiome, and largely stable. These findings demonstrate that the milk microbiome is a unique substrate that may selectively favor the establishment and persistence of particular microbes across lactation and highlights the need for future experimental studies on the origin of microbes in milk.     

Predicting Carriers of Ongoing Selective Sweeps without Knowledge of the Favored Allele

Methods for detecting the genomic signatures of natural selection have been heavily studied, and they have been successful in identifying many selective sweeps. For most of these sweeps, the favored allele remains unknown, making it difficult to distinguish carriers of the sweep from non-carriers. In an ongoing selective sweep, carriers of the favored allele are likely to contain a future most recent common ancestor. Therefore, identifying them may prove useful in predicting the evolutionary trajectory—for example, in contexts involving drug-resistant pathogen strains or cancer subclones. The main contribution of this paper is the development and analysis of a new statistic, the Haplotype Allele Frequency (HAF) score. The HAF score, assigned to individual haplotypes in a sample, naturally captures many of the properties shared by haplotypes carrying a favored allele. We provide a theoretical framework for computing expected HAF scores under different evolutionary scenarios, and we validate the theoretical predictions with simulations. As an application of HAF score computations, we develop an algorithm (PreCIOSS: Predicting Carriers of Ongoing Selective Sweeps) to identify carriers of the favored allele in selective sweeps, and we demonstrate its power on simulations of both hard and soft sweeps, as well as on data from well-known sweeps in human populations.