Release of 12 Adipokines by Adipose Tissue,Nonfat Cells,and Fat Cells From Obese Women

The relative release in vitro of endothelin‐1, zinc‐α2‐glycoprotein (ZAG), lipocalin‐2, CD14, RANTES (regulated on activation, normal T cell expressed and secreted protein), lipoprotein lipase (LPL), osteoprotegerin (OPG), fatty acid–binding protein 4 (FABP‐4), visfatin/PBEF/Nampt, glutathione peroxidase‐3 (GPX‐3), intracellular cell adhesion molecule 1 (ICAM‐1), and amyloid A was examined using explants of human adipose tissue as well as the nonfat cell fractions and adipocytes from obese women. Over a 48‐h incubation the majority of the release of LPL was by fat cells whereas that of lipocalin‐2, RANTES, and ICAM‐1 was by the nonfat cells present in human adipose tissue. In contrast appreciable amounts of OPG, amyloid A, ZAG, FABP‐4, GPX‐3, CD14, and visfatin/PBEF/Nampt were released by both fat cells and nonfat cells. There was an excellent correlation (r = 0.75) between the ratios of adipokine release by fat cells to nonfat cells over 48 h and the ratio of their mRNAs in fat cells to nonfat cells at the start of the incubation. The total release of ZAG, OPG, RANTES, and amyloid A by incubated adipose tissue explants from women with a fat mass of 65 kg was not different from that by women with a fat mass of 29 kg. In contrast that of ICAM‐1, FABP‐4, GPX‐3, visfatin/PBEF/Nampt, CD14, lipocalin‐2, LP, and endothelin‐1 was significantly greater in tissue from women with a total fat mass of 65 kg.     

Population Dynamics and Range Expansion in Nine-Banded Armadillos

Understanding why certain species can successfully colonize new areas while others do not is a central question in ecology. The nine-banded armadillo (Dasypus novemcinctus) is a conspicuous example of a successful invader, having colonized much of the southern United States in the last 200 years. We used 15 years (1992–2006) of capture-mark-recapture data from a population of armadillos in northern Florida in order to estimate, and examine relationships among, various demographic parameters that may have contributed to this ongoing range expansion. Modeling across a range of values for γ, the probability of juveniles surviving in the population until first capture, we found that population growth rates varied from 0.80 for γ = 0.1, to 1.03 for γ = 1.0. Growth rates approached 1.0 only when γ ≥0.80, a situation that might not occur commonly because of the high rate of disappearance of juveniles. Net reproductive rate increased linearly with γ, but life expectancy (estimated at 3 years) was independent of γ. We also found that growth rates were lower during a 3-year period of hardwood removal that removed preferred habitat than in the years preceding or following. Life-table response experiment (LTRE) analysis indicated the decrease in growth rate during logging was primarily due to changes in survival rates of adults. Likewise, elasticity analyses of both deterministic and stochastic population growth rates revealed that survival parameters were more influential on population growth than were those related to reproduction. Collectively, our results are consistent with recent theories regarding biological invasions which posit that populations no longer at the leading edge of range expansion do not exhibit strong positive growth rates, and that high reproductive output is less critical in predicting the likelihood of successful invasion than are life-history strategies that emphasize allocation of resources to future, as opposed to current, reproduction.     

Highly Adaptable Triple-Negative Breast Cancer Cells as a Functional Model for Testing Anticancer Agents

A major obstacle in developing effective therapies against solid tumors stems from an inability to adequately model the rare subpopulation of panresistant cancer cells that may often drive the disease. We describe a strategy for optimally modeling highly abnormal and highly adaptable human triple-negative breast cancer cells, and evaluating therapies for their ability to eradicate such cells. To overcome the shortcomings often associated with cell culture models, we incorporated several features in our model including a selection of highly adaptable cancer cells based on their ability to survive a metabolic challenge. We have previously shown that metabolically adaptable cancer cells efficiently metastasize to multiple organs in nude mice. Here we show that the cancer cells modeled in our system feature an embryo-like gene expression and amplification of the fat mass and obesity associated gene FTO. We also provide evidence of upregulation of ZEB1 and downregulation of GRHL2 indicating increased epithelial to mesenchymal transition in metabolically adaptable cancer cells. Our results obtained with a variety of anticancer agents support the validity of the model of realistic panresistance and suggest that it could be used for developing anticancer agents that would overcome panresistance.     

In vitro micropropagation of the tropical fruit tree Syzygium cuminii L.

Multiple shoots were obtained from nodal and shoot tip segments of 10 to 15-day-old seedlings of Syzygium cuminii L. on Murashige & Skoog (MS) revised medium supplemented with 6-benzyladenine (BA) (0.23–8.90 M) singly or in combination with -naphthaleneacetic acid (NAA), indole-3-acetic acid (IAA) or indole-3-butyric acid (IBA). Excised shoots were placed for root induction on MS medium containing NAA and/or IBA and then transferred to MS basal medium to form complete plantlets. The regenerated plantlets have been acclimatized and successfully transferred into the soil.     

Conditional Regression Based on a Multivariate Zero-Inflated Logistic-Normal Model for Microbiome Relative Abundance Data

The human microbiome plays critical roles in human health and has been linked to many diseases. While advanced sequencing technologies can characterize the composition of the microbiome in unprecedented detail, it remains challenging to disentangle the complex interplay between human microbiome and disease risk factors due to the complicated nature of microbiome data. Excessive numbers of zero values, high dimensionality, the hierarchical phylogenetic tree and compositional structure are compounded and consequently make existing methods inadequate to appropriately address these issues. We propose a multivariate two-part zero-inflated logistic-normal model to analyze the association of disease risk factors with individual microbial taxa and overall microbial community composition. This approach can naturally handle excessive numbers of zeros and the compositional data structure with the discrete part and the logistic-normal part of the model. For parameter estimation, an estimating equations approach is employed that enables us to address the complex inter-taxa correlation structure induced by the hierarchical phylogenetic tree structure and the compositional data structure. This model is able to incorporate standard regularization approaches to deal with high dimensionality. Simulation shows that our model outperforms existing methods. Our approach is also compared to others using the analysis of real microbiome data.