Mapping and Predicting Non-Linear Brassica rapa Growth Phenotypes Based on Bayesian and Frequentist Complex Trait Estimation.

Predicting phenotypes based on genotypes and understanding the effects of complex multi-locus traits on plant performance requires a description of the underlying developmental processes, growth trajectories, and their genomic architecture. Using data from Brassica rapa genotypes grown in multiple density settings and seasons, we applied a hierarchical Bayesian Function-Valued Trait (FVT) approach to fit logistic growth curves to leaf phenotypic data (length and width) and characterize leaf development. We found evidence of genetic variation in phenotypic plasticity of rate and duration of leaf growth to growing season. In contrast, the magnitude of the plastic response for maximum leaf size was relatively small, suggesting that growth dynamics vs. final leaf sizes have distinct patterns of environmental sensitivity. Consistent with patterns of phenotypic plasticity, several QTL-by-year interactions were significant for parameters describing leaf growth rates and durations but not leaf size. In comparison to frequentist approaches for estimating leaf FVT, Bayesian trait estimation resulted in more mapped QTL that tended to have greater average LOD scores and to explain a greater proportion of trait variance. We then constructed QTL-based predictive models for leaf growth rate and final size using data from one treatment (uncrowded plants in one growing season). Models successfully predicted non-linear developmental phenotypes for genotypes not used in model construction and, due to a lack of QTL-by-treatment interactions, predicted phenotypes across sites differing in plant density.

Signaling pathways governing osteoblast proliferation, differentiation and function.

Osteoblasts are bone forming cells that are responsible for bone growth and remodeling. They are derived from bone marrow mesenchymal stem cells through a series of processes including commitment, osteoprogenitor expansion, terminal differentiation and cell death. Osteoblastogenesis and bone formation are regulated by hormones, growth factors, cytokines, mechanical loading and aging. Osteoblasts can sense these external cues, transduce the signals through various signaling pathways and regulate the expression of specific genes, to determine the cell fate. In this review, we aim to update our current understanding of the signaling pathways that control different steps of osteoblast homeostasis, with special focus on how signaling events control cell fate through regulating gene expression.

A sorting system for hierarchical grading of diabetic fundus images: a preliminary study.

Diabetic retinopathy is a leading cause of blindness in developed countries. Diabetic patients can prevent severe visual loss by attending regular eye examinations and receiving timely treatments. In the United States, standard protocols have been developed and refined for years to provide better screening and evaluation procedures of the fundus images. Due to the emerging number of diabetic retinopathy cases, accurate and efficient evaluations of the fundus images have become a serious burden for the ophthalmologists or care providers. While diabetic retinopathy remains too complicated to call for an automatic diagnosis system, an efficient tool to facilitate the grading process with a limited number of personnel is in great demand. The current study is to develop a sorting system with a user-friendly interface, based upon the standardized early treatment diabetic retinopathy study (ETDRS) protocol, to assist the professional graders. The raw fundus images will be screened and assigned to different graders according to their skill levels and experiences. The developed hierarchical sorting process will greatly support the graders and enhance their efficiency and throughput. The proposed hybrid intelligent system with multilevel knowledge representation is used to construct this sorting system. A preliminary case study is conducted using only the features of the spot lesion group coupled with the ETDRS standard to demonstrate its feasibility and performance. The results obtained from the case study show a promising future.

Microarray and real-time RT-PCR analyses of differential human gene expression patterns induced by severe acute respiratory syndrome (SARS) coronavirus infection of Vero cells.

Vero E6 African green monkey kidney cells are highly susceptible to infection with the newly emerging severe acute respiratory syndrome coronavirus (SARS-CoV), and they are permissive for rapid viral replication, with resultant cytopathic effects. We employed cDNA microarray analysis to characterize the cellular transcriptional responses of homologous human genes at 12 h post-infection. Seventy mRNA transcripts belonging to various functional classes exhibited significant alterations in gene expression. There was considerable induction of heat shock proteins that are crucial to the immune response mechanism. Modified levels of several transcripts involved in pro-inflammatory and anti-inflammatory processes exemplified the balance between opposing forces during SARS pathogenesis. Other genes displaying altered transcription included those associated with host translation, cellular metabolism, cell cycle, signal transduction, transcriptional regulation, protein trafficking, protein modulators, and cytoskeletal proteins. Alterations in the levels of several novel transcripts encoding hypothetical proteins and expressed sequence tags were also identified. In addition, transcription of apoptosis-related genes DENN and hIAP1 was upregulated in contrast to FAIM. Elevated Mx1 expression signified a strong host response to mediate antiviral resistance. Also expressed in infected cells was the C-terminal alternative splice variant of the p53 tumor suppressor gene encoding a modified truncated protein that can influence the activity of wild-type p53. We observed the interplay between various mechanisms to favor virus multiplication before full-blown apoptosis and the triggering of several pathways in host cells in an attempt to eliminate the pathogen. Microarray analysis identifies the critical host-pathogen interactions during SARS-CoV infection and provides new insights into the pathophysiology of SARS.

Phylogenetic analysis of triple gene block viruses based on the TGB 1 homolog gene indicates a convergent evolution.

The complete nucleotide sequence of the triple gene block one (TGB 1) of cymbidium mosaic potexvirus (CymMV) was compared to those from other potex-, carla-, furo- and hordeiviruses. Seven conserved motifs in the TGB 1, including the ATP-GTP binding domain (P-Loop) consensus GXXGKTSTS, were found in all four virus genera. We propose that all TGBV can be classified into phylogenetic clusters based on their TGB 1 homolog genes. These clusters can be further delineated to form subgroups. The first cluster comprises the potexviruses which are further subdivided into three subgroups; BaMV, FMV, PlaMV and PapMV (subgroup Ia); CymMV, PAMV, NMV, SMYEaV and WC1MV (subgroup Ib) and PVX (subgroup Ic). The second cluster comprises carlaviruses with a dual subgrouping; CVB, LSV, PVM, PMV and ASPV (subgroup IIa) and LVX (subgroup IIb). The third cluster carries the most diverse of TGBV comprising furoviruses PCV, PMTV and BSBV (subgroup IIIa) and hordeiviruses PSLV, BSMV and LRSV (subgroup IIIb). The phylogenetic relationships of triple gene block viruses (TGBV) based on the TGB 1 homolog gene indicates a convergent evolution.