Automated assessment of histological features of non-alcoholic fatty liver disease (NAFLD) may reduce human variability and provide continuous rather than semiquantitative measurement of these features. As part of a larger effort, we perform automatic classification of steatosis, the cardinal feature of NAFLD, and other regions that manifest as white in images of hematoxylin and eosin-stained liver biopsy sections. These regions include macrosteatosis, central veins, portal veins, portal arteries, sinusoids and bile ducts. Digital images of hematoxylin and eosin-stained slides of 47 liver biopsies from patients with normal liver histology (n = 20) and NAFLD (n = 27) were obtained at 20× magnification. The images were analyzed using supervised machine learning classifiers created from annotations provided by two expert pathologists. The classification algorithm performs with 89% overall accuracy. It identified macrosteatosis, bile ducts, portal veins and sinusoids with high precision and recall (≥ 82%). Identification of central veins and portal arteries was less robust but still good. The accuracy of the classifier in identifying macrosteatosis is the best reported. The accurate automated identification of macrosteatosis achieved with this algorithm has useful clinical and research-related applications. The accurate detection of liver microscopic anatomical landmarks may facilitate important subsequent tasks, such as localization of other histological lesions according to liver microscopic anatomy.
Motivation:In order to understand transcription regulation in a given prokaryotic genome, it is critical to identify operons, the fundamental units of transcription, in such species. While there are a growing number of organisms whose sequence and gene coordinates are known, by and large their operons are not known. Results: We present a probabilistic approach to predicting operons using Bayesian networks. Our approach exploits diverse evidence sources such as sequence and expression data. We evaluate our approach on the Escherichia coli K-12 genome where our results indicate we are able to identify over 78% of its operons at a 10% false positive rate. Also, empirical evaluation using a reduced set of data sources suggests that our approach may have significant value for organisms that do not have as rich of evidence sources as E.coli. Availability: Our E.coli K-12 operon predictions are available at http://www.biostat.wisc.edu/gene-regulation Contact: joebock@biostat.wisc.edu INTRODUCTIONThe availability of complete genomic sequences and microarray expression data calls for new computational methods for uncovering the regulatory apparatus of a cell. We present a Bayesian network approach to predicting operons in prokaryotic genomes. Our approach is able to take into account several data sources including gene coordinates, codon usage statistics, predicted transcription signals, and expression data. We evaluate our approach using data from the E.coli K-12 genome (Blattner et al., 1997).In earlier work (Craven et al., 2000) we presented an operon prediction approach that involved two components: a simple probabilistic method for scoring candidate
Pregnant women are infected by specific variants of Plasmodium falciparum that adhere and accumulate in the placenta. Using serological and molecular approaches, we assessed the global antigenic diversity of surface antigens expressed by placenta-binding isolates to better understand immunity to malaria in pregnancy and evolution of polymorphisms and to inform vaccine development. We found that placenta-binding isolates originating from all major regions where malaria occurs were commonly recognized by antibodies in different populations of pregnant women. There was substantial antigenic overlap and sharing of epitopes between isolates, including isolates from distant geographic locations, suggesting that there are limitations to antigenic diversity; however, differences between populations and isolates were also seen. Many women had crossreactive antibodies and/or a broad repertoire of antibodies to different isolates. Studying VAR2CSA as the major antigen expressed by placenta-binding isolates, we identified antibody epitopes encoded by variable sequence blocks in the DBL3 domain. Analysis of global var2csa DBL3 sequences demonstrated that there was extensive sharing of variable blocks between Africa, Asia, Papua New Guinea, and Latin America, which likely contributes to the high level of antigenic overlap between different isolates. However, there was also evidence of geographic clustering of sequences and differences in VAR2CSA sequences between populations. The results indicate that there is limited antigenic diversity in placenta-binding isolates and may explain why immunity to malaria in pregnancy can be achieved after exposure during one pregnancy. Inclusion of a limited number of variants in a candidate vaccine may be sufficient for broad population coverage, but geographic considerations may also have to be included in vaccine design.
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