Target cells infected by avian erythroblastosis virus differentiate and become transformed

Transformation in vitro of bone marrow cells by avian erythroblastosis virus (AEV) gives rise to rapidly growing cells of erythroid nature. Target cells of neoplastic transformation by AEV are recruited among the early progenitors of the erythroid lineage, the burst-forming units-erythroid (BFU-E). They express a brain-related antigen at a high level and an immature antigen at a low level. We show that AEV-transformed cells express low levels of the brain antigen and high levels of the immature antigen. Their response to specific factors regulating the erythroid differentiation indicates that they are very sensitive to erythropoietin. Furthermore, cells transformed by a temperature-sensitive mutant of AEV differentiate into hemoglobin-synthesizing cells 4 days after being shifted to the nonpermissive temperature. All these properties are similar to those of late progenitors of the erythroid lineage, the colony-forming units-erythroid (CFU-E). These results indicate that the AEV-transformed cells are blocked in their differentiation at the CFU-E stage.     

Grouped sequential exploitation of food patches in a flock feeder,the feral pigeon

Feral and laboratory flocks of rock doves ($\text{Columba}$$\text{livia}$) show a pattern of grouped sequential exploitation when simultaneously presented with two dispersed, depleting patches of seed. This behavior contrasts with the ideal free distribution pattern shown when patches are small and concentrated. Grouped sequential exploitation consists of two phases: all pigeons first land together and feed at one patch, then leave one by one for the other patch. Departure times of individuals for the second patch are correlated with feeding rate at patch 1, which is in turn correlated with position in the dominance hierarchy. The decision to switch from patch 1 to patch 2 improves individual feeding rates in all cases, but is done slightly later than it should according to optimal foraging theory.     

Sequence Determinants of GLUT1 Oligomerization: ANALYSIS BY HOMOLOGY-SCANNING MUTAGENESIS*

The human blood-brain barrier glucose transport protein (GLUT1) forms homodimers and homotetramers in detergent micelles and in cell membranes, where the GLUT1 oligomeric state determines GLUT1 transport behavior. GLUT1 and the neuronal glucose transporter GLUT3 do not form heterocomplexes in human embryonic kidney 293 (HEK293) cells as judged by co-immunoprecipitation assays. Using homology-scanning mutagenesis in which GLUT1 domains are substituted with equivalent GLUT3 domains and vice versa, we show that GLUT1 transmembrane helix 9 (TM9) is necessary for optimal association of GLUT1-GLUT3 chimeras with parental GLUT1 in HEK cells. GLUT1 TMs 2, 5, 8, and 11 also contribute to a less abundant heterocomplex. Cell surface GLUT1 and GLUT3 containing GLUT1 TM9 are 4-fold more catalytically active than GLUT3 and GLUT1 containing GLUT3 TM9. GLUT1 and GLUT3 display allosteric transport behavior. Size exclusion chromatography of detergent solubilized, purified GLUT1 resolves GLUT1/lipid/detergent micelles as 6- and 10-nm Stokes radius particles, which correspond to GLUT1 dimers and tetramers, respectively. Studies with GLUTs expressed in and solubilized from HEK cells show that HEK cell GLUT1 resolves as 6- and 10-nm Stokes radius particles, whereas GLUT3 resolves as a 6-nm particle. Substitution of GLUT3 TM9 with GLUT1 TM9 causes chimeric GLUT3 to resolve as 6- and 10-nm Stokes radius particles. Substitution of GLUT1 TM9 with GLUT3 TM9 causes chimeric GLUT1 to resolve as a mixture of 6- and 4-nm particles. We discuss these findings in the context of determinants of GLUT oligomeric structure and transport function.     

Phenotype Classification of Zebrafish Embryos by Supervised Learning

Zebrafish is increasingly used to assess biological properties of chemical substances and thus is becoming a specific tool for toxicological and pharmacological studies. The effects of chemical substances on embryo survival and development are generally evaluated manually through microscopic observation by an expert and documented by several typical photographs. Here, we present a methodology to automatically classify brightfield images of wildtype zebrafish embryos according to their defects by using an image analysis approach based on supervised machine learning. We show that, compared to manual classification, automatic classification results in 90 to 100% agreement with consensus voting of biological experts in nine out of eleven considered defects in 3 days old zebrafish larvae. Automation of the analysis and classification of zebrafish embryo pictures reduces the workload and time required for the biological expert and increases the reproducibility and objectivity of this classification.