Carbohydrate metabolism in lactic acid bacteria

The term “lactic acid bacteria” is discussed. An overview of the following topics is given: main pathways of homo- and heterofermentation of hexoses, i.e. glycolysis, bifidus pathway, 6-phosphogluconate pathway; uptake and dissimilation of lactose (tagatose pathway); fermentation of pentoses and pentitols; alternative fates of pyruvate, i.e. splitting to formate and acetate, CO₂ and acetate or formation of acetoin and diacetyl; lactate oxidation; biochemical basis for the formation of different stereoisomers of lactate.     

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.     

Scale sensitivity of synthetic long‐term vegetation time series derived through overlay of short‐term field records

Questions: Is change in cover of dominant species driving the velocity of succession or is it species turnover (1)? Is the length of the time‐step chosen in sampling affecting our recognition of the long‐term rate of change (2)1 Location: 74 permanent plots located in the Swiss National Park, SE Switzerland, ca. 1900 m a.s.l. Methods: We superimpose several time‐series from permanent plots to one single series solely based on compositional dissimilarity. As shown earlier (Wildi & Schütz 2000) this results in a synthetic series covering about 400 to 650 yr length. Continuous power transformation of cover‐percentage scores is used to test if the dominance or the presence‐absence of species is governing secondary succession from pasture to forest. The effect of time step length is tested by sub‐samples of the time series. Results: Altering the weight of presence‐absence versus dominance of species affects the emerging time frame, while altering time step length is uncritical. Where species turnover is fast, different performance scales yield similar results. When cover change in dominant species prevails, the solutions vary considerably. Ordinations reveal that the synthetic time series seek for shortest paths of the temporal pattern whereas in the real system longer lasting alternatives exist. Conclusions: Superimposing time series differs from the classical space‐for‐time substitution approach. It is a computation‐based method to investigate temporal patterns of hundreds of years fitting between direct monitoring (usually limited to decades) and the analysis of proxy‐data (for time spans of thousands of years and more).