Replacement of Marine Fish Oil with de novo Omega-3 Oils from Transgenic Camelina sativa in Feeds for Gilthead Sea Bream (Sparus aurata L.)

Omega-3 (n-3) long-chain polyunsaturated fatty acids (LC-PUFA) are essential components of the diet of all vertebrates and. The major dietary source of n-3 LC-PUFA for humans has been fish and seafood but, paradoxically, farmed fish are also reliant on marine fisheries for fish meal and fish oil (FO), traditionally major ingredients of aquafeeds. Currently, the only sustainable alternatives to FO are vegetable oils, which are rich in C18 PUFA, but devoid of the eicosapentaenoic (EPA) and docosahexaenoic acids (DHA) abundant in FO. Two new n-3 LC-PUFA sources obtained from genetically modified (GM) Camelina sativa containing either EPA alone (ECO) or EPA and DHA (DCO) were compared to FO and wild-type camelina oil (WCO) in juvenile sea bream. Neither ECO nor DCO had any detrimental effects on fish performance, although final weight of ECO-fed fish (117 g) was slightly lower than that of FO- and DCO-fed fish (130 and 127 g, respectively). Inclusion of the GM-derived oils enhanced the n-3 LC-PUFA content in fish tissues compared to WCO, although limited biosynthesis was observed indicating accumulation of dietary fatty acids. The expression of genes involved in several lipid metabolic processes, as well as fish health and immune response, in both liver and anterior intestine were altered in fish fed the GM-derived oils. This showed a similar pattern to that observed in WCO-fed fish reflecting the hybrid fatty acid profile of the new oils. Overall the data indicated that the GM-derived oils could be suitable alternatives to dietary FO in sea bream

Eosinophils in glioblastoma biology

Glioblastoma multiforme (GBM) is the most common primary brain tumor in adults. The development of this malignant glial lesion involves a multi-faceted process that results in a loss of genetic or epigenetic gene control, un-regulated cell growth, and immune tolerance. Of interest, atopic diseases are characterized by a lack of immune tolerance and are inversely associated with glioma risk. One cell type that is an established effector cell in the pathobiology of atopic disease is the eosinophil. In response to various stimuli, the eosinophil is able to produce cytotoxic granules, neuromediators, and pro-inflammatory cytokines as well as pro-fibrotic and angiogenic factors involved in pathogen clearance and tissue remodeling and repair. These various biological properties reveal that the eosinophil is a key immunoregulatory cell capable of influencing the activity of both innate and adaptive immune responses. Of central importance to this report is the observation that eosinophil migration to the brain occurs in response to traumatic brain injury and following certain immunotherapeutic treatments for GBM. Although eosinophils have been identified in various central nervous system pathologies, and are known to operate in wound/repair and tumorstatic models, the potential roles of eosinophils in GBM development and the tumor immunological response are only beginning to be recognized and are therefore the subject of the present review

Shedding Light on the Galaxy Luminosity Function

From as early as the 1930s, astronomers have tried to quantify the statistical nature of the evolution and large-scale structure of galaxies by studying their luminosity distribution as a function of redshift - known as the galaxy luminosity function (LF). Accurately constructing the LF remains a popular and yet tricky pursuit in modern observational cosmology where the presence of observational selection effects due to e.g. detection thresholds in apparent magnitude, colour, surface brightness or some combination thereof can render any given galaxy survey incomplete and thus introduce bias into the LF. Over the last seventy years there have been numerous sophisticated statistical approaches devised to tackle these issues; all have advantages -- but not one is perfect. This review takes a broad historical look at the key statistical tools that have been developed over this period, discussing their relative merits and highlighting any significant extensions and modifications. In addition, the more generalised methods that have emerged within the last few years are examined. These methods propose a more rigorous statistical framework within which to determine the LF compared to some of the more traditional methods. I also look at how photometric redshift estimations are being incorporated into the LF methodology as well as considering the construction of bivariate LFs. Finally, I review the ongoing development of completeness estimators which test some of the fundamental assumptions going into LF estimators and can be powerful probes of any residual systematic effects inherent magnitude-redshift data.Comment: 95 pages, 23 figures, 3 tables. Now published in The Astronomy & Astrophysics Review. This version: bring in line with A&AR format requirements, also minor typo corrections made, additional citations and higher rez images adde