Interannual Variability of Fisheries Economic Returns and Energy Ratios Is Mostly Explained by Gear Type

According to portfolio theory applied to fisheries management, economic returns are stabilised by harvesting in a portfolio stocks of species whose returns are negatively correlated and for which the portfolio economic return variance is smaller than the sum of stock specific return variances. Also, variability is expected to decrease with portfolio width. Using a range of indicators, these predictions were tested for the French fishing fleets in the Bay of Biscay (Northeast Atlantic) during the period 2001–2009. For this, vessels were grouped into eight fishing fleets based on the gears used and exploited species were grouped into five functional groups. The portfolio width of fleets ranged from 1–3 functional groups, or 4–19 species. Economic fleet returns (sale revenues minus fishing costs) varied strongly between years; the interannual variability was independent of portfolio width (species or functional groups). Energy ratio expressed by the ratio between fuel energy used for fishing and energy contained in landings varied from 0.3 for purse seines to 9.7 for trawlers using bottom trawls alone or in combination with pelagic trawls independent of portfolio width. Interannual variability in total sale revenues was larger than the sum of species specific sales revenue variability, except for fleets using hooks and pelagic trawlers; it increased with the number of species exploited. In conclusion, the interannual variability of economic returns or energy ratios of French fisheries in the Bay of Biscay did not decrease with the number of species or functional groups exploited, though it varied between fleets.     

A semi-Markov model based on generalized Weibull distribution with an illustration for HIV disease

Multi-state stochastic models are useful tools for studying complex dynamics such as chronic diseases. Semi-Markov models explicitly define distributions of waiting times, giving an extension of continuous time and homogeneous Markov models based implicitly on exponential distributions. This paper develops a parametric model adapted to complex medical processes. (i) We introduced a hazard function of waiting times with a U or inverse U shape. (ii) These distributions were specifically selected for each transition. (iii) The vector of covariates was also selected for each transition. We applied this method to the evolution of HIV infected patients. We used a sample of 1244 patients followed up at the hospital in Nice, France.     

Reproductive characteristics of free-livingCalomys venustus (Rodentia, Muridae)

Calomys venustus showed a cyclic seasonal pattern of reproductive activity characterized by a period of repose and a variable length of the period of sexual activity between August—September to May—June. The study was done from 1983 to 1999. During this time the population density increased from October—November to the end of autumn and sharply decreased in winter. Three peaks in rate of pregnancy were recognized in the breeding period: spring, summer and late summer. The average litter size was different among the three peaks in rate of pregnancy. The reproductive pattern had a peak at the beginning of the spring produced by overwintering animals, and another peak in summer-autumn caused by the reproductive activity of young born in the same period in which they breed. The spring cohort exhibited the greatest breeding contribution suggesting that this cohort is principally responsible for the yearly peak in abundance.     

Aromatase expression in ovarian epithelial cancers

Our study focused on aromatase cytochrome P450 (CYP19) expression in ovarian epithelial normal and cancer cells and tissues. Aromatase mRNA expression was analyzed by real-time PCR in ovarian epithelial cancer cell lines, in human ovarian surface epithelial (HOSE) cell primary cultures, and in ovarian tissue specimens (n=94), including normal ovaries, ovarian cysts and cancers. Aromatase mRNA was found to be expressed in HOSE cells, in BG1, PEO4 and PEO14, but not in SKOV3 and NIH:OVCAR-3 ovarian cancer cell lines. Correlation analysis of aromatase expression was performed according to clinical, histological and biological parameters. Aromatase expression in ovarian tissue specimens was higher in normal ovaries and cysts than in cancers (P<0.0001). Using laser capture microdissection in normal postmenopausal ovaries, aromatase was found to be predominantly expressed in epithelial cells as compared to stromal component. Using immunohistochemistry (IHC), aromatase was also detected in the epithelium component. There was an inverse correlation between aromatase and ERalpha expression in ovarian tissues (P<0.001, r=-0.34). In the cancer group, no significant differences in aromatase expression were observed according to tumor histotype, grade, stage and survival. Aromatase activity was evaluated in ovarian epithelial cancer (OEC) cell lines by the tritiated water assay and the effects of third-generation aromatase inhibitors (AIs) on aromatase activity and growth were studied. Letrozole and exemestane were able to completely inhibit aromatase activity in BG1 and PEO14 cell lines. Interestingly, both AI showed an antiproliferative effect on the estrogen responsive BG1 cell line co-expressing aromatase and ERalpha. Aromatase expression was found in ovarian epithelial normal tissues and in some ovarian epithelial cancer cells and tissues. This finding raises the possibility that some tumors may respond to estrogen and provides a basis for ascertaining an antimitogenic effect of AI in a subgroup of ovarian epithelial cancers.