Phytoplankton distribution patterns in the northwestern Sargasso Sea revealed by small subunit rRNA genes from plastids.

Phytoplankton species vary in their physiological properties, and are expected to respond differently to seasonal changes in water column conditions. To assess these varying distribution patterns, we used 412 samples collected monthly over 12 years (1991-2004) at the Bermuda Atlantic Time-Series Study site, located in the northwestern Sargasso Sea. We measured plastid 16S ribosomal RNA gene abundances with a terminal restriction fragment length polymorphism approach and identified distribution patterns for members of the Prymnesiophyceae, Pelagophyceae, Chrysophyceae, Cryptophyceae, Bacillariophyceae and Prasinophyceae.

Seasonality and vertical structure of microbial communities in an ocean gyre.

Vertical, seasonal and geographical patterns in ocean microbial communities have been observed in many studies, but the resolution of community dynamics has been limited by the scope of data sets, which are seldom up to the task of illuminating the highly structured and rhythmic patterns of change found in ocean ecosystems. We studied vertical and temporal patterns in the microbial community composition in a set of 412 samples collected from the upper 300 m of the water column in the northwestern Sargasso Sea, on cruises between 1991 and 2004. The region sampled spans the extent of deep winter mixing and the transition between the euphotic and the upper mesopelagic zones, where most carbon fixation and reoxidation occurs.

Memory loss in a patient treated with fluoxetine.

Objective: To report a case of severe memory loss in an elderly patient after initiation of fluoxetine.

Case Summary: An 87-year-old white woman was started on fluoxetine for depression, and the dose was titrated to 20 mg/d. She developed progressive memory loss over the next 6 weeks for which she ultimately was hospitalized.

Universal approximation in p-mean by neural networks.

A feedforward neural net with d input neurons and with a single hidden layer of n neurons is given byg(x(1), em leader,x(d))= summation operator j=1na(j)sigma,where a(j), theta(j), w(ji) in R. In this paper we study the approximation of arbitrary functions F:R(d)-->R by a neural net in an L(p)(&mgr;) norm for some finite measure &mgr; on R(d). We prove that under natural moment conditions, a neural net with non-polynomial function can approximate any given function.