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. The analysis revealed dynamic bloom patterns by these phytoplankton taxa that begin early in the year, when the mixed layer is deep. Previously, unreported open-ocean prasinophyte blooms dominated the plastid gene signal during convective mixing events. Quantitative PCR confirmed the blooms and transitions of Bathycoccus, Micromonas and Ostreococcus populations. In contrast, taxa belonging to the pelagophytes and chrysophytes, as well as cryptophytes, reached annual peaks during mixed layer shoaling, while Bacillariophyceae (diatoms) were observed only episodically in the 12-year record. Prymnesiophytes dominated the integrated plastid gene signal. They were abundant throughout the water column before mixing events, but persisted in the deep chlorophyll maximum during stratified conditions. Various models have been used to describe mechanisms that drive vernal phytoplankton blooms in temperate seas. The range of taxon-specific bloom patterns observed here indicates that different 'spring bloom' models can aptly describe the behavior of different phytoplankton taxa at a single geographical location. These findings provide insight into the subdivision of niche space by phytoplankton and may lead to improved predictions of phytoplankton responses to changes in ocean conditions.

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. A bioinformatic pipeline was developed to de-noise, normalize and align terminal restriction fragment length polymorphism (T-RFLP) data from three restriction enzymes and link T-RFLP peaks to microbial clades. Non-metric multidimensional scaling statistics resolved three microbial communities with distinctive composition during seasonal stratification: a surface community in the region of lowest nutrients, a deep chlorophyll maximum community and an upper mesopelagic community. A fourth microbial community was associated with annual spring blooms of eukaryotic phytoplankton that occur in the northwestern Sargasso Sea as a consequence of winter convective mixing that entrains nutrients to the surface. Many bacterial clades bloomed in seasonal patterns that shifted with the progression of stratification. These richly detailed patterns of community change suggest that highly specialized adaptations and interactions govern the success of microbial populations in the oligotrophic ocean.

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. Other potential causes for her memory loss were ruled out. After fluoxetine was discontinued, the patient's memory improved significantly over the next 2 months. An objective causality assessment indicated a possible relationship between the memory loss and fluoxetine in this patient. DISCUSSION: Our report documents a case of severe reversible memory deterioration after initiating fluoxetine. Fluoxetine has a favorable adverse effect profile when compared with older classes of antidepressants. Postmarketing studies and isolated case reports, however, suggest that fluoxetine may harm memory in some patients. Some selective serotonin-reuptake inhibitors (SSRIs) appear to cause memory loss more frequently than others. CONCLUSIONS: Clinicians should be aware of the possible effects of fluoxetine (and possibly other SSRIs) on memory.

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.