A pigment-binding protein essential for regulation of photosynthetic light harvesting

Photosynthetic light harvesting in plants is regulated in response to changes in incident light intensity. Absorption of light that exceeds a plant's capacity for fixation of CO2 results in thermal dissipation of excitation energy in the pigment antenna of photosystem II by a poorly understood mechanism. This regulatory process, termed nonphotochemical quenching, maintains the balance between dissipation and utilization of light energy to minimize generation of oxidizing molecules, thereby protecting the plant against photo-oxidative damage. To identify specific proteins that are involved in nonphotochemical quenching, we have isolated mutants of Arabidopsis thaliana that cannot dissipate excess absorbed light energy. Here we show that the gene encoding PsbS, an intrinsic chlorophyll-binding protein of photosystem II, is necessary for nonphotochemical quenching but not for efficient light harvesting and photosynthesis. These results indicate that PsbS may be the site for nonphotochemical quenching, a finding that has implications for the functional evolution of pigment-binding proteins.     

Evidence for several waves of global transmission in the seventh cholera pandemic

Vibrio cholerae is a globally important pathogen that is endemic in many areas of the world and causes 3-5 million reported cases of cholera every year. Historically, there have been seven acknowledged cholera pandemics; recent outbreaks in Zimbabwe and Haiti are included in the seventh and ongoing pandemic. Only isolates in serogroup O1 (consisting of two biotypes known as 'classical' and 'El Tor') and the derivative O139 can cause epidemic cholera. It is believed that the first six cholera pandemics were caused by the classical biotype, but El Tor has subsequently spread globally and replaced the classical biotype in the current pandemic. Detailed molecular epidemiological mapping of cholera has been compromised by a reliance on sub-genomic regions such as mobile elements to infer relationships, making El Tor isolates associated with the seventh pandemic seem superficially diverse. To understand the underlying phylogeny of the lineage responsible for the current pandemic, we identified high-resolution markers (single nucleotide polymorphisms; SNPs) in 154 whole-genome sequences of globally and temporally representative V. cholerae isolates. Using this phylogeny, we show here that the seventh pandemic has spread from the Bay of Bengal in at least three independent but overlapping waves with a common ancestor in the 1950s, and identify several transcontinental transmission events. Additionally, we show how the acquisition of the SXT family of antibiotic resistance elements has shaped pandemic spread, and show that this family was first acquired at least ten years before its discovery in V. cholerae.     

Challenges and Opportunities in Urban Meteorology Research and Forecast

An international workshop on urban meteorology: observation and modeling, was jointly held by the Institute of Urban Meteorology (China) and the National Center for Atmospheric Research (US) in Beijing, October, 2004. The workshop was inten ded to share recent progress in urban meteorological research, discuss issues re lated to research and development priorities faced by diverse Chinese institutio ns, and explore collaboration opportunities between Chinese and US research inst itutions. This article summarizes the major issues discussed at the workshop, in cluding observation on urban boundary layer, urban landuse modeling, socioecon omic impacts of weather and climates, and air quality in urban environment. It i ncludes recommendations for future urban meteorology observational and modeling research, and potential collaborative opportunities between China and US.     

General conditions for predictivity in learning theory

Developing theoretical foundations for learning is a key step towards understanding intelligence. 'Learning from examples' is a paradigm in which systems (natural or artificial) learn a functional relationship from a training set of examples. Within this paradigm, a learning algorithm is a map from the space of training sets to the hypothesis space of possible functional solutions. A central question for the theory is to determine conditions under which a learning algorithm will generalize from its finite training set to novel examples. A milestone in learning theory was a characterization of conditions on the hypothesis space that ensure generalization for the natural class of empirical risk minimization (ERM) learning algorithms that are based on minimizing the error on the training set. Here we provide conditions for generalization in terms of a precise stability property of the learning process: when the training set is perturbed by deleting one example, the learned hypothesis does not change much. This stability property stipulates conditions on the learning map rather than on the hypothesis space, subsumes the classical theory for ERM algorithms, and is applicable to more general algorithms. The surprising connection between stability and predictivity has implications for the foundations of learning theory and for the design of novel algorithms, and provides insights into problems as diverse as language learning and inverse problems in physics and engineering.