The caribbean coastal marine productivity program (CARICOMP)

CARICOMP is a regional scientific program to study land-sea interaction processes in the Caribbean coastal zone. It has been collecting data since 1992, when a Data Management Centre was established at the University of the West Indies in Jamaica. Initially it focuses on documenting the structure and productivity of major coastal communities (mangrove forests, seagrass meadows and coral reefs) at relatively undisturbed sites in diverse physical settings. Second, by regular recording of physical and biological parameters, it monitors for change, seeking to distinguish natural from anthropogenic disturbance. Third, it constitutes a regional network of observers, able to collaborate on studies of region-wide events. Examples are presented of the diverse data sets collected by the Program.Fil: Alcolado, Pedro M.. Instituto de Oceanología; CubaFil: Alleng, Gerard. No especifíca;Fil: Bonair, Kurt. No especifíca;Fil: Bone, David. Universidad Simón Bolívar; VenezuelaFil: Buchan, Kenneth. No especifíca;Fil: Bush, Phillippe G.. Protection and Conservation Unit; Islas CaimánFil: De Meyer, Kalli. No especifíca;Fil: Garcia, Jorge R.. Universidad de Puerto Rico; Puerto RicoFil: Garzón Ferreira, Jaime. Instituto de Investigaciones Marinas y Costeras; ColombiaFil: Gayle, Peter M. H.. Discovery Bay Marine Laboratory; JamaicaFil: Gerace, Donald T.. Bahamian Field Station; BahamasFil: Geraldes, Francisco X.. Universidad Autonoma de Santo Domingo.; República DominicanaFil: Dahlgren, Eric Jordán. Universidad Nacional Autónoma de México; MéxicoFil: Kjferve, Björn. University of South Carolina; Estados UnidosFil: Klein, Eduardo. Universidad Simón Bolívar; VenezuelaFil: Koltes, Karen. Smithsonian Institution; Estados UnidosFil: Laydoo, Richard S.. No especifíca;Fil: Linton, Dulcie M.. University of the West Indies ; JamaicaFil: Ogden, John C.. Florida Institute of Oceanography; Estados UnidosFil: Oxenford, Hazel A.. McGill University; BarbadosFil: Parker, Christoph. McGill University; BarbadosFil: Penchaszadeh, Pablo Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Museo Argentino de Ciencias Naturales "Bernardino Rivadavia"; ArgentinaFil: Pors, Leon P. P. J.. Universidad Simón Bolívar; VenezuelaFil: Ramírez Ramírez, Javier. Instituto Politécnico Nacional. Centro de Investigación y de Estudios Avanzados. Departamento de Física; MéxicoFil: Ruiz Rentería, Francisco. Universidad Nacional Autónoma de México; MéxicoFil: Ryan, Joseph D.. Centro de Investigación y Documentación de la Costa Atlántica; NicaraguaFil: Smith, Struan R.. Bermuda Biological Station for Research; BermudasFil: Tschirky, John. Latin American and Caribbean Division; Estados UnidosFil: Varela, Ramon. Estación de Investigaciones Marinas de Margarita; VenezuelaFil: Walker, Susan. No especifíca;Fil: Weil, Ernesto. Universidad de Puerto Rico; Puerto RicoFil: Wiebe, William J.. University of Georgia; Estados UnidosFil: Woodley, Jeremy D.. University of the West Indies; JamaicaFil: Zieman, Joseph C.. University of Virginia; Estados Unido

Genetics of diabetic complications [Letter]

In 1965 Neel described diabetes mellitus as the “geneticist's nightmare”.1 Twin, family, and transracial studies suggested that inherited factors were important in the aetiology of the disease. It is now recognised that multiple genes, interacting with the environment, contribute to disease susceptibility. Despite the complexity of the picture, it has been possible to reduce the nightmare into just a “bad dream” by concentrating on specific subgroups of type 1 and type 2 diabetes. Thus in recent years the genes responsible for some cases of maturity-onset diabetes of the young (MODY),2–4 maternally inherited diabetes,5 and severe insulin resistance6 have been identified and several genome-wide searches have suggested possible loci for the more common forms of type 2 diabetes

Intrauterine environment and later disease development: Infertility treatment and the risk of diabetes in offspring

The phenotype of an individual, including their susceptibility to disease, is governed by several factors including parental genes and intrauterine environment. Thus, the risk of developing Type 2 diabetes is modulated by the inheritance of specific genetic variants that are slowly being characterised by the techniques of linkage analysis and population association studies using either a candidate gene or genome-wide scan approach. At the same time, evidence has accrued that alterations in the nutritional status of the developing foetus also increase the risk of diabetes in later life. Restricting protein intake in pregnant dams or interfering with placental function increases the risk of diabetes in offspring and light weight babies are more likely to develop Type 2 diabetes as adults than heavier ones. The oocyte plays a key role, since it contains not only the maternal haplotype but other information such as mitochondrial DNA and factors that modulate the expression of genes in the developing foetus. Although the ovaries contain a huge number of primordial follicles, generally each month only one oocyte matures to ovulation. Little is known about the processes that control this phenomenon. Certainly, primordial follicles and oocytes are not all the same, differing especially in mitochondrial DNA content. As women age, the oocytes released are more likely to contain genetic errors, explaining the increased risk of Trisomy 21 with maternal age. It is generally assumed that primordial follicle development and the selection of a single ooctye for ovulation is a random process. This paper suggests that this may not be the case but that a carefully controlled system may allow the mother to release an oocyte that is best suited to the prevailing environment. This would represent an important mechanism for species adaptation. Many human infertility treatments involve pharmacological superovulation, egg harvesting and culture prior to in vitro fertilisation and reimplantation. These will bypass any system of controlled ovulation and therefore might alter the risk of diseases such as Type 2 diabetes mellitus in later life. Although the offspring of human infertility treatments are generally born healthy, it is important to note that the oldest “test-tube” baby is still less than 30-years old, so the risk of late-onset diseases is still unknown