TRPV6 modulates proliferation of human pancreatic neuroendocrine BON-1 tumour cells

Highly Ca2+ permeable receptor potential channel vanilloid type 6 (TRPV6) modulates a variety of biological functions including calcium-dependent cell growth and apoptosis. So far, the role of TRPV6 in controlling growth of pancreatic neuroendocrine tumour (NET) cells is unknown. In the present study, we characterize the expression of TRPV6 in pancreatic BON-1 and QGP-1 NET cells. Furthermore, we evaluate the impact of TRPV6 on intracellular calcium, the activity of nuclear factor of activated T-cells (NFAT) and proliferation of BON-1 cells. TRPV6 expression was assessed by real-time PCR and Western blot. TRPV6 mRNA expression and protein production were down-regulated by siRNA. Changes in intracellular calcium levels were detected by fluorescence calcium imaging (fura-2/AM). NFAT activity was studied by NFAT reporter assay; cell proliferation by bromodeoxyuridine (BrdU), MTT and propidium iodine staining. TRPV6 mRNA and protein are present in BON-1 and QGP-1 NET-cells. Down-regulation of TRPV6 attenuates BON-1 cell proliferation. TRPV6 down- regulation is associated with decreased Ca2+ response pattern and reduced NFAT activity. In conclusion, TRPV6 is expressed in pancreatic NETs and modulates cell proliferation via Ca2+-dependent mechanism, which is accompanied by NFAT activation

Characteristics of myosin profile in human vastus lateralis muscle in relation to training background.

Twenty-four male volunteers (mean +/- SD: age 25.4+/-5.8 years, height 178.6+/-5.5 cm, body mass 72.1+/-7.7 kg) of different training background were investigated and classified into three groups according to their physical activity and sport discipline: untrained students (group A), national and sub-national level endurance athletes (group B, 7.8+/-2.9 years of specialised training) and sprint-power athletes (group C, 12.8+/-8.7 years of specialised training). Muscle biopsies of vastus lateralis were analysed histochemically for mATPase and SDH activities, immunohistochemically for fast and slow myosin, and electrophoretically followed by Western immunoblotting for myosin heavy chain (MyHC) composition. Significant differences (

Mathematical properties of neuronal TD-rules and differential Hebbian learning: a comparison

A confusingly wide variety of temporally asymmetric learning rules exists related to reinforcement learning and/or to spike-timing dependent plasticity, many of which look exceedingly similar, while displaying strongly different behavior. These rules often find their use in control tasks, for example in robotics and for this rigorous convergence and numerical stability is required. The goal of this article is to review these rules and compare them to provide a better overview over their different properties. Two main classes will be discussed: temporal difference (TD) rules and correlation based (differential hebbian) rules and some transition cases. In general we will focus on neuronal implementations with changeable synaptic weights and a time-continuous representation of activity. In a machine learning (non-neuronal) context, for TD-learning a solid mathematical theory has existed since several years. This can partly be transfered to a neuronal framework, too. On the other hand, only now a more complete theory has also emerged for differential Hebb rules. In general rules differ by their convergence conditions and their numerical stability, which can lead to very undesirable behavior, when wanting to apply them. For TD, convergence can be enforced with a certain output condition assuring that the δ-error drops on average to zero (output control). Correlation based rules, on the other hand, converge when one input drops to zero (input control). Temporally asymmetric learning rules treat situations where incoming stimuli follow each other in time. Thus, it is necessary to remember the first stimulus to be able to relate it to the later occurring second one. To this end different types of so-called eligibility traces are being used by these two different types of rules. This aspect leads again to different properties of TD and differential Hebbian learning as discussed here. Thus, this paper, while also presenting several novel mathematical results, is mainly meant to provide a road map through the different neuronally emulated temporal asymmetrical learning rules and their behavior to provide some guidance for possible applications

Nanostructures, Technology, Research, and Applications

Contains reports on the nanostructures laboratory, eighteen research projects and a list of publications.Joint Services Electronics Program Grant DAAH04-95-1-0038Semiconductor Research Corporation Contract 95-LJ-550National Science Foundation Grant ECS 94-07078U.S. Army Research Office Grant DAAH04-95-1-0564Defense Advanced Research Projects Agency/Naval Air Systems Command Contract N00019-95-K-0131National Aeronautics and Space Administration Contract NAS8-38249National Aeronautics and Space Administration Grant NAGW-2003IBM Corporation Contract 1622U.S. Navy- Office of Naval Research Grant N00014-95-1-1297U.S. Army Research Office Grant DAAH04-94-G-0377U.S. Air Force - Office of Scientific Research Grant F-49-620-92-J-0064U.S. Air Force - Office of Scientific Research Grant F-49-620-95-1-031

Nanostructures, Technology, Research, and Applications

Contains reports on twenty research projects and a list of publications.Joint Services Electronics Program Grant DAAH04-95-1-0038National Science Foundation Grant ECS-94-07078Semiconductor Research CorporationU.S. Army Research Office Grant DAAH04-95-1-0564Defense Advanced Research Projects Agency/Naval Air Systems Command Contract N00019-95-K-0131National Aeronautics and Space Administration Contract NAS8-38249National Aeronautics and Space Administration Grant NAGW-2003IBM Corporation Contract 1622National Science Foundation Graduate FellowshipU.S. Navy - Office of Naval Research Grant N00014-95-1-1297U.S. Army Research Office Contract DAAH04-94-G-0377U.S. Air Force - Office of Scientific Research Grant F49620-92-J-0064U.S. Air Force - Office of Scientific Research Grant F49620-95-1-0311National Science Foundation Contract DMR 94-0034U.S. Air Force - Office of Scientific Research Contract F49620-96-0126Harvard-Smithsonian Astrophysical Observatory Contract SV630304National Aeronautics and Space Administration Grant NAG5-5105Los Alamos National Laboratory Contract E57800017-9

Optics and Quantum Electronics

Contains table of contents for Section 3 and reports on twenty research projects.Charles S. Draper Laboratories Contract DL-H-467138Joint Services Electronics Program Contract DAAL03-92-C-0001Joint Services Electronics Program Grant DAAH04-95-1-0038U.S. Air Force - Office of Scientific Research Contract F49620-91-C-0091MIT Lincoln LaboratoryNational Science Foundation Grant ECS 90-12787Fujitsu LaboratoriesNational Center for Integrated PhotonicsHoneywell Technology CenterU.S. Navy - Office of Naval Research (MFEL) Contract N00014-94-1-0717U.S. Navy - Office of Naval Research (MFEL) Grant N00014-91-J-1956National Institutes of Health Grant NIH-5-R01-GM35459-09U.S. Air Force - Office of Scientific Research Grant F49620-93-1-0301MIT Lincoln Laboratory Contract BX-5098Electric Power Research Institute Contract RP3170-25ENEC

Optics and Quantum Electronics

Contains table of contents for Section 3 and reports on twenty-three research projects.Joint Services Electronics Program Contract DAAL03-92-C-0001U.S. Air Force - Office of Scientific Research Contract F49620-91-C-0091Charles S. Draper Laboratories Contract DL-H-441629MIT Lincoln LaboratoryNational Science Foundation Grant ECS 90-12787Fujitsu LaboratoriesU.S. Navy - Office of Naval Research Grant N00014-92-J-1302National Center for Integrated PhotonicsNational Center for Integrated Photonics TechnologyNational Science Foundation Grant EET 88-15834Joint Services Electronics Program Contract DAAL03-91-C-0001National Science Foundation Fellowship ECS-85-52701U.S. Navy - Office of Naval Research (MGH) Contract N00014-91-C-0084U.S. Navy - Office of Naval Research Grant N00014-91-J-1956National Institutes of Health Grant NIH-5-RO1-GM35459-08Bose CorporationLawrence Livermore National Laboratories Subcontract B160530U.S. Department of Energy Grant DE-FG02-89-ER14012Rockwell International CorporationSpace Exploration AssociatesFuture Energy Applied Technology, Inc