Deploying web-based visual exploration tools on the grid

Physical cues, such as extracellular matrix stiffness, direct cell differentiation and support tissue-specific function. Perturbation of these cues underlies diverse pathologies, including osteoarthritis, cardiovascular disease and cancer. However, the molecular mechanisms that establish tissue-specific material properties and link them to healthy tissue function are unknown. We show that Runx2, a key lineage-specific transcription factor, regulates the material properties of bone matrix through the same transforming growth factor-β (TGFβ)-responsive pathway that controls osteoblast differentiation. Deregulated TGFβ or Runx2 function compromises the distinctly hard cochlear bone matrix and causes hearing loss, as seen in human cleidocranial dysplasia. In Runx2+/⁻ mice, inhibition of TGFβ signalling rescues both the material properties of the defective matrix, and hearing. This study elucidates the unknown cause of hearing loss in cleidocranial dysplasia, and demonstrates that a molecular pathway controlling cell differentiation also defines material properties of extracellular matrix. Furthermore, our results suggest that the careful regulation of these properties is essential for healthy tissue function.

This issue of physica status solidi (b) provides an account of the most recent and interesting topical work presented at the 11th International Conference on High Pressure Semiconductor Physics (HPSP‐11), which was held in Berkeley, California, from 2 to 5 August 2004. This is a satellite of the 27th International Conference on the Physics of Semiconductors held in Flagstaff, Arizona, from 26 to 30 July 2004. About 65 registered participants from different countries gathered at the Clark Kerr Campus, Berkeley, for a lively and highly enjoyable conference both from the scientific and social point of view. In comparison to the previous conferences in this series, held at Guildford (2002), Sapporo (2000), Thessaloniki (1998) and Schwäbisch Gmünd (1996), the attendance was low, owing to the “post‐9/11 reality”. Several potential participants were discouraged to go through the difficult and time‐consuming procedures to obtain a visa while a few others were unable to get their visas on time. In the interest of scientific exchange and progress, we do sincerely hope that the situation improves soon enough, so the international scientific community is not forced to restrict the exchange of research results.As an advanced announcement and prelude to these proceedings, a Conference Report written by Prof. M. Cardona appeared in the October issue of physica status solidi (b) [1] and is reprinted at the end of this print edition. This issue begins with an historical article by Prof. William Paul [2], one of the pioneers of high‐pressure research in semiconductors, in which he describes the application of extremely high pressures as a powerful tool in solid‐state physics. His studies spanning over a decade (1950's and 60's) helped not only to unravel the behavior of solids, particularly semiconductors, under extreme conditions of pressure and temperature but also to obtain a deeper insight into ambient pressure properties, leading to a better understanding of a variety of physical phenomena. This conference covered several fundamental aspects of high‐pressure physics concerning the electronic band structure, the optical and vibrational properties and structural stability of many different semiconductor materials and compounds. Low‐dimensional semiconductor structures, such as quantum well heterostructures, quantum wires and self‐assembled quantum dot systems were discussed in several papers. Carbon nanotubes and organic semiconductors captured the attention of the audience for two morning sessions. Papers dealing with pressure effects on the performance and properties of diode lasers and materials like manganites and diluted magnetic semiconductors that are used in applications as storage devices were also presented at the meeting. Two prizes, one each for the best presentation in the two poster sessions, were awarded; the recipients are: (1) S. X. Li, J. Wu, E. E. Haller, W. Walukiewicz, W. Shan, Hai Lu, and W. J. Schaff, a joint collaboration between the University of California, Berkeley, and the Lawrence Berkeley National Laboratory; (2) V. Domnich, D. Ge, T. Juliano, and Y. Gogotsi, Drexel University, Philadelphia.This conference would not have been possible without the financial support from the Department of Physics, University of California, Berkeley, and from the Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley. We gratefully acknowledge the excellent secretarial support of Kate Hug and Chia‐Hua Yu as well as the cooperation of Andy Prins in all technical aspects of the meeting.The next conference on High Pressure Semiconductor Physics will be held in 2006 near Barcelona. We look forward to having another stimulating and enjoyable meeting in Spain and welcome all interesting suggestions to the planning in the two years ahead.

This SAND report provides the technical progress through April 2005 of the Sandia-led project, "Carbon Sequestration in Synechococcus Sp.: From Molecular Machines to Hierarchical Modeling," funded by the DOE Office of Science Genomics:GTL Program. Understanding, predicting, and perhaps manipulating carbon fixation in the oceans has long been a major focus of biological oceanography and has more recently been of interest to a broader audience of scientists and policy makers. It is clear that the oceanic sinks and sources of CO2 are important terms in the global environmental response to anthropogenic atmospheric inputs of CO2 and that oceanic microorganisms play a key role in this response. However, the relationship between this global phenomenon and the biochemical mechanisms of carbon fixation in these microorganisms is poorly understood. In this project, we will investigate the carbon sequestration behavior of Synechococcus Sp., an abundant marine cyanobacteria known to be important to environmental responses to carbon dioxide levels, through experimental and computational methods. This project is a combined experimental and computational effort with emphasis on developing and applying new computational tools and methods. Our experimental effort will provide the biology and data to drive the computational efforts and include significant investment in developing new experimental methods for uncovering protein partners, characterizing protein complexes, identifying new binding domains. We will also develop and apply new data measurement and statistical methods for analyzing microarray experiments. Computational tools will be essential to our efforts to discover and characterize the function of the molecular machines of Synechococcus. To this end, molecular simulation methods will be coupled with knowledge discovery from diverse biological data sets for high-throughput discovery and characterization of protein-protein complexes. In addition, we will develop a set of novel capabilities for inference of regulatory pathways in microbial genomes across multiple sources of information through the integration of computational and experimental technologies. These capabilities will be applied to Synechococcus regulatory pathways to characterize their interaction map and identify component proteins in these - 4 -pathways. We will also investigate methods for combining experimental and computational results with visualization and natural language tools to accelerate discovery of regulatory pathways. The ultimate goal of this effort is develop and apply new experimental and computational methods needed to generate a new level of understanding of how the Synechococcus genome affects carbon fixation at the global scale. Anticipated experimental and computational methods will provide ever-increasing insight about the individual elements and steps in the carbon fixation process, however relating an organism's genome to its cellular response in the presence of varying environments will require systems biology approaches. Thus a primary goal for this effort is to integrate the genomic data generated from experiments and lower level simulations with data from the existing body of literature into a whole cell model. We plan to accomplish this by developing and applying a set of tools for capturing the carbon fixation behavior of complex of Synechococcus at different levels of resolution. Finally, the explosion of data being produced by high-throughput experiments requires data analysis and models which are more computationally complex, more heterogeneous, and require coupling to ever increasing amounts of experimentally obtained data in varying formats. These challenges are unprecedented in high performance scientific computing and necessitate the development of a companion computational infrastructure to support this effort. More information about this project can be found at www.genomes-to-life.org Acknowledgment We want to gratefully acknowledge the contributions of: Grant Heffelfinger1*, Anthony Martino2, Brian Palenik6, Andrey Gorin3, Ying Xu10,3, Mark Daniel Rintoul1, Al Geist3, Matthew Ennis1, with Pratul Agrawal3, Hashim Al-Hashimi8, Andrea Belgrano12, Mike Brown1, Xin Chen9, Paul Crozier1, PguongAn Dam10, Jean-Loup Faulon2, Damian Gessler12, David Haaland1, Victor Havin4, C.F. Huang5, Tao Jiang9, Howland Jones1, David Jung3, Katherine Kang14, Michael Langston15, Shawn Martin1, Shawn Means1, Vijaya Natarajan4, Roy Nielson5, Frank Olken4, Victor Olman10, Ian Paulsen14, Steve Plimpton1, Andreas Reichsteiner5, Nagiza Samatova3, Arie Shoshani4, Michael Sinclair1, Alex Slepoy1, Shawn Stevens8, Charlie Strauss5, Zhengchang Su10, Ed Thomas1, Jerilyn Timlin1, WimVermaas13, Xiufeng Wan11, HongWei Wu10, Dong Xu11, Grover Yip8, Erik Zuiderweg8 *Author to whom correspondence should be addressed (gsheffe@sandia.gov) 1. Sandia National Laboratories, Albuquerque, NM 2. Sandia National Laboratories, Livermore, CA 3. Oak Ridge National Laboratory, Oak Ridge, TN 4. Lawrence Berkeley National Laboratory, Berkeley, CA 5. Los Alamos National Laboratory, Los Alamos, NM 6. University of California, San Diego 7. University of Illinois, Urbana/Champaign 8. University of Michigan, Ann Arbor 9. University of California, Riverside 10. University of Georgia, Athens 11. University of Missouri, Columbia 12. National Center for Genome Resources, Santa Fe, NM 13. Arizona State University 14. The Institute for Genomic Research 15. University of Tennessee 5 Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL8500.

A HIGH FREQUENCY ELECTROMAGNETIC IMPEDANCE IMAGING SYSTEM Hung-Wen Tseng, Lawrence Berkeley National Laboratory, Berkeley, CA Ki Ha Lee, Lawrence Berkeley National Laboratory, Berkeley, CA Alex Becker, University of California, Berkeley, CA Abstract Non-invasive, high resolution geophysical mapping of the shallow subsurface is necessary for delineation of buried hazardous wastes, detecting unexploded ordinance, verifying and monitoring of containment or moisture contents, and other environmental applications. Electromagnetic (EM) techniques can be used for this purpose since electrical conductivity and dielectric permittivity are representative of the subsurface media. Measurements in the EM frequency band between 1 and 100 MHz are very important for such applications, because the induction number of many targets is small and the ability to determine the subsurface distribution of both electrical properties is required. Earlier workers were successful in developing systems for detecting anomalous areas, but quantitative interpretation of the data was difficult. Accurate measurements are necessary, but difficult to achieve for high-resolution imaging of the subsurface. We are developing a broadband non-invasive method for accurately mapping the electrical conductivity and dielectric permittivity of the shallow subsurface using an EM impedance approach similar to the MT exploration technique. Electric and magnetic sensors were tested to ensure that stray EM scattering is minimized and the quality of the data collected with the high-frequency impedance (HFI) system is good enough to allow high-resolution, multi-dimensional imaging of hidden targets. Additional efforts are being made to modify and further develop existing sensors and transmitters to improve the imaging capability and data acquisition efficiency. Introduction Accurate information on the shallow (< 10 m) subsurface distribution in electrical conductivity and dielectric permittivity, which are related to local geochemistry and geohydrology, is useful in near- surface engineering, and increasingly more important in environmental studies. At the low end of the electromagnetic (EM) geophysical spectrum (< 300 kHz), DC resistivity and loop-loop induction measurements with small spacings have served adequately to map the near-surface resistivity variations since electrical conduction dominates in this low frequency band. However, the DC resistivity and induction methods suffer from lack of resolution and insensitivity to medium permittivity, which is primarily dependant on water content. Also, DC resistivity methods tend to be slow and expensive to deploy and the requirement of planting electrodes in the earth can be undesirably invasive in hazardous environments. Low-frequency inductive EM measurements are much more rapid but are frequently less informative, particularly in complex environments. At frequencies higher than 50 MHz, dielectric property (permittivity) starts dominating the EM response. For this reason ground-penetrating radar (GPR) is widely used to investigate shallow subsurface moisture content variations. Though GPR can provide excellent resolution with efficiency in field data acquisition, the method is often limited in its depth of investigation due to the strong attenuation of high-frequency EM waves in conductive media. Given the low amplitude induction response of many small near-surface targets and the need to determine both the electric conductivity and dielectric permittivity, use of frequencies between 100 kHz

Tropical forest soil microbial communities couple iron and carbon biogeochemistry Eric A. Dubinsky 1 , Whendee L. Silver and Mary K. Firestone 1 * Ecosystem Sciences Division, Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA 94720 *Lawrence Berkeley National Laboratory, Berkeley, CA 94720 Present address: Ecology Department, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd., MS 70A-3317, Berkeley, CA 94720. E-mail: eadubinsky@lbl.gov

Contrary to popular belief, “big science” did not begin with the Second World War or the Manhattan Project but had already taken off in the 1930s. Even though the US Department of Agriculture was at that time spending more on research than the Department of Defense (will those days ever return?), and even though “small science” was still by far the predominant mode, the first big science laboratories had clearly emerged well before the Second World War. The most important was the Lawrence Berkeley Laboratory – the Radiation Laboratory at the University of California at Berkeley – which is the subject of Robert Seidel’s opening chapter. Seidel's contribution sets the tone for the whole book and is in many ways the most interesting of all – though the general standard throughout is remarkably high – since it deals with the origins of the big science phenomenon and is full of informative anecdotes and detail.

Design of Smart Wellhead Controllers for Optimal Fluid Injection Policy and Producibility in Petroleum Reservoirs: A Neuro-Geometric Approach Masoud Nikravesh, Lawrence Berkeley National Laboratory, BISC Program, Computer Science Division, University of California at Berkeley, SPE, Masoud Soroush, Drexel University, M.R. Johnston, CalResources, LLC., SPE, and Tad W. Patzek, University of California at Berkeley, SPE Abstract In this paper, we present the next generation of "smart" controllers based on neural networks and geometric control techniques. In addition, we discuss an innovative Neural Network and Geometric Model-Based Control Strategy for developing and maintaining optimal fluid injection policy. First, the smart controller acquires data on wellhead pressures and rates continuously from the injectors. Second, a neural network model is "taught" the reservoir rate response to fluid injection pressure and vice versa. In the first case, the neural network learns how to predict the future injection rate based on injection pressure and rate. In the second case, the neural network learns how to predict the future injection pressure based on injection rate and pressure. The appropriately trained neural network can then recognize the symptoms of efficient or inefficient fluid injection around the operating point of the process. Third, feedback from the neural network models, in conjunction with geometric control, is used to design an optimal control strategy. In particular, the developed neural network-differential geometric models can be used to control and predict the behavior of individual and multiple fluid injectors. Introduction Oil Recovery from Petroleum Reservoirs: Fluids, such as water, carbon dioxide or steam, are injected into reservoirs to maintain pressure and displace oil. The process of fluid injection into petroleum reservoirs is known to exhibit an inherently complex, nonlinear, time varying and nonstationary behavior. Although an oil field is a complex and highly coupled system, injectors are usually controlled individually on the basis of past experience and pilots. Usually, simple single-input single-output controllers, such as Proportional-Integral-Derivative (PID) are used. Compared with carefully-controlled and maintained pilot injectors, field injectors have a more stochastic nature and their injectivity patterns are often different. These features have made the development of first-principle models for these injectors very challenging, if not impossible. Neural networks, however, have been able to provide sufficiently accurate models of fluid injectors by using historical process data such as flow rates and pressures. Despite our incomplete knowledge, neural network models are able to predict the complex behavior of petroleum reservoirs. These models can be used to synthesize model-based controllers that are capable of providing more effective control in these processes. In this project, we are creating an innovative Computer Assisted Operations (CAO) tool for developing and maintaining optimal injection policy for water or steam. Figure 1 shows a simplified schematic of optimal fluid injection policy. First, the CAO system acquires continuously data from the injectors (wellhead pressures (WHPs), rates, and temperatures) and producers (WHPs, rates, etc.). Second, the pattern-by-pattern responses are inverted to "back-out" the evolution of reservoir properties such as permeability and the extent of fracturing. P. 405^

Glucose metabolism in the temporal lobes in Alzheimer's disease: W. J. Jagust, J. L. Eberling, B. R. Richardson, M. G. Baker, B. R. Reed, T. E. Nordahl, and T. F. Budinger. Department of Neurology, University of California, Davis, and Lawrence Berkeley Laboratory, University of California, Berkeley

The dynamics of electronically excited helium nanodroplets are studied by femtosecond time-resolved photoelectron imaging. EUV excitation into a broad absorption band centered around 23.8 eV leads to an indirect photoemission process that generates ultraslow photoelectrons. A 1.58 eV probe pulse transiently depletes the indirect photoemission signal for pump-probe time delays &amp;lt;200 fs and enhances the signal beyond this delay. The depletion is due to suppression of the indirect ionization process by the probe photon, which generates a broad, isotropically emitted photoelectron band. Similar time scales in the decay of the high energy photoelectron signal and the enhancement of the indirect photoemission signal suggest an internal relaxation process that populates states in the range of a lower energy droplet absorption band located just below the droplet ionization potential (IP {approx} 23.0 eV). A nearly 70% enhancement of the ultraslow photoelectron signal indicates that interband relaxation plays a more dominant role for the droplet de-excitation mechanism than photoemission.

It is with great pleasure that we present this special issue focused on topological structures in ferroic materials. It is composed of invited contributions from the International Workshop on Topological Structures in Ferroic Materials held at the University of South Wales, Sydney, Australia from 19-21 st May 2015. The aim of the workshop was to capture current research interests regarding interfaces, domains, domain walls, vortices and skyrmions in ferroics as an emerging and highly active fi eld in solid state physics and materials science. Signifi cant emphasis was placed on understanding the fundamental commonalities that bridge topological defects across various types of ferroic materials, and therefore lessons learned on how new and groundbreaking functionalities afforded by the aforementioned concepts could be translated into emerging technologies.

ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTMolecular Characterization of Polymer and Polymer Blend Surfaces. Combined Sum Frequency Generation Surface Vibrational Spectroscopy and Scanning Force Microscopy StudiesD. H. Gracias, Z. Chen, Y. R. Shen, and G. A. SomorjaiView Author Information Departments of Chemistry and Physics, University of California, Berkeley, California 94720, and Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720 Cite this: Acc. Chem. Res. 1999, 32, 11, 930–940Publication Date (Web):July 8, 1999Publication History Received1 February 1999Published online8 July 1999Published inissue 1 November 1999https://doi.org/10.1021/ar990034fCopyright © 1999 American Chemical SocietyRIGHTS & PERMISSIONSArticle Views800Altmetric-Citations101LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit Read OnlinePDF (301 KB) Get e-AlertsSUBJECTS:Friction,Nonlinear optics,Polymer blends,Polymers,Thermoresponsive polymers Get e-Alerts

ProteinShop Gives Researchers a Hands-On Tool for Manipulating, Visualizing Protein Structures. The Human Genome Project and other biological research efforts are creating an avalanche of new data about the chemical makeup and genetic codes of living organisms. But in order to make sense of this raw data, researchers need software tools which let them explore and model data in a more intuitive fashion. With this in mind, researchers at Lawrence Berkeley National Laboratory and the University of California, Davis, have developed ProteinShop, a visualization and modeling program which allows researchers to manipulate protein structures with pinpoint control, guided in large part by their own biological and experimental instincts. Biologists have spent the last half century trying to unravel the ''protein folding problem,'' which refers to the way chains of amino acids physically fold themselves into three-dimensional proteins. This final shape, which resembles a crumpled ribbon or piece of origami, is what determines how the protein functions and translates genetic information. Understanding and modeling this geometrically complex formation is no easy matter. ProteinShop takes a given sequence of amino acids and uses visualization guides to help generate predictions about the secondary structures, identifying alpha helices and flat beta strands, and the coil regions that bind them. Once secondary structures are in place, researchers can twist and turn these pre-configurations until they come up with a number of possible tertiary structure conformations. In turn, these are fed into a computationally intensive optimization procedure that tries to find the final, three-dimensional protein structure. Most importantly, ProteinShop allows users to add human knowledge and intuition to the protein structure prediction process, thus bypassing bad configurations that would otherwise be fruitless for optimization. This saves compute cycles and accelerates the entire process, so that more and larger problems can be attempted. Currently, the program designers are working to make ProteinShop more applicable and adaptable to different protein folding methodologies. If users could manipulate structures from a biological point of view, and then put them back in the queue for more optimization, the process of experimentation and discovery in protein research could be greatly enhanced. The group is also investigating the use of stereoscopic rendering and three-dimensional input devices to remove the limitations of a two-dimensional interface. Clearly, protein-folding research will have far-reaching ramifications. It could lead to new insights about diseases ranging from Alzheimer's to Cystic fibrosis, which scientists believe are caused by protein folding gone wrong. A better understanding of protein structures could also lead to the engineering of altogether new proteins, and shed light on how drugs bind proteins to alter their structure and function. Above all, ProteinShop is an important tool that will help scientists unravel one of the most challenging problems that theoretical and computational chemistry has to offer.

Owen Chamberlain

"Perlmutter, Prof. Saul, (born 1959), Franklin W. and Karen Weber Dabby Professor, Physics Department, University of California, Berkeley; astrophysicist, Lawrence Berkeley National Laboratory" published on by Oxford University Press.

Now that physicists are able to routinely create Bose-Einstein condensates in the lab, their attention has turned to probing the behaviour of this unique quantum state of matter. Bose-Einstein condensation occurs when the de Broglie wavelength of the particles is greater than their average separation, This means that all the particles collapse into a single quantum state and can be described by a single wavefunction. Now Dan Butts and Daniel Rokhsar from the University of California at Berkeley and the Lawrence Berkeley National Laboratory in the US have calculated what will happen when a Bose condensate of weakly interacting alkali atoms is rotated (Nature 1999 397 327–329). In the figure, the black dots correspond to zeroes of the wavefunction or “vortices”, the black lines are contours of constant density, and the colours represent the phase of the wavefunction (see top left).