Grid Remote Execution (G-Rex) of Climate Models

G-Rex is light-weight Java middleware that allows scientific applications deployed on remote computer systems to be launched and controlled as if they are running on the user's own computer. G-Rex is particularly suited to ocean and climate modelling applications because output from the model is transferred back to the user while the run is in progress, which prevents the accumulation of large amounts of data on the remote cluster. The G-Rex server is a RESTful Web application that runs inside a servlet container on the remote system, and the client component is a Java command line program that can easily be incorporated into existing scientific work-flow scripts. The NEMO and POLCOMS ocean models have been deployed as G-Rex services in the NERC Cluster Grid, and G-Rex is the core grid middleware in the GCEP and GCOMS e-science projects.

Running Climate Models On The NERC Cluster Grid Using G-Rex

Compute grids are used widely in many areas of environmental science, but there has been limited uptake of grid computing by the climate modelling community, partly because the characteristics of many climate models make them difficult to use with popular grid middleware systems. In particular, climate models usually produce large volumes of output data, and running them usually involves complicated workflows implemented as shell scripts. For example, NEMO (Smith et al. 2008) is a state-of-the-art ocean model that is used currently for operational ocean forecasting in France, and will soon be used in the UK for both ocean forecasting and climate modelling. On a typical modern cluster, a particular one year global ocean simulation at 1-degree resolution takes about three hours when running on 40 processors, and produces roughly 20 GB of output as 50000 separate files. 50-year simulations are common, during which the model is resubmitted as a new job after each year. Running NEMO relies on a set of complicated shell scripts and command utilities for data pre-processing and post-processing prior to job resubmission. Grid Remote Execution (G-Rex) is a pure Java grid middleware system that allows scientific applications to be deployed as Web services on remote computer systems, and then launched and controlled as if they are running on the user's own computer. Although G-Rex is general purpose middleware it has two key features that make it particularly suitable for remote execution of climate models: (1) Output from the model is transferred back to the user while the run is in progress to prevent it from accumulating on the remote system and to allow the user to monitor the model; (2) The client component is a command-line program that can easily be incorporated into existing model work-flow scripts. G-Rex has a REST (Fielding, 2000) architectural style, which allows client programs to be very simple and lightweight and allows users to interact with model runs using only a basic HTTP client (such as a Web browser or the curl utility) if they wish. This design also allows for new client interfaces to be developed in other programming languages with relatively little effort. The G-Rex server is a standard Web application that runs inside a servlet container such as Apache Tomcat and is therefore easy to install and maintain by system administrators. G-Rex is employed as the middleware for the NERC1 Cluster Grid, a small grid of HPC2 clusters belonging to collaborating NERC research institutes. Currently the NEMO (Smith et al. 2008) and POLCOMS (Holt et al, 2008) ocean models are installed, and there are plans to install the Hadley Centre’s HadCM3 model for use in the decadal climate prediction project GCEP (Haines et al., 2008). The science projects involving NEMO on the Grid have a particular focus on data assimilation (Smith et al. 2008), a technique that involves constraining model simulations with observations. The POLCOMS model will play an important part in the GCOMS project (Holt et al, 2008), which aims to simulate the world’s coastal oceans. A typical use of G-Rex by a scientist to run a climate model on the NERC Cluster Grid proceeds as follows :(1) The scientist prepares input files on his or her local machine. (2) Using information provided by the Grid’s Ganglia3 monitoring system, the scientist selects an appropriate compute resource. (3) The scientist runs the relevant workflow script on his or her local machine. This is unmodified except that calls to run the model (e.g. with “mpirun”) are simply replaced with calls to "GRexRun" (4) The G-Rex middleware automatically handles the uploading of input files to the remote resource, and the downloading of output files back to the user, including their deletion from the remote system, during the run. (5) The scientist monitors the output files, using familiar analysis and visualization tools on his or her own local machine. G-Rex is well suited to climate modelling because it addresses many of the middleware usability issues that have led to limited uptake of grid computing by climate scientists. It is a lightweight, low-impact and easy-to-install solution that is currently designed for use in relatively small grids such as the NERC Cluster Grid. A current topic of research is the use of G-Rex as an easy-to-use front-end to larger-scale Grid resources such as the UK National Grid service.

Inverse amygdala and medial prefrontal cortex responses to surprised faces

Here we show inverse fMRI activation patterns in amygdala and medial prefrontal cortex (mPFC) depending upon whether subjects interpreted surprised facial expressions positively or negatively. More negative interpretations of surprised faces were associated with greater signal changes in the right ventral amygdala, while more positive interpretations were associated with greater signal changes in the ventral mPFC. Accordingly, signal change within these two areas was inversely correlated. Thus, individual differences in the judgment of surprised faces are related to a systematic inverse relationship between amygdala and mPFC activity, a circuitry that the animal literature suggests is critical to the assessment of stimuli that predict potential positive vs negative outcomes.

Contextual modulation of amygdala responsivity to surprised faces

We recently demonstrated a functional relationship between fMRI responses within the amygdala and the medial prefrontal cortex based upon whether subjects interpreted surprised facial expressions positively or negatively. In the present fMRI study, we sought to assess amygdala-medial prefrontal cortex responsivity when the interpretations of surprised faces were determined by contextual experimental stimuli, rather than subjective judgment. Subjects passively viewed individual presentations of surprised faces preceded by either a negatively or positively valenced contextual sentence (e. g., She just found $500 vs. She just lost $500). Negative and positive sentences were carefully matched in terms of length, situations described, and arousal level. Negatively cued surprised faces produced greater ventral amygdala activation compared to positively cued surprised faces. Responses to negative versus positive sentences were greater within the ventrolateral prefrontal cortex, whereas responses to positive versus negative sentences were greater within the ventromedial prefrontal cortex. The present study demonstrates that amygdala response to surprised facial expressions can be modulated by negatively versus positively valenced verbal contextual information. Connectivity analyses identified candidate cortical-subcortical systems subserving this modulation.

Human amygdala responses during presentation of happy and neutral faces: correlations with state anxiety

BACKGROUND: Previous functional imaging studies demonstrating amygdala response to happy facial expressions have all included the presentation of negatively valenced primary comparison expressions within the experimental context. This study assessed amygdala response to happy and neutral facial expressions in an experimental paradigm devoid of primary negatively valenced comparison expressions. METHODS: Sixteen human subjects (eight female) viewed 16-sec blocks of alternating happy and neutral faces interleaved with a baseline fixation condition during two functional magnetic resonance imaging scans. RESULTS: Within the ventral amygdala, a negative correlation between happy versus neutral signal changes and state anxiety was observed. The majority of the variability associated with this effect was explained by a positive relationship between state anxiety and signal change to neutral faces. CONCLUSIONS: Interpretation of amygdala responses to facial expressions of emotion will be influenced by considering the contribution of each constituent condition within a greater subtractive finding, as well as 1) their spatial location within the amygdaloid complex; and 2) the experimental context in which they were observed. Here, an observed relationship between state anxiety and ventral amygdala response to happy versus neutral faces was explained by response to neutral faces.

Variations in the human cannabinoid receptor (CNR1) gene modulate striatal responses to happy faces.

Happy facial expressions are innate social rewards and evoke a response in the striatum, a region known for its role in reward processing in rats, primates and humans. The cannabinoid receptor 1 (CNR1) is the best-characterized molecule of the endocannabinoid system, involved in processing rewards. We hypothesized that genetic variation in human CNR1 gene would predict differences in the striatal response to happy faces. In a 3T functional magnetic resonance imaging (fMRI) scanning study on 19 Caucasian volunteers, we report that four single nucleotide polymorphisms (SNPs) in the CNR1 locus modulate differential striatal response to happy but not to disgust faces. This suggests a role for the variations of the CNR1 gene in underlying social reward responsivity. Future studies should aim to replicate this finding with a balanced design in a larger sample, but these preliminary results suggest neural responsivity to emotional and socially rewarding stimuli varies as a function of CNR1 genotype. This has implications for medical conditions involving hypo-responsivity to emotional and social stimuli, such as autism.

Prediction of global and local model quality in CASP8 using the ModFOLD server

The development of effective methods for predicting the quality of three-dimensional (3D) models is fundamentally important for the success of tertiary structure (TS) prediction strategies. Since CASP7, the Quality Assessment (QA) category has existed to gauge the ability of various model quality assessment programs (MQAPs) at predicting the relative quality of individual 3D models. For the CASP8 experiment, automated predictions were submitted in the QA category using two methods from the ModFOLD server-ModFOLD version 1.1 and ModFOLDclust. ModFOLD version 1.1 is a single-model machine learning based method, which was used for automated predictions of global model quality (QMODE1). ModFOLDclust is a simple clustering based method, which was used for automated predictions of both global and local quality (QMODE2). In addition, manual predictions of model quality were made using ModFOLD version 2.0-an experimental method that combines the scores from ModFOLDclust and ModFOLD v1.1. Predictions from the ModFOLDclust method were the most successful of the three in terms of the global model quality, whilst the ModFOLD v1.1 method was comparable in performance to other single-model based methods. In addition, the ModFOLDclust method performed well at predicting the per-residue, or local, model quality scores. Predictions of the per-residue errors in our own 3D models, selected using the ModFOLD v2.0 method, were also the most accurate compared with those from other methods. All of the MQAPs described are publicly accessible via the ModFOLD server at: http://www.reading.ac.uk/bioinf/ModFOLD/. The methods are also freely available to download from: http://www.reading.ac.uk/bioinf/downloads/.

The ModFOLD server for the quality assessment of protein structural models

The reliable assessment of the quality of protein structural models is fundamental to the progress of structural bioinformatics. The ModFOLD server provides access to two accurate techniques for the global and local prediction of the quality of 3D models of proteins. Firstly ModFOLD, which is a fast Model Quality Assessment Program (MQAP) used for the global assessment of either single or multiple models. Secondly ModFOLDclust, which is a more intensive method that carries out clustering of multiple models and provides per-residue local quality assessment.

Processing of faces and emotional expressions in infants at risk of social phobia

Individuals with social phobia display social information processing biases yet their aetiological significance is unclear. Infants of mothers with social phobia and control infants' responses were assessed at 10 days, 10 and 16 weeks, and 10 months to faces versus non-faces, variations in intensity of emotional expressions, and gaze direction. Infant temperament and maternal behaviours were also assessed. Both groups showed a preference for faces over non-faces at 10 days and 10 weeks, and full faces over profiles at 16 weeks; they also looked more to high vs. low intensity angry faces at 10 weeks, and fearful faces at 10 months; however, index infants' initial orientation and overall looking to high-intensity fear faces was relatively less than controls at 10 weeks. This was not explained by infant temperament or maternal behaviours. The findings suggest that offspring of mothers with social phobia show processing biases to emotional expressions in infancy.

MPJ express: Towards thread safe Java HPC

MPJ Express is a thread-safe Java messaging library that provides a full implementation of the mpiJava 1.2 API specification. This specification defines a MPI-like bindings for the Java language. We have implemented two communication devices as part of our library, the first, called niodev is based on the Java New I/O package and the second, called mxdev is based on the Myrinet eXpress library MPJ Express comes with an experimental runtitne, which allows portable bootstrapping of Java Virtual Machines across a cluster or network of computers. In this paper we describe the implementation of MPJ Express. Also, we present a performance comparison against various other C and Java messaging systems. A beta version of MPJ Express was released in September 2005.

Parallel and distributed computing with Java

The Java language first came to public attention in 1995. Within a year, it was being speculated that Java may be a good language for parallel and distributed computing. Its core features, including being objected oriented and platform independence, as well as having built-in network support and threads, has encouraged this view. Today, Java is being used in almost every type of computer-based system, ranging from sensor networks to high performance computing platforms, and from enterprise applications through to complex research-based.simulations. In this paper the key features that make Java a good language for parallel and distributed computing are first discussed. Two Java-based middleware systems, namely MPJ Express, an MPI-like Java messaging system, and Tycho, a wide-area asynchronous messaging framework with an integrated virtual registry are then discussed. The paper concludes by highlighting the advantages of using Java as middleware to support distributed applications.