The CloudSat mission and the A-train: a new dimension of space-based observations of clouds and precipitation

CloudSat is a satellite experiment designed to measure the vertical structure of clouds from space. The expected launch of CloudSat is planned for 2004, and once launched, CloudSat will orbit in formation as part of a constellation of satellites (the A-Train) that includes NASA's Aqua and Aura satellites, a NASA-CNES lidar satellite (CALIPSO), and a CNES satellite carrying a polarimeter (PARASOL). A unique feature that CloudSat brings to this constellation is the ability to fly a precise orbit enabling the fields of view of the CloudSat radar to be overlapped with the CALIPSO lidar footprint and the other measurements of the constellation. The precision and near simultaneity of this overlap creates a unique multisatellite observing system for studying the atmospheric processes essential to the hydrological cycle.The vertical profiles of cloud properties provided by CloudSat on the global scale fill a critical gap in the investigation of feedback mechanisms linking clouds to climate. Measuring these profiles requires a combination of active and passive instruments, and this will be achieved by combining the radar data of CloudSat with data from other active and passive sensors of the constellation. This paper describes the underpinning science and general overview of the mission, provides some idea of the expected products and anticipated application of these products, and the potential capability of the A-Train for cloud observations. Notably, the CloudSat mission is expected to stimulate new areas of research on clouds. The mission also provides an important opportunity to demonstrate active sensor technology for future scientific and tactical applications. The CloudSat mission is a partnership between NASA's JPL, the Canadian Space Agency, Colorado State University, the U.S. Air Force, and the U.S. Department of Energy.

Endosomes: a legitimate platform for the signaling train

Although long regarded as a conduit for the degradation or recycling of cell surface receptors, the endosomal system is also an essential site of signal transduction. Activated receptors accumulate in endosomes, and certain signaling components are exclusively localized to endosomes. Receptors can continue to transmit signals from endosomes that are different from those that arise from the plasma membrane, resulting in distinct physiological responses. Endosomal signaling is widespread in metazoans and plants, where it transmits signals for diverse receptor families that regulate essential processes including growth, differentiation and survival. Receptor signaling at endosomal membranes is tightly regulated by mechanisms that control agonist availability, receptor coupling to signaling machinery, and the subcellular localization of signaling components. Drugs that target mechanisms that initiate and terminate receptor signaling at the plasma membrane are widespread and effective treatments for disease. Selective disruption of receptor signaling in endosomes, which can be accomplished by targeting endosomal-specific signaling pathways or by selective delivery of drugs to the endosomal network, may provide novel therapies for disease.

An “A-train” strategy for quantifying direct climate forcing by anthropogenic aerosols

This document outlines a practical strategy for achieving an observationally based quantification of direct climate forcing by anthropogenic aerosols. The strategy involves a four-step program for shifting the current assumption-laden estimates to an increasingly empirical basis using satellite observations coordinated with suborbital remote and in situ measurements and with chemical transport models. Conceptually, the problem is framed as a need for complete global mapping of four parameters: clear-sky aerosol optical depth δ, radiative efficiency per unit optical depth E, fine-mode fraction of optical depth ff, and the anthropogenic fraction of the fine mode faf. The first three parameters can be retrieved from satellites, but correlative, suborbital measurements are required for quantifying the aerosol properties that control E, for validating the retrieval of ff, and for partitioning fine-mode δ between natural and anthropogenic components. The satellite focus is on the “A-Train,” a constellation of six spacecraft that will fly in formation from about 2005 to 2008. Key satellite instruments for this report are the Moderate Resolution Imaging Spectroradiometer (MODIS) and Clouds and the Earth's Radiant Energy System (CERES) radiometers on Aqua, the Ozone Monitoring Instrument (OMI) radiometer on Aura, the Polarization and Directionality of Earth's Reflectances (POLDER) polarimeter on the Polarization and Anistropy of Reflectances for Atmospheric Sciences Coupled with Observations from a Lidar (PARASOL), and the Cloud and Aerosol Lider with Orthogonal Polarization (CALIOP) lidar on the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). This strategy is offered as an initial framework—subject to improvement over time—for scientists around the world to participate in the A-Train opportunity. It is a specific implementation of the Progressive Aerosol Retrieval and Assimilation Global Observing Network (PARAGON) program, presented earlier in this journal, which identified the integration of diverse data as the central challenge to progress in quantifying global-scale aerosol effects. By designing a strategy around this need for integration, we develop recommendations for both satellite data interpretation and correlative suborbital activities that represent, in many respects, departures from current practice

Generation of attosecond pulses in molecular nitrogen

We have generated attosecond pulse trains in an ensemble of randomly aligned nitrogen molecules. Measurements of the high-order harmonic relative phases and amplitudes allow us to reconstruct the temporal profile of the attosecond pulses. We show that in the considered spectral range, the latter is very similar to the pulse train generated in argon under the same conditions. We discuss the possible influence of the molecular structure in the generation process, and how it can induce subtle differences on the relative phases.

Stray light analysis of SALEX instrument

Discrepancies between recent global earth albedo anomaly data obtained from the climate models, space and ground observations call for a new and better earth reflectance measurement technique. The SALEX (Space Ashen Light Explorer) instrument is a space-based visible and IR instrument for precise estimation of the global earth albedo by measuring the ashen light reflected off the shadowy side of the Moon from the low earth orbit. The instrument consists of a conventional 2-mirror telescope, a pair of a 3-mirror visible imager and an IR bolometer. The performance of this unique multi-channel optical system is sensitive to the stray light contamination due to the complex optical train incorporating several reflecting and refracting elements, associated mounts and the payload mechanical enclosure. This could be further aggravated by the very bright and extended observation target (i.e. the Moon). In this paper, we report the details of extensive stray light analysis including ghosts and cross-talks, leading to the optimum set of stray light precautions for the highest signal-to-noise ratio attainable.

Can we harness computerised cognitive bias modification to treat anxiety in schizophrenia? A first step highlighting the role of mental imagery

A new wave of computerised therapy is under development which, rather than simulating talking therapies, uses bias modification techniques to target the core psychological process underlying anxiety. Such interventions are aimed at anxiety disorders, and are yet to be adapted for co-morbid anxiety in psychosis. The cognitive bias modification (CBM) paradigm delivers repeated exposure to stimuli in order to train individuals to resolve ambiguous information in a positive, rather than anxiety provoking, manner. The current study is the first to report data from a modified form of CBM which targets co-morbid anxiety within individuals diagnosed with schizophrenia. Our version of CBM involved exposure to one hundred vignettes presented over headphones. Participants were instructed to actively simulate the described scenarios via visual imagery. Twenty-one participants completed both a single session of CBM and a single control condition session in counter-balanced order. Within the whole sample, there was no significant improvement on interpretation bias of CBM or state anxiety, relative to the control condition. However, in line with previous research, those participants who engage in higher levels of visual imagery exhibited larger changes in interpretation bias. We discuss the implications for harnessing computerised CBM therapy developments for co-morbid anxiety in schizophrenia.

Control of a compliant two-axis robotic arm

In order to ease control, the links between actuators and robotic limbs are generally made to be as stiff as possible. This is in contrast to natural limbs, where compliance is present. Springs have been added to the drive train between the actuator and load to imitate this natural compliance. The majority of these springs have been in series between the actuator and load. However, a more biologically inspired approach is taken, here springs have been used in parallel to oppose each other. The paper will describe the application of parallel extension springs in a robot arm in order to give it compliance. Advantages and disadvantages of this application are discussed along with various control strategies.

Application of neural network to hybrid systems with binary inputs

Boolean input systems are in common used in the electric industry. Power supplies include such systems and the power converter represents these. For instance, in power electronics, the control variable are the switching ON and OFF of components as thyristors or transistors. The purpose of this paper is to use neural network (NN) to control continuous systems with Boolean inputs. This method is based on classification of system variations associated with input configurations. The classical supervised backpropagation algorithm is used to train the networks. The training of the artificial neural network and the control of Boolean input systems are presented. The design procedure of control systems is implemented on a nonlinear system. We apply those results to control an electrical system composed of an induction machine and its power converter.

Classification-based probabilistic modeling of texture transition for fast line search tracking and delineation

We introduce a classification-based approach to finding occluding texture boundaries. The classifier is composed of a set of weak learners, which operate on image intensity discriminative features that are defined on small patches and are fast to compute. A database that is designed to simulate digitized occluding contours of textured objects in natural images is used to train the weak learners. The trained classifier score is then used to obtain a probabilistic model for the presence of texture transitions, which can readily be used for line search texture boundary detection in the direction normal to an initial boundary estimate. This method is fast and therefore suitable for real-time and interactive applications. It works as a robust estimator, which requires a ribbon-like search region and can handle complex texture structures without requiring a large number of observations. We demonstrate results both in the context of interactive 2D delineation and of fast 3D tracking and compare its performance with other existing methods for line search boundary detection.

Mathematical modelling for the digital society

We argue the case for a new branch of mathematics and its applications: Mathematics for the Digital Society. There is a challenge for mathematics, a strong “pull” from new and emerging commercial and public activities; and a need to train and inspire a generation of quantitative scientists who will seek careers within the associated sectors. Although now going through an early phase of boiling up, prior to scholarly distillation, we discuss how data rich activities and applications may benefit from a wide range of continuous and discrete models, methods, analysis and inference. In ten years time such applications will be common place and associated courses may be embedded within the undergraduate curriculum.

Combined CloudSat-CALIPSO-MODIS retrievals of the properties of ice clouds

In this paper, data from spaceborne radar, lidar and infrared radiometers on the “A-Train” of satellites are combined in a variational algorithm to retrieve ice cloud properties. The method allows a seamless retrieval between regions where both radar and lidar are sensitive to the regions where one detects the cloud. We first implement a cloud phase identification method, including identification of supercooled water layers using the lidar signal and temperature to discriminate ice from liquid. We also include rigorous calculation of errors assigned in the variational scheme. We estimate the impact of the microphysical assumptions on the algorithm when radiances are not assimilated by evaluating the impact of the change in the area-diameter and the density-diameter relationships in the retrieval of cloud properties. We show that changes to these assumptions affect the radar-only and lidar-only retrieval more than the radar-lidar retrieval, although the lidar-only extinction retrieval is only weakly affected. We also show that making use of the molecular lidar signal beyond the cloud as a constraint on optical depth, when ice clouds are sufficiently thin to allow the lidar signal to penetrate them entirely, improves the retrieved extinction. When infrared radiances are available, they provide an extra constraint and allow the extinction-to-backscatter ratio to vary linearly with height instead of being constant, which improves the vertical distribution of retrieved cloud properties.

Prediction of Parkinson’s Disease tremor onset using a radial basis function neural network based on particle swarm optimization

Deep Brain Stimulation (DBS) has been successfully used throughout the world for the treatment of Parkinson's disease symptoms. To control abnormal spontaneous electrical activity in target brain areas DBS utilizes a continuous stimulation signal. This continuous power draw means that its implanted battery power source needs to be replaced every 18–24 months. To prolong the life span of the battery, a technique to accurately recognize and predict the onset of the Parkinson's disease tremors in human subjects and thus implement an on-demand stimulator is discussed here. The approach is to use a radial basis function neural network (RBFNN) based on particle swarm optimization (PSO) and principal component analysis (PCA) with Local Field Potential (LFP) data recorded via the stimulation electrodes to predict activity related to tremor onset. To test this approach, LFPs from the subthalamic nucleus (STN) obtained through deep brain electrodes implanted in a Parkinson patient are used to train the network. To validate the network's performance, electromyographic (EMG) signals from the patient's forearm are recorded in parallel with the LFPs to accurately determine occurrences of tremor, and these are compared to the performance of the network. It has been found that detection accuracies of up to 89% are possible. Performance comparisons have also been made between a conventional RBFNN and an RBFNN based on PSO which show a marginal decrease in performance but with notable reduction in computational overhead.

A novel shape feature for fast region-based pedestrian recognition

A new class of shape features for region classification and high-level recognition is introduced. The novel Randomised Region Ray (RRR) features can be used to train binary decision trees for object category classification using an abstract representation of the scene. In particular we address the problem of human detection using an over segmented input image. We therefore do not rely on pixel values for training, instead we design and train specialised classifiers on the sparse set of semantic regions which compose the image. Thanks to the abstract nature of the input, the trained classifier has the potential to be fast and applicable to extreme imagery conditions. We demonstrate and evaluate its performance in people detection using a pedestrian dataset.