Global observations with information content on water vapor content, ice water content and ice particle size distribution are needed to enhance knowledge of the impact of ice clouds on Earth's weather and climate. These observations may also help to reduce the uncertainty of global climate models. The Tropospheric Water and Cloud Ice (TWICE) microwave radiometer instrument has been designed to perform temperature and humidity sounding of the atmosphere near the 118.75, 183.31 and 380.20 GHz atmospheric absorption lines, as well as to retrieve ice cloud particle size information from radiometric measurements at 240, 310, 670 and 850 GHz. To acquire high-quality data, the TWICE instrument performs end-to-end, on-orbit calibration of all radiometer channels during each scan. The TWICE instrument is designed to fit within the mass, volume and power constraints of the 6U CubeSat platform.

The Tropospheric Water and Cloud ICE (TWICE) millimeter and sub-millimeter radiometer instrument is being developed to enable global observations of upper tropospheric/lower-stratospheric water vapor and ice particle size distribution in clouds. Global observations using the TWICE instrument are critically needed to reduce uncertainties in weather and climate models. A low-noise, power-efficient command and data handling (C&DH) subsystem has been designed and tested to control TWICE data acquisition and other subsystems. Considering the limited power resources available on such platforms, a highly-efficient power regulation board has been designed to minimize power losses and reduce system noise. Furthermore, heavy-ion radiation testing has been performed for some critical commercial-off-the-shelf components to analyze radiation tolerance in low-Earth orbit. The C&DH prototype board meets the functional, noise and size, weight and power (SWaP) requirements for deployment on a 6U -Class satellite.

Global measurements of upper tropospheric/lower-stratospheric water vapor and ice particle size distribution in clouds are critically needed to reduce uncertainties in global weather and climate models. To address this need, the conically scanning Tropospheric Water and Cloud ICE (TWICE) millimeter and submillimeter radiometer instrument is being developed. A low-noise, power-efficient command and data handling (C&DH) subsystem has been designed to control TWICE data acquisition and other subsystems. The C&DH prototype board meets functional, noise and size, weight and power (SWaP) requirements for deployment in a 6U-class satellite. Considering the limited power resources available on such platforms, a highly-efficient power regulation board has been designed to minimize power losses and reduce system noise. Furthermore, all of the components have been tested for radiation tolerance in low-Earth orbit.

A large family of graph-based semi-supervised algorithms have been developed intuitively and pragmatically for the multi-label learning problem. These methods, however, only implicitly exploited the label correlation, as either part of graph weight or an additional constraint, to improve overall classification performance. Despite their seemingly quite different formulations, we show that all existing approaches can be uniformly referred to as a Label Propagation (LP) or Random Walk with Restart (RWR) on a Cartesian Product Graph (CPG). Inspired by this discovery, we introduce a new framework for multi-label classification task, employing the Tensor Product Graph (TPG) — the tensor product of the data graph with the class (label) graph — in which not only the intra-class but also the inter-class associations are explicitly represented as weighted edges among graph vertices. In stead of computing directly on TPG, we derive an iterative algorithm, which is guaranteed to converge and with the same computational complexity and the same amount of storage as the standard label propagation on the original data graph. Applications to four benchmark multi-label data sets illustrate that our method outperforms several state-of-the-art approaches.

BACKGROUND: Proteins that interact in vivo tend to reside within the same or "adjacent" subcellular compartments. This observation provides opportunities to reveal protein subcellular localization in the context of the protein-protein interaction (PPI) network. However, so far, only a few efforts based on heuristic rules have been made in this regard. RESULTS: We systematically and quantitatively validate the hypothesis that proteins physically interacting with each other probably share at least one common subcellular localization. With the result, for the first time, four graph-based semi-supervised learning algorithms, Majority, χ2-score, GenMultiCut and FunFlow originally proposed for protein function prediction, are introduced to assign "multiplex localization" to proteins. We analyze these approaches by performing a large-scale cross validation on a Saccharomyces cerevisiae proteome compiled from BioGRID and comparing their predictions for 22 protein subcellular localizations. Furthermore, we build an ensemble classifier to associate 529 unlabeled and 137 ambiguously-annotated proteins with subcellular localizations, most of which have been verified in the previous experimental studies. CONCLUSIONS: Physical interaction of proteins has actually provided an essential clue for their co-localization. Compared to the local approaches, the global algorithms consistently achieve a superior performance.

Assigning biological functions to uncharacterized proteins is a fundamental problem in the postgenomic era. The increasing availability of large amounts of data on protein-protein interactions (PPIs) has led to the emergence of a considerable number of computational methods for determining protein function in the context of a network. These algorithms, however, treat each functional class in isolation and thereby often suffer from the difficulty of the scarcity of labeled data. In reality, different functional classes are naturally dependent on one another. We propose a new algorithm, Multi-label Correlated Semi-supervised Learning (MCSL), to incorporate the intrinsic correlations among functional classes into protein function prediction by leveraging the relationships provided by the PPI network and the functional class network. The guiding intuition is that the classification function should be sufficiently smooth on subgraphs where the respective topologies of these two networks are a good match. We encode this intuition as regularized learning with intraclass and interclass consistency, which can be understood as an extension of the graph-based learning with local and global consistency (LGC) method. Cross validation on the yeast proteome illustrates that MCSL consistently outperforms several state-of-the-art methods. Most notably, it effectively overcomes the problem associated with scarcity of label data. The supplementary files are freely available at http://sites.google.com/site/csaijiang/MCSL.