Stability enhancement of 129Xe hyperpolarizing system using alkali metal vapor in spin-exchange optical pumping cell to achieve high NMR sensitivity

Hyperpolarized (HP) 129Xe NMR and MRI have enabled 129Xe studies with extraordinarily enhanced sensitivity, stimulating new developments in magnetic resonance in chemistry, physics, biology and medicine. However, the standard method of HP 129Xe production inevitably demands Rb vapor for the excitation, which has made the method very sensitive to impurities such as water or oxygen. This is the case especially in the recirculating system. In the present study, stability of the hyperpolarizing system is discussed by proposing the "cell decay constant", which symbolizes the decay rate of the NMR signal obtained from the system. The cell decay constant is effectively decreased to 1/3 by introducing separated chambers and mechanical stirring of the alkali metals used in the system, making it effective for accumulating FIDs over 30 to 100 h. The newly developed hyperpolarizing system has been successfully applied for newly detecting a broad signal at 190 ppm with an industrial material Nanofiber

UPI: A Primary Index for Uncertain Databases

Uncertain data management has received growing attention from industry and academia. Many efforts have been made to optimize uncertain databases, including the development of special index data structures. However, none of these efforts have explored primary (clustered) indexes for uncertain databases, despite the fact that clustering has the potential to offer substantial speedups for non-selective analytic queries on large uncertain databases. In this paper, we propose a new index called a UPI (Uncertain Primary Index) that clusters heap files according to uncertain attributes with both discrete and continuous uncertainty distributions. Because uncertain attributes may have several possible values, a UPI on an uncertain attribute duplicates tuple data once for each possible value. To prevent the size of the UPI from becoming unmanageable, its size is kept small by placing low-probability tuples in a special Cutoff Index that is consulted only when queries for low-probability values are run. We also propose several other optimizations, including techniques to improve secondary index performance and techniques to reduce maintenance costs and fragmentation by buffering changes to the table and writing updates in sequential batches. Finally, we develop cost models for UPIs to estimate query performance in various settings to help automatically select tuning parameters of a UPI. We have implemented a prototype UPI and experimented on two real datasets. Our results show that UPIs can significantly (up to two orders of magnitude) improve the performance of uncertain queries both over clustered and unclustered attributes. We also show that our buffering techniques mitigate table fragmentation and keep the maintenance cost as low as or even lower than using an unclustered heap file.National Science Foundation (U.S.) (Grant IIS-0448124)National Science Foundation (U.S.) (Grant IIS-0905553)National Science Foundation (U.S.) (Grant IIS-0916691

Overview of research on tuna thermo-physiology using electric tags

The behavioral physiology of bigeye tuna was investigated using acoustic tags in the 1990s. They spend most of the daylight hours below the thermocline but make regular brief excursions into the mixed layer. In doing so, they reduce the efficacy of their vascular countercurrent heat exchangers while gaining heat from the warmer environment and then increase it again when they return below the thermocline. Recently, archival tags have been used on a range of tuna species including bluefin. This tag, recovered when the tuna is harvested, yields time-series data over longer periods than acoustic tags. Although immature Pacific bluefin spend most of their time at the surface, they often dive below the thermocline, maintaining a peritoneal temperature. This might be due either to heat conservation or high heat production. Further, while giant Atlantic bluefin show an ability to maintain their temperature above the ambient, there are occasions in cold water when some physiological process is clearly limiting their dive time. Solution of the growth conundrum that their warm body temperature may pose an ecological problem as they grow in body mass, should be clarified by using tag data. This will lead to investigation of their adaptation mechanisms to their habitats

Glycosylphosphatidylinositol-Anchored Cell Surface Proteins Regulate Position-Specific Cell Affinity in the Limb Bud

AbstractAlthough regional differences in mesenchymal cell affinity in the limb bud represent positional identity, the molecular basis for cell affinity is poorly understood. We found that treatment of the cell surface with bacterial phosphatidylinositol-specific phospholipase C (PI-PLC) could change cell affinity in culture. When PI-PLC was added to the culture medium, segregation of the progress zone (PZ) cells from different stage limb buds was inhibited. Similarly, sorting out of the cells from different positions along the proximodistal (PD) axis of the same stage limb buds was disturbed. Since PI-PLC can remove glycosylphosphatidylinositol (GPI)-anchored membrane bound proteins from the cell surface, the GPI-anchored cell surface proteins may be involved in sorting out. To define the GPI-anchored molecules that determine the segregation of limb mesenchymal cells, we examined the effect of neutralizing antibody on the EphA4 receptor that binds to GPI-anchored cell surface ligands, called ephrin-A. Sorting out of the PZ cells at different stages could be inhibited by the neutralizing antibody to EphA4. These results suggest that EphA4 and its GPI-anchored ligands are, at least in part, involved in sorting out of limb mesenchymal cells with different proximal–distal positional values, and that GPI-anchored cell surface proteins play important roles in determining cell affinity in the limb bud

CORADD: Correlation Aware Database Designer for Materialized Views and Indexes

We describe an automatic database design tool that exploits correlations between attributes when recommending materialized views (MVs) and indexes. Although there is a substantial body of related work exploring how to select an appropriate set of MVs and indexes for a given workload, none of this work has explored the effect of correlated attributes (e.g., attributes encoding related geographic information) on designs. Our tool identifies a set of MVs and secondary indexes such that correlations between the clustered attributes of the MVs and the secondary indexes are enhanced, which can dramatically improve query performance. It uses a form of Integer Linear Programming (ILP) called ILP Feedback to pick the best set of MVs and indexes for given database size constraints. We compare our tool with a state-of-the-art commercial database designer on two workloads, APB-1 and SSB (Star Schema Benchmark---similar to TPC-H). Our results show that a correlation-aware database designer can improve query performance up to 6 times within the same space budget when compared to a commercial database designer.National Science Foundation (U.S.) (Grant IIS-0704424)SAP Corporation (Grant