The relationship between histochemical enzyme activities of brain tumors and clinical features of the patients

Human brain turnors removed from 126 patients were histochemically examined and following results were obtained. 1. In general, alkaline phosphatase activity is decreased in poorly differentiated gliomas, but is not related to the tumor cell infiltration. 2. All the cases of alkaline phosphatase negative gliomas have poor reconvalescent course and most of the positive cases show good reconvalescence. 3. Alkaline phosphatase, leucine aminopeptidase and acid phosphatase activities are remarkable in fibroblastic meningioma, moderate or feeble in meningocytic meningioma, and negative in malignant meningioma. 4. The activities of alkaline phosphatase, &#946;-esterase, leucine aminopeptidase and acid phosphatase are decreased in most of meningocytic meningiomas when the duration of symptoms and signs is short. 5. Succinic dehydrogenase, malic dehydrogenase, isocitric dehydrogenase and &#946;-glucuronidase are strongly reactive in malignant meningioma; from strong to moderate in meningocytic meningioma and from moderate to feeble in fibroblastic meningioma. 6. There is a slight increasing tendency of the activities of succinic dehydrogenase, malic dehydrogenase, isocitric dehydrogenase in fibroblastic meningioma and p·glucuronidase for a short duration of symtoms and signs. 7. In the case of acoustic neurinomas the higher the alkaline phosphatase activity, the longer is the duration of symptoms and signs.</p

Wire-Speed Implementation of Sliding-Window Aggregate Operator over Out-of-Order Data Streams

This paper shows the design and evaluation of an FPGA-based accelerator for sliding-window aggregation over data streams with out-of-order data arrival. We propose an order-agnostic hardware implementation technique for windowing operators based on a one-pass query evaluation strategy called Window-ID, which is originally proposed for software implementation. The proposed implementation succeeds to process out-of-order data items, or tuples, at wire speed due to the simultaneous evaluations of overlapping sliding-windows. In order to verify the effectiveness of the proposed approach, we have also implemented an experimental system as a case study. Our experiments demonstrate that the proposed accelerator with a network interface achieves an effective throughput around 760 Mbps or equivalently nearly 6 million tuples per second, by fully utilizing the available bandwidth of the network interface

Anti-inflammatory Properties of Skeletal Muscle Protects Against Muscle Wasting in Colitis of Dextran Sulfate Sodium Model Mice

P(論文)Short Reportdepartmental bulletin pape

Relational Joins on GPUs: A Closer Look

The problem of scaling out relational join performance for large data sets in the database management system (DBMS) has been studied for years. Although in-memory DBMS engines can reduce load times by storing data in the main memory, join queries still remain computationally expensive. Modern graphics processing units (GPUs) provide massively parallel computing and may enhance the performance of such join queries; however, it is not clear yet in what condition relational joins perform well on GPUs. In this paper, we identify the performance characteristics of GPU computing for relational joins by implementing several well-known GPU-based join algorithms under various configurations. Experimental results indicate that the speedup ratio of GPU-based relational joins to CPU-based counterparts depends on the number of compute cores, the size of data sets, join conditions, and join algorithms. In the best case, the speedup ratios are up to 6.67 times for non-index joins, 9.41 times for sort index joins, and 2.55 times for hash joins. The execution time of GPU-based implementation for index joins, on the other hand, is only about 0.696 times less than the execution time of the CPU’s counterparts.journal articl

BATTLE: Genetically Engineered Strategies for Split-Tunable Allocation of Multiple Transgenes in the Nervous System

Elucidating fine architectures and functions of cellular and synaptic connections requires development of new flexible methods. Here, we created a concept called the “battle of transgenes,” based on which we generated strategies using genetically engineered battles of multiple recombinases. The strategies enabled split-tunable allocation of multiple transgenes. We demonstrated the versatility of these strategies and technologies in inducing strong and multi-sparse allocations of multiple transgenes. Furthermore, the combination of our transgenic strategy and expansion microscopy enabled three-dimensional high-resolution imaging of whole synaptic structures in the hippocampus with simultaneous visualizations of endogenous synaptic proteins. These strategies and technologies based on the battle of genes may accelerate the analysis of whole synaptic and cellular connections in diverse life science fields

BATTLE: Genetically Engineered Strategies for Split-Tunable Allocation of Multiple Transgenes in the Nervous System

Elucidating fine architectures and functions of cellular and synaptic connections requires development of new flexible methods. Here, we created a concept called the “battle of transgenes,” based on which we generated strategies using genetically engineered battles of multiple recombinases. The strategies enabled split-tunable allocation of multiple transgenes. We demonstrated the versatility of these strategies and technologies in inducing strong and multi-sparse allocations of multiple transgenes. Furthermore, the combination of our transgenic strategy and expansion microscopy enabled three-dimensional high-resolution imaging of whole synaptic structures in the hippocampus with simultaneous visualizations of endogenous synaptic proteins. These strategies and technologies based on the battle of genes may accelerate the analysis of whole synaptic and cellular connections in diverse life science fields