A Workload-Specific Memory Capacity Configuration Approach for In-Memory Data Analytic Platforms

We propose WSMC, a workload-specific memory capacity configuration approach for the Spark workloads, which guides users on the memory capacity configuration with the accurate prediction of the workload's memory requirement under various input data size and parameter settings.First, WSMC classifies the in-memory computing workloads into four categories according to the workloads' Data Expansion Ratio. Second, WSMC establishes a memory requirement prediction model with the consideration of the input data size, the shuffle data size, the parallelism of the workloads and the data block size. Finally, for each workload category, WSMC calculates the shuffle data size in the prediction model in a workload-specific way. For the ad-hoc workload, WSMC can profile its Data Expansion Ratio with small-sized input data and decide the category that the workload falls into. Users can then determine the accurate configuration in accordance with the corresponding memory requirement prediction.Through the comprehensive evaluations with SparkBench workloads, we found that, contrasting with the default configuration, configuration with the guide of WSMC can save over 40% memory capacity with the workload performance slight degradation (only 5%), and compared to the proper configuration found out manually, the configuration with the guide of WSMC leads to only 7% increase in the memory waste with the workload's performance slight improvement (about 1%

Microbial Air Contamination in an Intensive Care Unit

Unit layout affects every aspect of intensive care services, including patient safety. A previous study has shown that patients admitted to beds adjacent to the sink and to the door of a large bayroom had the highest number of positive blood cultures and the highest blood culture incidence density, respectively. The present study measures microbial air contamination in a medical intensive care unit of a medical center in central Taiwan. Of the 17 rooms, 8 rooms with distinct physical environmental characteristics were selected. Sampling tests were conducted between December 2013 and February 2014 with a microbial air sampler (MAS-100NT). TSA was used for bacteria collection and DG18 for fungi collection. The overall average bacterial and fungal concentrations were 83CFU/m3 and 69CFU/m3, respectively. The ranges were between 8-354 CFU/m3 and 0-1468 CFU/m3, respectively. A significant difference was found in the bacterial concentration (p=.005) between different room locations. The highest concentration was found in the rooms located at the front end of the circulation (99 CFU/m3), while the lowest was found in the rooms located at the rear end of the circulation (55CFU/m3). Differences in fungal concentrations for different room locations did not reach statistical significance. In addition, differences in bacterial and fungal concentrations for rooms with different sink locations did not reach statistical significance. Even though the microbial concentrations generally complied with standards, the results may help designers and hospital administrators develop a healthier environment for patients

Ligand Path: A Software Tool for Mapping Dynamic Ligand Migration Channel Networks

AbstractProteins are essential compositions of the living organisms and involved in the processes of different life events. Basically proteins are like amazing tiny bio-machines performing the functions in a stable and predictable manner and understanding the underline mechanisms can facilitate the pharmaceutical development. However, protein functions are not carried in a static style, so experimental observations of these dynamic movements of the drugs inside the proteins are difficult, so computational methods have an important and irreplaceable role.We developed a software tool called LigandPath for mapping the ligand migration channels in a constantly moving protein and this software can function with CADD (Computer aided drug design) software to map the possible migration pathways of candidate drugs inside a protein. Traditionally, biologists use MD (Molecular Dynamics) simulation to locate the ligand migration channels, but it takes long time for them to observe the complete migration paths. In order to overcome the limitations of the trajectory-based MD simulation, we adopt a computational method inspired from robotic motion planning called DyME (Dynamic Map Ensemble) and we develop the software tool LigandPath based on DyME. The software tool has already been successfully applied to map the potential migration channels of drugs candidates of three proteins, PPAR (peroxisome proliferator-activated receptors), UROD (uroporphyrinogen decarboxylase) and Sirt1 (silent information regulator 1) complexes in three publications

A Methylene Blue-Selective Membrane Electrode Using Methylene Blue-Phosphotungstate as Electroactive Material and its Pharmaceutical Applications

A methylene blue poly (vinyl chloride) membrane electrode based on methylene blue-phosphotungstate ion-pair complex as electroactive material is described. The linear response covered the range of 1 × 10–3 – 1 × 10–6 mol dm–3 methylene blue solution, with a slope of 51.5 ±0.8 mV/decade (pH range 3.0–10.0). The detection limit was 6.79 × 10–7 mol dm–3. The electrode showed stability, good reproducibility and a fast response. Interferences from common inorganic cations and some organic bases were negligible. These characteristics of the electrode enabled its successful use for determination of methylene blue in injection. There was good agreement for the results of methylene blue content in injection between the potentiometric method and the United States Pharmacopoeia standard procedure

Microbial Air Contamination in an Intensive Care Unit

Unit layout affects every aspect of intensive care services, including patient safety. A previous study has shown that patients admitted to beds adjacent to the sink and to the door of a large bayroom had the highest number of positive blood cultures and the highest blood culture incidence density, respectively. The present study measures microbial air contamination in a medical intensive care unit of a medical center in central Taiwan. Of the 17 rooms, 8 rooms with distinct physical environmental characteristics were selected. Sampling tests were conducted between December 2013 and February 2014 with a microbial air sampler (MAS-100NT). TSA was used for bacteria collection and DG18 for fungi collection. The overall average bacterial and fungal concentrations were 83CFU/m3 and 69CFU/m3, respectively. The ranges were between 8-354 CFU/m3 and 0-1468 CFU/m3, respectively. A significant difference was found in the bacterial concentration (p=.005) between different room locations. The highest concentration was found in the rooms located at the front end of the circulation (99 CFU/m3), while the lowest was found in the rooms located at the rear end of the circulation (55CFU/m3). Differences in fungal concentrations for different room locations did not reach statistical significance. In addition, differences in bacterial and fungal concentrations for rooms with different sink locations did not reach statistical significance. Even though the microbial concentrations generally complied with standards, the results may help designers and hospital administrators develop a healthier environment for patients