Isolation and Antimicrobial Susceptibility of Bacteria from Chronic Suppurative Otitis Media Patients in Kerman, Iran

Background: Chronic supportive otitis media (CSOM) is one of the commonest illnesses in ENT practice. This study was conducted to find out the various aerobic microorganisms associated with CSOM and their current antimicrobial susceptibility patterns to commonly used antimicrobials. Methods: samples were collected from 117 clinically diagnosed cases of CSOM and processed according to standard protocols. Results: Out of 117 CSOM cases, 105 (86) showed positive bacterial culture. The Staphylococcus aureus was the commonest aerobic isolate in CSOM. The sensitivity of Staphylococci spp. to commonly used antimicrobials varied from 27.2 for cefixime to 95.5 for gentamicin and coagulase positive. Pseudomonas isolates showed complete (100) resistance to amoxicillin/clavulanate (co-amoxiclave), cloxacillin and cefixime, and high sensitivity to ciprofloxacin (95) and cephalexin (90). Conclusion: An appropriate knowledge of antibacterial susceptibility of microorganisms would contribute to a rational antibiotic use and the success of treatment for chronic supportive otitis media. © Iranian Red Crescent Medical Journal

Nanotechnology for angiogenesis: Opportunities and challenges

Angiogenesis plays a critical role within the human body, from the early stages of life (i.e., embryonic development) to life-threatening diseases (e.g., cancer, heart attack, stroke, wound healing). Many pharmaceutical companies have expended huge efforts on both stimulation and inhibition of angiogenesis. During the last decade, the nanotechnology revolution has made a great impact in medicine, and regulatory approvals are starting to be achieved for nanomedicines to treat a wide range of diseases. Angiogenesis therapies involve the inhibition of angiogenesis in oncology and ophthalmology, and stimulation of angiogenesis in wound healing and tissue engineering. This review aims to summarize nanotechnology-based strategies that have been explored in the broad area of angiogenesis. Lipid-based, carbon-based and polymeric nanoparticles, and a wide range of inorganic and metallic nanoparticles are covered in detail. Theranostic and imaging approaches can be facilitated by nanoparticles. Many preparations have been reported to have a bimodal effect where they stimulate angiogenesis at low dose and inhibit it at higher doses. This journal i

BlinkDB: queries with bounded errors and bounded response times on very large data

In this paper, we present BlinkDB, a massively parallel, approximate query engine for running interactive SQL queries on large volumes of data. BlinkDB allows users to trade-off query accuracy for response time, enabling interactive queries over massive data by running queries on data samples and presenting results annotated with meaningful error bars. To achieve this, BlinkDB uses two key ideas: (1) an adaptive optimization framework that builds and maintains a set of multi-dimensional stratified samples from original data over time, and (2) a dynamic sample selection strategy that selects an appropriately sized sample based on a query's accuracy or response time requirements. We evaluate BlinkDB against the well-known TPC-H benchmarks and a real-world analytic workload derived from Conviva Inc., a company that manages video distribution over the Internet. Our experiments on a 100 node cluster show that BlinkDB can answer queries on up to 17 TBs of data in less than 2 seconds (over 200 x faster than Hive), within an error of 2-10%.National Science Foundation (U.S.) (CISE Expeditions Award CCF-1139158)United States. Defense Advanced Research Projects Agency (XData Award FA8750-12-2-0331)

Blink and it's done: Interactive queries on very large data

In this demonstration, we present BlinkDB, a massively parallel, sampling-based approximate query processing framework for running interactive queries on large volumes of data. The key observation in BlinkDB is that one can make reasonable decisions in the absence of perfect answers. BlinkDB extends the Hive/HDFS stack and can handle the same set of SPJA (selection, projection, join and aggregate) queries as supported by these systems. BlinkDB provides real-time answers along with statistical error guarantees, and can scale to petabytes of data and thousands of machines in a fault-tolerant manner. Our experiments using the TPC-H benchmark and on an anonymized real-world video content distribution workload from Conviva Inc. show that BlinkDB can execute a wide range of queries up to 150x faster than Hive on MapReduce and 10--150x faster than Shark (Hive on Spark) over tens of terabytes of data stored across 100 machines, all with an error of 2--10%.National Science Foundation (U.S.) (CISE Expeditions Award CCF-1139158)QUALCOMM Inc.Amazon.com (Firm)Google (Firm)SAP CorporationBlue GojiCisco Systems, Inc.Cloudera, Inc.Ericsson, Inc.General Electric CompanyHewlett-Packard CompanyIntel CorporationMarkLogic CorporationMicrosoft CorporationNetAppOracle CorporationSplunk Inc.VMware, Inc.United States. Defense Advanced Research Projects Agency (Contract FA8650-11-C-7136

Separation of Crocin/Betanin Using Aqueous Two-phase Systems Containing Ionic Liquid and Sorbitol

Betanin and crocin, two food additives with attractive colors, are bioactive compounds of plants that are widely used in food, pharmaceutical, and medical industries. These bioactive pigments are sensitive to light, heat, organic solvents, and pH. It seems that a benign economic method is needed to extract these biomolecules, especially for industrial applications. The aqueous two-phase system (ATPS) is a liquid-liquid extraction technique that has shown its potential in recent years to extract and separate biomolecules. In this study, an ATPS consisting of carbohydrate (sorbitol) and ionic liquid (tetrabutyl phosphonium bromide) has been proposed as a new separation system with unique properties to study the partition coefficient of crocin and betanin. The results indicated that crocin and betanin had more tendency to the ionic liquid (IL)-rich phase and carbohydrate-rich phase, respectively. The influence of the concentration of IL and sorbitol on the partition coefficient was studied. The results showed that an increase in the tie-line length (concentrations) increased the partition coefficient of crocin and betanin. Enhancement in temperature increased the partition coefficient of crocin. The highest values of crocin recovery (97.55 %) and partition coefficient (39.85) at 308 K show that our proposed ATPS can be considered for crocin separation in one step

Knowing when you're wrong: Building fast and reliable approximate query processing systems

Modern data analytics applications typically process massive amounts of data on clusters of tens, hundreds, or thousands of machines to support near-real-time decisions.The quantity of data and limitations of disk and memory bandwidth often make it infeasible to deliver answers at interactive speeds. However, it has been widely observed that many applications can tolerate some degree of inaccuracy. This is especially true for exploratory queries on data, where users are satisfied with "close-enough" answers if they can come quickly. A popular technique for speeding up queries at the cost of accuracy is to execute each query on a sample of data, rather than the whole dataset. To ensure that the returned result is not too inaccurate, past work on approximate query processing has used statistical techniques to estimate "error bars" on returned results. However, existing work in the sampling-based approximate query processing (S-AQP) community has not validated whether these techniques actually generate accurate error bars for real query workloads. In fact, we find that error bar estimation often fails on real world production workloads. Fortunately, it is possible to quickly and accurately diagnose the failure of error estimation for a query. In this paper, we show that it is possible to implement a query approximation pipeline that produces approximate answers and reliable error bars at interactive speeds.National Science Foundation (U.S.) (CISE Expeditions Award CCF-1139158)Lawrence Berkeley National Laboratory (Award 7076018)United States. Defense Advanced Research Projects Agency (XData Award FA8750-12-2-0331)Amazon.com (Firm)Google (Firm)SAP CorporationThomas and Stacey Siebel FoundationApple Computer, Inc.Cisco Systems, Inc.Cloudera, Inc.EMC CorporationEricsson, Inc.Facebook (Firm

BlinkML: Efficient Maximum Likelihood Estimation with Probabilistic Guarantees

The rising volume of datasets has made training machine learning (ML) models a major computational cost in the enterprise. Given the iterative nature of model and parameter tuning, many analysts use a small sample of their entire data during their initial stage of analysis to make quick decisions (e.g., what features or hyperparameters to use) and use the entire dataset only in later stages (i.e., when they have converged to a specific model). This sampling, however, is performed in an ad-hoc fashion. Most practitioners cannot precisely capture the effect of sampling on the quality of their model, and eventually on their decision-making process during the tuning phase. Moreover, without systematic support for sampling operators, many optimizations and reuse opportunities are lost. In this paper, we introduce BlinkML, a system for fast, quality-guaranteed ML training. BlinkML allows users to make error-computation tradeoffs: instead of training a model on their full data (i.e., full model), BlinkML can quickly train an approximate model with quality guarantees using a sample. The quality guarantees ensure that, with high probability, the approximate model makes the same predictions as the full model. BlinkML currently supports any ML model that relies on maximum likelihood estimation (MLE), which includes Generalized Linear Models (e.g., linear regression, logistic regression, max entropy classifier, Poisson regression) as well as PPCA (Probabilistic Principal Component Analysis). Our experiments show that BlinkML can speed up the training of large-scale ML tasks by 6.26x-629x while guaranteeing the same predictions, with 95% probability, as the full model.Comment: 22 pages, SIGMOD 201