An online data access prediction and optimization approach for distributed systems

Current scientific applications have been producing large amounts of data. The processing, handling and analysis of such data require large-scale computing infrastructures such as clusters and grids. In this area, studies aim at improving the performance of data-intensive applications by optimizing data accesses. In order to achieve this goal, distributed storage systems have been considering techniques of data replication, migration, distribution, and access parallelism. However, the main drawback of those studies is that they do not take into account application behavior to perform data access optimization. This limitation motivated this paper which applies strategies to support the online prediction of application behavior in order to optimize data access operations on distributed systems, without requiring any information on past executions. In order to accomplish such a goal, this approach organizes application behaviors as time series and, then, analyzes and classifies those series according to their properties. By knowing properties, the approach selects modeling techniques to represent series and perform predictions, which are, later on, used to optimize data access operations. This new approach was implemented and evaluated using the OptorSim simulator, sponsored by the LHC-CERN project and widely employed by the scientific community. Experiments confirm this new approach reduces application execution time in about 50 percent, specially when handling large amounts of data.

Prediction and probability of neonatal outcome in isolated congenital diaphragmatic hernia using multiple ultrasound parameters

Objectives To evaluate the accuracy and probabilities of different fetal ultrasound parameters to predict neonatal outcome in isolated congenital diaphragmatic hernia (CDH). Methods Between January 2004 and December 2010, we evaluated prospectively 108 fetuses with isolated CDH (82 left-sided and 26 right-sided). The following parameters were evaluated: gestational age at diagnosis, side of the diaphragmatic defect, presence of polyhydramnios, presence of liver herniated into the fetal thorax (liver-up), lung-to-head ratio (LHR) and observed/expected LHR (o/e-LHR), observed/expected contralateral and total fetal lung volume (o/e-ContFLV and o/e-TotFLV) ratios, ultrasonographic fetal lung volume/fetal weight ratio (US-FLW), observed/expected contralateral and main pulmonary artery diameter (o/e-ContPA and o/eMPA) ratios and the contralateral vascularization index (Cont-VI). The outcomes were neonatal death and severe postnatal pulmonary arterial hypertension (PAH). Results Neonatal mortality was 64.8% (70/108). Severe PAH was diagnosed in 68 (63.0%) cases, of which 63 died neonatally (92.6%) (P < 0.001). Gestational age at diagnosis, side of the defect and polyhydramnios were not associated with poor outcome (P > 0.05). LHR, o/eLHR, liver-up, o/e-ContFLV, o/e-TotFLV, US-FLW, o/eContPA, o/e-MPA and Cont-VI were associated with both neonatal death and severe postnatal PAH (P < 0.001). Receiver-operating characteristics curves indicated that measuring total lung volumes (o/e-TotFLV and US-FLW) was more accurate than was considering only the contralateral lung sizes (LHR, o/e-LHR and o/e-ContFLV; P < 0.05), and Cont-VI was the most accurate ultrasound parameter to predict neonatal death and severe PAH (P < 0.001). Conclusions Evaluating total lung volumes is more accurate than is measuring only the contralateral lung size. Evaluating pulmonary vascularization (Cont-VI) is the most accurate predictor of neonatal outcome. Estimating the probability of survival and severe PAH allows classification of cases according to prognosis. Copyright (C) 2011 ISUOG. Published by John Wiley & Sons, Ltd.

Prediction of enzymatic infarct size in ST-segment elevation myocardial infarction

Objectives Predictors of adverse outcomes following myocardial infarction (MI) are well established; however, little is known about what predicts enzymatically estimated infarct size in patients with acute ST-elevation MI. The Complement And Reduction of INfarct size after Angioplasty or Lytics trials of pexelizumab used creatine kinase (CK)-MB area under the curve to determine infarct size in patients treated with primary percutaneous coronary intervention (PCI) or fibrinolysis. Methods Prediction of infarct size was carried out by measuring CK-MB area under the curve in patients with ST-segment elevation MI treated with reperfusion therapy from January 2000 to April 2002. Infarct size was calculated in 1622 patients (PCI=817; fibrinolysis=805). Logistic regression was used to examine the relationship between baseline demographics, total ST-segment elevation, index angiographic findings (PCI group), and binary outcome of CK-MB area under the curve greater than 3000 ng/ml. Results Large infarcts occurred in 63% (515) of the PCI group and 69% (554) of the fibrinolysis group. Independent predictors of large infarcts differed depending on mode of reperfusion. In PCI, male sex, no prior coronary revascularization and diabetes, decreased systolic blood pressure, sum of ST-segment elevation, total (angiographic) occlusion, and nonright coronary artery culprit artery were independent predictors of larger infarcts (C index=0.73). In fibrinolysis, younger age, decreased heart rate, white race, no history of arrhythmia, increased time to fibrinolytic therapy in patients treated up to 2 h after symptom onset, and sum of ST-segment elevation were independently associated with a larger infarct size (C index=0.68). Conclusion Clinical and patient data can be used to predict larger infarcts on the basis of CK-MB quantification. These models may be helpful in designing future trials and in guiding the use of novel pharmacotherapies aimed at limiting infarct size in clinical practice. Coron Artery Dis 23:118-125 (C) 2012 Wolters Kluwer Health | Lippincott Williams & Wilkins.

The implementation of the Medical Regulation Office and Mobile Emergency Attendance System and its impact on the gravity profile of non-traumatic afflictions treated in a University Hospital: a research study

Abstract Background The public health system of Brazil is structured by a network of increasing complexity, but the low resolution of emergency care at pre-hospital units and the lack of organization of patient flow overloaded the hospitals, mainly the ones of higher complexity. The knowledge of this phenomenon induced Ribeirão Preto to implement the Medical Regulation Office and the Mobile Emergency Attendance System. The objective of this study was to analyze the impact of these services on the gravity profile of non-traumatic afflictions in a University Hospital. Methods The study conducted a retrospective analysis of the medical records of 906 patients older than 13 years of age who entered the Emergency Care Unit of the Hospital of the University of São Paulo School of Medicine at Ribeirão Preto. All presented acute non-traumatic afflictions and were admitted to the Internal Medicine, Surgery or Neurology Departments during two study periods: May 1996 (prior to) and May 2001 (after the implementation of the Medical Regulation Office and Mobile Emergency Attendance System). Demographics and mortality risk levels calculated by Acute Physiology and Chronic Health Evaluation II (APACHE II) were determined. Results From 1996 to 2001, the mean age increased from 49 ± 0.9 to 52 ± 0.9 (P = 0.021), as did the percentage of co-morbidities, from 66.6 to 77.0 (P = 0.0001), the number of in-hospital complications from 260 to 284 (P = 0.0001), the mean calculated APACHE II mortality risk increased from 12.0 ± 0.5 to 14.8 ± 0.6 (P = 0.0008) and mortality rate from 6.1 to 12.2 (P = 0.002). The differences were more significant for patients admitted to the Internal Medicine Department. Conclusion The implementation of the Medical Regulation and Mobile Emergency Attendance System contributed to directing patients with higher gravity scores to the Emergency Care Unit, demonstrating the potential of these services for hierarchical structuring of pre-hospital networks and referrals.

A fragment-based approach for the in silico prediction of blood-brain barrier permeation

Blood-brain barrier (BBB) permeation is an essential property for drugs that act in the central nervous system (CNS) for the treatment of human diseases, such as epilepsy, depression, Alzheimer's disease, Parkinson disease, schizophrenia, among others. In the present work, quantitative structure-property relationship (QSPR) studies were conducted for the development and validation of in silico models for the prediction of BBB permeation. The data set used has substantial chemical diversity and a relatively wide distribution of property values. The generated QSPR models showed good statistical parameters and were successfully employed for the prediction of a test set containing 48 compounds. The predictive models presented herein are useful in the identification, selection and design of new drug candidates having improved pharmacokinetic properties.