Optimization of F2 layer parameters using IRI-Plas model and IONOLAB Total Electron Content

In this study, the relation of the maximum ionization height (HmF2) and the critical frequency (FoF2) of F2 layer is examined within their parametric range through the International Reference Ionosphere extended towards the plasmasphere (IRI-Plas) model and the IONOLAB-TEC (Total Electron Content) observations. HmF2 and FoF2 are optimized using an iterational loop through Non-Linear Least Squares method by also using a physical relation constraint between these two parameters. Performance evaluation of optimization algorithm is performed separately for the cases running IRI-Plas with optimized parameters and TEC input; only with optimized parameters; only with TEC and finally with no optimized parameter and TEC input. As a conclusion, it is seen that using optimized parameters and TEC together as input produces best IRI-TEC estimates. But also using only optimized parameters (without TEC update) gives estimates with also very low RMS errors and is suitable to use in optimizations. HmF2 and FoF2 estimates are obtained separately for a quiet day, positively corrupted day, negatively corrupted day, a northern latitude and a southern latitude. HmF2 and FoF2 estimation results are compared with ionosonde data where available. This study enables the modification and update of empirical and deterministic IRI Model to include instantaneous variability of the ionosphere. © 2011 IEEE

Architecture conformance analysis approach within the context of multiple product line engineering

One of the important concerns in software product line engineering is the conformance of the application architecture to the product line architecture. Consistency with the product line architecture is important to ensure that the business rules and constraints that are defined for the entire product family are not violated. Usually, the conformance checking to the product line architecture is a manual and tedious process. A popular approach for ensuring architecture conformance is reflexion modeling which has been primarily used to check the consistency between the architecture and the code. In this paper we present an approach for product line conformance analysis based on reflexion modeling. We consider conformance analysis in product line engineering and extend our discussion to multiple product line engineering. Our study shows several important challenges regarding reflexion modeling within the context of product line engineering. © 2014 IEEE

Space-time interpolation and automatic mapping of TEC using TNPGN-active

Turkish National Permanent GPS Network (TNPGN) is the Reference Station Network of 146 continuously-operating GNSS stations o which are distributed uniformly across Turkey and North Cyprus Turkish Republic since May 2009. IONOLAB group, formed by researchers and students in Hacettepe University, Bilkent University and General Command of Mapping is currently investigating new techniques for space-time interpolation, and automatic mapping of TEC through a TUBITAK research grant. This study presents the developments in monitoring of space weather, and correction of geodetic positioning errors due to ionosphere using TNPGN. © 2011 IEEE

Space weather activities of IONOLAB group using TNPGN GPS Network

Characterization and constant monitoring of variability of the ionosphere is of utmost importance for the performance improvement of HF communication, Satellite communication, navigation and guidance systems, Low Earth Orbit (LEO) satellite systems, Space Craft exit and entry into the atmosphere and space weather. Turkish National Permanent GPS Network (TNPGN) is the Reference Station Network of 146 continuously-operating GNSS stations of which are distributed uniformly across Turkey and North Cyprus Turkish Republic since May 2009. IONOLAB group is currently investigating new techniques for space-time interpolation, and automatic mapping of TEC through a TUBITAK research grant. It is utmost importance to develop regional stochastic models for correction of ionospheric delay in geodetic systems and also form a scientific basis for communication link characterization. This study is a brief summary of the efforts of IONOLAB group in monitoring of space weather, and correction of geodetic positioning errors due to ionosphere using TNPGN. © 2011 IEEE

Can Machine Learning Models Predict Asparaginase-associated Pancreatitis in Childhood Acute Lymphoblastic Leukemia

Publisher Copyright: © 2021 Lippincott Williams and Wilkins. All rights reserved.Asparaginase-associated pancreatitis (AAP) frequently affects children treated for acute lymphoblastic leukemia (ALL) causing severe acute and persisting complications. Known risk factors such as asparaginase dosing, older age and single nucleotide polymorphisms (SNPs) have insufficient odds ratios to allow personalized asparaginase therapy. In this study, we explored machine learning strategies for prediction of individual AAP risk. We integrated information on age, sex, and SNPs based on Illumina Omni2.5exome-8 arrays of patients with childhood ALL (N=1564, 244 with AAP aged 1.0 to 17.9 y) from 10 international ALL consortia into machine learning models including regression, random forest, AdaBoost and artificial neural networks. A model with only age and sex had area under the receiver operating characteristic curve (ROC-AUC) of 0.62. Inclusion of 6 pancreatitis candidate gene SNPs or 4 validated pancreatitis SNPs boosted ROC-AUC somewhat (0.67) while 30 SNPs, identified through our AAP genome-wide association study cohort, boosted performance (0.80). Most predictive features included rs10273639 (PRSS1-PRSS2), rs10436957 (CTRC), rs13228878 (PRSS1/PRSS2), rs1505495 (GALNTL6), rs4655107 (EPHB2) and age (1 to 7 y). Second AAP following asparaginase re-exposure was predicted with ROC-AUC: 0.65. The machine learning models assist individual-level risk assessment of AAP for future prevention trials, and may legitimize asparaginase re-exposure when AAP risk is predicted to be low.Peer reviewe

Understanding and predicting ciprofloxacin minimum inhibitory concentration in Escherichia coli with machine learning

It is important that antibiotics prescriptions are based on antimicrobial susceptibility data to ensure effective treatment outcomes. The increasing availability of next-generation sequencing, bacterial whole genome sequencing (WGS) can facilitate a more reliable and faster alternative to traditional phenotyping for the detection and surveillance of AMR. This work proposes a machine learning approach that can predict the minimum inhibitory concentration (MIC) for a given antibiotic, here ciprofloxacin, on the basis of both genome-wide mutation profiles and profiles of acquired antimicrobial resistance genes. We analysed 704 Escherichia coli genomes combined with their respective MIC measurements for ciprofloxacin originating from different countries. The four most important predictors found by the model, mutations in gyrA residues Ser83 and Asp87, a mutation in parC residue Ser80 and presence of the qnrS1 gene, have been experimentally validated before. Using only these four predictors in a linear regression model, 65% and 93% of the test samples' MIC were correctly predicted within a two- and a four-fold dilution range, respectively. The presented work does not treat machine learning as a black box model concept, but also identifies the genomic features that determine susceptibility. The recent progress in WGS technology in combination with machine learning analysis approaches indicates that in the near future WGS of bacteria might become cheaper and faster than a MIC measurement

Supporting Incremental Product Development using Multiple Product Line Architecture

Software product line engineering (SPLE) has been successfully applied in various application domains to support systematic reuse. Besides of its benefits it is also acknowledged that the SPLE process can in practice be considered too time consuming and heavyweight due to the required planning and development of the asset base. For this reason more lightweight SPLE processes are required that can be integrated in the ongoing product development of the organization. In this context, the authors share their experiences in adopting a multiple product line architecture to support the incremental product development of Aselsan REHIS, a leading high technology company in Turkey. The authors first discuss the important business needs for defining a more lightweight multiple product line engineering (MPLE) process. Then they discuss the multiple product line architecture and how it has been used to guide the incremental product development

Detection of impaired cognitive function in rat with hepatosteatosis model and improving effect of GLP-1 analogs (exenatide) on cognitive function in hepatosteatosis

PubMed ID: 24741367The aims of the study were to evaluate (1) detection of cognitive function changing in rat with hepatosteatosis model and (2) evaluate the effect of GLP-1 analog (exenatide) on cognitive function in hepatosteatosis. In the study group, 30% fructose was given in nutrition water to perform hepatosteatosis for 8 weeks to 18 male rats. Six male rats were chosen as control group and had normal nutrition. Fructose nutrition group were stratified into 3 groups. In first group (n = 6), intracerebroventricular (ICV) infusion of exenatide (n = 6) was given. ICV infusion of NaCl (n = 6) was given to second group. And also, the third group had no treatment. And also, rats were evaluated for passive avoidance learning (PAL) and liver histopathology. Mean levels of latency time were statistically significantly decreased in rats with hepatosteatosis than those of normal rats (P < 0.00001). However, mean level of latency time in rats with hepatosteatosis treated with ICV exenatide was statistically significantly increased than that of rats treated with ICV NaCl (P < 0.001). Memory performance falls off in rats with hepatosteatosis feeding on fructose (decreased latency time). However, GLP-1 ameliorates cognitive functions (increased latency time) in rats with hepatosteatosis and releated metabolic syndrome. © 2014 Oytun Erbaş et al