Comparison Of Field And Laboratory Short Term Aging Of Asphalt Binder And Mixture

Aging is a complex physico-chemical phenomenon that influences asphalt binder and mixture rheological properties causing deterioration in asphalt mixture performance. To enhance the pavement performance, characterisation of the asphalt binder and asphalt mixture due to short term aging is required. This study is divided into two phases. The first phase evaluated and characterized the asphalt binder rheological properties before and after short term aging. The Rolling Thin Film Oven (RTFO), Dynamic Shear Rheometer (DSR), Rotational Viscometer (RV), Fourier Transform Infrared Spectroscopy (FTIR) and X-ray Diffraction (XRD) tests were used to evaluate behaviour of virgin, artificially aged and extracted asphalt binders. The second phase dwelt upon the behaviour of dense asphalt mixture produced by Kuad Quarry Sdn. Bhd, after subjected to short term aging. The indirect tensile strength, dynamic creep, resilient modulus and diametral fatigue tests were carried out to assess and determine the effects of short term aging on field, plant and artificially aged asphalt mixtures. All the binder and mixture tests results were compared to evaluate and determine the short term aging effect during production. The results showed that the aging increases the viscosity and hardening of the binder and mixture. The binder rheological test results indicated that aging increased the complex modulus, rutting factor, viscosity and torque but decreased the phase angle. The FTIR results indicated that aging only affected the binder carbonyl and sulfoxides bands. From the XRD results aging affect the range 2θ between 30° and 90° by decreasing the binder pattern intensity

Information Retrieval of Opioid Dependence Medications Reviews from Health-Related Social Media

Social media provides a convenient platform for patients to share their drug usage experience with others; consequently, health researchers can leverage this potential data to gain valuable information about users’ drug satisfaction. Since the 1990s, opioid drug abuse has become a national crisis. In order to reduce the dependency of opioids, several drugs have been presented to the market, but little is known about patient satisfaction with these treatments. Sentiment analysis is a method to measure and interpret patients’ satisfaction. In the first phase of this study, we aimed to utilize social media posts to predict patients’ sentiment towards opioid dependency treatment. We focused on Suboxone, a well-known opioid dependence medication, as our targeted treatment and Drugs.com, an online healthcare forum as our data source. For the purpose of our analysis, we first collected 1,532 posts to create a training dataset, split the posts to sentences, and annotated 1100 sentences for sentiment analysis. To predict patients’ sentiment, we extracted features from patients’ posts, including bigrams, trigrams, and features extracted from topic modeling. To develop the prediction model, we used two machine learning methods, Naïve Bayes and SVM, for predicting sentiment. We achieved the best performance using SVM, getting an accuracy of 61% for SVM. In the second phase of this study, we also aimed to understand the behavior of the patients toward the targeted medication. To accomplish this goal, we used the Health Belief Model (HBM), a social psychological model that describes and predicts patients’ health-related attitudes in action, benefit, barrier, and threat categories, for predicting such behavior from patients’ reviews. We also utilized the same combinations of features and machine learning methods that we used in the first phase of the study, and the best accuracy performance was 47% for the SVM classifier as compared to 43% as our baseline

Application of Response Surface Method for Analyzing Pavement Performance

Hot mix asphalt (HMA) is a common material that has been largely used in the road construction industries. The main constituents of HMA are asphalt binder, mineral aggregate, and filler. The asphalt binder bounds aggregate and filler particles together and also waterproofs the mixture. The aggregate acts as a stone skeleton to impart strength and toughness to the structure, while the filler fills pores in the mixture which can improve adhesion and cohesion as well as moisture resistance. The HMA behavior depends on individual component properties and their combined reaction in the mixture. Asphalt binder properties change due to different factors. Over the years, asphalt pavement materials age, causing binder embrittlement which adversely affects pavement service life. Response Surface Method (RSM) is a set of techniques that are used to develop a series of experiment designs, determining relationships between experimental factors and responses, and using these relationships to determine the optimum conditions. Incorporating RSM in pavement technologies can beneficially help researchers to develop a better experimental matrix and give them the opportunity to analyze the changes in pavement performance in a faster, more effective, and reliable way

Identification and characterization of metabolite quantitative trait loci in tomato leaves and comparison with those reported for fruits and seeds

Nunes Nesi, Adriano. Universidade Federal de Viçosa. Departamento de Biologia Vegetal. Viçosa, Minas Gerais, Brazil.Alseekh, Saleh. Max - Planck- Institute of Molecular Plant Physiology. Potsdam, Germany.Oliveira Silva, Franklin Magnum de. Universidade Federal de Viçosa. Departamento de Biologia Vegetal. Viçosa, Minas Gerais, Brazil.Omranian, Nooshin. Max - Planck- Institute of Molecular Plant Physiology. Potsdam, Germany.Lichtenstein, Gabriel. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología. Castelar, Buenos Aires, Argentina.Mirnezhad, Mohammad. Leiden University. Plant Ecology, Institute of Biology. The Netherlands.Romero González, Roman R. Leiden University. Plant Ecology. Institute of Biology. The Netherlands.Carrari, Fernando. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Biotecnología. Castelar, Buenos Aires, Argentina.1-13Introduction To date, most studies of natural variation and metabolite quantitative trait loci (mQTL) in tomato have focused on fruit metabolism, leaving aside the identification of genomic regions involved in the regulation of leaf metabolism. Objective This study was conducted to identify leaf mQTL in tomato and to assess the association of leaf metabolites and physiological traits with the metabolite levels from other tissues. Methods The analysis of components of leaf metabolism was performed by phenotypying 76 tomato ILs with chromosome segments of the wild species Solanum pennellii in the genetic background of a cultivated tomato (S. lycopersicum) variety M82. The plants were cultivated in two different environments in independent years and samples were harvested from mature leaves of non-flowering plants at the middle of the light period. The non-targeted metabolite profiling was obtained by gas chromatography time-of-flight mass spectrometry (GC-TOF-MS). With the data set obtained in this study and already published metabolomics data from seed and fruit, we performed QTL mapping, heritability and correlation analyses. Results Changes in metabolite contents were evident in the ILs that are potentially important with respect to stress responses and plant physiology. By analyzing the obtained data, we identified 42 positive and 76 negative mQTL involved in carbon and nitrogen metabolism. Conclusions Overall, these findings allowed the identification of S. lycopersicum genome regions involved in the regulation of leaf primary carbon and nitrogen metabolism, as well as the association of leaf metabolites with metabolites from seeds and fruits

Evaluation of effects of extended short-term aging on the rheological properties of asphalt binders at intermediate temperatures using respond surface method

Predicting the effects of short term aging on asphalt binders’ rheological properties can be a complicated task. This is due to the exposure of different binders to different conditions. Hence, the utilization of a Respond Surface Method (RSM) is a practical way to predict these effects. An experimental matrix was planned to predict asphalt binders behavior at intermediate temperatures based on the central composite design for aging duration and test temperature. The test results showed that prolonging aging increased the binder complex modulus, but decreased the phase angle, while increasing the test temperature decreased the complex modulus but increased the phase angle. However, the trends in aging differ and depend on the binder type, test temperature and aging conditions. It was also found that the RSM method is a fast, effective and reliable tool to predict the effects of aging on binders’ rheological behavio

Identification and characterization of metabolite quantitative trait loci in tomato leaves and comparison with those reported for fruits and seeds

Introduction: To date, most studies of natural variation and metabolite quantitative trait loci (mQTL) in tomato have focused on fruit metabolism, leaving aside the identification of genomic regions involved in the regulation of leaf metabolism. Objective: This study was conducted to identify leaf mQTL in tomato and to assess the association of leaf metabolites and physiological traits with the metabolite levels from other tissues. Methods: The analysis of components of leaf metabolism was performed by phenotypying 76 tomato ILs with chromosome segments of the wild species Solanum pennellii in the genetic background of a cultivated tomato (S. lycopersicum) variety M82. The plants were cultivated in two different environments in independent years and samples were harvested from mature leaves of non-flowering plants at the middle of the light period. The non-targeted metabolite profiling was obtained by gas chromatography time-of-flight mass spectrometry (GC-TOF-MS). With the data set obtained in this study and already published metabolomics data from seed and fruit, we performed QTL mapping, heritability and correlation analyses. Results: Changes in metabolite contents were evident in the ILs that are potentially important with respect to stress responses and plant physiology. By analyzing the obtained data, we identified 42 positive and 76 negative mQTL involved in carbon and nitrogen metabolism. Conclusions: Overall, these findings allowed the identification of S. lycopersicum genome regions involved in the regulation of leaf primary carbon and nitrogen metabolism, as well as the association of leaf metabolites with metabolites from seeds and fruits.Fil: Nunes Nesi, Adriano. Max Planck Institute Of Molecular Plant Physiology; Alemania. Universidade Federal de Viçosa.; BrasilFil: Alseekh, Saleh. Center Of Plant Systems Biology And Biotechnology; Bulgaria. Max Planck Institute Of Molecular Plant Physiology; AlemaniaFil: de Oliveira Silva, Franklin Magnum. Universidade Federal de Viçosa.; BrasilFil: Omranian, Nooshin. Max Planck Institute Of Molecular Plant Physiology; Alemania. Center Of Plant Systems Biology And Biotechnology; BulgariaFil: Lichtenstein, Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones en Microbiología y Parasitología Médica. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones en Microbiología y Parasitología Médica; ArgentinaFil: Mirnezhad, Mohammad. Leiden University; Países BajosFil: Romero González, Roman R.. Leiden University; Países BajosFil: Sabio y Garcia, Julia Veronica. Instituto Nacional de Tecnología Agropecuaria. Centro Nacional de Investigaciones Agropecuarias Castelar. Centro de Investigación en Ciencias Veterinarias y Agronómicas. Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Conte, Mariana. Instituto Nacional de Tecnología Agropecuaria. Centro Nacional de Investigaciones Agropecuarias Castelar. Centro de Investigación en Ciencias Veterinarias y Agronómicas. Instituto de Biotecnología; ArgentinaFil: Leiss, Kirsten A.. Leiden University; Países BajosFil: Klinkhamer, Peter G. L.. Leiden University; Países BajosFil: Nikoloski, Zoran. University of Potsdam; Alemania. Max Planck Institute of Molecular Plant Physiology; AlemaniaFil: Carrari, Fernando Oscar. Instituto Nacional de Tecnología Agropecuaria. Centro Nacional de Investigaciones Agropecuarias Castelar. Centro de Investigación en Ciencias Veterinarias y Agronómicas. Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; ArgentinaFil: Fernie, Alisdair R.. Max Planck Institute of Molecular Plant Physiology; Alemania. Center of Plant System Biology and Biotechnology; Bulgari

A selection operator for summary association statistics reveals allelic heterogeneity of complex traits

A general objective of genetic studies is to understand the genetic basis of complex traits such as height, body mass index (BMI), disease endpoints, etc. Such researches have been facilitated due to the completion of the human genome project and developments of high-throughput technologies. With the help of high-throughput genotyping and sequencing technologies, the information on millions of genetic markers can be measured for each individual. The most widely used strategy to detect the associations between genetic variants and a complex trait is genome-wide association study (GWAS). Because the genetic architecture of most complex traits is highly polygenic, the signal to noise ratio is usually tiny. Thus, especially in human populations, GWAS often requires large samples to obtain sufficient power. Unfortunately, given the restrictions on sharing individual-level data, it is often not feasible to pool data from different cohorts. Despite that, in each cohort, it is possible to report and share GWAS summary statistics, such as sample sizes, allele frequencies, estimates of genetic effect sizes, and their standard errors for the genetic markers across the genome. Therefore one recent focus in statistical methodology development for genetic studies has been on meta-analysis techniques using summary-level data. The objective of this thesis is to develop novel statistical genetics methods based on GWAS summary statistics and to apply these methods to better understand the genetic architecture underlying complex traits. In Study I, we developed a Selection Operator for JOint analyzing multiple SNPs (SOJO). We mathematically proved and empirically showed that the least absolute shrinkage and selection operator (LASSO) could be achieved using GWAS summary-level data. Compared to the stepwise selection procedures, SOJO performs better in variable selection. SOJO is useful for detecting additional variants with independent effects and assessing the magnitude of allelic heterogeneity within loci. In Study II, we developed a High-Definition Likelihood (HDL) method to improve the accuracy in genetic correlation estimation using GWAS summary statistics. Compared to the stateof- the-art method LD Score regression (LDSC), HDL achieves higher statistical power to detect genetic correlations between phenotypes by fully accounting for linkage disequilibrium (LD) information across the genome. In Study III, we introduced a four-level strategy for replication of loci detected by multi-trait GWAS methods. The four methods provide different degrees of replication strength, useful for providing additional evidence when a locus has been discovered and replicated by multivariate analysis of variance (MANOVA) or other multi-trait methods. The replication methods only require summary association statistics and are straightforward to be applied to multi-trait GWAS analyses. In Study IV, using GWAS summary statistics, we developed a method named Genetic Correlation Contrast for Causality (G3C) as a more robust test for the existence and direction of causal relationships between phenotypes. In contrast to Mendelian Randomization (MR), G3C does not rely on the assumption of no horizontal pleiotropy. G3C takes full advantage of genome-wide genetic association data and account for underlying genetic correlations between complex traits

Evaluation and improvement of the regulatory inference for large co-expression networks with limited sample size

Abstract Background Co-expression has been widely used to identify novel regulatory relationships using high throughput measurements, such as microarray and RNA-seq data. Evaluation studies on co-expression network analysis methods mostly focus on networks of small or medium size of up to a few hundred nodes. For large networks, simulated expression data usually consist of hundreds or thousands of profiles with different perturbations or knock-outs, which is uncommon in real experiments due to their cost and the amount of work required. Thus, the performances of co-expression network analysis methods on large co-expression networks consisting of a few thousand nodes, with only a small number of profiles with a single perturbation, which more accurately reflect normal experimental conditions, are generally uncharacterized and unknown. Methods We proposed a novel network inference methods based on Relevance Low order Partial Correlation (RLowPC). RLowPC method uses a two-step approach to select on the high-confidence edges first by reducing the search space by only picking the top ranked genes from an intial partial correlation analysis and, then computes the partial correlations in the confined search space by only removing the linear dependencies from the shared neighbours, largely ignoring the genes showing lower association. Results We selected six co-expression-based methods with good performance in evaluation studies from the literature: Partial correlation, PCIT, ARACNE, MRNET, MRNETB and CLR. The evaluation of these methods was carried out on simulated time-series data with various network sizes ranging from 100 to 3000 nodes. Simulation results show low precision and recall for all of the above methods for large networks with a small number of expression profiles. We improved the inference significantly by refinement of the top weighted edges in the pre-inferred partial correlation networks using RLowPC. We found improved performance by partitioning large networks into smaller co-expressed modules when assessing the method performance within these modules. Conclusions The evaluation results show that current methods suffer from low precision and recall for large co-expression networks where only a small number of profiles are available. The proposed RLowPC method effectively reduces the indirect edges predicted as regulatory relationships and increases the precision of top ranked predictions. Partitioning large networks into smaller highly co-expressed modules also helps to improve the performance of network inference methods. The RLowPC R package for network construction, refinement and evaluation is available at GitHub: https://github.com/wyguo/RLowPC