Inhibiting the gastric burst release of drugs from enteric microparticles: the influence of drug molecular mass and solubility.

Undesired drug release in acid medium from enteric microparticles has been widely reported. In this paper, we investigate the relative contribution of microparticle and drug properties, specifically microsphere size and drug's molecular weight and acid solubility, on the extent of such undesired release. A series of nine drugs with different physicochemical properties were successfully encapsulated into Eudragit S and Eudragit L microparticles using a novel emulsion solvent evaporation process. The process yielded spherical microparticles with a narrow size distribution (27-60 and 36-56 µm for Eudragit L and Eudragit S microparticles, respectively). Upon incubation in acid medium (pH 1.2) for 2 h, the release of dipyridamole, cinnarizine, amprenavir, bendroflumethiazide, budesonide and prednisolone from both Eudragit microparticles was less than 10% of drug load and conformed with the USP specifications for enteric dosage forms. In contrast, more than 10% of the entrapped paracetamol, salicylic acid and ketoprofen were released. Multiple regression revealed that the drug's molecular weight was the most important factor that determined its extent of release in the acid medium, while its acid solubility and microsphere's size had minor influences

Indoor environment propagation review

A survey of indoor propagation characteristics is presented, including different models for path loss, shadowing and fast fading mechanisms, different channel parameters including signal strength, power delay, coherence bandwidth, Doppler spread and angle of arrival. The concepts of MIMO channels are also covered. The study also explores many types of deterministic channel modelling, such as Finite Difference Time Domain, Finite Integration Method, Ray tracing and the Dominant path model. Electromagnetic properties of building materials, including frequency dependence, are also investigated and several models for propagation through buildings are reviewed

Modelling Driver Behaviour at Urban Signalised Intersections Using Logistic Regression and Machine Learning

This study investigated several factors that may influence driver actions throughout the yellow interval at urban signalised intersections. The selected samples include 2,168 observations. Almost 33% of drivers stopped ahead of the stop line, 60% passed the intersection through the yellow interval, and 7% passed after the yellow interval was complete (red light running, RLR violations). Binary logistic regression models showed that the chance of passing went up as vehicle speed went up and down as the gap between the vehicle and the traffic light and green interval went up. The movement type and vehicle position influenced the passing probability, but the vehicle type did not. Moreover, multinomial logistic regression models showed that the legal passing probability declined with the growth in the green time and vehicle distance to the traffic signal. It also increased with the growth in the speed of approaching vehicles. Also, movement type directly affected the chance of legally passing, but vehicle position and type did not. Furthermore, the driver’s performance during the yellow phase was studied using the k-nearest neighbours algorithm (KNN), support vector machines (SVM), random forest (RF) and AdaBoost machine learning techniques. The driver’s action run prediction was the most accurate, and the run-on-red camera was the least accurate

Nitrate Pathways, Processes, and Timing in an Agricultural Karst System: Development and Application of a Numerical Model

An edited version of this paper was published by AGU. Copyright 2019 American Geophysical Union.Nitrogen (N) contamination within agricultural‐karst landscapes and aquifers is widely reported; however, the complex hydrological pathways of karst make N fate difficult to ascertain. We developed a hydrologic and N numerical model for agricultural‐karst, including simulation of soil, epikarst, phreatic, and quick flow pathways as well as biochemical processes such as nitrification, mineralization, and denitrification. We tested the model on four years of nitrate (NO3−) data collected from a phreatic conduit and an overlying surface channel in the Cane Run watershed, Kentucky, USA. Model results indicate that slow to moderate flow pathways (phreatic and epikarst) dominate the N load and account for nearly 90% of downstream NO3− delivery. Further, quick flow pathways dilute NO3− concentrations relative to background aquifer levels. Net denitrification distributed across soil, epikarst, and phreatic water removes approximately 36% of the N inputs to the system at rates comparable to nonkarst systems. Evidence is provided by numerical modeling that NO3− accumulation via evapotranspiration in the soil followed by leaching through the epikarst acts as a control on spring NO3− concentration and loading. Compared to a fluvial‐dominated immature karst system, mature‐karst systems behave as natural detention basins for NO3−, temporarily delaying NO3− delivery to downstream waters and maintaining elevated NO3− concentrations for days to weeks after hydrologic activity ends. This study shows the efficacy of numerical modeling to elucidate complex pathways, processes, and timing of N in karst systems

Genetic regulation of gene expression of MIF family members in lung tissue.

Macrophage migration inhibitory factor (MIF) is a cytokine found to be associated with chronic obstructive pulmonary disease (COPD). However, there is no consensus on how MIF levels differ in COPD compared to control conditions and there are no reports on MIF expression in lung tissue. Here we studied gene expression of members of the MIF family MIF, D-Dopachrome Tautomerase (DDT) and DDT-like (DDTL) in a lung tissue dataset with 1087 subjects and identified single nucleotide polymorphisms (SNPs) regulating their gene expression. We found higher MIF and DDT expression in COPD patients compared to non-COPD subjects and found 71 SNPs significantly influencing gene expression of MIF and DDTL. Furthermore, the platform used to measure MIF (microarray or RNAseq) was found to influence the splice variants detected and subsequently the direction of the SNP effects on MIF expression. Among the SNPs found to regulate MIF expression, the major LD block identified was linked to rs5844572, a SNP previously found to be associated with lower diffusion capacity in COPD. This suggests that MIF may be contributing to the pathogenesis of COPD, as SNPs that influence MIF expression are also associated with symptoms of COPD. Our study shows that MIF levels are affected not only by disease but also by genetic diversity (i.e. SNPs). Since none of our significant eSNPs for MIF or DDTL have been described in GWAS for COPD or lung function, MIF expression in COPD patients is more likely a consequence of disease-related factors rather than a cause of the disease

Different genes interact with particulate matter and tobacco smoke exposure in affecting lung function decline in the general population

BACKGROUND: Oxidative stress related genes modify the effects of ambient air pollution or tobacco smoking on lung function decline. The impact of interactions might be substantial, but previous studies mostly focused on main effects of single genes. OBJECTIVES: We studied the interaction of both exposures with a broad set of oxidative-stress related candidate genes and pathways on lung function decline and contrasted interactions between exposures. METHODS: For 12679 single nucleotide polymorphisms (SNPs), change in forced expiratory volume in one second (FEV(1)), FEV(1) over forced vital capacity (FEV(1)/FVC), and mean forced expiratory flow between 25 and 75% of the FVC (FEF(25-75)) was regressed on interval exposure to particulate matter >10 microm in diameter (PM10) or packyears smoked (a), additive SNP effects (b), and interaction terms between (a) and (b) in 669 adults with GWAS data. Interaction p-values for 152 genes and 14 pathways were calculated by the adaptive rank truncation product (ARTP) method, and compared between exposures. Interaction effect sizes were contrasted for the strongest SNPs of nominally significant genes (p(interaction)>0.05). Replication was attempted for SNPs with MAF<10% in 3320 SAPALDIA participants without GWAS. RESULTS: On the SNP-level, rs2035268 in gene SNCA accelerated FEV(1)/FVC decline by 3.8% (p(interaction) = 2.5x10(-6)), and rs12190800 in PARK2 attenuated FEV1 decline by 95.1 ml p(interaction) = 9.7x10(-8)) over 11 years, while interacting with PM10. Genes and pathways nominally interacting with PM10 and packyears exposure differed substantially. Gene CRISP2 presented a significant interaction with PM10 (p(interaction) = 3.0x10(-4)) on FEV(1)/FVC decline. Pathway interactions were weak. Replications for the strongest SNPs in PARK2 and CRISP2 were not successful. CONCLUSIONS: Consistent with a stratified response to increasing oxidative stress, different genes and pathways potentially mediate PM10 and tobac smoke effects on lung function decline. Ignoring environmental exposures would miss these patterns, but achieving sufficient sample size and comparability across study samples is challengin

Hybrid multicriteria fuzzy classification of network traffic patterns, anomalies, and protocols

© 2017, Springer-Verlag London Ltd., part of Springer Nature. Traffic classification in computer networks has very significant roles in network operation, management, and security. Examples include controlling the flow of information, allocating resources effectively, provisioning quality of service, detecting intrusions, and blocking malicious and unauthorized access. This problem has attracted a growing attention over years and a number of techniques have been proposed ranging from traditional port-based and payload inspection of TCP/IP packets to supervised, unsupervised, and semi-supervised machine learning paradigms. With the increasing complexity of network environments and support for emerging mobility services and applications, more robust and accurate techniques need to be investigated. In this paper, we propose a new supervised hybrid machine-learning approach for ubiquitous traffic classification based on multicriteria fuzzy decision trees with attribute selection. Moreover, our approach can handle well the imbalanced datasets and zero-day applications (i.e., those without previously known traffic patterns). Evaluating the proposed methodology on several benchmark real-world traffic datasets of different nature demonstrated its capability to effectively discriminate a variety of traffic patterns, anomalies, and protocols for unencrypted and encrypted traffic flows. Comparing with other methods, the performance of the proposed methodology showed remarkably better classification accuracy

The state of HRM in the Middle East:Challenges and future research agenda

Based on a robust structured literature analysis, this paper highlights the key developments in the field of human resource management (HRM) in the Middle East. Utilizing the institutional perspective, the analysis contributes to the literature on HRM in the Middle East by focusing on four key themes. First, it highlights the topical need to analyze the context-specific nature of HRM in the region. Second, via the adoption of a systematic review, it highlights state of development in HRM in the research analysis set-up. Third, the analysis also helps to reveal the challenges facing the HRM function in the Middle East. Fourth, it presents an agenda for future research in the form of research directions. While doing the above, it revisits the notions of “universalistic” and “best practice” HRM (convergence) versus “best-fit” or context distinctive (divergence) and also alternate models/diffusion of HRM (crossvergence) in the Middle Eastern context. The analysis, based on the framework of cross-national HRM comparisons, helps to make both theoretical and practical implications

Whole exome re-sequencing implicates CCDC38 and cilia structure and function in resistance to smoking related airflow obstruction

Chronic obstructive pulmonary disease (COPD) is a leading cause of global morbidity and mortality and, whilst smoking remains the single most important risk factor, COPD risk is heritable. Of 26 independent genomic regions showing association with lung function in genome-wide association studies, eleven have been reported to show association with airflow obstruction. Although the main risk factor for COPD is smoking, some individuals are observed to have a high forced expired volume in 1 second (FEV1) despite many years of heavy smoking. We # hypothesised that these ‘‘resistant smokers’’ may harbour variants which protect against lung function decline caused by smoking and provide insight into the genetic determinants of lung health. We undertook whole exome re sequencing of 100 heavy smokers who had healthy lung function given their age, sex, height and smoking history and applied three complementary approaches to explore the genetic architecture of smoking resistance. Firstly, we identified novel functional variants in the ‘‘resistant smokers’’ and looked for enrichment of these novel variants within biological pathways. Secondly, we undertook association testing of all exonic variants individually with two independent control sets. Thirdly, we undertook gene-based association testing of all exonic variants. Our strongest signal of association with smoking resistance for a non-synonymous SNP was for rs10859974 (P = 2.3461024) in CCDC38, a gene which has previously been reported to show association with FEV1/FVC, and we demonstrate moderate expression of CCDC38 in bronchial epithelial cells. We identified an enrichment of novel putatively functional variants in genes related to cilia structure and function in resistant smokers. Ciliary function abnormalities are known to be associated with both smoking and reduced mucociliary clearance in patients with COPD. We suggest that genetic influences on the development or function of cilia in the bronchial epithelium may affect growth of cilia or the extent of damage caused by tobacco smoke