Base‐Pairing Behavior of a Carbocyclic Janus‐AT Nucleoside Analogue Capable of Recognizing A and T within a DNA Duplex

Janus‐type nucleosides are heterocycles with two faces, each of which is designed to complement the H‐bonding interactions of natural nucleosides comprising a canonical Watson–Crick base pair. By intercepting all of the hydrogen bonds contained within the base pair, oligomeric Janus nucleosides are expected to achieve sequence‐specific DNA recognition through the formation of J‐loops that will be more stable than D‐loops, which simply replaces one base‐pair with another. Herein, we report the synthesis of a novel Janus‐AT nucleoside analogue, J_AT, affixed on a carbocyclic analogue of deoxyribose that was converted to the corresponding phosphoramidite. A single J_AT was successfully incorporated into a DNA strand by solid phase for targeting both A and T bases, and characterized through biophysical and computational methods. Experimental UV‐melting and circular dichroism data demonstrated that within the context of a standard duplex, J_AT associates preferentially with T over A, and much more poorly with C and G. Density functional theory calculations confirm that the J_AT structure is well suited to associate only with A and T thereby highlighting the importance of the electronic structure in terms of H‐bonding. Finally, molecular dynamics simulations validated the observation that J_AT can substitute more effectively as an A‐analogue than as a T‐analogue without substantial distortion of the B‐helix. Overall, this new Janus nucleotide is a promising tool for the targeting of A–T base pairs in DNA, and will lead to the development of oligo‐Janus‐nucleotide strands for sequence‐specific DNA recognition.     

Size Distributions of Species in Fine Particles in Denver Using a Microorifice Impactor

Size-fractionated fine particles in Denver, Colorado, were collected by a microorifice impactor and analyzed for mass and elemental composition by a β-gauge and an X-ray fluorescence spectrometer. A least-squares procedure was used to fit a lognormal distribution function to the data in order to determine values and uncertainties for the mass median aerodynamic diameter and geometric standard deviation. The validity of the uncertainties so determined was tested by comparing them to uncertainties derived from Monte Carlo simulations.     

Toxicity and Metabolism of Layered Double Hydroxide Intercalated with Levodopa in a Parkinson’s Disease Model

Layered hydroxide nanoparticles are generally biocompatible, and less toxic than most inorganic nanoparticles, making them an acceptable alternative drug delivery system. Due to growing concern over animal welfare and the expense of in vivo experiments both the public and the government are interested to find alternatives to animal testing. The toxicity potential of zinc aluminum layered hydroxide (ZAL) nanocomposite containing anti-Parkinsonian agent may be determined using a PC 12 cell model. ZAL nanocomposite demonstrated a decreased cytotoxic effect when compared to levodopa on PC12 cells with more than 80% cell viability at 100 μg/mL compared to less than 20% cell viability in a direct levodopa exposure. Neither levodopa-loaded nanocomposite nor the un-intercalated nanocomposite disturbed the cytoskeletal structure of the neurogenic cells at their IC₅₀ concentration. Levodopa metabolite (HVA) released from the nanocomposite demonstrated the slow sustained and controlled release character of layered hydroxide nanoparticles unlike the burst uptake and release system shown with pure levodopa treatment.     

Retrieving relevant time‐course experiments: a study on Arabidopsis microarrays

Understanding time‐course regulation of genes in response to a stimulus is a major concern in current systems biology. The problem is usually approached by computational methods to model the gene behaviour or its networked interactions with the others by a set of latent parameters. The model parameters can be estimated through a meta‐analysis of available data obtained from other relevant experiments. The key question here is how to find the relevant experiments which are potentially useful in analysing current data. In this study, the authors address this problem in the context of time‐course gene expression experiments from an information retrieval perspective. To this end, they introduce a computational framework that takes a time‐course experiment as a query and reports a list of relevant experiments retrieved from a given repository. These retrieved experiments can then be used to associate the environmental factors of query experiment with the findings previously reported. The model is tested using a set of time‐course Arabidopsis microarrays. The experimental results show that relevant experiments can be successfully retrieved based on content similarity.Inspec keywords: botany, lab‐on‐a‐chip, genetics, bioinformatics, information retrieval, data mining, data analysis, associative processingOther keywords: relevant time‐course experiment retrieval, time‐course Arabidopsis microarray, time‐course gene regulation, stimulus response, systems biology, computational method, gene behaviour model, gene networked interaction, latent parameter, model parameter estimation, meta‐analysis, data analysis, time‐course gene expression experiment, information retrieval, computational framework, time‐course experiment query, relevant experiment list, repository, environmental factor, query experiment, experimental content similarity     

A numerical study on determination of volume averaged thermal transport properties of metal foam structures using X-ray microtomography technique

Abstract

Volume averaged thermal transport properties of two metal foams with 10 and 20 PPI are obtained by using microtomography technique. The digital 3D structures of samples are generated in computer environment. The governing equations are solved for the entire domain and the volume averaged technique is used to determine interfacial heat transfer coefficient, longitudinal and transverse thermal dispersion conductivity. The study is performed for the pore scale Reynolds number from 100 to 600. The obtained results are within the ranges of the suggested correlations in literature. The present study supports the correlations suggested by Calmidi and Mahajan (2000) and Zhang et al. (2016).     

Epithelial Layer Estimation Using Curvatures and Textural Features for Dysplastic Tissue Detection

Boundary effect in digital pathology is a phenomenon where the tissue shapes of biopsy samples get distorted during the sampling process. The morphological pattern of an epithelial layer is greatly affected. Theoretically, the shape deformation model can normalise the distortions, but it needs a 2D image. Curvatures theory, on the other hand, is not yet tested on digital pathology images. Therefore, this work proposed a curvature detection to reduce the boundary effects and estimates the epithelial layer. The boundary effect on the tissue surfaces is normalised using the frequency of a curve deviates from being a straight line. The epithelial layer’s depth is estimated from the tissue edges and the connected nucleolus only. Then, the textural and spatial features along the estimated layer are used for dysplastic tissue detection. The proposed method achieved better performance compared to the whole tissue regions in terms of detecting dysplastic tissue. The result shows a leap of kappa points from fair to a substantial agreement with the expert’s ground truth classification. The improved results demonstrate that curvatures have been effective in reducing the boundary effects on the epithelial layer of tissue. Thus, quantifying and classifying the morphological patterns for dysplasia can be automated. The textural and spatial features on the detected epithelial layer can capture the changes in tissue.     

Modeling the effect of elastane linear density,fabric thread density,and weave float on the stretch,recovery, and compression properties of bi-stretch woven fabrics for compression garments

The aim of this study was to investigate the effect of elastane linear density, thread density, and weave float on the stretch, recovery, and compression properties of bi-stretch woven fabrics for compression garments. Fabric samples were produced using elastane core-spun cotton yarns both in the warp and weft. The elastane linear density, fabric thread density, and weave float size were used as input variables while fabric contraction, subgarment pressure, fabric stretch, and recovery were taken as response variables. Two different elastane linear densities, i.e. 44 and 78 dtex, two different thread densities, and three different weave designs, i.e. 1/1 plain, 2/2 z-twill, and 3/3 z-twill were used. The results of fabric samples were analyzed in Minitab statistical software. The coefficients of determinations (R-sq values) of the regression equations showed good prediction ability of the developed statistical models. The findings of the study may be helpful in deciding appropriate manufacturing specifications of bi-stretch fabrics to attain specific stretch, recovery, and compression properties.     

Linking phosphorus sequestration to carbon humification in wetland soils by 31P and 13C NMR spectroscopy

Phosphorus sequestration in wetland soils is a prerequisite for long-term maintenance of water quality in downstream aquatic systems, but can be compromised if phosphorus is released following changes in nutrient status or hydrological regimen. The association of phosphorus with relatively refractory natural organic matter (e.g., humic substances) might protect soil phosphorus from such changes. Here we used hydrofluoric acid (HF) pretreatment to remove phosphorus associated with metals or anionic sorption sites, allowing us to isolate a pool of phosphorus associated with the soil organic fraction. Solution (31)P and solid state (13)C NMR spectra for wetland soils were acquired before and after hydrofluoric acid pretreatment to assess quantitatively and qualitatively the changes in phosphorus and carbon functional groups. Organic phosphorus was largely unaffected by HF treatment in soils dominated by refractory alkyl and aromatic carbon groups, indicating association of organic phosphorus with stable, humified soil organic matter. Conversely, a considerable decrease in organic phosphorus following HF pretreatment was detected in soils where O-alkyl groups represented the major fraction of the soil carbon. These correlations suggest that HF treatment can be used as a method to distinguish phosphorus fractions that are bound to the inorganic soil components from those fractions that are stabilized by incorporation into soil organic matter.     

Reinforced concrete deep beam shear strength capacity modelling using an integrative bio-inspired algorithm with an artificial intelligence model

The design and sustainability of reinforced concrete deep beam are still the main issues in the sector of structural engineering despite the existence of modern advancements in this area. Proper understanding of shear stress characteristics can assist in providing safer design and prevent failure in deep beams which consequently lead to saving lives and properties. In this investigation, a new intelligent model depending on the hybridization of support vector regression with bio-inspired optimization approach called genetic algorithm (SVR-GA) is employed to predict the shear strength of reinforced concrete (RC) deep beams based on dimensional, mechanical and material parameters properties. The adopted SVR-GA modelling approach is validated against three different well established artificial intelligent (AI) models, including classical SVR, artificial neural network (ANN) and gradient boosted decision trees (GBDTs). The comparison assessments provide a clear impression of the superior capability of the proposed SVR-GA model in the prediction of shear strength capability of simply supported deep beams. The simulated results gained by SVR-GA model are very close to the experimental ones. In quantitative results, the coefficient of determination (R²) during the testing phase (R² = 0.95), whereas the other comparable models generated relatively lower values of R² ranging from 0.884 to 0.941. All in all, the proposed SVR-GA model showed an applicable and robust computer aid technology for modelling RC deep beam shear strength that contributes to the base knowledge of material and structural engineering perspective.

     

An efficient recommender system algorithm using trust data

The Journal of Supercomputing - Smart services are a concept that provides services to the citizens in an efficient manner. The online shopping and recommender system can play an important role for...