Flexural behavior of two-layer beams made with normal and lightweight concrete layers

In this paper, twelve concrete beams with two different layers of concrete were evaluated as a simply supported beam under four-points loading. The beams assembled of two different types of concrete layers, one of which was normal-weight concrete (NWC) and the other was lightweight aggregate concrete (LWAC). The investigated parameters were the thickness of the lightweight concrete to the overall depth of beams (hLW/h), and the compressive strength of normal and lightweight concrete. Due to the weak lightweight aggregates used, lightweight aggregate concrete exhibits more brittleness and lower stiffness. Therefore, the viability of compensating for this degradation and providing a layer of normal concrete seems to be very interesting in such beams. The behavior of beams was evaluated based on cracking, failure mode, flexural strength, maximum deflection, stiffness, and toughness. The results showed slight variations on the majority of the above-mentioned performance aspects of two-layer beams compared to fully normal concrete beams. While there were great enhancements compared to fully LWAC beams. The variants were mainly attributable to the efficacy of using LWAC in providing lower stiffness and lower tensile strength. The experimental results have been compared to predicted values using the ACI 318-19, with some modifications for the equations to be matched with two-layer beams, the comparison was in terms of the deflection due to service load, moment capacity, and cracking moment

All-or-none amyloid disassembly via chaperone-triggered fibril unzipping favors clearance of α-synuclein toxic species

11 pags., 5 figs.α-synuclein aggregation is present in Parkinson’s disease and other neuropathologies. Among the assemblies that populate the amyloid formation process, oligomers and short fibrils are the most cytotoxic. The human Hsc70-based disaggregase system can resolve α-synuclein fibrils, but its ability to target other toxic assemblies has not been studied. Here, we show that this chaperone system preferentially dis-aggregates toxic oligomers and short fibrils, while its activity against large, less toxic amyloids is severely impaired. Biochemical and kinetic characterization of the disassembly process reveals that this behavior is the result of an all-or-none abrupt solubilization of individual aggregates. High-speed atomic force microscopy explicitly shows that disassembly starts with the destabilization of the tips and rapidly progresses to completion through protofilament unzipping and depolymerization without accumulation of harmful oligomeric intermediates. Our data provide molecular insights into the selective processing of toxic amyloids, which is critical to identify potential therapeutic targets against increasingly prevalent neurodegenerative disorders.This work was supported by MCI/AEI/FEDER, UE (Grants PGC2018-101282-B-I00 to J.M.G.V, PGC2018-096335-B-100 to N.C., and PID2019-111068GB-I00 to A.M.), MINECO/FEDER, UE (Grants RYC-2012-12068 and BFU2015-64119-P to N.C.), and by the Basque Government (Grant IT1201-19 to A.M. and A.P.). A.C. also acknowledges funding from MCIU, PID2019-111096GA-I00; MCIU/AEI/FEDER MINECOG19/P66 , RYC2018-024686-I, and Basque Government T1270-19. L.S. acknowledges support from the University of California, Davis. A.F. thanks a predoctoral fellowship from the Basque Government. The technical and human support provided by the microscopy service of SGIker (UPV/EHU/ERDF, EU) is acknowledged. We thank J. M. Valpuesta and J. Cuellar for the visualization of α-syn oligomers by E

Fibroblast viability and phenotypic changes within glycated stiffened three-dimensional collagen matrices

Background: There is growing interest in the development of cell culture assays that enable the rigidity of the extracellular matrix to be increased. A promising approach is based on three-dimensional collagen type I matrices that are stiffened by cross-linking through non-enzymatic glycation with reducing sugars. Methods: The present study evaluated the biomechanical changes in the non-enzymatically glycated type I collagen matrices, including collagen organization, the advanced glycation end products formation and stiffness achievement. Gels were glycated with ribose at different concentrations (0, 5, 15, 30 and 240 mM). The viability and the phenotypic changes of primary human lung fibroblasts cultured within the non-enzymatically glycated gels were also evaluated along three consecutive weeks. Statistical tests used for data analyze were MannWhitney U, Kruskal Wallis, Student's t-test, two-way ANOVA, multivariate ANOVA, linear regression test and mixed linear model. Results: Our findings indicated that the process of collagen glycation increases the stiffness of the matrices and generates advanced glycation end products in a ribose concentration-dependent manner. Furthermore, we identified optimal ribose concentrations and media conditions for cell viability and growth within the glycated matrices. The microenvironment of this collagen based three-dimensional culture induces α-smooth muscle actin and tenascin-C fibroblast protein expression. Finally, a progressive contractile phenotype cell differentiation was associated with the contraction of these gels. Conclusions: The use of non-enzymatic glycation with a low ribose concentration may provide a suitable model with a mechanic and oxidative modified environment with cell s embedded in it, which allowed cell proliferation and induced fibroblast phenotypic changes. Such culture model could be appropriate for investigations of the behavior and phenotypic changes in cells that occur during lung fibrosis as well as for testing different antifibrotic therapies in vitro

Serum microrna biomarkers for detection of non-small cell lung cancer

Non small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality world-wide and the majority of cases are diagnosed at late stages of disease. There is currently no cost-effective screening test for NSCLC, and the development of such a test is a public health imperative. Recent studies have suggested that chest computed tomography screening of patients at high risk of lung cancer can increase survival from disease, however, the cost effectiveness of such screening has not been established. In this Phase I/II biomarker study we examined the feasibility of using serum miRNA as biomarkers of NSCLC using RT-qPCR to examine the expression of 180 miRNAs in sera from 30 treatment naive NSCLC patients and 20 healthy controls. Receiver operating characteristic curves (ROC) and area under the curve were used to identify differentially expressed miRNA pairs that could distinguish NSCLC from healthy controls. Selected miRNA candidates were further validated in sera from an additional 55 NSCLC patients and 75 healthy controls. Examination of miRNA expression levels in serum from a multi-institutional cohort of 50 subjects (30 NSCLC patients and 20 healthy controls) identified differentially expressed miRNAs. A combination of two differentially expressed miRNAs miR-15b and miR-27b, was able to discriminate NSCLC from healthy controls with sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 100% in the training set. Upon further testing on additional 130 subjects (55 NSCLC and 75 healthy controls), this miRNA pair predicted NSCLC with a specificity of 84% (95% CI 0.73-0.91), sensitivity of 100% (95% CI; 0.93-1.0), NPV of 100%, and PPV of 82%. These data provide evidence that serum miRNAs have the potential to be sensitive, cost-effective biomarkers for the early detection of NSCLC. Further testing in a Phase III biomarker study in is necessary for validation of these results. © 2012 Hennessey et al

Hybridization interactions between probesets in short oligo microarrays lead to spurious correlations

BACKGROUND: Microarrays measure the binding of nucleotide sequences to a set of sequence specific probes. This information is combined with annotation specifying the relationship between probes and targets and used to make inferences about transcript- and, ultimately, gene expression. In some situations, a probe is capable of hybridizing to more than one transcript, in others, multiple probes can target a single sequence. These 'multiply targeted' probes can result in non-independence between measured expression levels. RESULTS: An analysis of these relationships for Affymetrix arrays considered both the extent and influence of exact matches between probe and transcript sequences. For the popular HGU133A array, approximately half of the probesets were found to interact in this way. Both real and simulated expression datasets were used to examine how these effects influenced the expression signal. It was found not only to lead to increased signal strength for the affected probesets, but the major effect is to significantly increase their correlation, even in situations when only a single probe from a probeset was involved. By building a network of probe-probeset-transcript relationships, it is possible to identify families of interacting probesets. More than 10% of the families contain members annotated to different genes or even different Unigene clusters. Within a family, a mixture of genuine biological and artefactual correlations can occur. CONCLUSION: Multiple targeting is not only prevalent, but also significant. The ability of probesets to hybridize to more than one gene product can lead to false positives when analysing gene expression. Comprehensive annotation describing multiple targeting is required when interpreting array data

Computational identification of condition-specific miRNA targets based on gene expression profiles and sequence information

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) are small and noncoding RNAs that play important roles in various biological processes. They regulate target mRNAs post-transcriptionally through complementary base pairing. Since the changes of miRNAs affect the expression of target genes, the expression levels of target genes in specific biological processes could be different from those of non-target genes. Here we demonstrate that gene expression profiles contain useful information in separating miRNA targets from non-targets.</p> <p>Results</p> <p>The gene expression profiles related to various developmental processes and stresses, as well as the sequences of miRNAs and mRNAs in <it>Arabidopsis</it>, were used to determine whether a given gene is a miRNA target. It is based on the model combining the support vector machine (SVM) classifier and the scoring method based on complementary base pairing between miRNAs and mRNAs. The proposed model yielded low false positive rate and retrieved condition-specific candidate targets through a genome-wide screening.</p> <p>Conclusion</p> <p>Our approach provides a novel framework into screening target genes by considering the gene regulation of miRNAs. It can be broadly applied to identify condition-specific targets computationally by embedding information of gene expression profiles.</p

MTar: a computational microRNA target prediction architecture for human transcriptome

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) play an essential task in gene regulatory networks by inhibiting the expression of target mRNAs. As their mRNA targets are genes involved in important cell functions, there is a growing interest in identifying the relationship between miRNAs and their target mRNAs. So, there is now a imperative need to develop a computational method by which we can identify the target mRNAs of existing miRNAs. Here, we proposed an efficient machine learning model to unravel the relationship between miRNAs and their target mRNAs.</p> <p>Results</p> <p>We present a novel computational architecture MTar for miRNA target prediction which reports 94.5% sensitivity and 90.5% specificity. We identified 16 positional, thermodynamic and structural parameters from the wet lab proven miRNA:mRNA pairs and MTar makes use of these parameters for miRNA target identification. It incorporates an Artificial Neural Network (ANN) verifier which is trained by wet lab proven microRNA targets. A number of hitherto unknown targets of many miRNA families were located using MTar. The method identifies all three potential miRNA targets (5' seed-only, 5' dominant, and 3' canonical) whereas the existing solutions focus on 5' complementarities alone.</p> <p>Conclusion</p> <p>MTar, an ANN based architecture for identifying functional regulatory miRNA-mRNA interaction using predicted miRNA targets. The area of target prediction has received a new momentum with the function of a thermodynamic model incorporating target accessibility. This model incorporates sixteen structural, thermodynamic and positional features of residues in miRNA: mRNA pairs were employed to select target candidates. So our novel machine learning architecture, MTar is found to be more comprehensive than the existing methods in predicting miRNA targets, especially human transcritome.</p

Identification of miRNA from Porphyra yezoensis by High-Throughput Sequencing and Bioinformatics Analysis

BACKGROUND: miRNAs are a class of non-coding, small RNAs that are approximately 22 nucleotides long and play important roles in the translational level regulation of gene expression by either directly binding or cleaving target mRNAs. The red alga, Porphyra yezoensis is one of the most important marine economic crops worldwide. To date, only a few miRNAs have been identified in green unicellar alga and there is no report about Porphyra miRNAs. METHODOLOGY/PRINCIPAL FINDINGS: To identify miRNAs in Porphyra yezoensis, a small RNA library was constructed. Solexa technology was used to perform high throughput sequencing of the library and subsequent bioinformatics analysis to identify novel miRNAs. Specifically, 180,557,942 reads produced 13,324 unique miRNAs representing 224 conserved miRNA families that have been identified in other plants species. In addition, seven novel putative miRNAs were predicted from a limited number of ESTs. The potential targets of these putative miRNAs were also predicted based on sequence homology search. CONCLUSIONS/SIGNIFICANCE: This study provides a first large scale cloning and characterization of Porphyra miRNAs and their potential targets. These miRNAs belong to 224 conserved miRNA families and 7 miRNAs are novel in Porphyra. These miRNAs add to the growing database of new miRNA and lay the foundation for further understanding of miRNA function in the regulation of Porphyra yezoensis development

MiR-RACE, a New Efficient Approach to Determine the Precise Sequences of Computationally Identified Trifoliate Orange (Poncirus trifoliata) MicroRNAs

BACKGROUND: Among the hundreds of genes encoding miRNAs in plants reported, much more were predicted by numerous computational methods. However, unlike protein-coding genes defined by start and stop codons, the ends of miRNA molecules do not have characteristics that can be used to define the mature miRNAs exactly, which made computational miRNA prediction methods often cannot predict the accurate location of the mature miRNA in a precursor with nucleotide-level precision. To our knowledge, there haven't been reports about comprehensive strategies determining the precise sequences, especially two termini, of these miRNAs. METHODS: In this study, we report an efficient method to determine the precise sequences of computationally predicted microRNAs (miRNAs) that combines miRNA-enriched library preparation, two specific 5' and 3' miRNA RACE (miR-RACE) PCR reactions, and sequence-directed cloning, in which the most challenging step is the two specific gene specific primers designed for the two RACE reactions. miRNA-mediated mRNA cleavage by RLM-5' RACE and sequencing were carried out to validate the miRNAs detected. Real-time PCR was used to analyze the expression of each miRNA. RESULTS: The efficiency of this newly developed method was validated using nine trifoliate orange (Poncirus trifoliata) miRNAs predicted computationally. The miRNAs computationally identified were validated by miR-RACE and sequencing. Quantitative analysis showed that they have variable expression. Eight target genes have been experimentally verified by detection of the miRNA-mediated mRNA cleavage in Poncirus trifoliate. CONCLUSION: The efficient and powerful approach developed herein can be successfully used to validate the sequences of miRNAs, especially the termini, which depict the complete miRNA sequence in the computationally predicted precursor

Using Sequence Similarity Networks for Visualization of Relationships Across Diverse Protein Superfamilies

The dramatic increase in heterogeneous types of biological data—in particular, the abundance of new protein sequences—requires fast and user-friendly methods for organizing this information in a way that enables functional inference. The most widely used strategy to link sequence or structure to function, homology-based function prediction, relies on the fundamental assumption that sequence or structural similarity implies functional similarity. New tools that extend this approach are still urgently needed to associate sequence data with biological information in ways that accommodate the real complexity of the problem, while being accessible to experimental as well as computational biologists. To address this, we have examined the application of sequence similarity networks for visualizing functional trends across protein superfamilies from the context of sequence similarity. Using three large groups of homologous proteins of varying types of structural and functional diversity—GPCRs and kinases from humans, and the crotonase superfamily of enzymes—we show that overlaying networks with orthogonal information is a powerful approach for observing functional themes and revealing outliers. In comparison to other primary methods, networks provide both a good representation of group-wise sequence similarity relationships and a strong visual and quantitative correlation with phylogenetic trees, while enabling analysis and visualization of much larger sets of sequences than trees or multiple sequence alignments can easily accommodate. We also define important limitations and caveats in the application of these networks. As a broadly accessible and effective tool for the exploration of protein superfamilies, sequence similarity networks show great potential for generating testable hypotheses about protein structure-function relationships