An experimental and numerical study on nonlinear impact responses of steel-plated structures in an Arctic environment

Ships and offshore platforms that operate in Arctic regions at low temperatures are likely subjected to impact loads that arise from collisions with icebergs. The aim of this paper was to examine the nonlinear impact response of steel-plated structures in an Arctic environment. In addition to material tensile tests for characterisation of the mechanical properties of polar-class high-tensile steel of grade DH36, an experimental study was undertaken in a dropped-object test facility on steel-plated structure models under impact loads and at low temperatures equivalent to those in Arctic regions. LS-DYNA nonlinear finite element computations were also performed for the corresponding test models. We conclude that nonlinear finite element analyses are useful in the analysis of the nonlinear impact structural responses involving yielding, crushing and brittle fracture at low temperatures as long as the modelling techniques are adequate. The conclusions and insights developed in this paper should be useful in the safety design of ships and offshore platforms intended for operation in Arctic regions

Test database of the mechanical properties of mild, high-tensile and stainless steel and aluminium alloy associated with cold temperatures and strain rates

In structural analysis and design, it is essential to define the material properties associated with the targeted structural systems. When harsh environmental or operational conditions are of primary concern, the mechanical properties of materials must be quantified by considering the effects of the conditions. The aim of this study is to develop a new test database on the mechanical properties of materials for marine applications, such as mild steel, high-tensile steel, aluminium alloy 5083-O and stainless steel 304L, focusing on the effects of cold temperatures and strain rates. Discussion of the new test database refers to extant test databases where available. Moreover, test coefficients that may be useful in the existing constitutive equations for the materials are suggested and the details of the test database are documented

Microarray data analysis in neoadjuvant biomarker studies in estrogen receptor-positive breast cancer

Microarray data have been widely utilized to discover biomarkers predictive of response to endocrine therapy in estrogen receptor-positive breast cancer. Typically, these data have focused on analyses conducted on the diagnostic specimen. However, dynamic temporal changes in gene expression associated with treatment may deliver significant improvements to the current generation of predictive models. We present and discuss some statistical issues relevant to the paper by Taylor and colleagues, who conducted studies to model the prognostic potential of gene expression changes that occur after endocrine treatment

Conversion of a molecular classifier obtained by gene expression profiling into a classifier based on real-time PCR: a prognosis predictor for gliomas

<p>Abstract</p> <p>Background</p> <p>The advent of gene expression profiling was expected to dramatically improve cancer diagnosis. However, despite intensive efforts and several successful examples, the development of profile-based diagnostic systems remains a difficult task. In the present work, we established a method to convert molecular classifiers based on adaptor-tagged competitive PCR (ATAC-PCR) (with a data format that is similar to that of microarrays) into classifiers based on real-time PCR.</p> <p>Methods</p> <p>Previously, we constructed a prognosis predictor for glioma using gene expression data obtained by ATAC-PCR, a high-throughput reverse-transcription PCR technique. The analysis of gene expression data obtained by ATAC-PCR is similar to the analysis of data from two-colour microarrays. The prognosis predictor was a linear classifier based on the first principal component (PC1) score, a weighted summation of the expression values of 58 genes. In the present study, we employed the delta-delta Ct method for measurement by real-time PCR. The predictor was converted to a Ct value-based predictor using linear regression.</p> <p>Results</p> <p>We selected <it>UBL5 </it>as the reference gene from the group of genes with expression patterns that were most similar to the median expression level from the previous profiling study. The number of diagnostic genes was reduced to 27 without affecting the performance of the prognosis predictor. PC1 scores calculated from the data obtained by real-time PCR showed a high linear correlation (r = 0.94) with those obtained by ATAC-PCR. The correlation for individual gene expression patterns (r = 0.43 to 0.91) was smaller than for PC1 scores, suggesting that errors of measurement were likely cancelled out during the weighted summation of the expression values. The classification of a test set (n = 36) by the new predictor was more accurate than histopathological diagnosis (log rank p-values, 0.023 and 0.137, respectively) for predicting prognosis.</p> <p>Conclusion</p> <p>We successfully converted a molecular classifier obtained by ATAC-PCR into a Ct value-based predictor. Our conversion procedure should also be applicable to linear classifiers obtained from microarray data. Because errors in measurement are likely to be cancelled out during the calculation, the conversion of individual gene expression is not an appropriate procedure. The predictor for gliomas is still in the preliminary stages of development and needs analytical clinical validation and clinical utility studies.</p

In-Plane Deformation Mechanics for Highly Stretchable Electronics

Scissoring in thick bars suppresses buckling behavior in serpentine traces that have thicknesses greater than their widths, as detailed in a systematic set of analytical and experimental studies. Scissoring in thick copper traces enables elastic stretchability as large as approximate to 350%, corresponding to a sixfold improvement over previously reported values for thin geometries (approximate to 60%).</p

Granular Assembly of α-Synuclein Leading to the Accelerated Amyloid Fibril Formation with Shear Stress

α-Synuclein participates in the Lewy body formation of Parkinson's disease. Elucidation of the underlying molecular mechanism of the amyloid fibril formation is crucial not only to develop a controlling strategy toward the disease, but also to apply the protein fibrils for future biotechnology. Discernable homogeneous granules of α-synuclein composed of approximately 11 monomers in average were isolated in the middle of a lag phase during the in vitro fibrillation process. They were demonstrated to experience almost instantaneous fibrillation during a single 12-min centrifugal membrane-filtration at 14,000×g. The granular assembly leading to the drastically accelerated fibril formation was demonstrated to be a result of the physical influence of shear force imposed on the preformed granular structures by either centrifugal filtration or rheometer. Structural rearrangement of the preformed oligomomeric structures is attributable for the suprastructure formation in which the granules act as a growing unit for the fibril formation. To parallel the prevailing notion of nucleation-dependent amyloidosis, we propose a double-concerted fibrillation model as one of the mechanisms to explain the in vitro fibrillation of α-synuclein, in which two consecutive concerted associations of monomers and subsequent oligomeric granular species are responsible for the eventual amyloid fibril formation

SpxA1 Involved in Hydrogen Peroxide Production, Stress Tolerance and Endocarditis Virulence in Streptococcus sanguinis

Streptococcus sanguinis is one of the most common agents of infective endocarditis. Spx proteins are a group of global regulators that negatively or positively control global transcription initiation. In this study, we characterized the spxA1 gene in S. sanguinis SK36. The spxA1 null mutant displayed opaque colony morphology, reduced hydrogen peroxide (H2O2) production, and reduced antagonistic activity against Streptococcus mutans UA159 relative to the wild type strain. The ΔspxA1 mutant also demonstrated decreased tolerance to high temperature, acidic and oxidative stresses. Further analysis revealed that ΔspxA1 also exhibited a ∼5-fold reduction in competitiveness in an animal model of endocarditis. Microarray studies indicated that expression of several oxidative stress genes was downregulated in the ΔspxA1 mutant. The expression of spxB and nox was significantly decreased in the ΔspxA1 mutant compared with the wild type. These results indicate that spxA1 plays a major role in H2O2 production, stress tolerance and endocarditis virulence in S. sanguinis SK36. The second spx gene, spxA2, was also found in S. sanguinis SK36. The spxA2 null mutant was found to be defective for growth under normal conditions and showed sensitivity to high temperature, acidic and oxidative stresses

Do Two Machine-Learning Based Prognostic Signatures for Breast Cancer Capture the Same Biological Processes?

The fact that there is very little if any overlap between the genes of different prognostic signatures for early-discovery breast cancer is well documented. The reasons for this apparent discrepancy have been explained by the limits of simple machine-learning identification and ranking techniques, and the biological relevance and meaning of the prognostic gene lists was questioned. Subsequently, proponents of the prognostic gene lists claimed that different lists do capture similar underlying biological processes and pathways. The present study places under scrutiny the validity of this claim, for two important gene lists that are at the focus of current large-scale validation efforts. We performed careful enrichment analysis, controlling the effects of multiple testing in a manner which takes into account the nested dependent structure of gene ontologies. In contradiction to several previous publications, we find that the only biological process or pathway for which statistically significant concordance can be claimed is cell proliferation, a process whose relevance and prognostic value was well known long before gene expression profiling. We found that the claims reported by others, of wider concordance between the biological processes captured by the two prognostic signatures studied, were found either to be lacking statistical rigor or were in fact based on addressing some other question

Reduced expression of a gene proliferation signature is associated with enhanced malignancy in colon cancer

The association between cell proliferation and the malignant potential of colon cancer is not well understood. Here, we evaluated this association using a colon-specific gene proliferation signature (GPS). The GPS was derived by combining gene expression data obtained from the analysis of a cancer cell line model and a published colon crypt profile. The GPS was overexpressed in both actively cycling cells in vitro and the proliferate compartment of colon crypts. K-means clustering was used to independantly stratify two cohorts of colon tumours into two groups with high and low GPS expression. Notably, we observed a significant association between reduced GPS expression and an increased likelihood of recurrence (P<0.05), leading to shorter disease-free survival in both cohorts. This finding was not a result of methodological bias as we verified the well-established association between breast cancer malignancy and increased proliferation, by applying our GPS to public breast cancer data. In this study, we show that reduced proliferation is a biological feature characterizing the majority of aggressive colon cancers. This contrasts with many other carcinomas such as breast cancer. Investigating the reasons underlying this unusual observation may provide important insight into the biology of colon cancer progression and putative novel therapy options