Ultrasonic NDE of Adhesive Bonds: The Inverse Problem

Over the past quarter century, a wide variety of ultrasonic techniques have been developed to determine the phase velocity and thickness of elastic plates. Techniques to measure the phase velocity include toneburst [1–4], separable pulse methods [5–7], and spectroscopy [8–11]. These classical methods require that the specimen be thick enough such that two successive echoes from the front and the back faces of the specimen, respectively, be separable in the time domain. Kinra and Dayal [12], developed a through transmission technique which removes this particular limitation of the classical methods. This technique works satisfactorily for the measurement of the phase velocity for specimens whose thickness is greater than one-half of the wavelength; for thinner specimens, however, their numerical algorithm runs into convergence problems. Moreover, their numerical algorithm cannot be used to determine thickness at any wavelength. The reasons for their convergence problems are discussed in detail by Iyer, Hanneman and Kinra [13]. They demonstrated that a detailed sensitivity analysis is a necessary pre-requisite for the development of a robust inversion algorithm. Accordingly, a new inversion scheme based on the method of least squares was developed by Iyer and Kinra to determine thickness from the measurements of phase, magnitude and complex spectrum, respectively, [14–17]. In all of the above ultrasonic methods only one parameter can be determined i.e., an accurate knowledge of thickness is required to determine the wavespeed and vice versa. This defines the central objective of the present work: In this paper we present a technique for determining, simultaneously, the thickness and wavespeed of a thin layer

Publishing perishing? Towards tomorrow's information architecture

Scientific articles are tailored to present information in human-readable aliquots. Although the Internet has revolutionized the way our society thinks about information, the traditional text-based framework of the scientific article remains largely unchanged. This format imposes sharp constraints upon the type and quantity of biological information published today. Academic journals alone cannot capture the findings of modern genome-scale inquiry. Like many other disciplines, molecular biology is a science of facts: information inherently suited to database storage. In the past decade, a proliferation of public and private databases has emerged to house genome sequence, protein structure information, functional genomics data and more; these digital repositories are now a vital component of scientific communication. The next challenge is to integrate this vast and ever-growing body of information with academic journals and other media. To truly integrate scientific information we must modernize academic publishing to exploit the power of the Internet. This means more than online access to articles, hyperlinked references and web-based supplemental data; it means making articles fully computer-readable with intelligent markup and Structured Digital Abstracts. Here, we examine the changing roles of scholarly journals and databases. We present our vision of the optimal information architecture for the biosciences, and close with tangible steps to improve our handling of scientific information today while paving the way for an expansive central index in the future

Enriching for correct prediction of biological processes using a combination of diverse classifiers

<p>Abstract</p> <p>Background</p> <p>Machine learning models (classifiers) for classifying genes to biological processes each have their own unique characteristics in what genes can be classified and to what biological processes. No single learning model is qualitatively superior to any other model and overall precision for each model tends to be low. The classification results for each classifier can be complementary and synergistic suggesting the benefit of a combination of algorithms, but often the prediction probability outputs of various learning models are neither comparable nor compatible for combining. A means to compare outputs regardless of the model and data used and combine the results into an improved comprehensive model is needed.</p> <p>Results</p> <p>Gene expression patterns from NCI's panel of 60 cell lines were used to train a Random Forest, a Support Vector Machine and a Neural Network model, plus two over-sampled models for classifying genes to biological processes. Each model produced unique characteristics in the classification results. We introduce the Precision Index measure (PIN) from the maximum posterior probability that allows assessing, comparing and combining multiple classifiers. The class specific precision measure (PIC) is introduced and used to select a subset of predictions across all classes and all classifiers with high precision. We developed a single classifier that combines the PINs from these five models in prediction and found that the PIN Combined Classifier (PINCom) significantly increased the number of correctly predicted genes over any single classifier. The PINCom applied to test genes that were not used in training also showed substantial improvement over any single model.</p> <p>Conclusions</p> <p>This paper introduces novel and effective ways of assessing predictions by their precision and recall plus a method that combines several machine learning models and capitalizes on synergy and complementation in class selection, resulting in higher precision and recall. Different machine learning models yielded incongruent results each of which were successfully combined into one superior model using the PIN measure we developed. Validation of the boosted predictions for gene functions showed the genes to be accurately predicted.</p

Nonparametric relevance-shifted multiple testing procedures for the analysis of high-dimensional multivariate data with small sample sizes

<p>Abstract</p> <p>Background</p> <p>In many research areas it is necessary to find differences between treatment groups with several variables. For example, studies of microarray data seek to find a significant difference in location parameters from zero or one for ratios thereof for each variable. However, in some studies a significant deviation of the difference in locations from zero (or 1 in terms of the ratio) is biologically meaningless. A relevant difference or ratio is sought in such cases.</p> <p>Results</p> <p>This article addresses the use of relevance-shifted tests on ratios for a multivariate parallel two-sample group design. Two empirical procedures are proposed which embed the relevance-shifted test on ratios. As both procedures test a hypothesis for each variable, the resulting multiple testing problem has to be considered. Hence, the procedures include a multiplicity correction. Both procedures are extensions of available procedures for point null hypotheses achieving exact control of the familywise error rate. Whereas the shift of the null hypothesis alone would give straight-forward solutions, the problems that are the reason for the empirical considerations discussed here arise by the fact that the shift is considered in both directions and the whole parameter space in between these two limits has to be accepted as null hypothesis.</p> <p>Conclusion</p> <p>The first algorithm to be discussed uses a permutation algorithm, and is appropriate for designs with a moderately large number of observations. However, many experiments have limited sample sizes. Then the second procedure might be more appropriate, where multiplicity is corrected according to a concept of data-driven order of hypotheses.</p

An Integrated Approach to Identifying Cis-Regulatory Modules in the Human Genome

In eukaryotic genomes, it is challenging to accurately determine target sites of transcription factors (TFs) by only using sequence information. Previous efforts were made to tackle this task by considering the fact that TF binding sites tend to be more conserved than other functional sites and the binding sites of several TFs are often clustered. Recently, ChIP-chip and ChIP-sequencing experiments have been accumulated to identify TF binding sites as well as survey the chromatin modification patterns at the regulatory elements such as promoters and enhancers. We propose here a hidden Markov model (HMM) to incorporate sequence motif information, TF-DNA interaction data and chromatin modification patterns to precisely identify cis-regulatory modules (CRMs). We conducted ChIP-chip experiments on four TFs, CREB, E2F1, MAX, and YY1 in 1% of the human genome. We then trained a hidden Markov model (HMM) to identify the labels of the CRMs by incorporating the sequence motifs recognized by these TFs and the ChIP-chip ratio. Chromatin modification data was used to predict the functional sites and to further remove false positives. Cross-validation showed that our integrated HMM had a performance superior to other existing methods on predicting CRMs. Incorporating histone signature information successfully penalized false prediction and improved the whole performance. The dataset we used and the software are available at http://nash.ucsd.edu/CIS/

Clonogenic growth of human breast cancer cells co-cultured in direct contact with serum-activated fibroblasts

INTRODUCTION: Accumulating evidence suggests that fibroblasts play a pivotal role in promoting the growth of breast cancer cells. The objective of the present study was to characterize and validate an in vitro model of the interaction between small numbers of human breast cancer cells and human fibroblasts. METHODS: We measured the clonogenic growth of small numbers of human breast cancer cells co-cultured in direct contact with serum-activated, normal human fibroblasts. Using DNA microarrays, we also characterized the gene expression profile of the serum-activated fibroblasts. In order to validate the in vivo relevance of our experiments, we then analyzed clinical samples of metastatic breast cancer for the presence of myofibroblasts expressing α-smooth muscle actin. RESULTS: Clonogenic growth of human breast cancer cells obtained directly from in situ and invasive tumors was dramatically and consistently enhanced when the tumor cells were co-cultured in direct contact with serum-activated fibroblasts. This effect was abolished when the cells were co-cultured in transwells separated by permeable inserts. The fibroblasts in our experimental model exhibited a gene expression signature characteristic of 'serum response' (i.e. myofibroblasts). Immunostaining of human samples of metastatic breast cancer tissue confirmed that myofibroblasts are in direct contact with breast cancer cells. CONCLUSION: Serum-activated fibroblasts promote the clonogenic growth of human breast cancer cells in vitro through a mechanism that involves direct physical contact between the cells. This model shares many important molecular and phenotypic similarities with the fibroblasts that are naturally found in breast cancers

Prioritization of gene regulatory interactions from large-scale modules in yeast

<p>Abstract</p> <p>Background</p> <p>The identification of groups of co-regulated genes and their transcription factors, called transcriptional modules, has been a focus of many studies about biological systems. While methods have been developed to derive numerous modules from genome-wide data, individual links between regulatory proteins and target genes still need experimental verification. In this work, we aim to prioritize regulator-target links within transcriptional modules based on three types of large-scale data sources.</p> <p>Results</p> <p>Starting with putative transcriptional modules from ChIP-chip data, we first derive modules in which target genes show both expression and function coherence. The most reliable regulatory links between transcription factors and target genes are established by identifying intersection of target genes in coherent modules for each enriched functional category. Using a combination of genome-wide yeast data in normal growth conditions and two different reference datasets, we show that our method predicts regulatory interactions with significantly higher predictive power than ChIP-chip binding data alone. A comparison with results from other studies highlights that our approach provides a reliable and complementary set of regulatory interactions. Based on our results, we can also identify functionally interacting target genes, for instance, a group of co-regulated proteins related to cell wall synthesis. Furthermore, we report novel conserved binding sites of a glycoprotein-encoding gene, CIS3, regulated by Swi6-Swi4 and Ndd1-Fkh2-Mcm1 complexes.</p> <p>Conclusion</p> <p>We provide a simple method to prioritize individual TF-gene interactions from large-scale transcriptional modules. In comparison with other published works, we predict a complementary set of regulatory interactions which yields a similar or higher prediction accuracy at the expense of sensitivity. Therefore, our method can serve as an alternative approach to prioritization for further experimental studies.</p

ChromaSig: A Probabilistic Approach to Finding Common Chromatin Signatures in the Human Genome

Computational methods to identify functional genomic elements using genetic information have been very successful in determining gene structure and in identifying a handful of cis-regulatory elements. But the vast majority of regulatory elements have yet to be discovered, and it has become increasingly apparent that their discovery will not come from using genetic information alone. Recently, high-throughput technologies have enabled the creation of information-rich epigenetic maps, most notably for histone modifications. However, tools that search for functional elements using this epigenetic information have been lacking. Here, we describe an unsupervised learning method called ChromaSig to find, in an unbiased fashion, commonly occurring chromatin signatures in both tiling microarray and sequencing data. Applying this algorithm to nine chromatin marks across a 1% sampling of the human genome in HeLa cells, we recover eight clusters of distinct chromatin signatures, five of which correspond to known patterns associated with transcriptional promoters and enhancers. Interestingly, we observe that the distinct chromatin signatures found at enhancers mark distinct functional classes of enhancers in terms of transcription factor and coactivator binding. In addition, we identify three clusters of novel chromatin signatures that contain evolutionarily conserved sequences and potential cis-regulatory elements. Applying ChromaSig to a panel of 21 chromatin marks mapped genomewide by ChIP-Seq reveals 16 classes of genomic elements marked by distinct chromatin signatures. Interestingly, four classes containing enrichment for repressive histone modifications appear to be locally heterochromatic sites and are enriched in quickly evolving regions of the genome. The utility of this approach in uncovering novel, functionally significant genomic elements will aid future efforts of genome annotation via chromatin modifications

Functional Identification of Neuroprotective Molecules

The central nervous system has the capacity to activate profound neuroprotection following sub-lethal stress in a process termed preconditioning. To gain insight into this potent survival response we developed a functional cloning strategy that identified 31 putative neuroprotective genes of which 28 were confirmed to provide protection against oxygen-glucose deprivation (OGD) or excitotoxic exposure to N-methyl-D-aspartate (NMDA) in primary rat cortical neurons. These results reveal that the brain possesses a wide and diverse repertoire of neuroprotective genes. Further characterization of these and other protective signals could provide new treatment opportunities for neurological injury from ischemia or neurodegenerative disease

Towards the reconstruction of integrated genome-scale models of metabolism and gene expression

The reconstruction of integrated genome-scale models of metabolism and gene expression has been a challenge for a while now. In fact, various methods that allow integrating reconstructions of Transcriptional Regulatory Networks, gene expression data or both into Genome-Scale Metabolic Models have been proposed. Several of these methods are surveyed in this article, which allowed identifying their strengths and weaknesses concerning the reconstruction of integrated models for multiple prokaryotic organisms. Additionally, the main resources of regulatory information were also surveyed, as the existence of novel sources of regulatory information and gene expression data may contribute for the improvement of methodologies referred herein.This study was supported by the Portuguese Foundation for Science andTechnology (FCT) under the scope of the strategic funding of UID/BIO/04469/2019 unit andBioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by the European RegionalDevelopment Fund under the scope of Norte2020-Programa Operacional Regional do Norte. Fernando Cruz holds a doctoral fellowship (SFRH/BD/139198/2018) funded by the FCT. The authors thank project SHIKIFACTORY100 - Modular cell factories for the production of 100 compounds from the shikimate pathway (814408) funded by the European Commission.info:eu-repo/semantics/publishedVersio