Reliability Abstracts and Technical Reviews January - December 1970

Reliability Abstracts and Technical Reviews is an abstract and critical analysis service covering published and report literature on reliability. The service is designed to provide information on theory and practice of reliability as applied to aerospace and an objective appraisal of the quality, significance, and applicability of the literature abstracted

Characterization of the Relationships between Soybean Yield, Trifoliolate Leaf Chloride Concentration, and Cultivar Chloride Inclusion/Exclusion Rating

Chloride toxicity is recognized as yield limiting problem in soybean [Glycine max (L.) Merr.] production. Limited information is available to accurately diagnose and manage Cl toxicity. The only recommendation for Cl toxicity management is to plant an excluder cultivar, however the cultivar Cl sensitivity rating system (excluder, includer, and mixed) does not appear to capture the variability in cultivar Cl tolerance. The objectives of this research were to i) develop critical tissue-Cl concentrations in which yield loss occurs for excluder and includer cultivars and ii) investigate the variability in cultivar Cl ratings. A study was conducted across five site-years using six soybean cultivars including three Cl-includer and three Cl-excluder cultivars. Solution containing Cl was applied to the soil beginning at late vegetative growth with final rates ranging from 0 to 1010 kg Cl ha-1. Critical trifoliolate leaflet-Cl concentrations at the R3 stage were developed by regressing relative soybean yield across leaf-Cl concentration for each cultivar Cl rating. For the second objective, composite trifoliolate leaflet and individual trifoliate leaf samples were collected during reproductive growth from variety trials and analyzed for Cl concentration. The research verified that the yield of Cl-includer cultivars is reduced more (4-20%) than Cl-excluder cultivars (0-8%) in high Cl environments. Relative grain yield declined linearly for cultivars within each Cl rating group with 5% yield loss expected when Cl concentrations at the R3 stage averaged 3923 mg Cl kg-1 for Cl includers and 1885 mg Cl kg-1 for Cl excluders. Across more than 100 cultivars sampled in three Arkansas Soybean Performance Tests, tissue-Cl concentration ranged from \u3c100 to \u3e5000 mg Cl kg-1 and showed no clear groupings of the three cultivar Cl-traits suggesting that many cultivars labeled as includers are a mixture of includer and excluder plants. Chloride concentrations of 528 individual plants from eleven cultivars showed 34% and 31% of the plants had Cl concentrations ≤500 or 1000-2000 mg Cl kg-1 with only one cultivar having a pure population of Cl excluder plants. A new rating system is warranted to more accurately characterize the proportion of Cl include and excluder plants of each cultivar

Comparison of heart valve flow dynamics assessment between echocardiography and pulse duplication

Published ThesisHeart valve surgery and valvular heart disease still pose a significant threat to patients worldwide. The aortic valve doesn't remain healthy and has largely been the focus of innovation and the development of replacement heart valves. Improving the ability of blood to flow througha prosthetic valve while minimizing the load on the heart is regarded as one of the performance objectives of prosthetic heart valves. In order to meet valvular performance objectives and to assess whether potential prosthetic heart valves meets hydrodynamic performance, testing simulated under in vivo flow conditions is necessary. Pulse duplication is widely accepted as a valid method to determine the performance of heart valves during their development. Few specialised centres exist to perform pulse duplication tests accurately and in accordance to the required ISO and FDA standards for cardiovascular implants. Real-time patient data of prosthetic heart valves is however not obtained with pulse duplication but with echocardiography. Modern day pulse duplicators come equipped with viewing chambers that can allow for echocardiographic measurements. Therefore, the aim of this study was to perform pulse duplication and echocardiography simultaneously on five different prosthetic heart valves using a commercial ViVitro pulse duplicator system. METHODS A hydrodynamic evaluation was performed on five prosthetic heart valves (i) Medtronic-Hall mechanical valve (tilting disc), (ii) Carbomedics mechanical valve (bileaflet), (iii) Glycar mechanical valve (Glycar), (iv) Edwards Perimount (tissue valve), (v) ViVitro reference (ViVitro) using pulse duplication and echocardiography. All the valves were inserted in the aortic position of the pulse duplicator and echocardiographic measurements was performed simultaneously. Each of the valves were tested at 5 different testing conditions by varying the stroke volume and beats per minute. The study concludes with a comparison between the pulse duplicator data and the echocardiography data acquired. RESULTS Pulse duplication: -The Glycar valve had the largest pressure drop across the valve at the lowest CO (3.6 L/min) of 17.15 mmHg, although it increased steadily at a slower rate than the other four valves. The Glycar and tissue valve had the highest EOA of 1.885 cm2 and 1.884 cm2 respectively at a peak CO of 9.6 L/min. The bi-leaflet valve had the highest EOA of 2.002 cm2 (CO 3.6 L/min), however the EOA deteriorated as the CO increased resulting in an EOA of 1.572 cm2 at a CO of 9.6L/min. The tissue valve had the largest RF for all testing conditions, ranging from 16.3% (CO 8.0 L/min) to 25.6% (4.9 L/min) where the bi-leaflet valve had the lowest (0.72% - 3.42%). Echocardiography: -The Glycar valve had the lowest overall pressure drop for all CO. The pulse duplicator pressure drop results were more consistent than three echocardiography results measured on the pulse duplicator. The bileaflet and Glycar valves EOA showed better consistency across the CO range than the ViVitro, tissue and tilting disk valves. The data showed that no definite correlation between all the valves exists between echocardiography and pulse duplication for EOA. However, a correlation for pressure drop between the pulse duplicator and echocardiographic data was demonstrated for both the tissue and bi-leaflet valve

Feasibility of predicting allele specific expression from DNA sequencing using machine learning

Allele specific expression (ASE) concerns divergent expression quantity of alternative alleles and is measured by RNA sequencing. Multiple studies show that ASE plays a role in hereditary diseases by modulating penetrance or phenotype severity. However, genome diagnostics is based on DNA sequencing and therefore neglects gene expression regulation such as ASE. To take advantage of ASE in absence of RNA sequencing, it must be predicted using only DNA variation. We have constructed ASE models from BIOS (n = 3432) and GTEx (n = 369) that predict ASE using DNA features. These models are highly reproducible and comprise many different feature types, highlighting the complex regulation that underlies ASE. We applied the BIOS-trained model to population variants in three genes in which ASE plays a clinically relevant role: BRCA2, RET and NF1. This resulted in predicted ASE effects for 27 variants, of which 10 were known pathogenic variants. We demonstrated that ASE can be predicted from DNA features using machine learning. Future efforts may improve sensitivity and translate these models into a new type of genome diagnostic tool that prioritizes candidate pathogenic variants or regulators thereof for follow-up validation by RNA sequencing. All used code and machine learning models are available at GitHub and Zenodo

Inferential stability in systems biology

The modern biological sciences are fraught with statistical difficulties. Biomolecular stochasticity, experimental noise, and the “large p, small n” problem all contribute to the challenge of data analysis. Nevertheless, we routinely seek to draw robust, meaningful conclusions from observations. In this thesis, we explore methods for assessing the effects of data variability upon downstream inference, in an attempt to quantify and promote the stability of the inferences we make. We start with a review of existing methods for addressing this problem, focusing upon the bootstrap and similar methods. The key requirement for all such approaches is a statistical model that approximates the data generating process. We move on to consider biomarker discovery problems. We present a novel algorithm for proposing putative biomarkers on the strength of both their predictive ability and the stability with which they are selected. In a simulation study, we find our approach to perform favourably in comparison to strategies that select on the basis of predictive performance alone. We then consider the real problem of identifying protein peak biomarkers for HAM/TSP, an inflammatory condition of the central nervous system caused by HTLV-1 infection. We apply our algorithm to a set of SELDI mass spectral data, and identify a number of putative biomarkers. Additional experimental work, together with known results from the literature, provides corroborating evidence for the validity of these putative biomarkers. Having focused on static observations, we then make the natural progression to time course data sets. We propose a (Bayesian) bootstrap approach for such data, and then apply our method in the context of gene network inference and the estimation of parameters in ordinary differential equation models. We find that the inferred gene networks are relatively unstable, and demonstrate the importance of finding distributions of ODE parameter estimates, rather than single point estimates

Defining the gene expression signature of rhabdomyosarcoma by meta-analysis

BACKGROUND: Rhabdomyosarcoma is a highly malignant soft tissue sarcoma in childhood and arises as a consequence of regulatory disruption of the growth and differentiation pathways of myogenic precursor cells. The pathogenic pathways involved in this tumor are mostly unknown and therefore a better characterization of RMS gene expression profile would represent a considerable advance. The availability of publicly available gene expression datasets have opened up new challenges especially for the integration of data generated by different research groups and different array platforms with the purpose of obtaining new insights on the biological process investigated. RESULTS: In this work we performed a meta-analysis on four microarray and two SAGE datasets of gene expression data on RMS in order to evaluate the degree of agreement of the biological results obtained by these different studies and to identify common regulatory pathways that could be responsible of tumor growth. Regulatory pathways and biological processes significantly enriched has been investigated and a list of differentially meta-profiles have been identified as possible candidate of aggressiveness of RMS. CONCLUSION: Our results point to a general down regulation of the energy production pathways, suggesting a hypoxic physiology for RMS cells. This result agrees with the high malignancy of RMS and with its resistance to most of the therapeutic treatments. In this context, different isoforms of the ANT gene have been consistently identified for the first time as differentially expressed in RMS. This gene is involved in anti-apoptotic processes when cells grow in low oxygen conditions. These new insights in the biological processes responsible of RMS growth and development demonstrate the effective advantage of the use of integrated analysis of gene expression studies

A new method for creating a visual plant identification key

Taxonomic keys are essential tools for species identification, used by students and professional biologists. In recent years, advancements in photography have allowed these keys to host high-quality photographs for aid in identification. However, most modern keys still rely heavily on text rather than images. Using text alone limits the user to a discrete number of characters, often described in esoteric terms. In order to create more effective keys, we developed a new method for constructing image-based taxonomic keys. These keys replace written characters with images – allowing the user to identify species using visual pattern recognition, rather than interpreting written text. In addition, we constructed our visual key using data on how different users assess the visual similarities between plant species. To ensure the strength of this methodology, our key focuses on the morphologically diverse genus, Quercus. A set of standardized photographs was taken of forty-three species of oak native or naturalized in the Southeast. These photographs were used to create a survey on how botanical experts and botanical novices rate the pair-wise similarity of different oak leaves. The mean of each rating was summarized into a distance matrix, which was then converted into a dendrogram. From the resulting dendrogram, a visual key was constructed using the standardized photographs of oak leaves. The key was then tested on against an existing dichotomous key using botanical novices and botanical experts. The resulting two-sample t-tests between the two identification keys demonstrated that users with our visual key produced between 22-30% more correct answers than users with the traditional key. Using this method of key creation, innovative keys could be constructed for other fields of biology

Lysophosphatidic Acid-Induced Transcriptional Profile Represents Serous Epithelial Ovarian Carcinoma and Worsened Prognosis

BACKGROUND:Lysophosphatidic acid (LPA) governs a number of physiologic and pathophysiological processes. Malignant ascites fluid is rich in LPA, and LPA receptors are aberrantly expressed by ovarian cancer cells, implicating LPA in the initiation and progression of ovarian cancer. However, there is an absence of systematic data critically analyzing the transcriptional changes induced by LPA in ovarian cancer. METHODOLOGY AND PRINCIPAL FINDINGS:In this study, gene expression profiling was used to examine LPA-mediated transcription by exogenously adding LPA to human epithelial ovarian cancer cells for 24 h to mimic long-term stimulation in the tumor microenvironment. The resultant transcriptional profile comprised a 39-gene signature that closely correlated to serous epithelial ovarian carcinoma. Hierarchical clustering of ovarian cancer patient specimens demonstrated that the signature is associated with worsened prognosis. Patients with LPA-signature-positive ovarian tumors have reduced disease-specific and progression-free survival times. They have a higher frequency of stage IIIc serous carcinoma and a greater proportion is deceased. Among the 39-gene signature, a group of seven genes associated with cell adhesion recapitulated the results. Out of those seven, claudin-1, an adhesion molecule and phenotypic epithelial marker, is the only independent biomarker of serous epithelial ovarian carcinoma. Knockdown of claudin-1 expression in ovarian cancer cells reduces LPA-mediated cellular adhesion, enhances suspended cells and reduces LPA-mediated migration. CONCLUSIONS:The data suggest that transcriptional events mediated by LPA in the tumor microenvironment influence tumor progression through modulation of cell adhesion molecules like claudin-1 and, for the first time, report an LPA-mediated expression signature in ovarian cancer that predicts a worse prognosis