An immunological approach to gibberellin purification and quantification

Gibberellin (GA) specific, high-affinity monoclonal antibodies have been used to assay the GA content of various plant tissues and to purify selected GAs by immunoaffinity chromatography. These immunological techniques may not stand alone as a general method of GA analysis. The results of this study indicate, however, that in conjunction with gas chromatography-mass spectrometry for positive GA identification, radioimmunoassay and immunoaffinity chromatography are extremely powerful tools for purifying and quantifying GAs from plant tissues.     

Protein turnover quantification in a multilabeling approach: from data calculation to evaluation

Liquid chromatography coupled to tandem mass spectrometry in combination with stable-isotope labeling is an established and widely spread method to measure gene expression on the protein level. However, it is often not considered that two opposing processes are responsible for the amount of a protein in a cell--the synthesis as well as the degradation. With this work, we provide an integrative, high-throughput method--from the experimental setup to the bioinformatics analysis--to measure synthesis and degradation rates of an organism's proteome. Applicability of the approach is demonstrated with an investigation of heat shock response, a well-understood regulatory mechanism in bacteria, on the biotechnologically relevant Corynebacterium glutamicum. Utilizing a multilabeling approach using both heavy stable nitrogen as well as carbon isotopes cells are metabolically labeled in a pulse-chase experiment to trace the labels' incorporation in newly synthesized proteins and its loss during protein degradation. Our work aims not only at the calculation of protein turnover rates but also at their statistical evaluation, including variance and hierarchical cluster analysis using the rich internet application QuPE.     

An immunocytochemical method for quantification of lung tissue fibronectin

A technique to quantify tissue fibronectin was developed, using peroxidase-antiperoxidase immunocytochemistry and automated scanning light microscopy. This technique was developed using isolated perfused rat lungs, some of which were subjected to acute oxidant lung injury. Both injured and control lungs were perfused with solutions containing heterologous fibronectin. The technique clearly demonstrated differences in the amount of tissue fibronectin in injured and noninjured lung as well as differences between lungs exposed to fibronectin and those not exposed. The described technique offers a reliable method for quantifying tissue fibronectin and is sensitive enough to detect differences in tissue fibronectin under experimental conditions of acute lung injury.     

An approach to measuring change in muscular tissue contraction

In this paper the authors consider a histological muscular component from the point of view of systems theory. Of the smooth and striated types of muscular fibres, the latter have a structural component that permits them to contract. Unlike the visceral striated and cardiac fibres, movements in the skeletal striated fibres are predominantly voluntary. An overlapping of certain fibre components--specifically a sliding movement of the filaments of which the fibre is composed--causes a variation in the overall size of the fibre and therefore of the muscle. The distances covered, whether in contraction or lengthening, vary according to the type of fibre from 1.8 to 3.8 microm, but depend only on the capacity of some filaments to slide over other filaments, with filament size itself remaining unchanged. Our objective is to obtain a metric of this system that will permit us to obtain some idea of the degree of variation in these distances. In future projects it will be possible to extend this theory to other involuntary muscular tissue movements (myocardial tissue, e.g.), in an endeavour to develop a mathematical approach to the study of myocardial processes damaged by electrical conduction problems.     

A flexible approach for on-line computerised quantification of intra-uterine pressure data

Utilising simple mathematical techniques of differential calculus, a computer program has been developed for real time, on-line processing of uterine contractions with a small computer. The area under the intra-uterine pressure-time curve is calculated and two distinctive values of this area are printed on-line every 512 sec. The ‘active area’, which is the area above the resting tone, is calculated in addition to the ‘total area’—that is, the area above 0 mmHg. This allows an objective and efficient assessment of uterine activity which may be used as a basis of comparison between individual patients and groups of patients.     

Engineering approach to mixing quantification in bioreactors

Homogeneity-time is defined and introduced as the criterion for mixing quality in bioreactors. The criterion could replace the mixing time, in the case, when more than one measuring point (sensors) is included in the measuring system. Results based on the homogeneity-time and the temperature pulse method, achieved in stirred tank reactors under aerated conditions as well as in a jet-mixed tank, are presented.List of Symbols C _p,p kJ/kg K Heat capacity of the pulse medium - C _p,s kJ/kg K Heat capacity of the reactor-medium - F m³/s Flow rate of the pulse-input - i Inhomogeneity - I _N Inhomogeneity-number - M (t) °C Ideal response curve - m deNumber of combinations for certain number of sensors acc. to Table 1 - n Number of sensor - _p kg/m³ Density of the pulse medium - kg/m³ Density of the tank medium - s ₁ °C Mean absolute deviation of the sensor temperatures related on the ideal response curve s₂ s Mean absolute deviation of the homogeneity-times related on the time achieved with 6 sensors - t s Time - t (i) s Homogeneity-time - t _ps s Starting time of tracer injection - t _PE s End time of tracer injection - T _E °C Mean medium temperature at the end of experiment - T _k °C Temperature at k-th sensor position - T _p °C Pulse temperature - T _s °C Mean medium temperature before the tracer injection - V _s m³ Tank volume before pulse input     

An approach to model clustered survival data with dependent censoring

In this study we introduce a likelihood-based method, via the Weibull and piecewise exponential distributions, capable of accommodating the dependence between failure and censoring times. The methodology is developed for the analysis of clustered survival data and it assumes that failure and censoring times are mutually independent conditional on a latent frailty. The dependent censoring mechanism is accounted through the frailty effect and this is accomplished by means of a key parameter accommodating the correlation between failure and censored observations. The full specification of the likelihood in our work simplifies the inference procedures with respect to Huang and Wolfe since it reduces the computation burden of working with the profile likelihood. In addition, the assumptions made for the baseline distributions lead to models with continuous survival functions. In order to carry out inferences, we devise a Monte Carlo EM algorithm. The performance of the proposed models is investigated through a simulation study. Finally, we explore a real application involving patients from the Dialysis Outcomes and Practice Patterns Study observed between 1996 and 2015.     

An integrated,modular approach to data science education in microbiology

We live in an increasingly data-driven world, where high-throughput sequencing and mass spectrometry platforms are transforming biology into an information science. This has shifted major challenges in biological research from data generation and processing to interpretation and knowledge translation. However, postsecondary training in bioinformatics, or more generally data science for life scientists, lags behind current demand. In particular, development of accessible, undergraduate data science curricula has the potential to improve research and learning outcomes as well as better prepare students in the life sciences to thrive in public and private sector careers. Here, we describe the Experiential Data science for Undergraduate Cross-Disciplinary Education (EDUCE) initiative, which aims to progressively build data science competency across several years of integrated practice. Through EDUCE, students complete data science modules integrated into required and elective courses augmented with coordinated cocurricular activities. The EDUCE initiative draws on a community of practice consisting of teaching assistants (TAs), postdocs, instructors, and research faculty from multiple disciplines to overcome several reported barriers to data science for life scientists, including instructor capacity, student prior knowledge, and relevance to discipline-specific problems. Preliminary survey results indicate that even a single module improves student self-reported interest and/or experience in bioinformatics and computer science. Thus, EDUCE provides a flexible and extensible active learning framework for integration of data science curriculum into undergraduate courses and programs across the life sciences.     

An integrated approach to optimization of Escherichia coli fermentations using historical data

Using a fermentation database for Escherichia coli producing green fluorescent protein (GFP), we have implemented a novel three-step optimization method to identify the process input variables most important in modeling the fermentation, as well as the values of those critical input variables that result in an increase in the desired output. In the first step of this algorithm, we use either decision-tree analysis (DTA) or information theoretic subset selection (ITSS) as a database mining technique to identify which process input variables best classify each of the process outputs (maximum cell concentration, maximum product concentration, and productivity) monitored in the experimental fermentations. The second step of the optimization method is to train an artificial neural network (ANN) model of the process input-output data, using the critical inputs identified in the first step. Finally, a hybrid genetic algorithm (hybrid GA), which includes both gradient and stochastic search methods, is used to identify the maximum output modeled by the ANN and the values of the input conditions that result in that maximum. The results of the database mining techniques are compared, both in terms of the inputs selected and the subsequent ANN performance. For the E. coli process used in this study, we identified 6 inputs from the original 13 that resulted in an ANN that best modeled the GFP fluorescence outputs of an independent test set. Values of the six inputs that resulted in a modeled maximum fluorescence were identified by applying a hybrid GA to the ANN model developed. When these conditions were tested in laboratory fermentors, an actual maximum fluorescence of 2.16E6 AU was obtained. The previous high value of fluorescence that was observed was 1.51E6 AU. Thus, this input condition set that was suggested by implementing the proposed optimization scheme on the available historical database increased the maximum fluorescence by 55%.     

Biomimetic approach to tissue engineering

The overall goal of tissue engineering is to create functional tissue grafts that can regenerate or replace our defective or worn out tissues and organs. Examples of grafts that are now in pre-clinical studies or clinical use include engineered skin, cartilage, bone, blood vessels, skeletal muscle, bladder, trachea, and myocardium. Engineered tissues are also finding applications as platforms for pharmacological and physiological studies in vitro. To fully mobilize the cell's biological potential, a new generation of tissue engineering systems is now being developed to more closely recapitulate the native developmental milieu, and mimic the physiologic mechanisms of transport and signaling. We discuss the interactions between regenerative biology and engineering, in the context of (i) creation of functional tissue grafts for regenerative medicine (where biological input is critical), and (ii) studies of stem cells, development and disease (where engineered tissues can serve as advanced 3D models).     

An approach to inferring transcriptional regulation among genes from large-scale expression data

The use of DNA microarrays opens up the possibility of measuring the expression levels of thousands of genes simultaneously under different conditions. Time-course experiments allow researchers to study the dynamics of gene interactions. The inference of genetic networks from such measures can give important insights for the understanding of a variety of biological problems. Most of the existing methods for genetic network reconstruction require many experimental data points, or can only be applied to the reconstruction of small subnetworks. Here we present a method that reduces the dimensionality of the dataset and then extracts the significant dynamic correlations among genes. The method requires a number of points achievable in common time-course experiments.     

An approach for Condition Based Maintenance strategy optimization oriented to multi-source data

Condition Based Maintenance (CBM) scheduling for power equipment is based on some types of data in power system, such as the health condition of components, the load level of the substation and Life Cycle Cost (LCC), etc. Due to the lack of necessary data, traditional CBM strategy optimization models are usually established without enough data, and the optimization results are usually difficult to obtain the enough accuracy. With the development of Chinese power industry, the data volume in power system is increasing dramatically in recent years, and the power industry is undoubtedly entering the era of Big Data. To handle and store these increasing data, more and more data management systems have been developed for different application. For example, Energy Management System, Production Management System and Management Information System have been widely applied. The multi-source data in these systems gives an opportunity to improve the accuracy and rationality for CBM optimization results. Thus, it is of great significance to make full use of these data in maintenance strategy optimization process. This paper presents a new approach to optimize the CBM strategy for components in the substation. After analyzing the interconnection relations of different types of components, the substation is divided into different maintenance units, the components in the same unit can be maintained together. To quantitatively evaluate the reliability of the components before and after repaired, two failure rate calculation models based on Health Index (HI) and age reduction factor are established respectively, and all the alternative maintenance strategies for the abnormal components are proposed based on the location and severity of the faults or defects of the components. According to the theory of LCC, the CBM optimization model is established when determines the minimum total cost as the optimization goal during the maintenance period. The total cost consists of the reparation cost, the interruption cost and the maintenance cost. Finally, an application example in a 220 kV substation is proposed, and these multi-source data are fully applied in the optimization process. The calculation results indicate that these types of data have benefit to improve the accuracy and rationality of the optimization results, and the strategy after optimized can obviously improve both the power supply reliability and the economy of the substation.     

An ancova approach to normalize microarray data, and its performance to existing methods

A microarray experiment includes many steps, and each one of them may include systematic variations. To have a sound analysis, the systematic bias must be identified and removed prior to the data being analyzed. Based on the M-A dependency observed by Dudoit et al. (2002), we suggest that, instead of using the lowess normalization, a new normalization method called ANCOVA be used for dealing with genes with replicates. Simulation studies have shown that the performance of the suggested ANCOVA method is superior to any of the available approaches with regards to the Fisher's Z score and concordance rate. We used a microarray data from bladder cancer to illustrate the application of our approach. The edge the ANCOVA method has over the existing normalization approaches is further confirmed through real-time PCR.     

EasyLCMS: An asynchronous web application for the automated quantification of LC-MS data

ABSTRACT: BACKGROUND: Downstream applications in metabolomics, as well as mathematical modelling, require data in a quantitative format, which may also necessitate the automated and simultaneous quantification of numerous metabolites. Although numerous applications have been previously developed for metabolomics data handling, automated calibration and calculation of the concentrations in terms of mumol have not been carried out. Moreover, most of the metabolomics applications are designed for GC-MS, and would not be suitable for LC-MS, since in LC, the deviation in the retention time is not linear, which is not taken into account in these applications. Moreover, only a few are web-based applications, which could improve stand-alone software in terms of compatibility, sharing capabilities and hardware requirements, even though a strong bandwidth is required. Furthermore, none of these incorporate asynchronous communication to allow real-time interaction with pre-processed results. FINDINGS: Here, we present EasyLCMS (http://www.easylcms.es/), a new application for automated quantification which was validated using more than 1000 concentration comparisons in real samples with manual operation. The results showed that only 1% of the quantifications presented a relative error higher than 15%. Using clustering analysis, the metabolites with the highest relative error distributions were identified and studied to solve recurrent mistakes. CONCLUSIONS: EasyLCMS is a new web application designed to quantify numerous metabolites, simultaneously integrating LC distortions and asynchronous web technology to present a visual interface with dynamic interaction which allows checking and correction of LC-MS raw data pre-processing results. Moreover, quantified data obtained with EasyLCMS are fully compatible with numerous downstream applications, as well as for mathematical modelling in the systems biology field.     

Biomimetic approach to cardiac tissue engineering

Here, we review an approach to tissue engineering of functional myocardium that is biomimetic in nature, as it involves the use of culture systems designed to recapitulate some aspects of the actual in vivo environment. To mimic the capillary network, subpopulations of neonatal rat heart cells were cultured on a highly porous elastomer scaffold with a parallel array of channels perfused with culture medium. To mimic oxygen supply by haemoglobin, the culture medium was supplemented with a perfluorocarbon (PFC) emulsion. Constructs cultivated in the presence of PFC contained higher amounts of DNA and cardiac markers and had significantly better contractile properties than control constructs cultured without PFC. To induce synchronous contractions of cultured constructs, electrical signals mimicking those in native heart were applied. Over only 8 days of cultivation, electrical stimulation induced cell alignment and coupling, markedly increased the amplitude of synchronous construct contractions and resulted in a remarkable level of ultrastructural organization. The biomimetic approach is discussed in the overall context of cardiac tissue engineering, and the possibility to engineer functional human cardiac grafts based on human stem cells.     

An eigenvalue ratio approach to inferring population structure from whole genome sequencing data

Inference of population structure from genetic data plays an important role in population and medical genetics studies. With the advancement and decreasing cost of sequencing technology, the increasingly available whole genome sequencing data provide much richer information about the underlying population structure. The traditional method originally developed for array-based genotype data for computing and selecting top principal components (PCs) that capture population structure may not perform well on sequencing data for two reasons. First, the number of genetic variants p is much larger than the sample size n in sequencing data such that the sample-to-marker ratio $n/p$ is nearly zero, violating the assumption of the Tracy-Widom test used in their method. Second, their method might not be able to handle the linkage disequilibrium well in sequencing data. To resolve those two practical issues, we propose a new method called ERStruct to determine the number of top informative PCs based on sequencing data. More specifically, we propose to use the ratio of consecutive eigenvalues as a more robust test statistic, and then we approximate its null distribution using modern random matrix theory. Both simulation studies and applications to two public data sets from the HapMap 3 and the 1000 Genomes Projects demonstrate the empirical performance of our ERStruct method.