76,561 research outputs found
Spectrophotometry for cerebrospinal fluid pigment analysis
The use of spectrophotometry for the analysis of the cerebrospinal fluid (CSF) is reviewed. The clinically relevant CSF pigments--oxyhemoglobin and bilirubin--are introduced and discussed with regard to clinical differential diagnosis and potentially confounding variables (the four T's: traumatic tap, timing, total protein, and total bilirubin). The practical laboratory aspects of spectrophotometry and automated techniques are presented in the context of analytical and clinical specificity and sensitivity. The perceptual limitations of human color vision are highlighted and the use of visual assessment of the CSF is discouraged in light of recent evidence from a national audit in the United Kingdom. Finally, future perspectives including the need for longitudinal CSF profiling and routine spectrophotometric calibration are outlined
Character analysis of oral activity: contact profiling
The article presents the results of our observations on syntactic, semantic and plot peculiarities of oral language activity, we find it justified to consider the above mentioned parameters as identification criteria for discovering characterological differences of Ukrainian-speaking and Russian-speaking objects of contact profiling. It describes the connection between mechanisms of psychological defenses as the character structural components, and agentive and non-agentive speech constructions, internal and external predicates. Localized and described plots of oral narratives inherent to representatives of different character types
Joining Forces of Bayesian and Frequentist Methodology: A Study for Inference in the Presence of Non-Identifiability
Increasingly complex applications involve large datasets in combination with
non-linear and high dimensional mathematical models. In this context,
statistical inference is a challenging issue that calls for pragmatic
approaches that take advantage of both Bayesian and frequentist methods. The
elegance of Bayesian methodology is founded in the propagation of information
content provided by experimental data and prior assumptions to the posterior
probability distribution of model predictions. However, for complex
applications experimental data and prior assumptions potentially constrain the
posterior probability distribution insufficiently. In these situations Bayesian
Markov chain Monte Carlo sampling can be infeasible. From a frequentist point
of view insufficient experimental data and prior assumptions can be interpreted
as non-identifiability. The profile likelihood approach offers to detect and to
resolve non-identifiability by experimental design iteratively. Therefore, it
allows one to better constrain the posterior probability distribution until
Markov chain Monte Carlo sampling can be used securely. Using an application
from cell biology we compare both methods and show that a successive
application of both methods facilitates a realistic assessment of uncertainty
in model predictions.Comment: Article to appear in Phil. Trans. Roy. Soc.
Genomic and proteomic profiling for cancer diagnosis in dogs
Global gene expression, whereby tumours are classified according to similar gene expression patterns or āsignaturesā regardless of cell morphology or tissue characteristics, is being increasingly used in both the human and veterinary fields to assist in cancer diagnosis and prognosis. Many studies on canine tumours have focussed on RNA expression using techniques such as microarrays or next generation sequencing. However, proteomic studies combining two-dimensional polyacrylamide gel electrophoresis or two-dimensional differential gel electrophoresis with mass spectrometry have also provided a wealth of data on gene expression in tumour tissues. In addition, proteomics has been instrumental in the search for tumour biomarkers in blood and other body fluids
Diverse correlation structures in gene expression data and their utility in improving statistical inference
It is well known that correlations in microarray data represent a serious
nuisance deteriorating the performance of gene selection procedures. This paper
is intended to demonstrate that the correlation structure of microarray data
provides a rich source of useful information. We discuss distinct correlation
substructures revealed in microarray gene expression data by an appropriate
ordering of genes. These substructures include stochastic proportionality of
expression signals in a large percentage of all gene pairs, negative
correlations hidden in ordered gene triples, and a long sequence of weakly
dependent random variables associated with ordered pairs of genes. The reported
striking regularities are of general biological interest and they also have
far-reaching implications for theory and practice of statistical methods of
microarray data analysis. We illustrate the latter point with a method for
testing differential expression of nonoverlapping gene pairs. While designed
for testing a different null hypothesis, this method provides an order of
magnitude more accurate control of type 1 error rate compared to conventional
methods of individual gene expression profiling. In addition, this method is
robust to the technical noise. Quantitative inference of the correlation
structure has the potential to extend the analysis of microarray data far
beyond currently practiced methods.Comment: Published in at http://dx.doi.org/10.1214/07-AOAS120 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Optimal Rate of Direct Estimators in Systems of Ordinary Differential Equations Linear in Functions of the Parameters
Many processes in biology, chemistry, physics, medicine, and engineering are
modeled by a system of differential equations. Such a system is usually
characterized via unknown parameters and estimating their 'true' value is thus
required. In this paper we focus on the quite common systems for which the
derivatives of the states may be written as sums of products of a function of
the states and a function of the parameters.
For such a system linear in functions of the unknown parameters we present a
necessary and sufficient condition for identifiability of the parameters. We
develop an estimation approach that bypasses the heavy computational burden of
numerical integration and avoids the estimation of system states derivatives,
drawbacks from which many classic estimation methods suffer. We also suggest an
experimental design for which smoothing can be circumvented. The optimal rate
of the proposed estimators, i.e., their -consistency, is proved and
simulation results illustrate their excellent finite sample performance and
compare it to other estimation approaches
Determining Structurally Identifiable Parameter Combinations Using Subset Profiling
Identifiability is a necessary condition for successful parameter estimation
of dynamic system models. A major component of identifiability analysis is
determining the identifiable parameter combinations, the functional forms for
the dependencies between unidentifiable parameters. Identifiable combinations
can help in model reparameterization and also in determining which parameters
may be experimentally measured to recover model identifiability. Several
numerical approaches to determining identifiability of differential equation
models have been developed, however the question of determining identifiable
combinations remains incompletely addressed. In this paper, we present a new
approach which uses parameter subset selection methods based on the Fisher
Information Matrix, together with the profile likelihood, to effectively
estimate identifiable combinations. We demonstrate this approach on several
example models in pharmacokinetics, cellular biology, and physiology
Optimization of miRNA-seq data preprocessing.
The past two decades of microRNA (miRNA) research has solidified the role of these small non-coding RNAs as key regulators of many biological processes and promising biomarkers for disease. The concurrent development in high-throughput profiling technology has further advanced our understanding of the impact of their dysregulation on a global scale. Currently, next-generation sequencing is the platform of choice for the discovery and quantification of miRNAs. Despite this, there is no clear consensus on how the data should be preprocessed before conducting downstream analyses. Often overlooked, data preprocessing is an essential step in data analysis: the presence of unreliable features and noise can affect the conclusions drawn from downstream analyses. Using a spike-in dilution study, we evaluated the effects of several general-purpose aligners (BWA, Bowtie, Bowtie 2 and Novoalign), and normalization methods (counts-per-million, total count scaling, upper quartile scaling, Trimmed Mean of M, DESeq, linear regression, cyclic loess and quantile) with respect to the final miRNA count data distribution, variance, bias and accuracy of differential expression analysis. We make practical recommendations on the optimal preprocessing methods for the extraction and interpretation of miRNA count data from small RNA-sequencing experiments
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