Cell Entries Research Articles

Macropinocytosis and SARS-CoV-2 cell entry

Macropinocytosis is a type of large-scale endocytosis that is triggered by the interaction of receptor proteins and ligands, such as growth factors, cytokines, chemokines, and lipopolysaccharide (LPS). Macropinocytosis ingests the extracellular fluid solutes and conveys them into the lysosome in the context of cell growth and differentiation. Aside from its physiological functions, macropinocytosis has been observed in viral infections. While the infectious mechanism of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is still unknown, recent studies suggest the involvement of macropinocytosis in its cell entry. In this review, we discuss the roles of endocytosis in SARS-CoV/SARS-CoV-2 cell entries and propose a hypothetical role of macropinocytosis in SARS-CoV-2 cell entry.

PARAMETER REDUNDANCY AND THE EXISTENCE OF MAXIMUM LIKELIHOOD ESTIMATES IN LOG-LINEAR MODELS

Log-linear models are typically fitted to contingency table data to describe and identify the relationship between different categorical variables. However, the data may include observed zero cell entries. The presence of zero cell entries can have an adverse effect on the estimability of parameters, due to parameter redundancy. We describe a general approach for determining whether a given log-linear model is parameter redundant for a pattern of observed zeros in the table, prior to fitting the model to the data. We derive the estimable parameters or functions of parameters and also explain how to reduce the unidentifiable model to an identifiable one. Parameter redundant models have a flat ridge in their likelihood function. We further explain when this ridge imposes some additional parameter constraints on the model, which can lead to obtaining unique maximum likelihood estimates for parameters that otherwise would not have been estimable. In contrast to other frameworks, the proposed novel approach informs on those constraints, elucidating the model that is actually being fitted.

An efficient charting scheme for multivariate categorical process with a sparse contingency table

Multivariate categorical quality characteristics, whose distribution can be displayed by a contingency table, are routinely encountered in many applications. When most of the cell entries in the contingency table are very small or zeros counts, which is so-called sparse contingency table in the literature, existing methods developed in the literature are often inadequate for use, due to the inaccuracy of the maximum likelihood estimate of its probability distribution, and the inflation of online charting statistics. This paper studies the multivariate statistical process control problem for such sparse contingency table. We integrate the group least absolute shrinkage and selection operator (LASSO) method with the Ridge method to estimate the in-control distribution of a contingency table and propose an efficient EWMA control chart, based on a modified Pearson χ2 statistic, to monitor the changes in it. Numerical results show that our proposed approach has the best overall performance, compared with its competitors. Finally, a real data example is used to demonstrate the effectiveness of the proposed control chart.

Transduction optimization of AAV vectors for human gene therapy of glaucoma and their reversed cell entry characteristics

The trabecular meshwork (TM) of the eye is responsible for maintaining physiological intraocular pressure (IOP). Dysfunction of this tissue results in elevated IOP, subsequent optic nerve damage and glaucoma, the world’s leading cause of irreversible blindness. IOP regulation by delivering candidate TM genes would offer an enormous clinical advantage to the current daily-drops/surgery treatment. Initially, we showed that a double-stranded AAV2 (scAAV2) transduced the human TM very efficiently, while its single-stranded form (ssAAV2) did not. Here, we quantified transduction and entry of single- and double-strand serotypes 1, 2.5, 5, 6, 8, and 9 in primary, single individual-derived human TM cells (HTM). scAAV2 exhibited highest transduction in all individuals, distantly followed by scAAV2.5, scAAV6, and scAAV5. Transduction of scAAV1, scAAV8, and scAAV9 was negligible. None of the ssAAV serotypes transduced, but their cell entries were significantly higher than those of their corresponding scAAV. Tyrosine scAAV2 capsid mutants increased transduction in HTM cultured cells and all TM-outflow layers of perfused postmortem human eyes. These studies provide the first serotype optimization for gene therapy of glaucoma in humans. They further reveal biological differences between the AAV forms in HTM cells, whose understanding could contribute to the development of gene therapy of glaucoma.

Generalized Fréchet Bounds for Cell Entries in Multidimensional Contingency Tables

We consider the lattice, $\mathcal{L}$, of all subsets of a multidimensional contingency table and establish the properties of monotonicity and supermodularity for the marginalization function, $n(\cdot)$, on $\mathcal{L}$. We derive from the supermodularity of $n(\cdot)$ some generalized Fr\'echet inequalities complementing and extending inequalities of Dobra and Fienberg. Further, we construct new monotonic and supermodular functions from $n(\cdot)$, and we remark on the connection between supermodularity and some correlation inequalities for probability distributions on lattices. We also apply an inequality of Ky Fan to derive a new approach to Fr\'echet inequalities for multidimensional contingency tables.

Who introduced Western mathematicians to Latin squares?

A Latin square is a grid or array containing the same number of rows and columns. The cell entries are a sequence of symbols inserted in such a way that each symbol occurs only once in each row and only once in each column. Since the beginning of the twentieth century, Latin squares have been studied in the field of combinatorics. This article is concerned with the issue of who, according to the published literature, introduced Western mathematicians to Latin squares. The first mathematician to provide a depiction of Latin squares seems to have been Jacques Ozanam (1640–1718), who demonstrated the use of Latin squares in a problem based on playing cards. However, he did not discuss the analytic properties or the potential applications of Latin squares. The first mathematician to provide an analytic description of Latin squares seems to have been Leonhard Euler (1707–1783).

QTLTableMiner++: semantic mining of QTL tables in scientific articles

BackgroundA quantitative trait locus (QTL) is a genomic region that correlates with a phenotype. Most of the experimental information about QTL mapping studies is described in tables of scientific publications. Traditional text mining techniques aim to extract information from unstructured text rather than from tables. We present QTLTableMiner++ (QTM), a table mining tool that extracts and semantically annotates QTL information buried in (heterogeneous) tables of plant science literature.QTM is a command line tool written in the Java programming language. This tool takes scientific articles from the Europe PMC repository as input, extracts QTL tables using keyword matching and ontology-based concept identification. The tables are further normalized using rules derived from table properties such as captions, column headers and table footers. Furthermore, table columns are classified into three categories namely column descriptors, properties and values based on column headers and data types of cell entries. Abbreviations found in the tables are expanded using the Schwartz and Hearst algorithm. Finally, the content of QTL tables is semantically enriched with domain-specific ontologies (e.g. Crop Ontology, Plant Ontology and Trait Ontology) using the Apache Solr search platform and the results are stored in a relational database and a text file.ResultsThe performance of the QTM tool was assessed by precision and recall based on the information retrieved from two manually annotated corpora of open access articles, i.e. QTL mapping studies in tomato (Solanum lycopersicum) and in potato (S. tuberosum). In summary, QTM detected QTL statements in tomato with 74.53% precision and 92.56% recall and in potato with 82.82% precision and 98.94% recall.ConclusionQTM is a unique tool that aids in providing QTL information in machine-readable and semantically interoperable formats.

The use of Latin-square designs in educational and psychological research

A Latin square is a matrix containing the same number of rows and columns. The cell entries are a sequence of symbols inserted in such a way that each symbol occurs only once in each row and only once in each column. Fisher (1925) proposed that Latin squares could be useful in experimental designs for controlling the effects of extraneous variables. He argued that a Latin square should be chosen at random from the set of possible Latin squares that would fit a research design and that the Latin-square design should be carried through into the data analysis. Psychological researchers have advanced our appreciation of Latin-square designs, but they have made only moderate use of them and have not heeded Fisher's prescriptions. Educational researchers have used them even less and are vulnerable to similar criticisms. Nevertheless, the judicious use of Latin-square designs is a powerful tool for experimental researchers.

Correction to: Current Methodologies and Implications of Phase Identification of the Vertical Jump: A Systematic Review and Meta-analysis.

Pages 1313-5, table1, column 1: The cell entries in the 'Reference' column, which previously read.

CDC42 Use in Viral Cell Entry Processes by RNA Viruses.

The cellular actin cytoskeleton presents a barrier that must be overcome by many viruses, and it has become increasingly apparent many viral species have developed a diverse repertoire of mechanisms to hijack cellular actin-regulating signalling pathways as part of their cell entry processes. The Rho family GTPase Cdc42 is appreciated as a key moderator of cellular actin dynamics, and the development of specific Cdc42-inhibiting agents has given us an unprecedented ability to investigate its individual role in signalling pathways. However, investigative use of said agents, and the subsequent characterisation of the role Cdc42 plays in viral entry processes has been lacking. Here, we describe the current literature on the role of Cdc42 in human immunodeficiency virus (HIV)-1 cell entry, which represents the most investigated instance of Cdc42 function in viral cell entry processes, and also review evidence of Cdc42 use in other RNA virus cell entries, demonstrating prime areas for more extensive research using similar techniques.

The Mantel-Haenszel Procedure Revisited: Models and Generalizations

Several statistical methods have been developed for adjusting the Odds Ratio of the relation between two dichotomous variables X and Y for some confounders Z. With the exception of the Mantel-Haenszel method, commonly used methods, notably binary logistic regression, are not symmetrical in X and Y. The classical Mantel-Haenszel method however only works for confounders with a limited number of discrete strata, which limits its utility, and appears to have no basis in statistical models. Here we revisit the Mantel-Haenszel method and propose an extension to continuous and vector valued Z. The idea is to replace the observed cell entries in strata of the Mantel-Haenszel procedure by subject specific classification probabilities for the four possible values of (X,Y) predicted by a suitable statistical model. For situations where X and Y can be treated symmetrically we propose and explore the multinomial logistic model. Under the homogeneity hypothesis, which states that the odds ratio does not depend on Z, the logarithm of the odds ratio estimator can be expressed as a simple linear combination of three parameters of this model. Methods for testing the homogeneity hypothesis are proposed. The relationship between this method and binary logistic regression is explored. A numerical example using survey data is presented.

On measures of association for multiple-cause mortality: Do we need more measures?

More than 70 measures exist for analyzing the binary association of a 2x2 contingency table. Of these, only five are used in multiple-cause mortality. The aim of the paper is to answer the question of whether these measures are adequate. Building on comparative reviews of measures of association, the paper identifies three additional measures as suitable candidates. These additional measures, together with the five existing ones, are assessed for their theoretical utility based on seven criteria laid out in the paper. Subsequently, the same measures are applied to South African multiple-cause data that comprises over four million records. The multiple-causesoftware Cause_limp v1.1 was used to extract the data for the cell entries of the 2x2 contingency table, with diabetes as a multiple-cause and cardiac arrest as a co-morbid condition. The paperconcludes that existing measures of multiple-cause mortality need to be supplemented with other measures, in particular the Positive Matching Index (PMI). This measure is found to satisfy allthe criteria laid out, and produces the most consistent results among all the measures compared.

Visualizing space, time, and agents: production, performance, and preference

Visualizations of space, time, and agents (or objects) are ubiquitous in science, business, and everyday life, from weather maps to scheduling meetings. Effective communications, including visual ones, emerge from use in the field, but no conventional visualization form has yet emerged for this confluence of information. The real-world spiral of production, comprehension, and use that fine-tunes communications can be accelerated in the laboratory. Here, we do so in search of effective visualizations of space, time, and agents. Users' production, preference, and performance aligned to favor matrix representations with time as rows or columns and space and agents as entries. Overall, performance and preference were greater for matrices with discrete dots representing cell entries than for matrices with lines, but lines connecting cells may provide an advantage when evaluating temporal sequence. Both the diagram type and the technique have broader applications.

Pseudocontingencies can override genuine contingencies between multiple cues

The cognitive process of contingency assessment has traditionally been conceived as an inference from the joint frequencies (table cell entries) of correlated cues. Alternatively, pseudocontingency (PC) inferences are derived from the alignment of separate base rate trends (table marginals). The present research extends PCs to the simultaneous assessment of six contingencies between four personality cues. Consistently obtained PC effects, reflecting accurately assessed base rate trends, were unaffected by the actual cue intercorrelations, which were zero or consistent or inconsistent with the PCs. The functional value of PC inferences is discussed. Although PCs can be misleading, they afford approximations of actually existing correlations that suffice under many, although not under all, conditions.

Markov bases and subbases for bounded contingency tables

In this paper we study the computation of Markov bases for contingency tables whose cell entries have an upper bound. It is known that in this case one has to compute universal Gröbner bases, and this is often infeasible also in small- and medium-sized problems. Here we focus on bounded two-way contingency tables under independence model. We show that when these bounds on cells are positive the set of basic moves of all 2 × 2 minors connects all tables with given margins. We also give some results about bounded incomplete table and we conclude with an open problem on the necessary and sufficient condition on the set of structural zeros so that the set of basic moves of all 2 × 2 minors connects all incomplete contingency tables with given positive margins.

Small Contingency Tables with Large Gaps

We construct examples of contingency tables on n binary random variables where the gap between the linear programming lower/upper bound and the true integer lower/upper bounds on cell entries is exponentially large. These examples provide evidence that linear programming may not be an effective heuristic for detecting disclosures when releasing margins of multiway tables.

Data augmentation in multi-way contingency tables with fixed marginal totals

We describe and illustrate approaches to data augmentation in multi-way contingency tables for which partial information, in the form of subsets of marginal totals, is available. In such problems, interest lies in questions of inference about the parameters of models underlying the table together with imputation for the individual cell entries. We discuss questions of structure related to the implications for inference on cell counts arising from assumptions about log-linear model forms, and a class of simple and useful prior distributions on the parameters of log-linear models. We then discuss “local move” and “global move” Metropolis–Hastings simulation methods for exploring the posterior distributions for parameters and cell counts, focusing particularly on higher-dimensional problems. As a by-product, we note potential uses of the “global move” approach for inference about numbers of tables consistent with a prescribed subset of marginal counts. Illustration and comparison of MCMC approaches is given, and we conclude with discussion of areas for further developments and current open issues.

Bayesian identifiability and misclassification in multinomial data

AbstractThe authors consider the Bayesian analysis of multinomial data in the presence of misclassification. Misclassification of the multinomial cell entries leads to problems of identifiability which are categorized into two types. The first type, referred to as the permutation‐type nonidentifiabilities, may be handled with constraints that are suggested by the structure of the problem. Problems of identifiability of the second type are addressed with informative prior information via Dirichlet distributions. Computations are carried out using a Gibbs sampling algorithm.

Disambiguation Data: Extracting Information from Anonymized Sources

Privacy protection is an important consideration when releasing medical databases to the research community. We show that while recent advances in anonymization algorithms provide increased levels of protection, it is still possible to calculate approximations to the original data set. In some cases, one can even uniquely reconstruct entries in a table before anonymization. In this paper, we demonstrate how knowledge of an anonymization algorithm based on ambiguating data cell entries can be used to undo the anonymization process. We investigate the effect of this algorithm and its reversal on data sets of varying sizes and distributions. It is shown that by using a computationally complex disambiguation process, information on individuals can be extracted from an anonymized data set.

A qualitative method to consider leakage effects from CDM and JI projects

Indirect effects (“leakage”) are an important issue in baseline development for CDM and JI projects but so far no coherent methodology for their determination has been suggested. We propose a simplified method to correct direct emission reduction values for leakage. A matrix of different project types should be elaborated that shows leakages in qualitative form. Depending on the number of entries in the matrix columns, an overall leakage factor will be calculated by summing up factors related to the cell entries. The factors have predetermined values denominated in per cent to cover the different qualitative degrees of leakage.