Binomial transformation applied to presence-absence community data

Community data is often transformed or standardized to meet the requirements and assumptions of multivariate analysis. While these methods are usually appropriate for abundance data, they are seldom applied to presence-absence data. Here, a method of transforming a binary matrix using the binomial probability is described. Number of trials (n), number of successes (x) and probability of success (p) are necessary to compute the binomial probability. Successes were defined as the number of sites where the species occurrence can be considered; trials were equal and greater than the number of successes. The actual occurrence of each species along the gradient was considered the probability of success. The Mantel statistic associated with the binomially transformed distance matrix and the distance matrix based on binary data were used to choose an appropriate binomial transformation. The chosen binomial transformation gave greater value to species indicating habitat typologies. Binomially transformed data rendered results closer to expectations.     

Medoidshift clustering applied to genomic bulk tumor data

Despite the enormous medical impact of cancers and intensive study of their biology, detailed characterization of tumor growth and development remains elusive. This difficulty occurs in large part because of enormous heterogeneity in the molecular mechanisms of cancer progression, both tumor-to-tumor and cell-to-cell in single tumors. Advances in genomic technologies, especially at the single-cell level, are improving the situation, but these approaches are held back by limitations of the biotechnologies for gathering genomic data from heterogeneous cell populations and the computational methods for making sense of those data. One popular way to gain the advantages of whole-genome methods without the cost of single-cell genomics has been the use of computational deconvolution (unmixing) methods to reconstruct clonal heterogeneity from bulk genomic data. These methods, too, are limited by the difficulty of inferring genomic profiles of rare or subtly varying clonal subpopulations from bulk data, a problem that can be computationally reduced to that of reconstructing the geometry of point clouds of tumor samples in a genome space. Here, we present a new method to improve that reconstruction by better identifying subspaces corresponding to tumors produced from mixtures of distinct combinations of clonal subpopulations. We develop a nonparametric clustering method based on medoidshift clustering for identifying subgroups of tumors expected to correspond to distinct trajectories of evolutionary progression. We show on synthetic and real tumor copy-number data that this new method substantially improves our ability to resolve discrete tumor subgroups, a key step in the process of accurately deconvolving tumor genomic data and inferring clonal heterogeneity from bulk data.

     

Photopolymers as an immobilized matrix in biosensing

Biosensors are perspective devices for analysis of substances in chemistry, biological chemistry, medicine, biotechnology and also for environment status monitoring. Their advantages are compact size, short time of analysis, display high response and simplicity in usage. The working characteristics of biosensors often depend on efficacy of a biological stuff immobilization on the surface of transducer. In this context there is a need for development of immobilization methods capable to provide for execution of the following demands: 1) compatibility of this process with technology of building transducer; 2) simplicity in fulfillment, cheapness and expressness at manufacture of biomembranes; 3) ability to provide the maximal safety of a biological stuff activity and its high adhesion to a surface of transducer; 4) reproducibility at serial application and capacity of standardizing. Usage of photocrosslinked and photopolymerized compound at the immobilization of a biological stuff allows to provide execution of the listed above demands. The present review is devoted to features of application of the given class of compound at building of biosensors.     

ROC analysis as an additional method to characterize chronobiological data

Receiver Operating Characteristic (ROC) analysis was originally developed for the detection of radiosignals in the presence of noise, and is nowadays widely used for the assessement of diagnostic test discrimination. The method is also useful for characterising chronobiological data sets based on predefined clinically or experimentally suitable limits of variation. The method also helps to decide whether the chronogram reflects a relevant periodicity.     

Benford’s law applied to aerobiological data and its potential as a quality control tool

Benford’s phenomenological law gives the expected frequencies of the first significant digits of any given series of numbers. According to this law, the frequency of 1 is higher than that of 2; this in turn appears more often 3, and so on. Similarly, Benford’s law can also be applied to the first two significant digits (i.e., from 10 to 99), and so on. Here, we show that gross data sets of daily pollen counts from three aerobiological stations (located in European cities with different features regarding vegetation and climatology) fit Benford’s law for the first significant digits, but this is not always true for the data transformed by a correction factor used in aerobiological studies. That is to say, the biases introduced by rounding and lower and upper built-in limits in pollen counts are detected by Benford’s law analysis. The analysis of the first two significant digits from transformed data is better explained by a Power law than Benford’s law. We propose that Benford’s law could be used as a quality control tool for numerical aerobiological data sets.     

A data assimilation technique applied to a predator-prey model

A new approach for data assimilation, which is based on the adjoint method, but allows the computer code for the adjoint to be constructed directly from the model computer code, is described. This technique is straightforward and reduces the chance of introducing errors in the construction of the adjoint code. Implementation of the technique is illustrated by applying it to a simple predator-prey model in a model fitting mode. A series of identical twin numerical experiments are used to show that this data assimilation approach can successfully recover model parameters as well as initial conditions. However, the ease with which these values are recovered is dependent on the form of the model equations as well as on the type and amount of data that are available. Additional numerical experiments show that sufficient coefficient and parameter recoveries are possible even when the assimilated data contain significant random noise. Thus, for biological systems that can be described by ecosystem models, the adjoint method represents a powerful approach for estimating values for little-known biological parameters, such as initial conditions, growth rates, and mortality rates.     

Performance of several variable-selection methods applied to real ecological data

I evaluated the predictive ability of statistical models obtained by applying seven methods of variable selection to 12 ecological and environmental data sets. Cross-validation, involving repeated splits of each data set into training and validation subsets, was used to obtain honest estimates of predictive ability that could be fairly compared among methods. There was surprisingly little difference in predictive ability among five methods based on multiple linear regression. Stepwise methods performed similarly to exhaustive algorithms for subset selection, and the choice of criterion for comparing models (Akaike's information criterion, Schwarz's Bayesian information criterion or F statistics) had little effect on predictive ability. For most of the data sets, two methods based on regression trees yielded models with substantially lower predictive ability. I argue that there is no 'best' method of variable selection and that any of the regression-based approaches discussed here is capable of yielding useful predictive models.     

Trophoblast concept as applied to therian mammals

Available evidence provides little support for a recent proposal that the term "trophoblast" be applied solely to eutherian mammals. Arguments for such a restricted usage are based on a dichotomous interpretation of therian reproduction that underestimates the developmental, structural, and functional diversity of trophoblastic tissues occurring within the infraclass Eutherian. The occurrence of developmental patterns that are phenotypically intermediate between those of commonly studied eutherians and metatherians suggests that blastocyst development is not fundamentally different in marsupials and eutherians. The trophoblast of marsupials accomplishes most or all of the major functions of the eutherian trophoblast, including maternal-fetal physiological exchange, implantation, contribution to placental membranes, steroid metabolism, and possibly, immunological protection of the conceptus. Furthermore, application of the term "trophoblast" to marsupials is consistent with present and past usage, as well as with the original definition and etymological derivation of the term. Therefore, we recommend that the term "trophoblast" continue to be applied in a functional-morphological sense to the appropriate extraembryonic tissues of marsupials. Such use of functional (rather than taxonomic) criteria for application of this term avoids biasing interpretations of mammalian reproductive evolution.     

Liquid photo-polymerized compositions as an immobilized matrix of biosensors

Series of liquid photopolimerized compositions based on oligourethanemetacrylate (OUM-1000T and OUM-2000T) and oligocarbonatemethacrylate (OCM-2), butilmethacrylate, methacrylic that acid, monomethacrylic ether of ethylene glycol and vinylpirrolidone (VP) were tested. It was shown the optimal variant of enzyme sensor development was a composition containing VP (a basic hydrophylic matrix), OCM-2 (crosslinked components) and OUM-2000T (crosslinked and increasing adsorption of polymer component). The blend contains 3% of enzyme. The obtained biosensors as based on immobilized beta-glucose oxidase and ureases have the following charachteristics: the linear response in the range of the concentration 0.1-10 mM, 0.05-20 mM, angle of slope of curve 30 mV/pC, 38 mV/pC, and response time 10-15, 5-10 mines, respectively. The maximal response of urease sensor was in the diapazon of pH 6.0-6.5. The increase of NaCl concentration in the solution to 300 mM caused reduction of sensor response. Under this concentration the was latter equal to half of initial response. Further increase of NaCl concentration (to 500 mM) doesn't lead to further response reduction. K(m) was calculated and it was shown, that amount of immobilized urease and beta-glucose oxidase in photopolymer material was equal 0.85 and 3.1 mM respectively.