Quantifying the success of feral cat eradication, San Nicolas Island, California

It is usually uncertain when to declare success and stop control in pest eradication operations that rely on successive reductions of the population. We used the data collected during a project to eradicate feral cats from San Nicolas Island, California to estimate both the number of cats remaining towards the end of the project, and the amount and type of surveillance effort required to declare successful eradication after the last known cat was removed. Fifty seven cats were removed between June 2009 and April 2010 and our model estimated that there was a 95% chance that a further 1 to 4 cats remained, with 1 cat being the most likely number. After this time a further two cats were detected and removed and the model predicted this outcome with a probability of 0.25. If managers wished to confirm eradication success at this point, we estimated that 55 km of effort searching for recent evidence of cats over the whole island without detecting any would provide 99% certainty that no cats remained (stopping rule 1). Alternatively, the optimal amount of search effort for evidence that minimized the joint cost of searching and the cost of wrongly declaring eradication was 75 km (stopping rule 2). The equivalent amount of camera-nights (26 cameras were available) required to declare successful eradication were 416 (stopping rule 1) and 1196 camera nights (stopping rule 2). During the confirmation phase, 270 km of sign search effort and 3294 camera-nights surveillance were used from late June 2010, when the last cat was removed, through August 2010, without detecting signs of survivors. Managers can be very confident that eradication has been successful.     

Further evaluation of the practical applicability of nuclear morphometry for the prediction of the outcome of atypical endometrial hyperplasia

A previously developed morphometric classification rule has been shown to be successful in identifying approximately 30% of patients with atypical hyperplasia of the endometrium in whom the finding does not imply a progression to malignancy. The reproducibility of the nuclear classification rule was tested in blind, duplicate morphometric assessments by different technicians. The results showed a satisfactorily high degree of consistency and reproducibility, with only one of ten cases classified differently. In a second series of experiments, the nuclear classification rule was applied to samples from 101 nonmalignant cases (39 proliferative endometriums, 7 secretory endometriums, 55 mildly atypical hyperplasias), 8 markedly atypical hyperplasias and 43 malignant cases (20 well-differentiated adenocarcinomas and 23 moderately to poorly differentiated adenocarcinomas of the endometrium). Ideally, the rule should classify all nonhyperplastic and mildly hyperplastic cases as nonprogressive and all carcinomas as progressive; there were, however, a considerable number of false positives and false negatives based on application of the classification rule to these cases. Therefore, the sensitivity and specificity of nuclear morphometry using the classification rule developed for atypical hyperplasias is too low to allow its random application. This emphasizes the selective nature of diagnostic morphometry, in which the full diagnostic capacity of the pathologist must be used in selection of the proper cases to be studied.     

Flexible implementations of group sequential stopping rules using constrained boundaries

Group sequential stopping rules are often used during the conduct of clinical trials in order to attain more ethical treatment of patients and to better address efficiency concerns. Because the use of such stopping rules materially affects the frequentist operating characteristics of the hypothesis test, it is necessary to choose an appropriate stopping rule during the planning of the study. It is often the case, however, that the number and timing of interim analyses are not precisely known at the time of trial design, and thus the implementation of a particular stopping rule must allow for flexible determination of the schedule of interim analyses. In this article, we consider the use of constrained stopping boundaries in the implementation of stopping rules. We compare this approach when used on various scales for the test statistic. When implemented on the scale of boundary crossing probabilities, this approach is identical to the error spending function approach of Lan and DeMets (1983).     

A brute‐force approach to vegetation classification

Aim: Introduction of a novel approach to the classification of vegetation data (species by plot matrices). This approach copes with a large amount of noise, groups irregularly shaped in attribute space and species turnover within groups. Method: The proposed algorithm (Isopam) is based on the classification of ordination scores from isometric feature mapping. Ordination and classification are repeated in a search for either high overall fidelity of species to groups of sites, or high quantity and quality of indicator species for groups of sites. The classification is performed either as a hierarchical, divisive method or as non‐hierarchical partitioning. In divisive clustering, resulting groups are subdivided until a stopping criterion is met. Isopam was tested on 20 real‐world data sets. The resulting classifications were compared with solutions from eight widely used clustering algorithms. Results: When looking at the significance of species fidelities to groups of sites, and at quantity and quality of indicator species, Isopam often achieved high ranks as compared with other algorithms.     

The principles and advances of quantitative pathology

This article gives an overview of the quantitative pathologic techniques used today, with special emphasis on interactive morphometry and its application in the clinical setting. At present, stereologic calculations may be necessary, but in a diagnostic setting have only rarely proved to be essential. The reproducibility of the measurements is discussed in relation to the definition of the particles and staining methods. A number of technical factors that cause random errors are mentioned, such as quality of the slides, magnification, definition of the particles to be measured and measuring protocol. Large-scale experiments have revealed that the means of nuclear morphometric and certain stereologic features are reproducible, such as in the volume percentage of epithelium, the surface densities of glands and the mitotic activity index. The diagnostic applications of the standard deviation and shape factors of nuclear quantitative features require additional precautions, however. Having quantified cell and tissue features, multivariate analysis may result in a better discrimination of two or more groups under study. The quantitative pathologic examination of cells and tissues can provide important diagnostic and prognostic information. Quantitation in pathology is especially useful in so-called continuous lesions, in which interobserver and intraobserver disagreement is considerable. An important requirement of diagnostic morphometry is object selection by a skilled pathologist; the use of morphometry as a black box can result in dramatic errors. The criteria used for a morphometric classification rule that can be used for clinical applications are summarized. Quality control of the whole measuring system is essential. Application of these techniques for more than six years in diagnostic pathology has repeatedly corrected previous qualitative diagnoses.(ABSTRACT TRUNCATED AT 250 WORDS)     

Solution of Revecz-Krushinskii test by animals of different taxonomic groups

There has been compared behavior of rats, corvid birds, and monkeys of different species at their performance of the Revecz-Krushinskii test (RKT) developed by L.V. Krushinskii to estimate the human capability for revealing rule of discrete translocation of hidden target object. RKT was introduced as an addition to the test for extrapolation of the movement direction of the lure seen only at the initial pathway fragment; this test is close to Piaget’s test (stage 6) evaluating the capability for mental representation and location of the moving hidden object. During RKT, the lure, hidden from animals, was placed, near where it was previous time: at the first test presentation—under the 1st cylinder, at the 2nd one—under the 2nd cylinder, etc. The animals were tested once. It was shown that they did not catch the necessary for successful solution rule of the lure translocation, direction and step of its translocation at each presentation. Only some of the animals solved RKT, found the lure 3 and more times in succession with no errors or with one error. Nevertheless, in all groups the number of errors was lower than that in the model situation of random search. Such optimization was a consequence of universal for all groups’ strategy of search in the places where the lure was found recently. With the similar number of errors, rats, birds, and monkeys performed the search differently. Rats were looking for lure mainly among the cylinders where they had found it previously, whereas monkeys and birds the first the new cylinders located near the target one, which implies the existence, to the weak extent, of elements of prognosis. For all groups of animals, RKT turned out to be more difficult both of the test for extrapolation and of the Piaget’s test.     

Which is better for cDNA-microarray-based classification: ratios or direct intensities

MOTIVATION: There are two general methods for making gene-expression microarrays: one is to hybridize a single test set of labeled targets to the probe, and measure the background-subtracted intensity at each probe site; the other is to hybridize both a test and a reference set of differentially labeled targets to a single detector array, and measure the ratio of the background-subtracted intensities at each probe site. Which method is better depends on the variability in the cell system and the random factors resulting from the microarray technology. It also depends on the purpose for which the microarray is being used. Classification is a fundamental application and it is the one considered here. RESULTS: This paper describes a model-based simulation paradigm that compares the classification accuracy provided by these methods over a variety of noise types and presents the results of a study modeled on noise typical of cDNA microarray data. The model consists of four parts: (1) the measurement equation for genes in the reference state; (2) the measurement equation for genes in the test state; (3) the ratio and normalization procedure for a dual-channel system; and (4) the intensity and normalization procedure for a single-channel system. In the reference state, the mean intensities are modeled as a shifted exponential distribution, and the intensity for a particular gene is modeled via a normal distribution, Normal(I, alphaI), about its mean intensity I, with alpha being the coefficient of variation of the cell system. In the test state, some genes have their intensities up-regulated by a random factor. The model includes a number of random factors affecting intensity measurement: deposition gain d, labeling gain, and post-image-processing residual noise. The key conclusion resulting from the study is that the coefficient of variation governing the randomness of the intensities and the deposition gain are the most important factors for determining whether a single-channel or dual-channel system provides superior classification, and the decision region in the alpha-d plane is approximately linear.     

Classification using partial least squares with penalized logistic regression

MOTIVATION: One important aspect of data-mining of microarray data is to discover the molecular variation among cancers. In microarray studies, the number n of samples is relatively small compared to the number p of genes per sample (usually in thousands). It is known that standard statistical methods in classification are efficient (i.e. in the present case, yield successful classifiers) particularly when n is (far) larger than p. This naturally calls for the use of a dimension reduction procedure together with the classification one. RESULTS: In this paper, the question of classification in such a high-dimensional setting is addressed. We view the classification problem as a regression one with few observations and many predictor variables. We propose a new method combining partial least squares (PLS) and Ridge penalized logistic regression. We review the existing methods based on PLS and/or penalized likelihood techniques, outline their interest in some cases and theoretically explain their sometimes poor behavior. Our procedure is compared with these other classifiers. The predictive performance of the resulting classification rule is illustrated on three data sets: Leukemia, Colon and Prostate.     

Linking Moose Population and Plant Growth Models with a Moose Energetics Model

Selective foraging by large mammals can change ecosystem properties such as plant species composition, nutrient cycling rates, and soil fertility. These changes, in turn, alter the availability of forage and could affect the relative efficiencies of foraging strategies used by these animals. We used a simulation model to predict how alternate foraging strategies affected the net annual energy balance of moose (Alces alces), moose density, and distribution of browse across the landscape. The model simulates the spatial distribution of vegetation in an 8-ha landscape of 1-m² cells with seasonal changes in the energetic needs of free-ranging moose and plant phenology. The energetics model was integrated with a moose population model and a plant-growth model for long-term simulations. Changes in bite density in each feeding station are predicted with height and biomass logistic curves modified by a quadratic response to browsing. We tested foraging strategies using random, fractional, and marginal value theorem (MVT) algorithms on landscapes with a range of bite densities and differing spatial distributions. Small-scale disturbances (that is, tree-fall gaps) were required to maintain browse supply and prevent moose population extinction under all foraging strategies. Populations using a fractional stopping rule survived the 100-year simulations because moose browsed across much of the landscape and did not overbrowse patches with high bite density. Populations using random and MVT stopping rules became extinct in about 25 and about 50 years, respectively. Moose using a random stopping rule were in negative energy balance because travel time was high and the net energy intake rate was low on an annual basis. Moose using the MVT stopping rule were initially in positive energy balance, but as the high-density browse patches were overbrowsed and low-density unbrowsed patches grew out of reach, bite density decreased, and energy balance became negative in subsequent years. Thus, the foraging strategy used by individual moose resulted in creation of landscapes that strongly affected browse density, browse distribution, moose population density, and moose survival. Received 30 April 1997; accepted 5 August 1997.     

Interpretable gene expression classifier with an accurate and compact fuzzy rule base for microarray data analysis

An accurate classifier with linguistic interpretability using a small number of relevant genes is beneficial to microarray data analysis and development of inexpensive diagnostic tests. Several frequently used techniques for designing classifiers of microarray data, such as support vector machine, neural networks, k-nearest neighbor, and logistic regression model, suffer from low interpretabilities. This paper proposes an interpretable gene expression classifier (named iGEC) with an accurate and compact fuzzy rule base for microarray data analysis. The design of iGEC has three objectives to be simultaneously optimized: maximal classification accuracy, minimal number of rules, and minimal number of used genes. An "intelligent" genetic algorithm IGA is used to efficiently solve the design problem with a large number of tuning parameters. The performance of iGEC is evaluated using eight commonly-used data sets. It is shown that iGEC has an accurate, concise, and interpretable rule base (1.1 rules per class) on average in terms of test classification accuracy (87.9%), rule number (3.9), and used gene number (5.0). Moreover, iGEC not only has better performance than the existing fuzzy rule-based classifier in terms of the above-mentioned objectives, but also is more accurate than some existing non-rule-based classifiers.     

A faster circular binary segmentation algorithm for the analysis of array CGH data

MOTIVATION: Array CGH technologies enable the simultaneous measurement of DNA copy number for thousands of sites on a genome. We developed the circular binary segmentation (CBS) algorithm to divide the genome into regions of equal copy number. The algorithm tests for change-points using a maximal t-statistic with a permutation reference distribution to obtain the corresponding P-value. The number of computations required for the maximal test statistic is O(N2), where N is the number of markers. This makes the full permutation approach computationally prohibitive for the newer arrays that contain tens of thousands markers and highlights the need for a faster algorithm. RESULTS: We present a hybrid approach to obtain the P-value of the test statistic in linear time. We also introduce a rule for stopping early when there is strong evidence for the presence of a change. We show through simulations that the hybrid approach provides a substantial gain in speed with only a negligible loss in accuracy and that the stopping rule further increases speed. We also present the analyses of array CGH data from breast cancer cell lines to show the impact of the new approaches on the analysis of real data. AVAILABILITY: An R version of the CBS algorithm has been implemented in the "DNAcopy" package of the Bioconductor project. The proposed hybrid method for the P-value is available in version 1.2.1 or higher and the stopping rule for declaring a change early is available in version 1.5.1 or higher.     

Diffraction imaging of single particles and biomolecules

Theory predicts that with a very short and very intense X-ray pulse, the image of a single diffraction pattern may be recorded from a large macromolecule, a virus, or a nanocluster of proteins without the need for a crystal. A three-dimensional data set can be assembled from such images when many copies of the molecule are exposed to the beam one by one in random orientations. We outline a method for structure reconstruction from such a data set in which no independent information is available about the orientation of the images. The basic requirement for reconstruction and/or signal averaging is the ability to tell whether two noisy diffraction patterns represent the same view of the sample or two different views. With this knowledge, averaging techniques can be used to enhance the signal and extend the resolution in a redundant data set. Based on statistical properties of the diffraction pattern, we present an analytical solution to the classification problem. The solution connects the number of incident X-ray photons with the particle size and the achievable resolution. The results are surprising in that they show that classification can be done with less than one photon per pixel in the limiting resolution shell, assuming Poisson-type photon noise in the image. The results can also be used to provide criteria for improvements in other image classification procedures, e.g., those used in electron tomography or diffraction.     

Random Whole Metagenomic Sequencing for Forensic Discrimination of Soils

Here we assess the ability of random whole metagenomic sequencing approaches to discriminate between similar soils from two geographically distinct urban sites for application in forensic science. Repeat samples from two parklands in residential areas separated by approximately 3 km were collected and the DNA was extracted. Shotgun, whole genome amplification (WGA) and single arbitrarily primed DNA amplification (AP-PCR) based sequencing techniques were then used to generate soil metagenomic profiles. Full and subsampled metagenomic datasets were then annotated against M5NR/M5RNA (taxonomic classification) and SEED Subsystems (metabolic classification) databases. Further comparative analyses were performed using a number of statistical tools including: hierarchical agglomerative clustering (CLUSTER); similarity profile analysis (SIMPROF); non-metric multidimensional scaling (NMDS); and canonical analysis of principal coordinates (CAP) at all major levels of taxonomic and metabolic classification. Our data showed that shotgun and WGA-based approaches generated highly similar metagenomic profiles for the soil samples such that the soil samples could not be distinguished accurately. An AP-PCR based approach was shown to be successful at obtaining reproducible site-specific metagenomic DNA profiles, which in turn were employed for successful discrimination of visually similar soil samples collected from two different locations.     

Minimizing animal numbers: the variable-criteria sequential stopping rule

The variable-criteria sequential stopping rule (SSR) allows an investigator to use a few subjects at a time to determine whether a planned experiment is worth pursuing without increasing the rate of false discoveries (type I errors). The SSR is appropriate whenever testing a null hypothesis if the experiment can be conducted in stages. The investigator adds a predetermined number of subjects at each stage and tests repeatedly for significance until the experiment is stopped because: (1) a significant effect is detected; (2) the effect is clearly not going to be significant; or (3) the predetermined maximal number of subjects has been reached. Two crucial features of the SSR are that it holds the probability of a type I error constant and maintains excellent power. The method is more efficient than is performing a typical significance test after a power analysis because SSR can require 30% fewer subjects to achieve the same power. The variable-criteria SSR provides a formal method for assuring the use of a minimal number of animals. This article provides practical examples of how to use the SSR in combination with a t test, one-way ANOVA, one-way ANOVA with a planned contrast as the focus of the stopping rule, or, in limited circumstances, multifactorial ANOVA.     

Superimposing noise linearizes the responses of primary muscle spindle afferents to sinusoidal muscle stretch

Experiments were conducted in anaesthetized and spinalized cats to measure the extent to which the non-linear response of Ia afferent fibers to sinusoidal muscle stretch as expressed by the peristimulus-time-histograms, PSTHs, can be transformed into a linear one by means of the superposition of random stretch ("mechanical noise"). The gastrocnemius muscles of one hind leg were stretched and the response to sinewave muscle stretch (amplitudes between 0.01 and 4.0 mm, frequencies between 0.1 and 20 Hz) were investigated while band-limited mechanical noise was superimposed on the sinewave stretch. The random stretch upper cut-off frequency was varied between 60 and 300 Hz; the displacements were normally distributed. The noise amplitude sigma, i.e. the standard deviation of the displacement distributions, was varied systematically between 0.002 and 0.4 mm. Mechanical noise was very effective in raising the mean discharge rate. Added to the sinusoidal stretch it prevented the cessation of firing during the release phase of the stretch cycle, or at least reduced the duration of discharge pauses, i.e., a linearization occurred. In general, the larger the noise amplitude, the more the amplitude of the fundamental harmonic component was attenuated and the phase lead reduced. Apart from this rule the particular combination of superimposing small noise (sigma less than 0.02 mm) on small sinewave stretch (A less than 0.02 mm) could enhance the depth of sinusoidal modulation of cycle histograms (compared with responses to pure sinusoids). Linearizing the sinewave response by additional noise allowed the estimation of frequency response characteristics in the otherwise non-linear range of amplitudes (sinewave amplitude 0.5-1.0 mm).(ABSTRACT TRUNCATED AT 250 WORDS)     

Prediction of Cancer Class with Majority Voting Genetic Programming Classifier Using Gene Expression Data

In order to get a better understanding of different types of cancers and to find the possible biomarkers for diseases, recently, many researchers are analyzing the gene expression data using various machine learning techniques. However, due to a very small number of training samples compared to the huge number of genes and class imbalance, most of these methods suffer from overfitting. In this paper, we present a majority voting genetic programming classifier (MVGPC) for the classification of microarray data. Instead of a single rule or a single set of rules, we evolve multiple rules with genetic programming (GP) and then apply those rules to test samples to determine their labels with majority voting technique. By performing experiments on four different public cancer data sets, including multiclass data sets, we have found that the test accuracies of MVGPC are better than those of other methods, including AdaBoost with GP. Moreover, some of the more frequently occurring genes in the classification rules are known to be associated with the types of cancers being studied in this paper.     

The dry weight estimate of biomass in a selection of Cladocera,Copepoda and Rotifera from the plankton,periphyton and benthos of continental waters

Summary A procedure for determining dry weights has been standardized and applied to a number of Cladocera, Copepoda and Rotatoria. In most of the Cladocera, regression equations of the exponential type, relating dry weight to body length, were computed. In the Copepoda, one equation per suborder was computed, and suggestions for future refinements are made. In both groups, a fairly satisfactory agreement was found with literature data where these exist. In both groups, the egg and embryo weight proved to be considerable, relative to the weight of the adult female. In Rotatoria, 4 species could be dealt with in size-classes, and their weight increment per unit length was found to be lower than in the Cladocera and Copepoda. A large number of species were weighed as adults only. A conclusion applicable to the 3 groups is that, as a rule, limnetic species weigh relatively less than littoral, periphytic or benthic species. Even within a species, populations with a more pronounced limnetic way of life weigh less than populations of littoral nature.     

Reverse correlation analysis of the stretch response of primary muscle spindle afferent fibers

The nonlinear responses of deefferented primary muscle spindle afferent fibers to muscle stretching consisted of a train of action potentials which was analyzed when random changes in muscle length (band-limited gaussian white noise) were applied in cats. The upper cutoff frequency of the applied noise (the source stimulus) was varied between 1.6 and 570 Hz; the amplitude of the random input was varied between 0.002 and 1.2 mm. In a previous report the reverse correlation of 1st and 2nd order was studied for its ability to analyze data of a continuous input signal and pulsatile events in the output. Computations of the Wiener kernelsh ₁ andh ₂ or their equivalents, the perispike averages of the 1st and 2nd order, were computed from the random stretch responses of muscle-spindle afferents. Then the 1st- and the 2ndorder predictions and the summation of both to random muscle stretch was estimated. A general finding was that the 1st-order component was approximately 10 times that of the 2nd-order component, when both were combined in approximation procedures to give the closest prediction of observed responses to random test stimuli. The approximation was poor when the source stimulus was less than 0.03 mm and improved when it was greater. With the increase in the upper cutoff frequency of the random source input, the approximation worsened continuously. Predictions to ramp-and-hold stimuli were computed, as well as responses to random stimulation. Limiting the upper cutoff frequency did not diminish the value of the techniques applied.     

Classifying breast carcinomas with DNA measurements and morphometry

The relation between the DNA distribution pattern and the variables DNA content and nuclear area was investigated in 73 breast carcinomas. According to the DNA patterns, the tumors were subclassified into groups I, II, III, and IV (Auer's classification). The Auer's classes correlate with prognosis. In this investigation it was shown that Auer's classes strongly correlate with the standard deviation of DNA content and mean nuclear area. The carcinomas were subclassified using data of DNA content and nuclear area. For the establishment of this subclassification, multivariate analysis was applied. It was evident that Auer's group III was a hybrid group. Therefore a second classification rule was calculated without the data of Auer's group III. This second classification rule was even more successful in determining the Auer's group of each individual case. Our investigation shows that it is possible to subclassify breast carcinomas objectively using DNA content and nuclear area of the tumor cells.