MORPHOMETRICAL CHARACTERIZATION OF CYTISUS AND ALLIES (GENISTEAE: LEGUMINOSAE) AS AN AID IN TAXONOMIC DISCRIMINATION

Twenty-four accessions belonging to the genus Cytisus and allied taxa were characterized by adult plant morphometry. Twenty-six characters were measured in flowers, 9 in leaves, and 5 in fruits. Two data sets were prepared, the first including only floral parameters and the second with all the parameters. Two different multivariate analyses were carried out for every data set: cluster analysis and principal components analysis. All these studies produced a similar grouping of the operational taxonomic units. Four clear groups were defined: (i) Cytisophyllum sessilifolium; (ii) Cytisus baeticus, C. reverchonii, C. scoparius., (iii) Chamaecytisus species; (iv) Genista species. On the other hand, Cytisus villosus showed an intermediate position between Cytisus and Chamaecytisus, and Cytisus heterochrous and C. purgans an intermediate position between Cytisus and Genista. This grouping agrees with that obtained by other recent seed morphometry and biochemical studies, and supports the generic arrangement presented by Bisby (1981).

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Chato Murciano pig breed: genetic and ethnozoological characterization

Animal Genetic Resources Information ◽

10.1017/s1014233900002078 ◽

2006 ◽

Vol 38 ◽

pp. 77-86 ◽

Cited By ~ 1

Author(s):

B. Peinado ◽

J.L. Vega-Pla ◽

M.A. Martínez ◽

M. Galián ◽

C. Barba ◽

...

Keyword(s):

Morphological Characterization ◽

Data Sets ◽

Large White ◽

Data Set ◽

Genetic Studies ◽

Original Population ◽

Accuracy And Precision ◽

Qualitative Variables ◽

Made In

SummaryThe Chato Murciano is the only surviving breed of pig of those historically farmed in the region of Murcia for their quality meat. At present, it is on the verge of extinction, having a population of only 260 reproductive animals. This paper describes the genetic studies made in the conservation and recovery programme of this breed of pig. A study of the morphological characterization of these animals was carried out first, measuring thirteen quantitative and six qualitative variables in a sample of 24 adult animals, 8 males and 16 females.Subsequently, investigation was made of the consanguinity of the individuals and of the population as well as the future influence of inbreeding in each generation. Finally, the accuracy and precision of the heterozygote-excess method was evaluated using two data sets from the Chato Murciano pig. One data set is an original population and the other is a F3+F4+F5 generation of a line created from mating a Chato Murciano female with a Large White boar as part of an absorption programme based on backcrosses with Chato Murciano boars.

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Regenerative potential of the Bichat fat pad determined by the quantification of multilineage differentiating stress enduring cells

European Journal of Histochemistry ◽

10.4081/ejh.2018.2900 ◽

2018 ◽

Vol 62 (4) ◽

Cited By ~ 3

Author(s):

Giamaica Conti ◽

Dario Bertossi ◽

Elena Dai Prè ◽

Chiara Cavallini ◽

Maria Teresa Scupoli ◽

...

Keyword(s):

Mesenchymal Cell ◽

Cellular Level ◽

Data Sets ◽

Cellular Composition ◽

Fat Pad ◽

Cell Compartment ◽

Data Set ◽

Fat Pads ◽

Scanning Electron

Published studies regarding Bichat fat pad focused, quite exclusively, on the implant of this adipose depot for different facial portions reconstruction. The regenerative components of Bichat fat pad were poorly investigated. The present study aimed to describe by an ultrastructural approach the Bichat fat pad, providing novel data at the ultrastructural and cellular level. This data sets improve the knowledge about the usefulness of the Bichat fat pad in regenerative and reconstructive surgery. Bichat fat pads were harvested form eight patients subjected to maxillofacial, dental and aesthetic surgeries. Biopsies were used for the isolation of mesenchymal cell compartment and for ultrastructural analysis. Respectively, Bichat fat pads were either digested and placed in culture for the characterization of mesenchymal stem cells (MSCs) or, were fixed in glutaraldehyde 2% and processed for transmission or scanning electron microscopy. Collected data showed very interesting features regarding the cellular composition of the Bichat fat pad and, in particular, experiments aimed to characterized the MSCs showed the presence of a sub-population of MSCs characterized by the expression of specific markers that allow to classify them as multilineage differentiating stress enduring cells. This data set allows to collect novel information about regenerative potential of Bichat fat pad that could explain the success of its employment in reconstructive and regenerative medicine.

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Haplotype Classification Using Copy Number Variation and Principal Components Analysis

The Open Bioinformatics Journal ◽

10.2174/1875036201307010019 ◽

2013 ◽

Vol 7 (1) ◽

pp. 19-24

Author(s):

Kevin Blighe

Keyword(s):

Principal Components Analysis ◽

Principal Components ◽

Large Scale ◽

Large Data ◽

Large Data Sets ◽

Data Sets ◽

Data Set ◽

Reduction Techniques ◽

Number Variation ◽

Components Analysis

Elaborate downstream methods are required to analyze large microarray data-sets. At times, where the end goal is to look for relationships between (or patterns within) different subgroups or even just individual samples, large data-sets must first be filtered using statistical thresholds in order to reduce their overall volume. As an example, in anthropological microarray studies, such ‘dimension reduction’ techniques are essential to elucidate any links between polymorphisms and phenotypes for given populations. In such large data-sets, a subset can first be taken to represent the larger data-set. For example, polling results taken during elections are used to infer the opinions of the population at large. However, what is the best and easiest method of capturing a sub-set of variation in a data-set that can represent the overall portrait of variation? In this article, principal components analysis (PCA) is discussed in detail, including its history, the mathematics behind the process, and in which ways it can be applied to modern large-scale biological datasets. New methods of analysis using PCA are also suggested, with tentative results outlined.

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Demonstration of Improved Quantitative Mobility Spectrum Analysis (i-QMSA)

MRS Proceedings ◽

10.1557/proc-490-245 ◽

1997 ◽

Vol 490 ◽

Author(s):

I. Vurgaftman ◽

J. R. Meyer ◽

C. A. Hoffman ◽

D. Redfern ◽

J. Antoszewski ◽

...

Keyword(s):

Spectrum Analysis ◽

Real Data ◽

Semiconductor Materials ◽

Data Sets ◽

Mobility Spectrum ◽

Data Set ◽

Standard Tool ◽

Field Dependent ◽

Quantitative Mobility Spectrum Analysis

ABSTRACTWe discuss an improved quantitative mobility spectrum analysis (i-QMSA) of magnetic-field-dependent Hall and resistivity data, which can determine multiple electron and hole densities and mobilities. A fully automated computer implementation of i-QMSA is applied to a variety of synthetic and real data sets. The results show that the new algorithm increases the information available from a given data set and is suitable for use as a standard tool in the characterization of semiconductor materials and devices.

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Systematic exploration of unsupervised methods for mapping behavior

10.1101/051300 ◽

2016 ◽

Author(s):

Jeremy G. Todd ◽

Jamey S. Kain ◽

Benjamin L. de Bivort

Keyword(s):

Gaussian Mixture ◽

Data Sets ◽

Clustering Methods ◽

Cluster Assignment ◽

Data Set ◽

Behavioral Transitions ◽

Assignment Algorithm ◽

Higher Dimensional ◽

Systematic Exploration ◽

Components Analysis

AbstractTo fully understand the mechanisms giving rise to behavior, we need to be able to precisely measure it. When coupled with large behavioral data sets, unsupervised clustering methods offer the potential of unbiased mapping of behavioral spaces. However, unsupervised techniques to map behavioral spaces are in their infancy, and there have been few systematic considerations of all the methodological options. We compared the performance of seven distinct mapping methods in clustering a data set consisting of the x-and y-positions of the six legs of individual flies. Legs were automatically tracked by small pieces of fluorescent dye, while the fly was tethered and walking on an air-suspended ball. We find that there is considerable variation in the performance of these mapping methods, and that better performance is attained when clustering is done in higher dimensional spaces (which are otherwise less preferable because they are hard to visualize). High dimensionality means that some algorithms, including the non-parametric watershed cluster assignment algorithm, cannot be used. We developed an alternative watershed algorithm which can be used in high-dimensional spaces when the probability density estimate can be computed directly. With these tools in hand, we examined the behavioral space of fly leg postural dynamics and locomotion. We find a striking division of behavior into modes involving the fore legs and modes involving the hind legs, with few direct transitions between them. By computing behavioral clusters using the data from all flies simultaneously, we show that this division appears to be common to all flies. We also identify individual-to-individual differences in behavior and behavioral transitions. Lastly, we suggest a computational pipeline that can achieve satisfactory levels of performance without the taxing computational demands of a systematic combinatorial approach.AbbreviationsGMM: Gaussian mixture model; PCA: principal components analysis; SW: sparse watershed; t-SNE: t-distributed stochastic neighbor embedding

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Great differences in performance and outcome of high-throughput sequencing data analysis platforms for fungal metabarcoding

10.7287/peerj.preprints.27019v2 ◽

2018 ◽

Author(s):

Sten Anslan ◽

Henrik Nilsson ◽

Christian Wurzbacher ◽

Petr Baldrian ◽

Leho Tedersoo ◽

...

Keyword(s):

High Throughput ◽

High Throughput Sequencing ◽

Computation Time ◽

Potential Effect ◽

Data Sets ◽

Sequencing Data ◽

Data Set ◽

Operational Taxonomic Units ◽

High Throughput Sequencing Data ◽

Recent Developments

Along with recent developments in high-throughput sequencing (HTS) technologies and thus fast accumulation of HTS data, there has been a growing need and interest for developing tools for HTS data processing and communication. In particular, a number of bioinformatics tools have been designed for analysing metabarcoding data, each with specific features, assumptions and outputs. To evaluate the potential effect of the application of different bioinformatics workflow on the results, we compared the performance of different analysis platforms on two contrasting high-throughput sequencing data sets. Our analysis revealed that the computation time, quality of error filtering and hence output of specific bioinformatics process largely depends on the platform used. Our results show that none of the bioinformatics workflows appear to perfectly filter out the accumulated errors and generate Operational Taxonomic Units, although PipeCraft, LotuS and PIPITS perform better than QIIME2 and Galaxy for the tested fungal amplicon data set. We conclude that the output of each platform require manual validation of the OTUs by examining the taxonomy assignment values.

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Relationships between Human Brain Structural Connectomes and Traits

10.1101/256933 ◽

2018 ◽

Cited By ~ 5

Author(s):

Zhengwu Zhang ◽

Genevera I. Allen ◽

Hongtu Zhu ◽

David Dunson

Keyword(s):

Dimensionality Reduction ◽

Fluid Intelligence ◽

Imaging Techniques ◽

Brain Connectivity ◽

Image Resolution ◽

Data Set ◽

Human Connectome Project ◽

Dimensionality Reduction Technique ◽

Components Analysis

AbstractAdvanced brain imaging techniques make it possible to measure individuals’ structural connectomes in large cohort studies non-invasively. However, due to limitations in image resolution and pre-processing, questions remain about whether reconstructed connectomes are measured accurately enough to detect relationships with human traits and behaviors. Using a state-of-the-art structural connectome processing pipeline and a novel dimensionality reduction technique applied to data from the Human Connectome Project (HCP), we show strong relationships between connectome structure and various human traits. Our dimensionality reduction approach uses a tensor characterization of the connectomes and relies on a generalization of principal components analysis. We analyze over 1100 scans for 1076 subjects from the HCP and the Sherbrooke test-retest data set as well as 175 human traits that measure domains including cognition, substance use, motor, sensory and emotion. We find that brain connectomes are associated with many traits. Specifically, fluid intelligence, language comprehension, and motor skills are associated with increased cortical-cortical brain connectivity, while the use of alcohol, tobacco, and marijuana are associated with decreased cortical-cortical connectivity.

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Comparing perturbation models for evaluating stability of neuroimaging pipelines

The International Journal of High Performance Computing Applications ◽

10.1177/1094342020926237 ◽

2020 ◽

Vol 34 (5) ◽

pp. 491-501 ◽

Cited By ~ 2

Author(s):

Gregory Kiar ◽

Pablo de Oliveira Castro ◽

Pierre Rioux ◽

Eric Petit ◽

Shawn T Brown ◽

...

Keyword(s):

Monte Carlo ◽

Error Detection ◽

Data Sets ◽

Data Set ◽

Systems Software ◽

Numerical Instabilities ◽

Software Implementations ◽

The Stability ◽

Future Work

With an increase in awareness regarding a troubling lack of reproducibility in analytical software tools, the degree of validity in scientific derivatives and their downstream results has become unclear. The nature of reproducibility issues may vary across domains, tools, data sets, and computational infrastructures, but numerical instabilities are thought to be a core contributor. In neuroimaging, unexpected deviations have been observed when varying operating systems, software implementations, or adding negligible quantities of noise. In the field of numerical analysis, these issues have recently been explored through Monte Carlo Arithmetic, a method involving the instrumentation of floating-point operations with probabilistic noise injections at a target precision. Exploring multiple simulations in this context allows the characterization of the result space for a given tool or operation. In this article, we compare various perturbation models to introduce instabilities within a typical neuroimaging pipeline, including (i) targeted noise, (ii) Monte Carlo Arithmetic, and (iii) operating system variation, to identify the significance and quality of their impact on the resulting derivatives. We demonstrate that even low-order models in neuroimaging such as the structural connectome estimation pipeline evaluated here are sensitive to numerical instabilities, suggesting that stability is a relevant axis upon which tools are compared, alongside more traditional criteria such as biological feasibility, computational efficiency, or, when possible, accuracy. Heterogeneity was observed across participants which clearly illustrates a strong interaction between the tool and data set being processed, requiring that the stability of a given tool be evaluated with respect to a given cohort. We identify use cases for each perturbation method tested, including quality assurance, pipeline error detection, and local sensitivity analysis, and make recommendations for the evaluation of stability in a practical and analytically focused setting. Identifying how these relationships and recommendations scale to higher order computational tools, distinct data sets, and their implication on biological feasibility remain exciting avenues for future work.

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FRACTAL CHARACTERIZATION OF BPN WEIGHTS EVOLUTION

International Journal of Neural Systems ◽

10.1142/s0129065704001929 ◽

2004 ◽

Vol 14 (02) ◽

pp. 139-145 ◽

Cited By ~ 1

Author(s):

S. GUNASEKARAN ◽

B. VENKATESH ◽

B. S. D. SAGAR

Keyword(s):

Fractal Dimension ◽

Dimensional Analysis ◽

Back Propagation ◽

Training Data ◽

Data Sets ◽

Training Phase ◽

Data Set ◽

Training Methodology ◽

Propagation Network

Training methodology of the Back Propagation Network (BPN) is well documented. One aspect of BPN that requires investigation is whether or not the BPN would get trained for a given training data set and architecture. In this paper the behavior of the BPN is analyzed during its training phase considering convergent and divergent training data sets. Evolution of the weights during the training phase was monitored for the purpose of analysis. The evolution of weights was plotted as return map and was characterized by means of fractal dimension. This fractal dimensional analysis of the weight evolution trajectories is used to provide a new insight to understand the behavior of BPN and dynamics in the evolution of weights.

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