Journal articles on the topic 'Tree set heterogeneity'
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GLEISER, PABLO M., LUCE PRIGNANO, CONRAD J. PÉREZ-VICENTE, and ALBERT DÍAZ-GUILERA. "PACEMAKERS IN A CAYLEY TREE OF KURAMOTO OSCILLATORS." International Journal of Bifurcation and Chaos 22, no. 07 (July 2012): 1250161. http://dx.doi.org/10.1142/s0218127412501611.
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In this work, we study a system of Kuramoto oscillators with identical frequencies in a Cayley tree. Heterogeneity in the frequency distribution is introduced in the root of the tree, allowing for analytical calculations of the phase evolution. In this work, we study a system of Kuramoto oscillators with identical frequencies in a Cayley tree. Heterogeneity in the frequency distribution is introduced in the root of the tree, allowing for analytical calculations of the phase evolution. This simple case can be regarded as a starting point in order to understand how to extract topological features of the connectivity pattern from the dynamic state of the system, and vice versa, for the general situation of a set of phase oscillators located on a tree-like network.
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Keren, Srđan, Miroslav Svoboda, Pavel Janda, and Thomas A. Nagel. "Relationships between Structural Indices and Conventional Stand Attributes in an Old-Growth Forest in Southeast Europe." Forests 11, no. 1 (December 18, 2019): 4. http://dx.doi.org/10.3390/f11010004.
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Structural indices are often proposed as guiding measures for increasing structural heterogeneity. However, few studies have examined the association between such indices and conventional stand attributes. The primary objectives of this study were to evaluate changes in structural heterogeneity and tree species diversity at different plot sizes and to quantify the relationships between conventional stand attributes (mean tree diameter, absolute tree density, basal area, species proportion) and structural indices in a mixed old-growth forest in Southeast Europe. Paired tests were used to identify significant changes in structural heterogeneity with increased plot area, while the relationships between stand attributes and analyzed indices (Gini, diameter differentiation, species mingling, and Shannon’s index) were evaluated with Pearson’s correlations. The index values of Gini, diameter differentiation, and tree species mingling were rather stable with the increase of plot size, whereas tree species diversity increased significantly with the increase of plot area from 200 m2 to 1500 m2. The measures of tree species mingling and tree species diversity were strongly associated with each other, while their association with diameter variability was weak to moderately strong. Tree species mingling index was strongly associated with the changes in tree species proportions. However, conventional stand attributes were generally not strongly correlated with the examined indices. For restoring and maintaining old-growth characteristics, forest managers may use structural indices to increase small-scale structural heterogeneity, tree species mingling, and diversity, but only as an additional set of measures, not as surrogates for conventional stand attributes.
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Karch, Rudolf, Friederike Neumann, Bruno K. Podesser, Martin Neumann, Paul Szawlowski, and Wolfgang Schreiner. "Fractal Properties of Perfusion Heterogeneity in Optimized Arterial Trees." Journal of General Physiology 122, no. 3 (August 11, 2003): 307–22. http://dx.doi.org/10.1085/jgp.200208747.
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Regional blood flows in the heart muscle are remarkably heterogeneous. It is very likely that the most important factor for this heterogeneity is the metabolic need of the tissue rather than flow dispersion by the branching network of the coronary vasculature. To model the contribution of tissue needs to the observed flow heterogeneities we use arterial trees generated on the computer by constrained constructive optimization. This method allows to prescribe terminal flows as independent boundary conditions, rather than obtaining these flows by the dispersive effects of the tree structure. We study two specific cases: equal terminal flows (model 1) and terminal flows set proportional to the volumes of Voronoi polyhedra used as a model for blood supply regions of terminal segments (model 2). Model 1 predicts, depending on the number Nterm of end-points, fractal dimensions D of perfusion heterogeneities in the range 1.20 to 1.40 and positively correlated nearest-neighbor regional flows, in good agreement with experimental data of the normal heart. Although model 2 yields reasonable terminal flows well approximated by a lognormal distribution, it fails to predict D and nearest-neighbor correlation coefficients r1 of regional flows under normal physiologic conditions: model 2 gives D = 1.69 ± 0.02 and r1 = −0.18 ± 0.03 (n = 5), independent of Nterm and consistent with experimental data observed under coronary stenosis and under the reduction of coronary perfusion pressure. In conclusion, flow heterogeneity can be modeled by terminal positions compatible with an existing tree structure without resorting to the flow-dispersive effects of a specific branching tree model to assign terminal flows.
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Chaddad, Ahmad, and Camel Tanougast. "High-Throughput Quantification of Phenotype Heterogeneity Using Statistical Features." Advances in Bioinformatics 2015 (October 20, 2015): 1–7. http://dx.doi.org/10.1155/2015/728164.
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Statistical features are widely used in radiology for tumor heterogeneity assessment using magnetic resonance (MR) imaging technique. In this paper, feature selection based on decision tree is examined to determine the relevant subset of glioblastoma (GBM) phenotypes in the statistical domain. To discriminate between active tumor (vAT) and edema/invasion (vE) phenotype, we selected the significant features using analysis of variance (ANOVA) with p value < 0.01. Then, we implemented the decision tree to define the optimal subset features of phenotype classifier. Naïve Bayes (NB), support vector machine (SVM), and decision tree (DT) classifier were considered to evaluate the performance of the feature based scheme in terms of its capability to discriminate vAT from vE. Whole nine features were statistically significant to classify the vAT from vE with p value < 0.01. Feature selection based on decision tree showed the best performance by the comparative study using full feature set. The feature selected showed that the two features Kurtosis and Skewness achieved a highest range value of 58.33–75.00% accuracy classifier and 73.88–92.50% AUC. This study demonstrated the ability of statistical features to provide a quantitative, individualized measurement of glioblastoma patient and assess the phenotype progression.
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Duduman, Gabriel, Ionuț Barnoaiea, Daniel Avăcăriței, Cătălina-Oana Barbu, Vasile-Cosmin Coșofreț, Iulian-Constantin Dănilă, Mihai-Leonard Duduman, Anca Măciucă, and Marian Drăgoi. "Aboveground Biomass of Living Trees Depends on Topographic Conditions and Tree Diversity in Temperate Montane Forests from the Slătioara-Rarău Area (Romania)." Forests 12, no. 11 (October 31, 2021): 1507. http://dx.doi.org/10.3390/f12111507.
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The study zone includes one of the largest montane old-growth forests in Europe (Slatioara UNESCO site), and understanding the structure and functioning of sill intact forests in Europe is essential for grounding management strategies for secondary forests. For this reason, we set out to analyze the dependencies between aboveground biomass (AgB), tree species and size diversity and terrain morphology, as well as the relationship between biomass and diversity, since neither of these issues have been sufficiently explored. We found that tree species diversity decreases with increased solar radiation and elevation. Tree size heterogeneity reaches its highest mean values at elevations between 1001 and 1100 m, on slopes between 50 and 60 degrees. AgB is differentiated with elevation; the highest mean AgB (293 tonnes per hectare) is recorded at elevations between 801 and 900 m, while it decreases to 79 tonnes per hectare at more than 1500 m a.s.l. It is also influenced by tree species diversity and tree size heterogeneity, with the highest AgB reached in the most complex forest ecosystems in terms of structural diversity. We showed that intact temperate montane forests develop maximum biomass for optimum species diversity and highest size heterogeneity; all three are modulated mainly by elevation.
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LIŠKA, Jiří, and Tomáš HERBEN. "Long-term changes of epiphytic lichen species composition over landscape gradients: an 18 year time series." Lichenologist 40, no. 05 (August 26, 2008): 437–48. http://dx.doi.org/10.1017/s0024282908006610.
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Abstract:The study aimed to determine how the response of the epiphytic lichen vegetation to sulphur air pollution is affected by interaction with other factors (distance from pollution sources, habitat, altitude, initial eutrophication of the tree bark). It was based on a series of four successive recordings taken over a period of 18 years with increasing pollution levels and on the same set of 139 solitary trees. Relationships between habitat variables and lichen community composition are assessed using canonical correspondence analysis. The data set comprised 69 lichen species. Ordination of initial species composition on trees revealed two major gradients: eutrophication and acidity.The species composition significantly changed with time, with a general decrease of the total number of lichen species per tree. In general, species sensitive to air pollution decreased, while tolerant species increased in number. Change over time differed depending on the position of the tree within the landscape (relative to one major source of pollution, the town of Tábor, whereas the distance to the other source, Sezimovo Ústí, was not significant) and on the initial species composition found on the tree. Trees under the effect of eutrophication changed their species composition less, indicating that the effect of eutrophication (mainly increased bark pH) may ameliorate the effects of air pollution; a local effect of eutrophication also seems to play an important role. Distance to pollution sources had only a small impact on the rate of change and perhaps other local conditions (sheltered or humid position, altitude) play a role in this interaction. There was a decrease of the overall heterogeneity of the data set over time. This means that the gradients in species composition attributable to these variables became less important over time. Therefore, one of the effects of air pollution is also a general homogenization of the lichen vegetation of the solitary trees.
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Adams, Richard H., and Todd A. Castoe. "Probabilistic Species Tree Distances: Implementing the Multispecies Coalescent to Compare Species Trees Within the Same Model-Based Framework Used to Estimate Them." Systematic Biology 69, no. 1 (May 14, 2019): 194–207. http://dx.doi.org/10.1093/sysbio/syz031.
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Abstract Despite the ubiquitous use of statistical models for phylogenomic and population genomic inferences, this model-based rigor is rarely applied to post hoc comparison of trees. In a recent study, Garba et al. derived new methods for measuring the distance between two gene trees computed as the difference in their site pattern probability distributions. Unlike traditional metrics that compare trees solely in terms of geometry, these measures consider gene trees and associated parameters as probabilistic models that can be compared using standard information theoretic approaches. Consequently, probabilistic measures of phylogenetic tree distance can be far more informative than simply comparisons of topology and/or branch lengths alone. However, in their current form, these distance measures are not suitable for the comparison of species tree models in the presence of gene tree heterogeneity. Here, we demonstrate an approach for how the theory of Garba et al. (2018), which is based on gene tree distances, can be extended naturally to the comparison of species tree models. Multispecies coalescent (MSC) models parameterize the discrete probability distribution of gene trees conditioned upon a species tree with a particular topology and set of divergence times (in coalescent units), and thus provide a framework for measuring distances between species tree models in terms of their corresponding gene tree topology probabilities. We describe the computation of probabilistic species tree distances in the context of standard MSC models, which assume complete genetic isolation postspeciation, as well as recent theoretical extensions to the MSC in the form of network-based MSC models that relax this assumption and permit hybridization among taxa. We demonstrate these metrics using simulations and empirical species tree estimates and discuss both the benefits and limitations of these approaches. We make our species tree distance approach available as an R package called pSTDistanceR, for open use by the community.
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Hime, Paul M., Alan R. Lemmon, Emily C. Moriarty Lemmon, Elizabeth Prendini, Jeremy M. Brown, Robert C. Thomson, Justin D. Kratovil, et al. "Phylogenomics Reveals Ancient Gene Tree Discordance in the Amphibian Tree of Life." Systematic Biology 70, no. 1 (June 30, 2020): 49–66. http://dx.doi.org/10.1093/sysbio/syaa034.
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Abstract Molecular phylogenies have yielded strong support for many parts of the amphibian Tree of Life, but poor support for the resolution of deeper nodes, including relationships among families and orders. To clarify these relationships, we provide a phylogenomic perspective on amphibian relationships by developing a taxon-specific Anchored Hybrid Enrichment protocol targeting hundreds of conserved exons which are effective across the class. After obtaining data from 220 loci for 286 species (representing 94% of the families and 44% of the genera), we estimate a phylogeny for extant amphibians and identify gene tree–species tree conflict across the deepest branches of the amphibian phylogeny. We perform locus-by-locus genealogical interrogation of alternative topological hypotheses for amphibian monophyly, focusing on interordinal relationships. We find that phylogenetic signal deep in the amphibian phylogeny varies greatly across loci in a manner that is consistent with incomplete lineage sorting in the ancestral lineage of extant amphibians. Our results overwhelmingly support amphibian monophyly and a sister relationship between frogs and salamanders, consistent with the Batrachia hypothesis. Species tree analyses converge on a small set of topological hypotheses for the relationships among extant amphibian families. These results clarify several contentious portions of the amphibian Tree of Life, which in conjunction with a set of vetted fossil calibrations, support a surprisingly younger timescale for crown and ordinal amphibian diversification than previously reported. More broadly, our study provides insight into the sources, magnitudes, and heterogeneity of support across loci in phylogenomic data sets.[AIC; Amphibia; Batrachia; Phylogeny; gene tree–species tree discordance; genomics; information theory.]
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Schierup, Mikkel H., and Jotun Hein. "Consequences of Recombination on Traditional Phylogenetic Analysis." Genetics 156, no. 2 (October 1, 2000): 879–91. http://dx.doi.org/10.1093/genetics/156.2.879.
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Abstract We investigate the shape of a phylogenetic tree reconstructed from sequences evolving under the coalescent with recombination. The motivation is that evolutionary inferences are often made from phylogenetic trees reconstructed from population data even though recombination may well occur (mtDNA or viral sequences) or does occur (nuclear sequences). We investigate the size and direction of biases when a single tree is reconstructed ignoring recombination. Standard software (PHYLIP) was used to construct the best phylogenetic tree from sequences simulated under the coalescent with recombination. With recombination present, the length of terminal branches and the total branch length are larger, and the time to the most recent common ancestor smaller, than for a tree reconstructed from sequences evolving with no recombination. The effects are pronounced even for small levels of recombination that may not be immediately detectable in a data set. The phylogenies when recombination is present superficially resemble phylogenies for sequences from an exponentially growing population. However, exponential growth has a different effect on statistics such as Tajima's D. Furthermore, ignoring recombination leads to a large overestimation of the substitution rate heterogeneity and the loss of the molecular clock. These results are discussed in relation to viral and mtDNA data sets.
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Revell, Liam J., Ken S. Toyama, and D. Luke Mahler. "A simple hierarchical model for heterogeneity in the evolutionary correlation on a phylogenetic tree." PeerJ 10 (August 18, 2022): e13910. http://dx.doi.org/10.7717/peerj.13910.
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Numerous questions in phylogenetic comparative biology revolve around the correlated evolution of two or more phenotypic traits on a phylogeny. In many cases, it may be sufficient to assume a constant value for the evolutionary correlation between characters across all the clades and branches of the tree. Under other circumstances, however, it is desirable or necessary to account for the possibility that the evolutionary correlation differs through time or in different sections of the phylogeny. Here, we present a method designed to fit a hierarchical series of models for heterogeneity in the evolutionary rates and correlation of two quantitative traits on a phylogenetic tree. We apply the method to two datasets: one for different attributes of the buccal morphology in sunfishes (Centrarchidae); and a second for overall body length and relative body depth in rock- and non-rock-dwelling South American iguanian lizards. We also examine the performance of the method for parameter estimation and model selection using a small set of numerical simulations.
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Tahvonen, Olli, Janne Rämö, and Mikko Mönkkönen. "Economics of mixed-species forestry with ecosystem services." Canadian Journal of Forest Research 49, no. 10 (October 2019): 1219–32. http://dx.doi.org/10.1139/cjfr-2018-0514.
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The Faustmann–Hartman setup is widely established for specifying the economics of forest values besides timber, but it is criticized as restrictive for capturing diversity values. We show that extending the model to cover diversity attributes, i.e., mixed species and internal heterogeneity within species, is not enough to overcome these restrictions. Additionally, it is necessary to extend forest harvesting regimes to cover thinning, continuous cover forestry, and the management of commercially useless trees. Restrictions in the Faustmann–Hartman setup are first shown analytically with optimized thinning but without tree size structures. The empirical significance of these findings is shown by a model that includes four tree species, tree size structures, an extended set of forest management activities, a detailed description of harvesting costs, and a measure for stand diversity as a key factor behind ecosystem services. We show how an optimal harvesting regime, net revenues, wood output, and stand diversity depend on model flexibility, economic parameters, and the valuation of ecosystem services. In a setup allowing flexible management regimes, the costs of reaching a specified level of ecosystem services are negligible compared with those of the Faustmann–Hartman specification.
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Holder, Mark T., Derrick J. Zwickl, and Christophe Dessimoz. "Evaluating the robustness of phylogenetic methods to among-site variability in substitution processes." Philosophical Transactions of the Royal Society B: Biological Sciences 363, no. 1512 (October 7, 2008): 4013–21. http://dx.doi.org/10.1098/rstb.2008.0162.
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Computer simulations provide a flexible method for assessing the power and robustness of phylogenetic inference methods. Unfortunately, simulated data are often obviously atypical of data encountered in studies of molecular evolution. Unrealistic simulations can lead to conclusions that are irrelevant to real-data analyses or can provide a biased view of which methods perform well. Here, we present a software tool designed to generate data under a complex codon model that allows each residue in the protein sequence to have a different set of equilibrium amino acid frequencies. The software can obtain maximum-likelihood estimates of the parameters of the Halpern and Bruno model from empirical data and a fixed tree; given an arbitrary tree and a fixed set of parameters, the software can then simulate artificial datasets. We present the results of a simulation experiment using randomly generated tree shapes and substitution parameters estimated from 1610 mammalian cytochrome b sequences. We tested tree inference at the amino acid, nucleotide and codon levels and under parsimony, maximum-likelihood, Bayesian and distance criteria (for a total of more than 650 analyses on each dataset). Based on these simulations, nucleotide-level analyses seem to be more accurate than amino acid and codon analyses. The performance of distance-based phylogenetic methods appears to be quite sensitive to the choice of model and the form of rate heterogeneity used. Further studies are needed to assess the generality of these conclusions. For example, fitting parameters of the Halpern Bruno model to sequences from other genes will reveal the extent to which our conclusions were influenced by the choice of cytochrome b . Incorporating codon bias and more sources heterogeneity into the simulator will be crucial to determining whether the current results are caused by a bias in the current simulation study in favour of nucleotide analyses.
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Yrttimaa, Tuomas, Ninni Saarinen, Ville Kankare, Xinlian Liang, Juha Hyyppä, Markus Holopainen, and Mikko Vastaranta. "Investigating the Feasibility of Multi-Scan Terrestrial Laser Scanning to Characterize Tree Communities in Southern Boreal Forests." Remote Sensing 11, no. 12 (June 14, 2019): 1423. http://dx.doi.org/10.3390/rs11121423.
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Terrestrial laser scanning (TLS) has proven to accurately represent individual trees, while the use of TLS for plot-level forest characterization has been studied less. We used 91 sample plots to assess the feasibility of TLS in estimating plot-level forest inventory attributes, namely the stem number (N), basal area (G), and volume (V) as well as the basal area weighed mean diameter (Dg) and height (Hg). The effect of the sample plot size was investigated by using different-sized sample plots with a fixed scan set-up to also observe possible differences in the quality of point clouds. The Gini coefficient was used to measure the variation in tree size distribution at the plot-level to investigate the relationship between stand heterogeneity and the performance of the TLS-based method. Higher performances in tree detection and forest attribute estimation were recorded for sample plots with a low degree of tree size variation. The TLS-based approach captured 95% of the variation in Hg and V, 85% of the variation in Dg and G, and 67% of the variation in N. By increasing the sample plot size, the tree detection rate was decreased, and the accuracy of the estimates, especially G and N, decreased. This study emphasizes the feasibility of TLS-based approaches in plot-level forest inventories in varying southern boreal forest conditions.
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Zhang, C., X. Pan, S. Q. Zhang, H. P. Li, and P. M. Atkinson. "A ROUGH SET DECISION TREE BASED MLP-CNN FOR VERY HIGH RESOLUTION REMOTELY SENSED IMAGE CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 14, 2017): 1451–54. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-1451-2017.
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Recent advances in remote sensing have witnessed a great amount of very high resolution (VHR) images acquired at sub-metre spatial resolution. These VHR remotely sensed data has post enormous challenges in processing, analysing and classifying them effectively due to the high spatial complexity and heterogeneity. Although many computer-aid classification methods that based on machine learning approaches have been developed over the past decades, most of them are developed toward pixel level spectral differentiation, e.g. Multi-Layer Perceptron (MLP), which are unable to exploit abundant spatial details within VHR images. <br><br> This paper introduced a rough set model as a general framework to objectively characterize the uncertainty in CNN classification results, and further partition them into correctness and incorrectness on the map. The correct classification regions of CNN were trusted and maintained, whereas the misclassification areas were reclassified using a decision tree with both CNN and MLP. The effectiveness of the proposed rough set decision tree based MLP-CNN was tested using an urban area at Bournemouth, United Kingdom. The MLP-CNN, well capturing the complementarity between CNN and MLP through the rough set based decision tree, achieved the best classification performance both visually and numerically. Therefore, this research paves the way to achieve fully automatic and effective VHR image classification.
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Pagel, Mark, and Andrew Meade. "Modelling heterotachy in phylogenetic inference by reversible-jump Markov chain Monte Carlo." Philosophical Transactions of the Royal Society B: Biological Sciences 363, no. 1512 (October 7, 2008): 3955–64. http://dx.doi.org/10.1098/rstb.2008.0178.
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The rate at which a given site in a gene sequence alignment evolves over time may vary. This phenomenon—known as heterotachy—can bias or distort phylogenetic trees inferred from models of sequence evolution that assume rates of evolution are constant. Here, we describe a phylogenetic mixture model designed to accommodate heterotachy. The method sums the likelihood of the data at each site over more than one set of branch lengths on the same tree topology. A branch-length set that is best for one site may differ from the branch-length set that is best for some other site, thereby allowing different sites to have different rates of change throughout the tree. Because rate variation may not be present in all branches, we use a reversible-jump Markov chain Monte Carlo algorithm to identify those branches in which reliable amounts of heterotachy occur. We implement the method in combination with our ‘pattern-heterogeneity’ mixture model, applying it to simulated data and five published datasets. We find that complex evolutionary signals of heterotachy are routinely present over and above variation in the rate or pattern of evolution across sites, that the reversible-jump method requires far fewer parameters than conventional mixture models to describe it, and serves to identify the regions of the tree in which heterotachy is most pronounced. The reversible-jump procedure also removes the need for a posteriori tests of ‘significance’ such as the Akaike or Bayesian information criterion tests, or Bayes factors. Heterotachy has important consequences for the correct reconstruction of phylogenies as well as for tests of hypotheses that rely on accurate branch-length information. These include molecular clocks, analyses of tempo and mode of evolution, comparative studies and ancestral state reconstruction. The model is available from the authors' website, and can be used for the analysis of both nucleotide and morphological data.
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Bergstrom, D. M., and C. E. Tweedie. "A Conceptual Model for Integrative Studies of Epiphytes: Nitrogen Utilisation, a Case Study." Australian Journal of Botany 46, no. 2 (1998): 273. http://dx.doi.org/10.1071/bt96104.
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A conceptual, hierarchical model for the study of epiphytes in complex rainforest ecosystems is illustrated. The model incorporates an integrative, multidisciplinary approach with ecological parameters compartmentalised into relevant spatial, temporal and organisational scales. The model can be applied to other spatially complex ecosystems such as coral reefs. An example showing the use of the model is presented. The natural abundance of 15N in three species of epiphyte growing on one tree is examined. In this pilot study, interpretation of a complex data set has been aided by the use of a hierarchical approach. A high degree of spatial heterogeneity in the nitrogen pool within the tree was found, with epiphytes accessing at least three sources of nitrogen: an atmospheric source, a nitrogen fixed source and the phorophyte itself through decomposed litter.
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Ceriani, Luca, Giuseppe Gritti, Luciano Cascione, Maria Cristina Pirosa, Angela Polino, Teresa Ruberto, Anastasios Stathis, et al. "SAKK38/07 study: integration of baseline metabolic heterogeneity and metabolic tumor volume in DLBCL prognostic model." Blood Advances 4, no. 6 (March 20, 2020): 1082–92. http://dx.doi.org/10.1182/bloodadvances.2019001201.
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Abstract Several functional parameters from baseline (18)F-fluorodeoxyglucose positron emission tomography (PET)/computed tomography have been proposed as promising biomarkers of treatment efficacy in diffuse large B-cell lymphoma (DLBCL). We tested their ability to predict outcome in 2 cohorts of DLBCL patients receiving conventional immunochemotherapy (rituximab, cyclophosphamide, doxorubicin hydrochloride, vincristine sulfate, and prednisone [R-CHOP] regimen), either every 14 (R-CHOP14) or 21 days (R-CHOP21). Baseline PET analysis was performed in 141 patients with DLBCL treated with R-CHOP14 in the prospective SAKK38/07 study (NCT00544219) of the Swiss Group for Clinical Cancer Research (testing set). Reproducibility was examined in a validation set of 113 patients treated with R-CHOP21. In the SAKK38/07 cohort, progression-free survival (PFS) at 5 years was 83% for patients with low metabolic tumor volume (MTV) and 59% for those with high MTV (hazard ratio [HR], 3.4; 95% confidence interval [CI], 1.6-7.0; P = .0005), whereas overall survival (OS) was 91% and 64%, respectively (HR, 4.4; 95% CI, 1.9-10; P = .0001). MTV was the most powerful predictor of outcome also in the validation set. Elevated metabolic heterogeneity (MH) significantly predicted poorer outcomes in the subgroups of patients with elevated MTV. A model integrating MTV and MH identified high-risk patients with shorter PFS (testing set: HR, 5.6; 95% CI, 1.8-17; P < .0001; validation set: HR, 5.6; 95% CI, 1.7-18; P = .0002) and shorter OS (testing set: HR, 9.5; 95% CI, 1.7-52; P < .0001; validation set: HR, 7.6; 95% CI, 2.0-28; P = .0003). This finding was confirmed by an unsupervised regression tree analysis indicating that prognostic models based on MTV and MH may allow early identification of refractory patients who might benefit from treatment intensification. This trial was registered at www.clinicaltrials.gov as #NCT00544219.
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Gimotty, Phyllis A., David E. Elder, Douglas L. Fraker, Jeffrey Botbyl, Kimberly Sellers, Rosalie Elenitsas, Michael E. Ming, et al. "Identification of High-Risk Patients Among Those Diagnosed With Thin Cutaneous Melanomas." Journal of Clinical Oncology 25, no. 9 (March 20, 2007): 1129–34. http://dx.doi.org/10.1200/jco.2006.08.1463.
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Purpose Most patients with melanoma have microscopically thin (≤ 1 mm) primary lesions and are cured with excision. However, some develop metastatic disease that is often fatal. We evaluated established prognostic factors to develop classification schemes with better discrimination than current American Joint Committee on Cancer (AJCC) staging. Patients and Methods We studied patients with thin melanomas from the US population-based Surveillance, Epidemiology, and End Results (SEER) cancer registry (1988 to 2001; n = 26,291) and those seen by the University of Pennsylvania's Pigmented Lesion Group (PLG; 1972 to 2001; n = 2,389; Philadelphia, PA). AJCC prognostic factors were thickness, anatomic level, ulceration, site, sex, and age; PLG prognostic factors also included a set of biologically based candidate prognostic factors. Recursive partitioning was used to develop a SEER-based classification tree that was validated using PLG data. Next, a new PLG-based classification tree was developed using the expanded set of prognostic factors. Results The SEER-based classification tree identified additional criteria to explain survival heterogeneity among patients with thin, nonulcerated lesions; 10-year survival rates ranged from 89.1% to 99%. The new PLG-based tree identified groups using level, tumor cell mitotic rate, and sex. With survival rates from 83.4% to 100%, it had better discrimination. Conclusion Prognostication and related clinical decision making in the majority of patients with melanoma can be improved now using the validated, SEER-based classification. Tumor cell mitotic rate should be incorporated into the next iteration of AJCC staging.
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Zhao, Zhifeng, Mingyuan Chen, Heng Fan, and Nailu Zhang. "Data Analysis and Knowledge Mining of Machine Learning in Soil Corrosion Factors of the Pipeline Safety." Computational Intelligence and Neuroscience 2022 (May 6, 2022): 1–9. http://dx.doi.org/10.1155/2022/9523878.
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The purpose of this research is to enhance the ability of data analysis and knowledge mining in soil corrosion factors of the pipeline. According to its multifactor characteristics, the rough set algorithm is directly used to analyze and process the observation data without considering any prior information. We apply rough set algorithm to delete the duplicate same information and redundant items and simplify the condition attributes and decision indicators from the decision table. Combined with the simplified index, the decision tree method is used to analyze the root node and branch node of it, and the knowledge decision model is constructed. With the Python machine learning language and PyCharm Community Edition software, the algorithm functions of rough set and decision tree are realized, so as to carry out artificial intelligence analysis and judgment of the soil corrosion factor data in pipeline. Taking the area of loam soil corrosion as an example, the data analysis and knowledge mining of its multifactors original data are carried out through the model. The example verifies that the evaluation and classification rules of the model meet the requirements, and there are no problems such as inconsistency and heterogeneity. It provides decision-making service and theoretical basis for the soil corrosion management of pipeline.
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Souza, Cléber Rodrigo De, Polyanne Aparecida Coelho, Henrique Faria de Oliveira, Jean Daniel Morel, Felipe De Carvalho Araújo, Maria Teresa Rodrigues Costa, Alisson Borges Miranda Santos, Gislene De Carvalho Castro, and Rubens Manoel dos Santos. "Floristic-structural relationships between Canga ferruginous tree communities and adjacent vegetation types." Acta Scientiarum. Biological Sciences 40, no. 1 (April 1, 2018): 39466. http://dx.doi.org/10.4025/actascibiolsci.v40i1.39466.
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Our objective was to evaluate the floristic and structural relationships between Canga tree communities and other vegetation types inserted in the physiognomic units set, in order to test the hypothesis that these communities have unique characteristics due to their specific environmental conditions. For this, we compared the structural and floristic attributes of Canga vegetation with adjacent semideciduous seasonal forest, Savanna and ecotone areas, evaluating the similarity in its behavior. Our results demonstrate the existence of distinct relationships among vegetation types in relation to different perspectives, related to macro-scale environmental attributes and to the phytogeographic context. In general, Canga tree vegetation has its structure associated with open vegetation types, such as the Cerrado (Savanna) and its composition associated with forest vegetation types, constituted by a community with specific ecological characteristics. Our results suggest the presence of ferruginous soils as a factor that contributes to environmental and ecological heterogeneity in vegetation matrices.
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Minh, Bui Quang, Cuong Cao Dang, Le Sy Vinh, and Robert Lanfear. "QMaker: Fast and Accurate Method to Estimate Empirical Models of Protein Evolution." Systematic Biology 70, no. 5 (February 22, 2021): 1046–60. http://dx.doi.org/10.1093/sysbio/syab010.
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Abstract Amino acid substitution models play a crucial role in phylogenetic analyses. Maximum likelihood (ML) methods have been proposed to estimate amino acid substitution models; however, they are typically complicated and slow. In this article, we propose QMaker, a new ML method to estimate a general time-reversible $Q$ matrix from a large protein data set consisting of multiple sequence alignments. QMaker combines an efficient ML tree search algorithm, a model selection for handling the model heterogeneity among alignments, and the consideration of rate mixture models among sites. We provide QMaker as a user-friendly function in the IQ-TREE software package (http://www.iqtree.org) supporting the use of multiple CPU cores so that biologists can easily estimate amino acid substitution models from their own protein alignments. We used QMaker to estimate new empirical general amino acid substitution models from the current Pfam database as well as five clade-specific models for mammals, birds, insects, yeasts, and plants. Our results show that the new models considerably improve the fit between model and data and in some cases influence the inference of phylogenetic tree topologies.[Amino acid replacement matrices; amino acid substitution models; maximum likelihood estimation; phylogenetic inferences.]
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Adamo, Irene, Edgar Ortiz-Malavasi, Robin Chazdon, Priscila Chaverri, Hans ter Steege, and József Geml. "Soil Fungal Community Composition Correlates with Site-Specific Abiotic Factors, Tree Community Structure, and Forest Age in Regenerating Tropical Rainforests." Biology 10, no. 11 (October 31, 2021): 1120. http://dx.doi.org/10.3390/biology10111120.
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Successional dynamics of plants and animals during tropical forest regeneration have been thoroughly studied, while fungal compositional dynamics during tropical forest succession remain unknown, despite the crucial roles of fungi in ecological processes. We combined tree data and soil fungal DNA metabarcoding data to compare richness and community composition along secondary forest succession in Costa Rica and assessed the potential roles of abiotic factors influencing them. We found a strong coupling of tree and soil fungal community structure in wet tropical primary and regenerating secondary forests. Forest age, edaphic variables, and regional differences in climatic conditions all had significant effects on tree and fungal richness and community composition in all functional groups. Furthermore, we observed larger site-to-site compositional differences and greater influence of edaphic and climatic factors in secondary than in primary forests. The results suggest greater environmental heterogeneity and greater stochasticity in community assembly in the early stages of secondary forest succession and a certain convergence on a set of taxa with a competitive advantage in the more persisting environmental conditions in old-growth forests. Our work provides unprecedented insights into the successional dynamics of fungal communities during secondary tropical forest succession.
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Fortin, Michel, and Yves Mauffette. "The suitability of leaves from different canopy layers for a generalist herbivore (Lepidoptera: Lasiocampidae) foraging on sugar maple." Canadian Journal of Forest Research 32, no. 3 (March 1, 2002): 379–89. http://dx.doi.org/10.1139/x01-205.
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Variations of leaf suitability within forest canopies may have important consequences for the biology of phytophagous insects. In this study we examined over 4 consecutive years (19941997), the influence of vertical stratification of leaves within a sugar maple (Acer saccharum Marsh.) stand on biological performance and feeding preference of Malacosoma disstria Hbn. Each year, 10 healthy sugar maple trees and about 15 understory sugar maple seedlings were selected. Leaves were collected from the lower (36 m above ground) and the upper crown (2025 m above ground) sections of the trees and from seedlings. Sampled leaves were set in Petri dishes for insect rearings in controlled environment. The performance of the insect, especially pupal masses and the number of eggs of adult females, was higher when larvae were fed with leaves from the upper crown section of trees. Results for the feeding preference tests showed that larvae of fourth instars consumed more surface area from leaves collected in the upper crown section of the trees. More total nitrogen found in leaves from the upper tree crown could explain the higher performance of this insect. Our results confirm the importance of the heterogeneity in leaf suitability along a vertical stratification in forests by its influence on biological performance and feeding preference of M. disstria.
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Zeller, Laura, Astor Toraño Caicoya, and Hans Pretzsch. "Analyzing the effect of silvicultural management on the trade-off between stand structural heterogeneity and productivity over time." European Journal of Forest Research 140, no. 3 (February 1, 2021): 615–34. http://dx.doi.org/10.1007/s10342-020-01350-z.
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AbstractThis study combined an empirically based simulation with an analysis of the trade-off between structural heterogeneity and stand productivity depending on time, spatial scale, and silvicultural management, whereas volume growth and tree species diversity have been examined in detail, the role of forest structure and its interdependencies with stand productivity has only lately become a stronger research focus. We used the growth simulator SILVA to examine the development of stand structural heterogeneity and its trade-off with stand productivity in age-class versus uneven-aged pure and mixed spruce and beech stands at different spatial scales over 100 years. Those stands were based on typical forest types in Bavaria and were representative of forests in Central Europe. We examined how stand structure and its trade-off with productivity were modified by a multifunctional, a production-oriented, and a set-aside management scenario. The production-oriented management scenario applied to uneven-aged stands led to a reduction in structural heterogeneity per unit of productivity over time. In age-class stands, the production-oriented scenario was able to maintain the initial structural heterogeneity. The structural heterogeneity per unit of productivity increased more strongly with increasing spatial scale in age-class stands compared to uneven-aged stands. Combining forest stand simulation with scenario analyses is an exemplary method for testing the effect of silvicultural management alternatives on forest structure. This approach can later be connected to climate models considering long-term changes in growing conditions and support the planning of multifunctional forests.
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Queiroz, A. C. M., and C. R. Ribas. "Canopy cover negatively affects arboreal ant species richness in a tropical open habitat." Brazilian Journal of Biology 76, no. 4 (May 3, 2016): 864–70. http://dx.doi.org/10.1590/1519-6984.02015.
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Abstract We tested the hypothesis of a negative relationship between vegetation characteristics and ant species richness in a Brazilian open vegetation habitat, called candeial. We set up arboreal pitfalls to sample arboreal ants and measured the following environmental variables, which were used as surrogate of environmental heterogeneity: tree richness, tree density, tree height, circumference at the base of the plants, and canopy cover. Only canopy cover had a negative effect on the arboreal ant species richness. Vegetation characteristics and plant species composition are probably homogeneous in candeial, which explains the lack of relationship between other environmental variables and ant richness. Open vegetation habitats harbor a large number of opportunistic and generalist species, besides specialist ants from habitats with high temperatures. An increase in canopy cover decreases sunlight incidence and may cause local microclimatic differences, which negatively affect the species richness of specialist ants from open areas. Canopy cover regulates the richness of arboreal ants in open areas, since only few ant species are able to colonize sites with dense vegetation; most species are present in sites with high temperature and luminosity. Within open vegetation habitats the relationship between vegetation characteristics and species richness seems to be the opposite from closed vegetation areas, like forests.
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Worku, Mekuannet, Tefera Berihun Taw, and Malaku Tarekegn. "Economic Valuation of Local Environmental Amenities: A Case Study of Bahir Dar City, Amhara Regional State, Ethiopia." April 2021, Volume 10(2) (April 30, 2021): 698–711. http://dx.doi.org/10.46222/ajhtl.19770720-127.
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This study estimates the economic value of local environmental amenities in Bahir Dar city which is one of the tourist attraction sites in Ethiopia. The study employed choice experiment valuation method by identifying four environmental amenities attributes (Lake Tana, urban park, palm tree and street cleanliness). The study used probability multi-stage random sampling technique. The analysis was based on primary data surveyed from households in Bahir Dar city. The study presented nine choices set for each respondent; each choice set has three alternatives including the status quo option. The study employed a mixed logit model. The result showed that all improved attribute levels have positive signs and statistically significant. As expected and consistent with economic theory the monetary cost has negative signs and significant. The mixed logit model showed that there is preference heterogeneity in some attribute levels. Based on the finding, the study recommends that the city administration and the concerned body expected to implement the hypothetical policy scenario so as to improve environmental amenity.
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Liu, Hongliang, Jinpeng Tan, Kyongson Jon, and Wensheng Zhu. "A New Case-Mix Classification Method for Medical Insurance Payment." Mathematics 10, no. 10 (May 11, 2022): 1640. http://dx.doi.org/10.3390/math10101640.
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Rapidly rising medical expenses can be controlled by a well-designed medical insurance payment system with the ability to ensure the stability and development of medical insurance funds. At present, China is in the stage of exploring the reform of the medical insurance payment system. One of the significant tasks is to establish an appropriate reimbursement model for disease treatment expenses, so as to meet the needs of patients for medical services. In this paper, we propose a case-mixed decision tree method that considers the homogeneity within the same case subgroup as well as the heterogeneity between different case subgroups. The optimal case mix is determined by maximizing the inter-group difference and minimizing the intra-group difference. In order to handle the instability of the tree-based method with a small amount of data, we propose a multi-model ensemble decision tree method. This method first extracts and merges the inherent rules of the data by the stacking-based ensemble learning method, then generates a new sample set by aggregating the original data with the additional samples obtained by applying these rules, and finally trains the case-mix decision tree with the augmented dataset. The proposed method ensures the interpretability of the grouping rules and the stability of the grouping at the same time. The experimental results on real-world data demonstrate that our case-mix method can provide reasonable medical insurance payment standards and the appropriate medical insurance compensation payment for different patient groups.
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Návar, José. "Pan tropical biomass equations for Mexico's dry forests." Agronomía Colombiana 32, no. 3 (September 1, 2014): 367–76. http://dx.doi.org/10.15446/agron.colomb.v32n3.45627.
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This study reports a set of robust regional M-tree allometric equations for Mexico's tropical dry forests and their application to a forest inventory dataset for the States of Durango and Sinaloa, Mexico. Calculated M data from 15 reported equations were fitted, applied and validated for regional and global models. Proposed theoretical models, empirically derived equations, as well as global and local reported equations were fitted and applied to calculated M-tree data using wood specific gravity, diameter at breast height, and top height as exogenous variables. Empirically-derived, computer-based equations assessed the M-tree evaluations slightly better than the theoretical, the global and the local models. However, the theoretical models projected compatible M-tree values and deserve further attention once wood specific gravity data are collected in the field. Using the best fit equation, mean M plot density values of 30, 41 and 35 Mg ha-1 were estimated from 57 plots (1,600 m2 each), 217 plots (1,000 m2 each) and 166 plots (1,000 m2 each) in the tropical dry forests of the States of Durango, Tiniaquis and Vado Hondo (Sinaloa), respectively. The large sample size, the richness of the tested allometric models, the economic and ecological importance of this data-source, and the spatial coverage of these equations made this dataset uniquely useful for biomass, charcoal, and other bio-energy estimations, as well as for understanding the inherent heterogeneity of the stand-structure in dynamic tropical forest environments.
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Pandey, Akanksha, and Edward L. Braun. "Phylogenetic Analyses of Sites in Different Protein Structural Environments Result in Distinct Placements of the Metazoan Root." Biology 9, no. 4 (March 28, 2020): 64. http://dx.doi.org/10.3390/biology9040064.
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Phylogenomics, the use of large datasets to examine phylogeny, has revolutionized the study of evolutionary relationships. However, genome-scale data have not been able to resolve all relationships in the tree of life; this could reflect, at least in part, the poor-fit of the models used to analyze heterogeneous datasets. Some of the heterogeneity may reflect the different patterns of selection on proteins based on their structures. To test that hypothesis, we developed a pipeline to divide phylogenomic protein datasets into subsets based on secondary structure and relative solvent accessibility. We then tested whether amino acids in different structural environments had distinct signals for the topology of the deepest branches in the metazoan tree. We focused on a dataset that appeared to have a mixture of signals and we found that the most striking difference in phylogenetic signal reflected relative solvent accessibility. Analyses of exposed sites (residues located on the surface of proteins) yielded a tree that placed ctenophores sister to all other animals whereas sites buried inside proteins yielded a tree with a sponge+ctenophore clade. These differences in phylogenetic signal were not ameliorated when we conducted analyses using a set of maximum-likelihood profile mixture models. These models are very similar to the Bayesian CAT model, which has been used in many analyses of deep metazoan phylogeny. In contrast, analyses conducted after recoding amino acids to limit the impact of deviations from compositional stationarity increased the congruence in the estimates of phylogeny for exposed and buried sites; after recoding amino acid trees estimated using the exposed and buried site both supported placement of ctenophores sister to all other animals. Although the central conclusion of our analyses is that sites in different structural environments yield distinct trees when analyzed using models of protein evolution, our amino acid recoding analyses also have implications for metazoan evolution. Specifically, our results add to the evidence that ctenophores are the sister group of all other animals and they further suggest that the placozoa+cnidaria clade found in some other studies deserves more attention. Taken as a whole, these results provide striking evidence that it is necessary to achieve a better understanding of the constraints due to protein structure to improve phylogenetic estimation.
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Falco, Gennaro, and Kristen M. Waring. "Community Classification of Piñon-Juniper Vegetation in the Four Corners Region, USA." Forest Science 66, no. 6 (September 10, 2020): 687–99. http://dx.doi.org/10.1093/forsci/fxaa024.
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Abstract Piñon-juniper is one of the most common vegetation types in the Four Corners states of the western United States (Arizona, Colorado, New Mexico, and Utah). Because of its high degree of community heterogeneity across the landscape, development of a more detailed and statistically supported classification system for piñon-juniper has been requested by regional land managers. We used a USDA Forest Service Forest Inventory and Analysis (FIA) data set from the Four Corners states to develop a statistics-based classification system for piñon-juniper vegetation. Cluster analysis was used to group piñon-juniper FIA data into community classes. Classification and regression tree analysis was then used to develop a model for predicting piñon-juniper community types. To determine which variables contributed most to classifying piñon-juniper FIA data, a random forest analysis was conducted. Results from these analyses support a six-class piñon-juniper community-type model within the Four Corners states. Using the classification tree, membership of FIA piñon-juniper communities can be accurately predicted (r2 = 0.81) using only relative overstory species abundance. Our dominance-based classification system was useful in classifying piñon-juniper community types and could be used in the field to identify broad community types and complement more refined tools available for stand-scale decisionmaking. Study Implications: Piñon-juniper vegetation communities commonly occur in the Four Corners region of the United States. We used a regional data set to develop a statistically based classification system for piñon-juniper communities. We found support for a dominance-based approach supporting initial classification into six community classes. Classes were based on different overstory species dominance patterns, stand structural characteristics (stand density index, basal area [square meters per hectare], trees per hectare, and stand age), and precipitation patterns (mean annual precipitation and monsoonal index) (Table S2). Community type can be predicted using relative overstory abundance to help managers prioritize regional areas (~6,000 acres [2,428 hectares]) for management and predict responses based on precipitation patterns, current understory tree regeneration, and plant community abundance. This system could lead to better planning documents and management decisions on a regional scale to complement more refined tools available for stand-scale management such as plant associations and detailed soil maps.
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Ismaili, Ahmad, Farhad Karami, Omidali Akbarpour, and Abdolhossein Rezaei Nejad. "Estimation of genotypic correlation and heritability of apricot traits, using restricted maximum likelihood in repeated measures data." Canadian Journal of Plant Science 96, no. 3 (June 1, 2016): 439–47. http://dx.doi.org/10.1139/cjps-2015-0253.
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In estimation of genetic parameters in perennial tree species on the basis of analysis of variance (ANOVA), heterogeneity of years and genotype × environment interaction for data sets during the juvenility to maturity life period is ignored. Therefore, a linear mixed model based on restricted maximum likelihood (REML) approximation for modeling of covariance structure of longitudinal data can improve our ability to analyze repeated measures data. In the present research, a modeling of variance-covariance structure by mixed model based on the REML approach has been used for characteristics of 26 apricot genotypes recorded during three years. Fitting unstructured covariance (UN) models for all traits indicated a great heterogeneity of variances among repeated years and the trends of response variables in the genotypes (except for RWC) was due to imperfect correlation of subjects measured in different years. Based on the same structure, positive correlations were estimated among fruit set, potassium content, and yield of pistil in repetitive years, and most traits showed high heritability estimation. To our knowledge, this is the first report in plant that genotypic correlation and heritability and their standard errors are estimated in a repeated measures data over years using REML approximation.
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Betz, Linda, Nora Penzel, and Joseph Kambeitz. "S4. IDENTIFYING HETEROGENEITY IN SYMPTOM NETWORKS IN THE GENERAL POPULATION: A RECURSIVE PARTITIONING APPROACH." Schizophrenia Bulletin 46, Supplement_1 (April 2020): S31. http://dx.doi.org/10.1093/schbul/sbaa031.070.
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Abstract Background Network models of psychopathology have gained increasing ground recently. It is suggested that psychopathology arises from the reciprocal associations between symptoms and other psycho-biological factors. Given the heterogeneity in psychopathological phenomena, it seems likely that subgroups with distinct network structures may emerge given different demographic and environmental risk factors. Thus, the identification of heterogeneity in symptom networks associated with specific variables may promote an understanding of the mechanisms that underlie the relation between environmental factors and psychopathology. Methods We took a recursive partitioning approach based on conditional inference trees that iteratively splits the sample of interest based on a predefined set of covariates to detect subgroups with significantly different network structures, resulting in a network tree. We used general population data from the 2000 and 2007 English National Survey of Psychiatric Morbidity, with a combined sample size of n = 15,983 (age range: 16–95 years, 55.9% female), to model networks of psychotic experiences (hallucinations, persecutory ideation) and affective symptoms (worry, mood instability, depression, anxiety, sleep problems). Split variables explored as sources of heterogeneity in networks were sex, age, and exposure to environmental risk factors (cannabis use in past month, lifetime sexual abuse, lifetime experience of bullying). We used a stop-splitting rule based on Bonferroni-adjusted p-values to determine the final tree size (α = .01). Results Environmental factors were the primary sources of heterogeneity in network structures, with exposure to these factors being linked to more densely connected networks. Globally, cannabis use was associated with particularly strong connections between hallucinations and persecutory ideation, depression and persecutory ideation, and depression and mood instability. In those participants with cannabis use and experiences of sexual abuse, the association between depression and persecutory ideation was particularly strong, and further, strong connections were present between the affective symptoms. Similarly, those with exposure to both cannabis and bullying showed stronger associations involving sleep problems than participants exposed to either bullying or cannabis alone. Exposure to either bullying or sexual abuse without concurrent cannabis use was linked to a strongly connected cluster of worry, anxiety, and depression, with only weak associations to other symptoms. Lastly, the sample was split at 60 years of age. The younger group was divided further by age, with participants younger than 26 years showing stronger associations between hallucinations and persecutory ideation and worry and depression than those older than 26 years. In participants older than 60 years, another split was made by gender: women showed a more densely connected network than men. Discussion Findings from this exploratory analysis document substantial heterogeneity in symptom network structures in a large general population sample. Exposure to risk factors is linked to more strongly connected, probably less resilient symptom networks, with evidence for additive vulnerability given the presence of several risk factors. Exposure to sexual abuse or bullying mainly seems to relate to higher connectivity of affective symptoms, while cannabis use links to higher connection of psychotic symptoms with each other, but also with affective symptoms. The analysis also highlights demographic variables as sources of heterogeneity in symptom networks, pointing to specifically relevant symptom interactions in subgroups of age and gender.
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Mentil, Lorenzo, Corrado Battisti, and Giuseppe Maria Carpaneto. "The older the richer: significant increase in breeding bird diversity along an age gradient of different coppiced woods." Web Ecology 18, no. 2 (October 2, 2018): 143–51. http://dx.doi.org/10.5194/we-18-143-2018.
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Abstract. Forest structural complexity could be a good predictor of overall species diversity. Since tree harvesting has a negative effect on forest structure, it is important to analyse the effects of this disturbance on sensitive groups, as forest birds. In this study, we aimed to shed light on this aspect by analysing a set of univariate metrics in bird communities breeding in three coppiced forest habitats (coppiced of chestnut, coppiced of Turkey oak and high forest of beech) along a gradient in age classes. We hypothesised that, with increasing forest age, (i) breeding bird communities will progressively increase in diversity and, (ii) due to higher habitat heterogeneity due to coppicing, a higher species turnover in the first age classes could appear. In each forest habitat, all the metrics significantly increased, from recently coppiced to more mature forests, due to progressively higher availability of resources and niches along the gradient. When comparing paired forest habitats, abundance and richness were significantly different only in the two oldest age classes, highlighting that responses to different tree composition were more marked in the mature phase. In all forest habitats, species turnover (βw diversity) decreased progressively along the age gradient and was highest in the youngest age classes where many vegetation layers were present. Due to different coppice management practices, growth regime and consequent habitat heterogeneity, chestnuts showed a different pattern when compared to other forest habitats, with an increase in species turnover (βw diversity) at intermediate level. With increasing age of the forests, all the diversity metrics increased and species turnover decreased, highlighting the role of older forests as strategic habitats for highly structured bird communities.
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Zhukov, Olexander, Olga Kunah, Yulia Dubinina, Yulia Zhukova, and Dmytro Ganzha. "The effect of soil on spatial variation of the herbaceous layer modulated by overstorey in an Eastern European poplar-willow forest." Ekológia (Bratislava) 38, no. 3 (September 1, 2019): 253–72. http://dx.doi.org/10.2478/eko-2019-0020.
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AbstractThe tree species composition can influence the dynamics of herbaceous species and enhance the spatial heterogeneity of the soil. But there is very little evidence on how both overstorey structure and soil properties affect the spatial variation of the herb layer. The aim of this study is to evaluate the factors of the soil and overstorey structure by which it is possible to explain the fine-scale variation of herbaceous layer communities in an Eastern European poplar-willow forest. The research was conducted in the “Dnipro-Orils’kiy” Nature Reserve (Ukraine). The research polygon (48°30′51″N, 34°49″02″E) was laid in an Eastern European poplar-willow forest in the floodplain of the River Protich, which is a left inflow of the River Dnipro. The site consists of 7 transects. Each transect was made up of 15 test points. The distance between rows in the site was 3 m. At the site, we established a plot of 45×21 m, with 105 subplots of 3×3 m organized in a regular grid. The adjacent subplots were in close proximity. Vascular plant species lists were recorded at each 3×3 m subplot along with visual estimates of species cover using the nine-degree Braun-Blanquet scale. Within the plot, all woody stems ≥ 1 cm in diameter at breast height were measured and mapped. Dixon’s segregation index was calculated for tree species to quantify their relative spatial mixing. Based on geobotanical descriptions, a phytoindicative assessment of environmental factors according to the Didukh scale was made. The redundancy analysis was used for the analysis of variance in the herbaceous layer species composition. The geographic coordinates of sampling locations were used to generate a set of orthogonal eigenvector-based spatial variables. Two measurements of the overstorey spatial structure were applied: the distances from the nearest tree of each species and the distance based on the evaluation of spatial density of point objects, which are separate trees. In both cases, the distance matrix of sampling locations was calculated, which provided the opportunity to generate eigenvector-based spatial variables. A kernel smoothed intensity function was used to compute the density of the trees’ spatial distribution from the point patterns’ data. Gaussian kernel functions with various bandwidths were used. The coordinates of sampling locations in the space obtained after the conversion of the trees’ spatial distribution densities were used to generate a set of orthogonal eigenvector-based spatial variables, each of them representing a pattern of particular scale within the extent of the bandwidth area structured according to distance and reciprocal placement of the trees. An overall test of random labelling reveals the total nonrandom distribution of the tree stems within the site. The unexplained variation consists of 43.8%. The variation explained solely by soil variables is equal to 15.5%, while the variation explained both by spatial and soil variables is 18.0%. The measure of the overstorey spatial structure, which is based on the evaluation of its density enables us to obtain different estimations depending on the bandwidth. The bandwidth affects the explanatory capacity of the tree stand. A considerable part of the plant community variation explained by soil factors was spatially structured. The orthogonal eigenvector-based spatial variables (dbMEMs) approach can be extended to quantifying the effect of forest structures on the herbaceous layer community. The measure of the overstorey spatial structure, which is based on the evaluation of its density, was very useful in explaining herbaceous layer community variation.
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Ellis, Christopher J., and Sally Eaton. "Climate change refugia: landscape, stand and tree-scale microclimates in epiphyte community composition." Lichenologist 53, no. 1 (January 2021): 135–48. http://dx.doi.org/10.1017/s0024282920000523.
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AbstractThere is growing evidence that species and communities are responding to, and will continue to be affected by, climate change. For species at risk, vulnerability can be reduced by ensuring that their habitat is extensive, connected and provides opportunities for dispersal and/or gene flow, facilitating a biological response through migration or adaptation. For woodland epiphytes, vulnerability might also be reduced by ensuring sufficient habitat heterogeneity, so that microhabitats provide suitable local microclimates, even as the larger scale climate continues to change (i.e. microrefugia). This study used fuzzy set ordination to compare bryophyte and lichen epiphyte community composition to a large-scale gradient from an oceanic to a relatively more continental macroclimate. The residuals from this relationship identified microhabitats in which species composition reflected a climate that was more oceanic or more continental than would be expected given the prevailing macroclimate. Comparing these residuals to features that operate at different scales to create the microclimate (landscape, stand and tree-scale), it was possible to identify how one might engineer microrefugia into existing or new woodland, in order to reduce epiphyte vulnerability to climate change. Multimodel inference was used to identify the most important features for consideration, which included local effects such as height on the bole, angle of bole lean and bark water holding capacity, as well as tree species and tree age, and within the landscape, topographic wetness and physical exposure.
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Magnabosco Marra, Daniel, Niro Higuchi, Susan E. Trumbore, Gabriel H. P. M. Ribeiro, Joaquim dos Santos, Vilany M. C. Carneiro, Adriano J. N. Lima, et al. "Predicting biomass of hyperdiverse and structurally complex central Amazonian forests – a virtual approach using extensive field data." Biogeosciences 13, no. 5 (March 11, 2016): 1553–70. http://dx.doi.org/10.5194/bg-13-1553-2016.
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Abstract. Old-growth forests are subject to substantial changes in structure and species composition due to the intensification of human activities, gradual climate change and extreme weather events. Trees store ca. 90 % of the total aboveground biomass (AGB) in tropical forests and precise tree biomass estimation models are crucial for management and conservation. In the central Amazon, predicting AGB at large spatial scales is a challenging task due to the heterogeneity of successional stages, high tree species diversity and inherent variations in tree allometry and architecture. We parameterized generic AGB estimation models applicable across species and a wide range of structural and compositional variation related to species sorting into height layers as well as frequent natural disturbances. We used 727 trees (diameter at breast height ≥ 5 cm) from 101 genera and at least 135 species harvested in a contiguous forest near Manaus, Brazil. Sampling from this data set we assembled six scenarios designed to span existing gradients in floristic composition and size distribution in order to select models that best predict AGB at the landscape level across successional gradients. We found that good individual tree model fits do not necessarily translate into reliable predictions of AGB at the landscape level. When predicting AGB (dry mass) over scenarios using our different models and an available pantropical model, we observed systematic biases ranging from −31 % (pantropical) to +39 %, with root-mean-square error (RMSE) values of up to 130 Mg ha−1 (pantropical). Our first and second best models had both low mean biases (0.8 and 3.9 %, respectively) and RMSE (9.4 and 18.6 Mg ha−1) when applied over scenarios. Predicting biomass correctly at the landscape level in hyperdiverse and structurally complex tropical forests, especially allowing good performance at the margins of data availability for model construction/calibration, requires the inclusion of predictors that express inherent variations in species architecture. The model of interest should comprise the floristic composition and size-distribution variability of the target forest, implying that even generic global or pantropical biomass estimation models can lead to strong biases. Reliable biomass assessments for the Amazon basin (i.e., secondary forests) still depend on the collection of allometric data at the local/regional scale and forest inventories including species-specific attributes, which are often unavailable or estimated imprecisely in most regions.
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Huancayo Ramos, Katherinne Shirley, Marco Antonio Sotelo Monge, and Jorge Maestre Vidal. "Benchmark-Based Reference Model for Evaluating Botnet Detection Tools Driven by Traffic-Flow Analytics." Sensors 20, no. 16 (August 12, 2020): 4501. http://dx.doi.org/10.3390/s20164501.
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Botnets are some of the most recurrent cyber-threats, which take advantage of the wide heterogeneity of endpoint devices at the Edge of the emerging communication environments for enabling the malicious enforcement of fraud and other adversarial tactics, including malware, data leaks or denial of service. There have been significant research advances in the development of accurate botnet detection methods underpinned on supervised analysis but assessing the accuracy and performance of such detection methods requires a clear evaluation model in the pursuit of enforcing proper defensive strategies. In order to contribute to the mitigation of botnets, this paper introduces a novel evaluation scheme grounded on supervised machine learning algorithms that enable the detection and discrimination of different botnets families on real operational environments. The proposal relies on observing, understanding and inferring the behavior of each botnet family based on network indicators measured at flow-level. The assumed evaluation methodology contemplates six phases that allow building a detection model against botnet-related malware distributed through the network, for which five supervised classifiers were instantiated were instantiated for further comparisons—Decision Tree, Random Forest, Naive Bayes Gaussian, Support Vector Machine and K-Neighbors. The experimental validation was performed on two public datasets of real botnet traffic—CIC-AWS-2018 and ISOT HTTP Botnet. Bearing the heterogeneity of the datasets, optimizing the analysis with the Grid Search algorithm led to improve the classification results of the instantiated algorithms. An exhaustive evaluation was carried out demonstrating the adequateness of our proposal which prompted that Random Forest and Decision Tree models are the most suitable for detecting different botnet specimens among the chosen algorithms. They exhibited higher precision rates whilst analyzing a large number of samples with less processing time. The variety of testing scenarios were deeply assessed and reported to set baseline results for future benchmark analysis targeted on flow-based behavioral patterns.
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Chen, Ziwei, Fuzhou Gong, Lin Wan, and Liang Ma. "RobustClone: a robust PCA method for tumor clone and evolution inference from single-cell sequencing data." Bioinformatics 36, no. 11 (March 11, 2020): 3299–306. http://dx.doi.org/10.1093/bioinformatics/btaa172.
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Abstract Motivation Single-cell sequencing (SCS) data provide unprecedented insights into intratumoral heterogeneity. With SCS, we can better characterize clonal genotypes and reconstruct phylogenetic relationships of tumor cells/clones. However, SCS data are often error-prone, making their computational analysis challenging. Results To infer the clonal evolution in tumor from the error-prone SCS data, we developed an efficient computational framework, termed RobustClone. It recovers the true genotypes of subclones based on the extended robust principal component analysis, a low-rank matrix decomposition method, and reconstructs the subclonal evolutionary tree. RobustClone is a model-free method, which can be applied to both single-cell single nucleotide variation (scSNV) and single-cell copy-number variation (scCNV) data. It is efficient and scalable to large-scale datasets. We conducted a set of systematic evaluations on simulated datasets and demonstrated that RobustClone outperforms state-of-the-art methods in large-scale data both in accuracy and efficiency. We further validated RobustClone on two scSNV and two scCNV datasets and demonstrated that RobustClone could recover genotype matrix and infer the subclonal evolution tree accurately under various scenarios. In particular, RobustClone revealed the spatial progression patterns of subclonal evolution on the large-scale 10X Genomics scCNV breast cancer dataset. Availability and implementation RobustClone software is available at https://github.com/ucasdp/RobustClone. Contact lwan@amss.ac.cn or maliang@ioz.ac.cn Supplementary information Supplementary data are available at Bioinformatics online.
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Hoffmann, Janik, Javier Muro, and Olena Dubovyk. "Predicting Species and Structural Diversity of Temperate Forests with Satellite Remote Sensing and Deep Learning." Remote Sensing 14, no. 7 (March 29, 2022): 1631. http://dx.doi.org/10.3390/rs14071631.
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Anthropogenically-driven climate change, land-use changes, and related biodiversity losses are threatening the capability of forests to provide a variety of valuable ecosystem services. The magnitude and diversity of these services are governed by tree species richness and structural complexity as essential regulators of forest biodiversity. Sound conservation and sustainable management strategies rely on information from biodiversity indicators that is conventionally derived by field-based, periodical inventory campaigns. However, these data are usually site-specific and not spatially explicit, hampering their use for large-scale monitoring applications. Therefore, the main objective of our study was to build a robust method for spatially explicit modeling of biodiversity variables across temperate forest types using open-access satellite data and deep learning models. Field data were obtained from the Biodiversity Exploratories, a research infrastructure platform that supports ecological research in Germany. A total of 150 forest plots were sampled between 2014 and 2018, covering a broad range of environmental and forest management gradients across Germany. From field data, we derived key indicators of tree species diversity (Shannon Wiener Index) and structural heterogeneity (standard deviation of tree diameter) as proxies of forest biodiversity. Deep neural networks were used to predict the selected biodiversity variables based on Sentinel-1 and Sentinel-2 images from 2017. Predictions of tree diameter variation achieved good accuracy (r2 = 0.51) using Sentinel-1 winter-based backscatter data. The best models of species diversity used a set of Sentinel-1 and Sentinel-2 features but achieved lower accuracies (r2 = 0.25). Our results demonstrate the potential of deep learning and satellite remote sensing to predict forest parameters across a broad range of environmental and management gradients at the landscape scale, in contrast to most studies that focus on very homogeneous settings. These highly generalizable and spatially continuous models can be used for monitoring ecosystem status and functions, contributing to sustainable management practices, and answering complex ecological questions.
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Potterf, Mária, Kyle Eyvindson, Clemens Blattert, Daniel Burgas, Ryan Burner, Jörg G. Stephan, and Mikko Mönkkönen. "Interpreting wind damage risk–how multifunctional forest management impacts standing timber at risk of wind felling." European Journal of Forest Research 141, no. 2 (March 22, 2022): 347–61. http://dx.doi.org/10.1007/s10342-022-01442-y.
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AbstractLandscape multifunctionality, a widely accepted challenge for boreal forests, aims to simultaneously provide timber, non-timber ecosystem services, and shelter for biodiversity. However, multifunctionality requires the use of novel forest management regimes optimally combined over the landscape, and an increased share of sets asides. It remains unclear how this combination will shape stand vulnerability to wind disturbances and exposed timber volume. We combined forest growth simulations and multi-objective optimization to create alternative landscape level forest management scenarios. Management choices were restricted to 1) rotation forestry, 2) continuous cover forestry, and 3) all regimes allowed over a harvest intensity gradient from completely set aside landscapes to maximal economic gain. Estimates for the stands’ structural and environmental characteristics were used to predict the stand level wind damage probability. We evaluated averaged wind-exposed standing timber volume and changing forest structure under management scenarios. Intensive rotation forestry reduced tree heights and wind damage risk, but also reduced landscape multifunctionality. Conversely, continuous cover forestry maintained multifunctionality but increased wind damage probability due to taller trees and higher thinning frequency. Overall, continuous cover forestry lowers the total volume of wind exposed timber at any given time compared with rotation forestry. Nevertheless, a selective application of rotation forestry contributes to high economic gains and increases landscape heterogeneity. A combination of management approaches across landscapes provides an efficient way to reduce the amount of wind-exposed timber volume while also increasing habitat for vertebrate and non-vertebrate species and satisfying high timber demands.
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Chen, Ziwei, Shaokun An, Xiangqi Bai, Fuzhou Gong, Liang Ma, and Lin Wan. "DensityPath: an algorithm to visualize and reconstruct cell state-transition path on density landscape for single-cell RNA sequencing data." Bioinformatics 35, no. 15 (December 7, 2018): 2593–601. http://dx.doi.org/10.1093/bioinformatics/bty1009.
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Abstract Motivation Visualizing and reconstructing cell developmental trajectories intrinsically embedded in high-dimensional expression profiles of single-cell RNA sequencing (scRNA-seq) snapshot data are computationally intriguing, but challenging. Results We propose DensityPath, an algorithm allowing (i) visualization of the intrinsic structure of scRNA-seq data on an embedded 2-d space and (ii) reconstruction of an optimal cell state-transition path on the density landscape. DensityPath powerfully handles high dimensionality and heterogeneity of scRNA-seq data by (i) revealing the intrinsic structures of data, while adopting a non-linear dimension reduction algorithm, termed elastic embedding, which can preserve both local and global structures of the data; and (ii) extracting the topological features of high-density, level-set clusters from a single-cell multimodal density landscape of transcriptional heterogeneity, as the representative cell states. DensityPath reconstructs the optimal cell state-transition path by finding the geodesic minimum spanning tree of representative cell states on the density landscape, establishing a least action path with the minimum-transition-energy of cell fate decisions. We demonstrate that DensityPath can ably reconstruct complex trajectories of cell development, e.g. those with multiple bifurcating and trifurcating branches, while maintaining computational efficiency. Moreover, DensityPath has high accuracy for pseudotime calculation and branch assignment on real scRNA-seq, as well as simulated datasets. DensityPath is robust to parameter choices, as well as permutations of data. Availability and implementation DensityPath software is available at https://github.com/ucasdp/DensityPath. Supplementary information Supplementary data are available at Bioinformatics online.
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Han, Lina, Zhihong Zeng, Peng Qiu, Jeffrey L. Jorgensen, Duncan H. Mak, Jared K. Burks, Teresa McQueen, et al. "Single-Cell Mass Cytometry Reveals Phenotypic and Functional Heterogeneity In Acute Myeloid Leukemia At Diagnosis and In Remission." Blood 122, no. 21 (November 15, 2013): 1311. http://dx.doi.org/10.1182/blood.v122.21.1311.1311.
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Abstract Acute myeloid leukemia (AML) is organized in a hierarchy with a rare population known as leukemia stem cells (LSC) capable of self-renewal and propagation of the disease. Characterization of the unique phenotypes and complex signaling pathways in LSCs that survive induction chemotherapy is essential for understanding of the mechanisms of chemoresistance and designing the strategies to eliminate residual leukemia clones. In this study, we compared signaling profiles of distinct phenotypic AML subsets in paired bone marrow (BM) samples collected at diagnosis and after achieving the complete remission (CR). Cell surface characteristics and signaling pathways activated within sub-populations of AML samples were defined using the novel technology of time-of-flight mass cytometry (CyTOF) that has the ability to perform up to 100 mutiparameter assays in single cells (Bendall et al, Science 2011). First, we validated CyTOF measurements by performing cross-comparisons of surface markers and intracellular proteins measured in AML cells with traditional multi-parametric flow cytometry (FCM). Frequencies of CD123+CD99+ population within CD34+CD38- cells were 73.7%±1.8% and 78.5%±3.7% by CyTOF and FCM. Patterns of specific activation of the intracellular proteins pSTAT5, pERK1/2 and pAKT by GM-CSF, PMA and SCF, and inhibition by selective kinase inhibitors showed excellent cross-platform consistency between CyTOF, FCM and immunoblotting. Next, mononuclear cells of 5 paired AML (at diagnosis and in CR) and of 3 normal BM (NBM) were stained with 11 cell surface markers (CD34, CD38, CD123, CD99, CD45, CD33, CD117, CD7, CD4, CD90 and CD133) and 8 intracellular markers (p-4EBP1, p-NF¦ÊB, p-STAT3, p-AKT, p-mTOR, p-ERK, p-S6 and p-STAT5). A SPADE (spanning-tree progression analysis of density-normalized events) (Qiu et al, Nat Biotechnol. 2011) tree plot was generated, representing clustered expression of the cell-surface antigens. Boundaries and annotations of the AML cells were manually defined to represent distinct cell subsets (Figure 1). We used the pooled data from NBM samples, which showed identical patterns, as a reference. SPADE analysis revealed several subsets unique to the diagnostic AML samples, which were eliminated by chemotherapy; and phenotypically distinct subsets in diagnostic samples that persisted in CR. Notably, a subset defined by the “traditional LSC” markers (CD45dimCD34+CD38lowCD90-CD33-CD117+; annotation #2) was readily identified in diagnostic samples and was significantly reduced by induction chemotherapy in 2 of the 5 AML samples. In one of these samples we identified a distinct subset co-expressing LSC markers CD45dimCD34+CD38lowCD33-CD117-CD99lowCD133low (annotation #3) that was present in both diagnostic (1.1%) and CR (1.7%) BM; this subset may have contributed to the MRD detected by standard leukemia-associated immunophenotypes.Figure 1The tree plot was generated using 11 cell surface proteins in AML and NBM, and colored by the median intensity of individual markers (CD34 is shown). Phenotypes of each annotation are indicated.Figure 1. The tree plot was generated using 11 cell surface proteins in AML and NBM, and colored by the median intensity of individual markers (CD34 is shown). Phenotypes of each annotation are indicated. We next investigated intracellular signaling pathways in antigen-defined AML subpopulations using CyTOF. Activation of p-AKT and pS6 showed similar pattern in subsets defined by annotations 1, 9 and 10 at diagnosis (Figure 2A), and was largely reduced in the CR BM. In turn, activation of p-4EBP1 and p-mTOR were observed in multiple subsets (#1-5 and 9-11) in all diagnostic AML samples, especially in a subset 1 characterized by the “Progenitor” phenotype, and remained heightened in the CR samples (Figure 2B).Figure 2The heat map of the average expression of intracellular proteins in selected populations from individual samples. (A) Each column represents individual sample, and each row reflects expression of a certain protein for each annotation. (B) Signaling pathways in annotation #1 in individual samples.Figure 2. The heat map of the average expression of intracellular proteins in selected populations from individual samples. (A) Each column represents individual sample, and each row reflects expression of a certain protein for each annotation. (B) Signaling pathways in annotation #1 in individual samples. In summary, using CyTOF and SPADE, we characterized phenotype-specific intracellular signaling pathways in AML samples at diagnosis and in CR. Persistent activation of p-mTOR and p-4EBP1 are identified in the subpopulations of AML progenitors in CR, and may present the potentially targetable pathways in AML. The study is ongoing with prospective CyTOF analysis of a larger set of paired AML samples at diagnosis, CR and relapse coupled with the molecular analysis of the distinct subpopulations. Disclosures: No relevant conflicts of interest to declare.
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Zhang, Duo, Shangping Wang, Yinglong Zhang, Qian Zhang, and Yaling Zhang. "A Secure and Privacy-Preserving Medical Data Sharing via Consortium Blockchain." Security and Communication Networks 2022 (May 18, 2022): 1–15. http://dx.doi.org/10.1155/2022/2759787.
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Medical data sharing is of great significance in promoting smart medicine. However, the heterogeneity of information systems used by various medical institutions makes sharing difficult. In addition, since medical data involves a great deal of sensitive information, sharing it could easily lead to the leakage of personal privacy. Blockchain, gained popularity as a distributed ledger technology, has great potential to connect heterogeneous systems and provides authenticity and integrity guarantees for medical data sharing. Focusing on the issues of medical data sharing and privacy protection, we propose a medical data sharing scheme based on consortium blockchain. To achieve access control, attribute-based access control technique is implemented, where patients preset attribute-specific access policies for their medical records, and record requesters are described by a set of attributes. For patients, we devise a hybrid storage mode to write access policies of medical records on the consortium blockchain network and store encrypted medical records off-chain. Leveraging blockchain and smart contracts, access privilege control and access history tracking can be realized. To enhance the key management, a tree of medical records is constructed for each patient, and by simply keeping the medical record trees, patients can recover their encryption keys at any time. Furthermore, we carry out an extensive analysis to show the high security and efficiency of our proposed scheme. Finally, we build a Quorum consortium blockchain on the Tencent Cloud and deploy smart contracts on the chain to simulate transactions in our scheme. The experiment results indicate the proposed scheme achieves good feasibility.
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Audureau, Etienne, Pierre-Louis Soubeyran, Claudia Martinez-Tapia, Carine A. Bellera, Sylvie Bastuji-Garin, Pascaline Boudou-Rouquette, Muriel Rainfray, et al. "Using machine learning to predict mortality in older patients with cancer: Decision tree and random forest analyses from the ELCAPA and ONCODAGE prospective cohorts." Journal of Clinical Oncology 37, no. 15_suppl (May 20, 2019): 11516. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.11516.
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11516 Background: Accurate prognosis is crucial to decision making in oncology, but remains challenging in older patients due to the heterogeneity of this population and the lack of ability of current models to capture complex interactions between oncological and geriatric predictors. We aimed to develop new predictive algorithms based on machine learning to refine individualized prognosis in older patients with cancer. Methods: Data were collected from 3409 patients ≥70 years referred to geriatric oncology clinics for completion of a geriatric assessment (GA), including 2012 and 1397 patients from the ELCAPA (training set) and ONCODAGE (validation set) French prospective cohorts, respectively. Candidate predictors included baseline oncological and geriatric parameters, G-8 score and routine biological data (CRP/albumin ratio). Prognostic models for 12-months mortality were built using Cox regression model, single decision tree (DT) and random survival forest (RSF). Models performance was compared based on externally validated Harrell’s C-indexes. Results: During the 1-year study period, 875 (43%) and 219 (16%) patients died in the training and validation sets, respectively (mean age: 81±6 / 78±5, women 47% / 70%, metastasis 50% / 34%). Cox model identified 9 independent predictors of mortality: tumor site/metastatic status, anticancer treatment, weight loss > 3kg, drugs > 5, renal failure, increased CRP/Albumin, ECOG-PS≥2, ADL≤5 and altered TGUG. DT identified more complex combinations between features, yielding 16 patient groups with highly differentiated survival, notably depending on the G-8 ( < 10 vs. ≥10 as the root node). RFS had the highest C-index (0.86 [RFS], 0.82 [Cox], 0.81 [DT]), identifying the G-8, CRP/albumin and tumor site/metastasis as the most important features. Conclusions: While Cox modeling confirmed known independent prognostic factors, DT revealed more complex interactions between them and random forest achieved superior prognostic performance by better capturing patient’s complexity. The latter model has been implemented into an interactive web interface for easy and direct use in clinical practice. Clinical trial information: NCT02884375.
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Cintra, Renato, and Jessica Cancelli. "Effects of forest heterogeneity on occurrence and abundance of the scale-backed antbird, Hylophylax poecilinotus (Aves: Thamnophilidae), in the Amazon forest." Revista Brasileira de Zoologia 25, no. 4 (December 2008): 630–39. http://dx.doi.org/10.1590/s0101-81752008000400008.
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Recently, a significant number of studies on neotropical forest bird communities have focused on factors influencing their richness, abundance, and habitat selection. However, few of them have considered populations or individual species, and how habitat structure affects their distribution and abundance. In this study, we investigated how the combined effects of some forest structure components affect the occurrence and abundance of a resident bird species, the scale-backed antbird Hylophylax poecilinotus (Cabanis, 1847). We tested the null hypothesis of no difference between the variation in forest structure components at locations where birds occurred and at locations where they did not. In a pristine Terra Firme forest at the Ducke Reserve, Manaus, we recorded bird occurrence and abundance using mist nets in 56 transects (1 km long each) within a 9 x 9 km trail grid covering 6400 ha. Also in the same 56 transects, we set 50 x 50 m plots and recorded the following seven components of forest structure and landscape: 1) canopy opening, 2) leaf litter, 3) tree abundance, 4) logs, 5) snags, 6) streams, and 7) elevation. We evaluated their effects on avian occurrence and abundance by using models of Multiple Logistic Regression (for bird occurrence) and Multiple Linear Regression (for bird abundance). The results suggested that H. poecilinotus occurred significantly more often in lowland areas, in areas located farther away from streams, and in areas bearing thicker leaf litter. Hylophylax poecilinotus was also more abundant in lowland areas and in areas located further away from streams. Overall, the results indicated that environmental heterogeneity produced by variation in forest structure components affects habitat use by this bird species in the Amazon forest.
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Jiang, Kui, Yujuan Shang, Lei Wang, Zheqing Zhang, Siwei Zhou, Jiancheng Dong, and Huiqun Wu. "A framework for meaningful use of clinical decision model: A diabetic nephropathy prediction modeling based on real world data." Journal of Intelligent & Fuzzy Systems 40, no. 5 (April 22, 2021): 9597–608. http://dx.doi.org/10.3233/jifs-202030.
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This study aims to propose a framework for developing a sharable predictive model of diabetic nephropathy (DN) to improve the clinical efficiency of automatic DN detection in data intensive clinical scenario. Different classifiers have been developed for early detection, while the heterogeneity of data makes meaningful use of such developed models difficult. Decision tree (DT) and random forest (RF) were adopted as training classifiers in de-identified electronic medical record dataset from 6,745 patients with diabetes. After model construction, the obtained classification rules from classifier were coded in a standard PMML file. A total of 39 clinical features from 2159 labeled patients were included as risk factors in DN prediction after data preprocessing. The mean testing accuracy of the DT classifier was 0.8, which was consistent to that of the RF classifier (0.823). The DT classifier was choose to recode as a set of operable rules in PMML file that could be transferred and shared, which indicates the proposed framework of constructing a sharable prediction model via PMML is feasible and will promote the interoperability of trained classifiers among different institutions, thus achieving meaningful use of clinical decision making. This study will be applied to multiple sites to further verify feasibility.
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Pantelis, Athanasios G., Panagiota A. Panagopoulou, and Dimitris P. Lapatsanis. "Artificial Intelligence and Machine Learning in the Diagnosis and Management of Gastroenteropancreatic Neuroendocrine Neoplasms—A Scoping Review." Diagnostics 12, no. 4 (March 31, 2022): 874. http://dx.doi.org/10.3390/diagnostics12040874.
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Neuroendocrine neoplasms (NENs) and tumors (NETs) are rare neoplasms that may affect any part of the gastrointestinal system. In this scoping review, we attempt to map existing evidence on the role of artificial intelligence, machine learning and deep learning in the diagnosis and management of NENs of the gastrointestinal system. After implementation of inclusion and exclusion criteria, we retrieved 44 studies with 53 outcome analyses. We then classified the papers according to the type of studied NET (26 Pan-NETs, 59.1%; 3 metastatic liver NETs (6.8%), 2 small intestinal NETs, 4.5%; colorectal, rectal, non-specified gastroenteropancreatic and non-specified gastrointestinal NETs had from 1 study each, 2.3%). The most frequently used AI algorithms were Supporting Vector Classification/Machine (14 analyses, 29.8%), Convolutional Neural Network and Random Forest (10 analyses each, 21.3%), Random Forest (9 analyses, 19.1%), Logistic Regression (8 analyses, 17.0%), and Decision Tree (6 analyses, 12.8%). There was high heterogeneity on the description of the prediction model, structure of datasets, and performance metrics, whereas the majority of studies did not report any external validation set. Future studies should aim at incorporating a uniform structure in accordance with existing guidelines for purposes of reproducibility and research quality, which are prerequisites for integration into clinical practice.
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Lemos, Helena Lara, José Roberto Rodrigues Pinto, Henrique Augusto Mews, and Eddie Lenza. "Structure and floristic relationships between Cerrado sensu stricto sites on two types of substrate in northern Cerrado, Brazil." Biota Neotropica 13, no. 4 (December 2013): 121–32. http://dx.doi.org/10.1590/s1676-06032013000400013.
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We described and compared the floristic composition, richness, species diversity and structure of the tree-shrub component in pairs of Typical Cerrado (Cerrado Típico) and rocky outcrop Cerrado (Cerrado Rupestre) in two localities in Tocantins State. In each locality, we set up 10 plots of 20 × 50 m at a site, the Cerrado Típico and other Cerrado Rupestre, and sampled the individuals with Db30cm ≥ 5 cm. The rocky outcrop Cerrado did not present any trend towards lower richness and basal area compared to the Cerrado on deep soil. Few species occurred across the four sites and only two important species (Anacardium occidentale and Qualea parviflora) in the four vegetation structure were common to both environments assessed. Furthermore, the occurrence of habitat-specialist species of rocky outcrops and high altitudes (Mimosa claussenii, Tibouchina papyrus, Schwartzia adamantium and Wunderlichia cruelsiana) and the high dissimilarity among sites suggest that altitude is the main responsible for the floristic dissimilarity, followed by the influence of substrate type. Therefore, the information with respect to phytophysiognomy type as a parameter to select areas for conservation, by itself, does not effectively ensure biodiversity preservation, owing to the existing flora heterogeneity not only at local but also at regional scale, revealed by the floristic and structural particularity of each site.
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Вечканова, N. Vechkanova, Бушукина, and O. Bushukina. "Adaptive-compensatory reorganization of neural tissue multi-chambered stomach." Journal of New Medical Technologies. eJournal 9, no. 1 (April 17, 2015): 0. http://dx.doi.org/10.12737/7833.
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Based on the comparative complex neuromorphological, morphometric and histochemical studies of ganglia of intermuscular plexus multichamber stomach (its departments: the rumen, reticulum, omasum and abomasum) of lambs of edilbaev’s breed located on the induction effect with ewes (control group) and bottle-fed sheep milk substitute (ZOM) Colvo-Start (experience) of adaptive-compensatory changes in the nervous tissue of the organ are set. Adapting to artificial feeding is manifested in the dairy phase (15 days), a decrease in the growth potential of the main population nerve-cell ganglia scar delayed cytodifferentiation in the ganglia of the grid and of rennet, the asynchronous nature of cell differentiation in a medium-sized portion of the stomach, which leads to increased morphological and functional heterogeneity of the nervous tissue of the stomach multicam . Adaptive-compensatory changes are associated with the hypertrophic growth of large neurons, a decrease in their content in the ganglia, change neuro-glial relations, increased ferningdendro-axonal tree and subsequent to the period of transition to the definitive animal feed, reduction of secondary branching dendrites. Changes due to the changing nature of breastfeeding are a reflection of the complex and dynamic processes of compensatory adaptation, which is peculiar to the entire nervous system as a whole, which has a high potency for self-regulation.
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MASUD, MD MEHEDI, ILUJU KIRINGA, and HASAN URAL. "UPDATE PROCESSING IN INSTANCE-MAPPED P2P DATA SHARING SYSTEMS." International Journal of Cooperative Information Systems 18, no. 03n04 (September 2009): 339–79. http://dx.doi.org/10.1142/s021884300900204x.
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We consider the problem of update processing in a peer-to-peer (P2P) database network where each peer consists of an independently created relational database. We assume that peers store related data, but data has heterogeneity wrt instances and schemas. The differences in schema and data vocabulary are bridged by value correspondences called mapping tables. Peers build an overlay network called acquaintance network, in which each peer may get acquainted with any other peer that stores related data. In this setting, the updates are free to initiate in any peer and are executed over other peers which are acquainted directly or indirectly with the updates initiator. The execution of an update is achieved by translating, through mapping tables, the update into a set of updates that are executed against the acquainted peers. We consider both the soundness and completeness of update translation. When updates are generated and propagated in the network initiated from a peer, a tree is built dynamically called Update Dependency Tree (UDT). The UDT depicts the relationships among the component updates generated from the initial update. We also discuss the issues of the update propagation when a peer is temporarily unavailable or offline. Our propagation mechanism keeps track of a peer when the peer is not available for a certain period of time and once the peer comes back online the system propagates the updates destined to the returning peer to keep it's database synchronized. Moreover, conflict detection and resolution strategies have been proposed for such a dynamic P2P database network. We have implemented and experimentally tested a prototype of our update processing mechanism on a small P2P database network. We show the results of our experiments.