Demographic model selection using random forests and the site frequency spectrum

Phylogeographic data sets have grown from tens to thousands of loci in recent years, but extant statistical methods do not take full advantage of these large data sets. For example, approximate Bayesian computation (ABC) is a commonly used method for the explicit comparison of alternate demographic histories, but it is limited by the “curse of dimensionality” and issues related to the simulation and summarization of data when applied to next‐generation sequencing (NGS) data sets. We implement here several improvements to overcome these difficulties. We use a Random Forest (RF) classifier for model selection to circumvent the curse of dimensionality and apply a binned representation of the multidimensional site frequency spectrum (mSFS) to address issues related to the simulation and summarization of large SNP data sets. We evaluate the performance of these improvements using simulation and find low overall error rates (~7%). We then apply the approach to data from Haplotrema vancouverense, a land snail endemic to the Pacific Northwest of North America. Fifteen demographic models were compared, and our results support a model of recent dispersal from coastal to inland rainforests. Our results demonstrate that binning is an effective strategy for the construction of a mSFS and imply that the statistical power of RF when applied to demographic model selection is at least comparable to traditional ABC algorithms. Importantly, by combining these strategies, large sets of models with differing numbers of populations can be evaluated.     

Bioinformatics and data mining in proteomics

Proteomic studies involve the identification as well as qualitative and quantitative comparison of proteins expressed under different conditions, and elucidation of their properties and functions, usually in a large-scale, high-throughput format. The high dimensionality of data generated from these studies will require the development of improved bioinformatics tools and data-mining approaches for efficient and accurate data analysis of biological specimens from healthy and diseased individuals. Mining large proteomics data sets provides a better understanding of the complexities between the normal and abnormal cell proteome of various biological systems, including environmental hazards, infectious agents (bioterrorism) and cancers. This review will shed light on recent developments in bioinformatics and data-mining approaches, and their limitations when applied to proteomics data sets, in order to strengthen the interdependence between proteomic technologies and bioinformatics tools.     

Bioinformatics and data mining in proteomics

Proteomic studies involve the identification as well as qualitative and quantitative comparison of proteins expressed under different conditions, and elucidation of their properties and functions, usually in a large-scale, high-throughput format. The high dimensionality of data generated from these studies will require the development of improved bioinformatics tools and data-mining approaches for efficient and accurate data analysis of biological specimens from healthy and diseased individuals. Mining large proteomics data sets provides a better understanding of the complexities between the normal and abnormal cell proteome of various biological systems, including environmental hazards, infectious agents (bioterrorism) and cancers. This review will shed light on recent developments in bioinformatics and data-mining approaches, and their limitations when applied to proteomics data sets, in order to strengthen the interdependence between proteomic technologies and bioinformatics tools.     

In vitro and in vivo immunosuppressive effects of supernatants from human choriocarcinoma cell lines

Local immunosuppression mediated by placental suppressor factors may contribute to the absence of consistently demonstrable cellular immunity against the fetus. In this context, we have investigated the immunosuppressive capabilities of supernatants from human trophoblastic choriocarcinoma cell lines (HCS) by testing the effects of HCS on immune responses in vitro and in vivo in the human and murine systems. HCS suppresses mitogen-induced proliferation and mixed lymphocyte reactions in humans and in mice, as well as antigen-induced T cell proliferation in mice. HCS also suppresses the in vivo response of mice to allogeneic cells. Furthermore, HCS when injected intraperitoneally causes the induction of suppressor cells in mice which in turn prevent the mounting of an allogeneic response in other strains of mice. These results indicate that human choriocarcinoma cell lines secrete a suppressor factor(s) which induces suppression in vitro as well as in vivo through the generation of suppressor cells.     

SVM-based learning control of space robots in capturing operation

In this paper, we presents a novel approach for tracking and catching operation of space robots using learning and transferring human control strategies (HCS). We firstly use an efficient support vector machine (SVM) to parametrize the model of HCS. Then we develop a new SVM-based learning structure to better implement human control strategy learning in tracking and capturing control. The approach is fundamentally valuable in dealing with some problems such as small sample data and local minima, and so on. Therefore this approach is efficient in modeling, understanding and transferring its learning process. The simulation results attest that this approach is useful and feasible in generating tracking trajectory and catching objects autonomously.     

Monitoring the phase space of ecosystems: Concept and examples from the Quillow catchment,Uckermark

Ecosystem research benefits enormously from the fact that comprehensive data sets of high quality, and covering long time periods are now increasingly more available. However, facing apparently complex interdependencies between numerous ecosystem components, there is urgent need rethinking our approaches in ecosystem research and applying new tools of data analysis.The concept presented in this paper is based on two pillars. Firstly, it postulates that ecosystems are multiple feedback systems and thus are highly constrained. Consequently, the effective dimensionality of multivariate ecosystem data sets is expected to be rather low compared to the number of observables. Secondly, it assumes that ecosystems are characterized by continuity in time and space as well as between entities which are often treated as distinct units.Implementing this concept in ecosystem research requires new tools for analysing large multivariate data sets. This study presents some of them, which were applied to a comprehensive water quality data set from a long-term monitoring program in Northeast Germany in the Uckermark region, one of the LTER-D (Long Term Ecological Research network, Germany) sites.The effective dimensionality was assessed by the Correlation Dimension approach as well as by a Principal Component Analysis and was in fact substantially lower than the number of observables. Continuity in time, space and between different types of water bodies was studied by combining Self-Organizing Maps with Sammon's Mapping. Groundwater, kettle hole and stream water samples exhibited some overlap, confirming continuity between different types of water bodies. Clear long-term shifts were found at the stream sampling sites. There was strong evidence that the intensity of single processes had changed at these sites rather than that new processes developed. Thus the more recent data did not occupy new subregions of the phase space of observations.Short-term variability of the kettle hole water samples differed substantially from that of the stream water samples, suggesting different processes generating the dynamics in these two types of water bodies. However, again, this seemed to be due to differing intensities of single processes rather than to completely different processes.We feel that research aiming at elucidating apparently complex interactions in ecosystems could make much more efficient use from now available large monitoring data sets by implementing the suggested concept and using corresponding innovative tools of system analysis.     

Impact of image segmentation on high-content screening data quality for SK-BR-3 cells

Background  

High content screening (HCS) is a powerful method for the exploration of cellular signalling and morphology that is rapidly being adopted in cancer research. HCS uses automated microscopy to collect images of cultured cells. The images are subjected to segmentation algorithms to identify cellular structures and quantitate their morphology, for hundreds to millions of individual cells. However, image analysis may be imperfect, especially for "HCS-unfriendly" cell lines whose morphology is not well handled by current image segmentation algorithms. We asked if segmentation errors were common for a clinically relevant cell line, if such errors had measurable effects on the data, and if HCS data could be improved by automated identification of well-segmented cells.     

Phaedra, a protocol-driven system for analysis and validation of high-content imaging and flow cytometry

High-content screening has brought new dimensions to cellular assays by generating rich data sets that characterize cell populations in great detail and detect subtle phenotypes. To derive relevant, reliable conclusions from these complex data, it is crucial to have informatics tools supporting quality control, data reduction, and data mining. These tools must reconcile the complexity of advanced analysis methods with the user-friendliness demanded by the user community. After review of existing applications, we realized the possibility of adding innovative new analysis options. Phaedra was developed to support workflows for drug screening and target discovery, interact with several laboratory information management systems, and process data generated by a range of techniques including high-content imaging, multicolor flow cytometry, and traditional high-throughput screening assays. The application is modular and flexible, with an interface that can be tuned to specific user roles. It offers user-friendly data visualization and reduction tools for HCS but also integrates Matlab for custom image analysis and the Konstanz Information Miner (KNIME) framework for data mining. Phaedra features efficient JPEG2000 compression and full drill-down functionality from dose-response curves down to individual cells, with exclusion and annotation options, cell classification, statistical quality controls, and reporting.     

A picture is worth a thousand words: Genomics to phenomics in the yeast Saccharomyces cerevisiae

Large scale cell biological experiments are beginning to be applied as a systems-level approach to decipher mechanisms that govern cellular function in health and disease. The use of automated microscopes combined with digital imaging, machine learning and other analytical tools has enabled high-content screening (HCS) in a variety of experimental systems. Successful HCS screens demand careful attention to assay development, data acquisition methods and available genomic tools. In this minireview, we highlight developments in this field pertaining to yeast cell biology and discuss how we have combined HCS with methods for automated yeast genetics (synthetic genetic array (SGA) analysis) to enable systematic analysis of cell biological phenotypes in a variety of genetic backgrounds.     

In-depth analysis of tandem mass spectrometry data from disparate instrument types

Mass spectrometric analyses of protein digests produce large numbers of fragmentation spectra that are not identified by routine database searching strategies. Some of these spectra could be identified by development of improved search engines. However, many of these spectra represent fragmentation of peptide components bearing modifications that are not routinely considered in database searches. Here we present new software within Protein Prospector that allows comprehensive analysis of data sets by analyzing the data at increasing levels of depth. Analysis of published data sets is presented to illustrate that the software is not biased to any instrument types. The results show that these data sets contain many modified peptides. As well as searching for known modification types, Protein Prospector permits the detection and identification of unexpected or novel modifications by searching for any mass shift within a user-specified mass range to any chosen amino acid(s). Several modifications never previously reported in proteomics data were identified in these standard data sets using this mass modification searching approach.     

Effects of a placental hormone, human chorionic somatomammotropin on normal and malignant cells

Studies were made on the effects of human chorionic somatomammotropin (HCS) on normal and malignant cells and tissues in Swiss mice. HCS was found to produce a significant increase in the fresh weight of normal liver, kidney, spleen, testis, ovary and uterus. Total cell counts of leukocytes and erythrocytes were elevated. The percentage of granulocytes in blood was found to be increased and the percentage of lymphocytes was decreased following HCS treatment. HCS stimulated the growth of ascitic Ehrlich's carcinoma and Sarcoma 180, and nucleic acid synthesis by these tumor cells. A depression in the mitogen induced blastogenesis of lymphocytes was also noted following HCS treatment.     

Hollow Carbon Spheres and Their Hybrid Nanomaterials in Electrochemical Energy Storage

Electrochemical energy storage is of extraordinary importance for fulfilling the utilization of renewable and sustainable energy sources. There is an increasing demand for energy storage devices with high energy and power densities, prolonged stability, safety, and low cost. In the past decade, numerous research efforts have been devoted to achieving these requirements, especially in the design of advanced electrode materials. Hollow carbon spheres (HCS) derived nanomaterials combining the advantages of 3D HCS and porous structures have been considered as alternative electrode materials for advanced energy storage applications, due to their unique features such as high surface‐to‐volume ratios, encapsulation capability, together with outstanding chemical and thermal stability. In this review, the authors first present a comprehensive overview of the synthetic strategies of HCS, and elucidate the design and synthesis of HCS‐derived nanomaterials including various types of HCS and their nanohybrids. Additionally, their significant roles as electrode materials for supercapacitors, lithium‐ion or sodium‐ion batteries, and sulfur hosts for lithium sulfur batteries are highlighted. Finally, current challenges in the synthesis of HCS and future directions in HCS‐derived nanomaterials for energy storage applications are proposed.     

The space-lifetime hypothesis: viewing organisms in four dimensions, literally

Much of the debate about alternative scaling exponents may result from unawareness of the dimensionality appropriate for different data and questions; in some cases, analysis has to include a fourth temporal dimension, and in others, it does not. Proportional scaling simultaneously applied to an organism and its generation time, treating the latter as a natural fourth dimension, produces a simple explanation for the 3/4 power in large-scale interspecies comparisons. Analysis of data sets of reduced dimensionality (e.g., data sets constructed such that one or more of the four dimensions are fixed), results in predictably lower metabolic exponents of 2/3 and 1/2 under one and two constraints, respectively. Our space-lifetime view offers a predictive framework that may be useful in developing a more complete mechanistic theory of metabolic scaling.     

Kinetic mechanism of histidine-tagged homocitrate synthase from Saccharomyces cerevisiae

Kinetic data have been collected suggesting a preferred sequential ordered kinetic mechanism for the histidine-tagged homocitrate synthase (HCS) from Saccharomyces cerevisiae with alpha-ketoglutarate binding before AcCoA and CoA released before homocitrate. Oxaloacetate is also a substrate for HCS, but with lower affinity than alpha-ketoglutarate. In agreement with the ordered kinetic mechanism desulfo-CoA is uncompetitive and citrate is competitive vs alpha-ketoglutarate. Varying AcCoA, citrate is a noncompetitive inhibitor as predicted, but CoA is noncompetitive vs AcCoA suggesting binding of CoA to E:homocitrate and E:alpha-ketoglutarate. The product CoA behaves in a manner identical to the dead-end analogue desulfo-CoA, suggesting an E:alpha-ketoglutarate:CoA dead-end complex. Data further suggest an irreversible reaction overall, in agreement with the downhill nature of the reaction as a result of homocitryl-CoA hydrolysis. Fluorescence titration data generally agree with the steady state data, but show finite binding of CoA and AcCoA to free enzyme, suggesting that the mechanism may be random with a high degree of synergism of binding between the reactants.     

High Content Screening as High Quality Assay for Biological Evaluation of Photosensitizers In Vitro

A novel single step assay approach to screen a library of photdynamic therapy (PDT) compounds was developed. Utilizing high content analysis (HCA) technologies several robust cellular parameters were identified, which can be used to determine the phototoxic effects of porphyrin compounds which have been developed as potential anticancer agents directed against esophageal carcinoma. To demonstrate the proof of principle of this approach a small detailed study on five porphyrin based compounds was performed utilizing two relevant esophageal cancer cell lines (OE21 and SKGT-4). The measurable outputs from these early studies were then evaluated by performing a pilot screen using a set of 22 compounds. These data were evaluated and validated by performing comparative studies using a traditional colorimetric assay (MTT). The studies demonstrated that the HCS assay offers significant advantages over and above the currently used methods (directly related to the intracellular presence of the compounds by analysis of their integrated intensity and area within the cells). A high correlation was found between the high content screening (HCS) and MTT data. However, the HCS approach provides additional information that allows a better understanding of the behavior of these compounds when interacting at the cellular level. This is the first step towards an automated high-throughput screening of photosensitizer drug candidates and the beginnings of an integrated and comprehensive quantitative structure action relationship (QSAR) study for photosensitizer libraries.     

Variables associated with peripartum traits in dairy cows. II. Interrelationships among disorders and their effects on intake of feed and on reproductive efficiency

Daily individual voluntary intakes of dry matter (DM % of body weight) and estimated net energy (ENE, Mcal/100 kg) by 89 Holstein cows were compared between day 220 of gestation and day 30 postpartum over a 21-month period. The purpose was to compare effects of diet and health status (control vs. abnormal) on intakes of DM and ENE. The cows were fed either chopped hay, hay crop silage (HCS) or corn silage (CS). Compared to controls, voluntary intakes of DM and ENE were decreased (most to least) in cows with fat cow syndrome (FCS), parturient paresis (PP), mastitis (MST), retained fetal membranes-metritis (RFM-M), and displaced abomasum (DA) prepartum and FCS, DA, PP, RFM-M and MST peripartum. Cows fed hay had a lower incidence of RFM-M (28%) than cows fed HCS (57%) and CS (47%) as well as superior reproductive efficiency subsequently (89% conceived vs. 72% and 78%, respectively).     

The importance of data partitioning and the utility of Bayes factors in Bayesian phylogenetics

As larger, more complex data sets are being used to infer phylogenies, accuracy of these phylogenies increasingly requires models of evolution that accommodate heterogeneity in the processes of molecular evolution. We investigated the effect of improper data partitioning on phylogenetic accuracy, as well as the type I error rate and sensitivity of Bayes factors, a commonly used method for choosing among different partitioning strategies in Bayesian analyses. We also used Bayes factors to test empirical data for the need to divide data in a manner that has no expected biological meaning. Posterior probability estimates are misleading when an incorrect partitioning strategy is assumed. The error was greatest when the assumed model was underpartitioned. These results suggest that model partitioning is important for large data sets. Bayes factors performed well, giving a 5% type I error rate, which is remarkably consistent with standard frequentist hypothesis tests. The sensitivity of Bayes factors was found to be quite high when the across-class model heterogeneity reflected that of empirical data. These results suggest that Bayes factors represent a robust method of choosing among partitioning strategies. Lastly, results of tests for the inclusion of unexpected divisions in empirical data mirrored the simulation results, although the outcome of such tests is highly dependent on accounting for rate variation among classes. We conclude by discussing other approaches for partitioning data, as well as other applications of Bayes factors.