Multivariate Data Analysis for Neuroimaging Data: Overview and Application to Alzheimer’s Disease

As clinical and cognitive neuroscience mature, the need for sophisticated neuroimaging analysis becomes more apparent. Multivariate analysis techniques have recently received increasing attention as they have many attractive features that cannot be easily realized by the more commonly used univariate, voxel-wise, techniques. Multivariate approaches evaluate correlation/covariance of activation across brain regions, rather than proceeding on a voxel-by-voxel basis. Thus, their results can be more easily interpreted as a signature of neural networks. Univariate approaches, on the other hand, cannot directly address functional connectivity in the brain. The covariance approach can also result in greater statistical power when compared with univariate techniques, which are forced to employ very stringent, and often overly conservative, corrections for voxel-wise multiple comparisons. Multivariate techniques also lend themselves much better to prospective application of results from the analysis of one dataset to entirely new datasets. Multivariate techniques are thus well placed to provide information about mean differences and correlations with behavior, similarly to univariate approaches, with potentially greater statistical power and better reproducibility checks. In contrast to these advantages is the high barrier of entry to the use of multivariate approaches, preventing more widespread application in the community. To the neuroscientist becoming familiar with multivariate analysis techniques, an initial survey of the field might present a bewildering variety of approaches that, although algorithmically similar, are presented with different emphases, typically by people with mathematics backgrounds. We believe that multivariate analysis techniques have sufficient potential to warrant better dissemination. Researchers should be able to employ them in an informed and accessible manner. The following article attempts to provide a basic introduction with sample applications to simulated and real-world data sets.     

A Linear Mixed Model Spline Framework for Analysing Time Course ‘Omics’ Data

Time course ‘omics’ experiments are becoming increasingly important to study system-wide dynamic regulation. Despite their high information content, analysis remains challenging. ‘Omics’ technologies capture quantitative measurements on tens of thousands of molecules. Therefore, in a time course ‘omics’ experiment molecules are measured for multiple subjects over multiple time points. This results in a large, high-dimensional dataset, which requires computationally efficient approaches for statistical analysis. Moreover, methods need to be able to handle missing values and various levels of noise. We present a novel, robust and powerful framework to analyze time course ‘omics’ data that consists of three stages: quality assessment and filtering, profile modelling, and analysis. The first step consists of removing molecules for which expression or abundance is highly variable over time. The second step models each molecular expression profile in a linear mixed model framework which takes into account subject-specific variability. The best model is selected through a serial model selection approach and results in dimension reduction of the time course data. The final step includes two types of analysis of the modelled trajectories, namely, clustering analysis to identify groups of correlated profiles over time, and differential expression analysis to identify profiles which differ over time and/or between treatment groups. Through simulation studies we demonstrate the high sensitivity and specificity of our approach for differential expression analysis. We then illustrate how our framework can bring novel insights on two time course ‘omics’ studies in breast cancer and kidney rejection. The methods are publicly available, implemented in the R CRAN package lmms.     

Gene Network and Database for Genes of Wheat’s Resistance to Pathogenic Fungi

An overview describing a gene network that controls the formation of plant responses to diseases caused by pathogenic fungi (http://wwwmgs.bionet.nsc.ru/mgs/gnw/genenet//viewer/Plant%20fungus%20pathogen.html) is presented. The gene network represents the coordinated interactions of genes, proteins, and regulatory molecules, including integrated defense mechanisms that prevent the development of infection, localize the lesion, and minimize damage. The gene network was reconstructed on the basis of literature data, and the elements of the gene network were associated with the records of the PGR database (Pathogenesis-Related Genes, http://srs6.bionet.nsc.ru/srs6bin/cgi-bin/wgetz?-page+top+-newId), where information on plant genes resistant to pathogenic fungi is accumulated. Reconstruction of the gene network allows us to formalize, visualize, and systematize possible mechanisms for the response of plant cells to fungal infection, which may be useful for the planning of experiments and interpretation of experimental data in this field of science.     

Data warehouse for assessing animal health,welfare, risk management and –communication

The objective of this paper is to give an overview of existing databases in Denmark and describe some of the most important of these in relation to establishment of the Danish Veterinary and Food Administrations’ veterinary data warehouse. The purpose of the data warehouse and possible use of the data are described. Finally, sharing of data and validity of data is discussed. There are databases in other countries describing animal husbandry and veterinary antimicrobial consumption, but Denmark will be the first country relating all data concerning animal husbandry, -health and -welfare in Danish production animals to each other in a data warehouse. Moreover, creating access to these data for researchers and authorities will hopefully result in easier and more substantial risk based control, risk management and risk communication by the authorities and access to data for researchers for epidemiological studies in animal health and welfare.     

The NAD World: A New Systemic Regulatory Network for Metabolism and Aging—Sirt1, Systemic NAD Biosynthesis,and Their Importance

For the past several years, it has been demonstrated that the NAD-dependent protein deacetylase Sirt1 and nicotinamide phosphoribosyltransferase (Nampt)-mediated systemic NAD biosynthesis together play a critical role in the regulation of metabolism and possibly aging in mammals. Based on our recent studies on these two critical components, we have developed a hypothesis of a novel systemic regulatory network, named “NAD World”, for mammalian aging. Conceptually, in the NAD World, systemic NAD biosynthesis mediated by intra- and extracellular Nampt functions as a driver that keeps up the pace of metabolism in multiple tissues/organs, and the NAD-dependent deacetylase Sirt1 serves as a universal mediator that executes metabolic effects in a tissue-dependent manner in response to changes in systemic NAD biosynthesis. This new concept of the NAD World provides important insights into a systemic regulatory mechanism that fundamentally connects metabolism and aging and also conveys the ideas of functional hierarchy and frailty for the regulation of metabolic robustness and aging in mammals.     

A Neural Network Model for K(λ) Retrieval and Application to Global K par Monitoring

Accurate estimation of diffuse attenuation coefficients in the visible wavelengths K _d(λ) from remotely sensed data is particularly challenging in global oceanic and coastal waters. The objectives of the present study are to evaluate the applicability of a semi-analytical K _d(λ) retrieval model (SAKM) and Jamet’s neural network model (JNNM), and then develop a new neural network K _d(λ) retrieval model (NNKM). Based on the comparison of K _d(λ) predicted by these models with in situ measurements taken from the global oceanic and coastal waters, all of the NNKM, SAKM, and JNNM models work well in K _d(λ) retrievals, but the NNKM model works more stable and accurate than both SAKM and JNNM models. The near-infrared band-based and shortwave infrared band-based combined model is used to remove the atmospheric effects on MODIS data. The K _d(λ) data was determined from the atmospheric corrected MODIS data using the NNKM, JNNM, and SAKM models. The results show that the NNKM model produces <30% uncertainty in deriving K _d(λ) from global oceanic and coastal waters, which is 4.88-17.18% more accurate than SAKM and JNNM models. Furthermore, we employ an empirical approach to calculate K _par from the NNKM model-derived diffuse attenuation coefficient at visible bands (443, 488, 555, and 667 nm). The results show that our model presents a satisfactory performance in deriving K _par from the global oceanic and coastal waters with 20.2% uncertainty. The K _par are quantified from MODIS data atmospheric correction using our model. Comparing with field measurements, our model produces ~31.0% uncertainty in deriving K _par from Bohai Sea. Finally, the applicability of our model for general oceanographic studies is briefly illuminated by applying it to climatological monthly mean remote sensing reflectance for time ranging from July, 2002- July 2014 at the global scale. The results indicate that the high K _d(λ) and K _par values are usually found around the coastal zones in the high latitude regions, while low K _d(λ) and K _par values are usually found in the open oceans around the low-latitude regions. These results could improve our knowledge about the light field under waters at either the global or basin scales, and be potentially used into general circulation models to estimate the heat flux between atmosphere and ocean.     

Estimating Temporal Transmission Parameters from Infectious Disease Household Data, with Application to Taiwan SARS Data

Taking households having at least one infective as standard units and considering both a within-household infection rate and a global infection rate, we propose a Bayesian two level mixing S-I-R (susceptible-infective-removed) counting process model in which the transmission parameters may change over time and the parameters of interest are the within-household infection rate and the removal rate. Customized Markov chain Monte Carlo methods are developed for generating samples from the posterior distribution for inference purpose, based only on the removal times. The numerical performance of this method is examined in a simulation study. Applying this method to 2003 Taiwan SARS data, we find that the within-household infection rate decreases, the removal rate increases and their ratio is less than one and decreases significantly during the epidemic. This method allows the estimation of these parameters during the epidemic. For a rapidly transmitted disease, it provides a method to nearly real-time tracking of infection measures.     

A Jump-Growth Model for Predator–Prey Dynamics: Derivation and Application to Marine Ecosystems

This paper investigates the dynamics of biomass in a marine ecosystem. A stochastic process is defined in which organisms undergo jumps in body size as they catch and eat smaller organisms. Using a systematic expansion of the master equation, we derive a deterministic equation for the macroscopic dynamics, which we call the deterministic jump-growth equation, and a linear Fokker–Planck equation for the stochastic fluctuations. The McKendrick–von Foerster equation, used in previous studies, is shown to be a first-order approximation, appropriate in equilibrium systems where predators are much larger than their prey. The model has a power-law steady state consistent with the approximate constancy of mass density in logarithmic intervals of body mass often observed in marine ecosystems. The behaviours of the stochastic process, the deterministic jump-growth equation, and the McKendrick–von Foerster equation are compared using numerical methods. The numerical analysis shows two classes of attractors: steady states and travelling waves.     

Network analysis to assess landscape connectivity trends: Application to European forests (1990–2000)

Landscape networks and ecosystems worldwide are undergoing changes that may impact in different ways relevant ecological processes such as gene flow, pollination, or wildlife dispersal. A myriad of indices have been developed to characterize landscape patterns, but not all of them are equally suited to evaluate temporal changes in landscape connectivity as is increasingly needed for biodiversity monitoring and operational indicator delivery. Relevant advancements in this direction have been recently proposed based on graph theoretical methods to analyze landscape network connectivity and on the measurement of habitat availability at the landscape scale. Building from these developments, we modify a recent index and present the equivalent connected area (ECA) index, defined as the size of a single patch (maximally connected) that would provide the same probability of connectivity than the actual habitat pattern in the landscape. The temporal changes in ECA can be directly compared with the changes in total habitat area. This allows for additional and straightforward insights on the degree to which the gains or losses in habitat amount can be beneficial or deleterious by affecting landscape elements that uphold connectivity in a wider landscape context. We provide a demonstrative example of application and interpretation of this index and approach to monitor changes in functional landscape connectivity. We focus on the trends in European forests at the province level in the period 1990–2000 from Corine land cover data, considering both changes in the forest spatial pattern and in the average permeability of the landscape matrix. The degree of connectivity was rather stable over most of the study area, with a slight overall increase in forest connectivity in Europe. However, a few countries and regions concentrated remarkably high changes in the analyzed period, particularly those with a low forest cover. The species traits also affected the responses to landscape pattern changes, which were more prominent for those species with limited dispersal abilities. We conclude discussing the potential of this approach for consistent indicator delivery, as well as the limitations and possibilities of application to a variety of situations, for which the required quantitative tools are freely available as open source projects.     

NORMA: The Network Makeup Artist——A Web Tool for Network Annotation Visualization

The Network Makeup Artist(NORMA) is a web tool for interactive network annotation visualization and topological analysis, able to handle multiple networks and annotations simultaneously. Precalculated annotations(e.g., Gene Ontology, Pathway enrichment, community detection,or clustering results) can be uploaded and visualized in a network, either as colored pie-chart nodes or as color-filled areas in a 2D/3D Venn-diagram-like style. In the case where no annotation exists,algorithms for automated...     

Azido-Coronatine: A Useful Platform for “Click Chemistry”-Mediated Probe Synthesis for Bioorganic Studies

We report on the development of azide-coronatine as a useful platform for azide alkyne cycloaddition (“click chemistry”)-mediated synthesis of molecular probes. (+)-Azido-coronatine was synthesized in 10 steps with 11% yield using improved synthesis of coronafacic acid, in which the highly exo-selective Diels-Alder reaction (endo:exo > 1:25) is the key step. Azido coronatine was as effective as the original coronatine in a stomatal opening assay, and was easily modified to a fluorescein isothiocyanate (FITC)-labeled probe with high yield.     

Statistical considerations for in vitro research: II — Data to presentation

Summary The paper is the second of two papers about statistical considerations that researchers should make while doing in vitro plant biology research. The first paper focused on aspects from developing a plan to do research through the collection of data. This paper continues with information about editing data, handling outliers, analyzing quantitative and qualitative data, comparing treatment means, preparing graphs and tables, and presenting results.     

Sterile Products: Advances and Challenges in Formulation,Manufacturing and Regulatory Aspects—A Regulatory Review Perspective

For several decades, the FDA has undertaken many initiatives to improve the quality and safety of sterile drug products. In recent years, efforts have also been undertaken to accelerate the rate for application approval by adding earlier involvement of microbiology reviewers in drug development. Product and manufacturing process development, as well as safe use and product design, are among the elements of enhanced technical involvement. An overview of the product quality microbiology aspects for sterile drugs is provided.     

Vascular Endothelial Growth Factor +936C/T, –634G/C, –2578C/A,and –1154G/A Polymorphisms with Risk of Preeclampsia: A Meta-Analysis

Background

Emerging evidence showed that VEGF gene polymorphisms are involved in the regulation of VEGF protein expression, thus increasing an individual''s susceptibility to preeclampsia (PE); but individually published results are inconclusive. The aim of this meta-analysis was to investigate the associations between VEGF gene polymorphisms and PE risk.

Methods

A systematic literature search of MEDLINE, Embase, Web of Science, and CNKI (Chinese National Knowledge Infrastructure) databases was conducted. Statistical analyses were performed using STATA 12.0 software and Review manager 5.1. Odds ratios (ORs) with 95% confidence intervals (CIs) were used to assess the strength of associations.

Results

According to the inclusion criteria, 11 case-control studies were finally included in this meta-analysis. A total of 1,069 PE cases and 1,315 controls were included in this study. Our meta-analysis indicated that VEGF +936C/T (T vs. C, OR = 1.52, 95%CI = 1.08–2.12) or −634G/C polymorphism (C vs. G, OR = 1.24, 95% CI = 1.03–1.50) was associated with the risk of PE, whereas there was no association between −2578C/A (A vs. C, OR = 0.98, 95%CI = 0.82–1.16) or −1154G/A (A vs. G, OR = 1.30, 95%CI = 0.94–1.78) polymorphism and PE risk in our study.

Conclusion

Our meta-analysis suggested that VEGF −2578C/A or −1154G/A polymorphism had no association with PE risk in all examined patients, whereas there was an association between VEGF +936C/T or −634G/C polymorphism and risk of PE.     

Night-Time Light Data: A Good Proxy Measure for Economic Activity?

Much research has suggested that night-time light (NTL) can be used as a proxy for a number of variables, including urbanization, density, and economic growth. As governments around the world either collect census data infrequently or are scaling back the amount of detail collected, alternate sources of population and economic information like NTL are being considered. But, just how close is the statistical relationship between NTL and economic activity at a fine-grained geographical level? This paper uses a combination of correlation analysis and geographically weighted regressions in order to examine if light can function as a proxy for economic activities at a finer level. We use a fine-grained geo-coded residential and industrial full sample micro-data set for Sweden, and match it with both radiance and saturated light emissions. We find that the correlation between NTL and economic activity is strong enough to make it a relatively good proxy for population and establishment density, but the correlation is weaker in relation to wages. In general, we find a stronger relation between light and density values, than with light and total values. We also find a closer connection between radiance light and economic activity, than with saturated light. Further, we find the link between light and economic activity, especially estimated by wages, to be slightly overestimated in large urban areas and underestimated in rural areas.     

FreshOmics: A manually curated and standardized –omics database for investigating freshwater microbiomes

Freshwater is a critical resource for human survival but severely threatened by anthropogenic activities and climate change. These changes strongly impact the abundance and diversity of the microbial communities which are key players in the functioning of these aquatic ecosystems. Although widely documented since the emergence of high-throughput sequencing approaches, the information on these natural microbial communities is scattered among thousands of publications and it is therefore difficult to investigate the temporal dynamics and the spatial distribution of microbial taxa within or across ecosystems. To fill this gap and in the FAIR principles context we built a manually curated and standardized microbial freshwater –omics database (FreshOmics). Based on recognized ontologies (ENVO, MIMICS, GO, ISO), FreshOmics describes 29 different types of freshwater ecosystems and uses standardized attributes to depict biological samples, sequencing protocols and article attributes for more than 2487 geographical locations across 71 countries around the world. The database contains 24,808 sequence identifiers (i.e., Run_Id / Exp_ID, mainly from SRA/DDBJ SRA/ENA, GSA and MG-RAST repositories) covering all sequence-based -omics approaches used to investigate bacteria, archaea, microbial eukaryotes, and viruses. Therefore, FreshOmics allows accurate and comprehensive analyses of microbial communities to answer questions related to their roles in freshwater ecosystems functioning and resilience, especially through meta-analysis studies. This collection also highlights different sort of errors in published works (e.g., wrong coordinates, sample type, material, spelling).