Population Physiology: Leveraging Electronic Health Record Data to Understand Human Endocrine Dynamics

Studying physiology and pathophysiology over a broad population for long periods of time is difficult primarily because collecting human physiologic data can be intrusive, dangerous, and expensive. One solution is to use data that have been collected for a different purpose. Electronic health record (EHR) data promise to support the development and testing of mechanistic physiologic models on diverse populations and allow correlation with clinical outcomes, but limitations in the data have thus far thwarted such use. For example, using uncontrolled population-scale EHR data to verify the outcome of time dependent behavior of mechanistic, constructive models can be difficult because: (i) aggregation of the population can obscure or generate a signal, (ii) there is often no control population with a well understood health state, and (iii) diversity in how the population is measured can make the data difficult to fit into conventional analysis techniques. This paper shows that it is possible to use EHR data to test a physiological model for a population and over long time scales. Specifically, a methodology is developed and demonstrated for testing a mechanistic, time-dependent, physiological model of serum glucose dynamics with uncontrolled, population-scale, physiological patient data extracted from an EHR repository. It is shown that there is no observable daily variation the normalized mean glucose for any EHR subpopulations. In contrast, a derived value, daily variation in nonlinear correlation quantified by the time-delayed mutual information (TDMI), did reveal the intuitively expected diurnal variation in glucose levels amongst a random population of humans. Moreover, in a population of continuously (tube) fed patients, there was no observable TDMI-based diurnal signal. These TDMI-based signals, via a glucose insulin model, were then connected with human feeding patterns. In particular, a constructive physiological model was shown to correctly predict the difference between the general uncontrolled population and a subpopulation whose feeding was controlled.     

Tensor‐structured decomposition improves systems serology analysis

Systems serology provides a broad view of humoral immunity by profiling both the antigen‐binding and Fc properties of antibodies. These studies contain structured biophysical profiling across disease‐relevant antigen targets, alongside additional measurements made for single antigens or in an antigen‐generic manner. Identifying patterns in these measurements helps guide vaccine and therapeutic antibody development, improve our understanding of diseases, and discover conserved regulatory mechanisms. Here, we report that coupled matrix–tensor factorization (CMTF) can reduce these data into consistent patterns by recognizing the intrinsic structure of these data. We use measurements from two previous studies of HIV‐ and SARS‐CoV‐2‐infected subjects as examples. CMTF outperforms standard methods like principal components analysis in the extent of data reduction while maintaining equivalent prediction of immune functional responses and disease status. Under CMTF, model interpretation improves through effective data reduction, separation of the Fc and antigen‐binding effects, and recognition of consistent patterns across individual measurements. Data reduction also helps make prediction models more replicable. Therefore, we propose that CMTF is an effective general strategy for data exploration in systems serology.     

How do insect nuclear ribosomal genes compare to protein-coding genes in phylogenetic utility and nucleotide substitution patterns?

Abstract. The expanding data set on insect molecular systematics allows examination of phylogenetic performance and molecular evolution of different types of gene. Studies combining more than one gene in the same analysis allow examination of the relative contribution and performance of each gene partition and can help inform gene choice for resolving deep and/or problematic divergences. We compared results obtained from analyses of twelve insect data sets in which authors combined one or more nuclear ribosomal genes (28S and/or 18S) with one or more protein-coding genes [elongation factor-1α (EF-1α), histone H3, carbamoylphosphate synthetase domain (CPS domain of CAD, or rudimentary), long-wavelength rhodopsin (LW opsin), glucose-6-phosphate dehydrogenase (G₆pd), phosphoenolpyruvate carboxykinase (PEPCK), arginine kinase, and white]. Data sets examined spanned eight orders of insects (Odonata, Ephemeroptera, Hemiptera, Coleoptera, Trichoptera, Lepidoptera, Diptera and Hymenoptera), providing a broad range of divergence times and taxonomic levels. We estimated the phylogenetic utility of the individual genes (using parsimony methods) and characterized the nucleotide substitution patterns (using Bayesian methods) to ask which type of data is preferable for phylogenetic analysis in insects. Nuclear ribosomal and protein coding genes differed little in our measures of phylogenetic performance and patterns of nucleotide substitution. We recommend combining nuclear ribosomal gene data with nuclear protein-coding gene data because each data set has distinct advantages. We do not recommend using mitochondrial genes for higher-level studies of insect phylogeny because reviewed studies demonstrate substitution patterns that lead to high levels of homoplasy.     

A large population parental care game: Polymorphisms and feedback between patterns of care and the operational sex ratio

This article presents a game theoretic model of parental care which models the feedback between patterns of care and the operational sex ratio. It is assumed here that males can be in one of two states: searching for a mate or breeding (including caring for their offspring). Females can be in one of three states: receptive (searching), non-receptive or breeding. However, these sets of states can be adapted to the physiology of a particular species. The length of time that an individual remains in the breeding state depends on the level of care an individual gives. When in the searching state, individuals find partners at a rate dependent on the proportion of members of the opposite sex searching. These rates are defined to satisfy the Fisher condition that the total number of offspring of males equals the total number of offspring of females. The operational sex ratio is not defined exogenously, but can be derived from the adult sex ratio and the pattern of parental care. Pure strategy profiles and so-called single sex stable polymorphisms, in which behaviour is varied within one sex, are derived analytically. The difference between mixed evolutionarily stable strategies and stable polymorphisms within this framework is highlighted. The effects of various physiological and demographic parameters on patterns of care are considered.     

VizStruct: exploratory visualization for gene expression profiling

MOTIVATION: DNA arrays provide a broad snapshot of the state of the cell by measuring the expression levels of thousands of genes simultaneously. Visualization techniques can enable the exploration and detection of patterns and relationships in a complex data set by presenting the data in a graphical format in which the key characteristics become more apparent. The dimensionality and size of array data sets however present significant challenges to visualization. The purpose of this study is to present an interactive approach for visualizing variations in gene expression profiles and to assess its usefulness for classifying samples. RESULTS: The first Fourier harmonic projection was used to map multi-dimensional gene expression data to two dimensions in an implementation called VizStruct. The visualization method was tested using the differentially expressed genes identified in eight separate gene expression data sets. The samples were classified using the oblique decision tree (OC1) algorithm to provide a procedure for visualization-driven classification. The classifiers were evaluated by the holdout and the cross-validation techniques. The proposed method was found to achieve high accuracy. AVAILABILITY: Detailed mathematical derivation of all mapping properties as well as figures in color can be found as supplementary on the web page http://www.cse.buffalo.edu/DBGROUP/bioinformatics/supplementary/vizstruct. All programs were written in Java and Matlab and software code is available by request from the first author.     

Physiological state-specific models in estimation of recombinant Escherichia coli fermentation performance

Implementation of advanced control strategies in bioprocesses is often hindered by the lack of on-line measurements reflecting the physiological state of the culture. Although a number of techniques have been used to estimate key variables from data monitored on-line, these often do not explicitly take into account changes in physiological state and information on many aspects of physiological state that may not be present in on-line data. Here we demonstrate that data obtained from chemical fingerprinting methods, such as pyrolysis mass spectrometry, can be used to identify changes in the physiological state during cultivation. This information can be utilized for the estimation of the physiological state and can enable physiological state-specific-model development for on-line bioprocess control.     

Small-world connectivity, motif composition, and complexity of fractal neuronal connections

Connection patterns of the cerebral cortex consist of pathways linking neuronal populations across multiple levels of scale, from whole brain regions to local minicolumns. This nested interconnectivity suggests the hypothesis that cortical connections are arranged in fractal or self-similar patterns. We describe a simple procedure to generate fractal connection patterns that aim at capturing the potential self-similarity and hierarchical ordering of neuronal connections. We examine these connection patterns by calculating a broad range of structural measures, including small-world attributes and motif composition, as well as some global measures of functional connectivity, including complexity. As we vary fractal patterns by changing a critical control parameter, we find strongly correlated changes in several structural and functional measures, suggesting that they emerge together and are mutually linked. Measures obtained from some modeled fractal patterns closely resemble those of real neuroanatomical data sets, supporting the original hypothesis.     

Community structure – Ecosystem function relationships in the Congo Basin methane cycle depend on the physiological scale of function

Belowground ecosystem processes can be highly variable and difficult to predict using microbial community data. Here, we argue that this stems from at least three issues: (a) complex covariance structure of samples (with environmental conditions or spatial proximity) can make distinguishing biotic drivers a challenge; (b) communities can control ecosystem processes through multiple mechanisms, making the identification of these controls a challenge; and (c) ecosystem function assessments can be broad in physiological scale, encapsulating multiple processes with unique microbially mediated controls. We test these assertions using methane (CH₄)‐cycling processes in soil samples collected along a wetland‐to‐upland habitat gradient in the Congo Basin. We perform our measurements of function under controlled laboratory conditions and statistically control for environmental covariates to aid in identifying biotic drivers. We divide measurements of microbial communities into four attributes (abundance, activity, composition, and diversity) that represent different forms of community control. Lastly, our process measurements differ in physiological scale, including broader processes (gross methanogenesis and methanotrophy) that involve more mediating groups, to finer processes (hydrogenotrophic methanogenesis and high‐affinity CH₄ oxidation) with fewer mediating groups. We observed that finer scale processes can be more readily predicted from microbial community structure than broader scale processes. In addition, the nature of those relationships differed, with broad processes limited by abundance while fine‐scale processes were associated with diversity and composition. These findings demonstrate the importance of carefully defining the physiological scale of ecosystem function and performing community measurements that represent the range of possible controls on ecosystem processes.     

Broad‐scale ecological implications of ectothermy and endothermy in changing environments

Aim Physiology is emerging as a basis for understanding the distribution and diversity of organisms, and ultimately for predicting their responses to climate change. Here we review how the difference in physiology of terrestrial vertebrate ectotherms (amphibians and reptiles) and endotherms (birds and mammals) is expected to influence broad‐scale ecological patterns. Location Global terrestrial ecosystems. Methods We use data from the literature and modelling to analyse geographic gradients in energy use and thermal limits. We then compare broad‐scale ecological patterns for both groups with expectations stemming from these geographic gradients. Results The differences in thermal physiology between ectotherms and endotherms result in geographically disparate macrophysiological constraints. Field metabolic rate (FMR) is stable or decreases slightly with temperature for endotherms, while it generally increases for ectotherms, leading to opposing latitudinal gradients of expected FMR. Potential activity time is a greater constraint on the distributions of ectotherms than endotherms, particularly at high latitudes. Differences in the primary correlates of abundance and species richness for two representative taxonomic groups are consistent with the consequences of these basic physiological differences. Ectotherm richness is better predicted by temperature, whereas endotherm richness is more strongly associated with primary productivity. Finally, in contrast to endotherms, ectotherm richness is not strongly related to abundance. Main conclusions Differences in thermal physiology affect how organisms interact with and are constrained by their environment, and may ultimately explain differences in the geographic pattern of biodiversity for endotherms and ectotherms. Linking the fields of physiological and broad‐scale ecology should yield a more mechanistic understanding of how biodiversity will respond to environmental change.     

The fingerprint of phantom mutations in mitochondrial DNA data

Phantom mutations are systematic artifacts generated in the course of the sequencing process itself. In sequenced mitochondrial DNA (mtDNA), they generate a hotspot pattern quite different from that of natural mutations in the cell. To identify the telltale patterns of a particular phantom mutation process, one first filters out the well-established frequent mutations (inferred from various data sets with additional coding region information). The filtered data are represented by their full (quasi-)median network, to visualize the character conflicts, which can be expressed numerically by the cube spectrum. Permutation tests are used to evaluate the overall phylogenetic content of the filtered data. Comparison with benchmark data sets helps to sort out suspicious data and to infer features and potential causes for the phantom mutation process. This approach, performed either in the lab or at the desk of a reviewer, will help to avoid errors that otherwise would go into print and could lead to erroneous evolutionary interpretations. The filtering procedure is illustrated with two mtDNA data sets that were severely affected by phantom mutations.     

Atlas versus range maps: robustness of chorological relationships to distribution data types in European mammals

Aim Chorological relationships describe the patterns of distributional overlap among species. In addition to revealing biogeographical structure, the resulting clusters of species with similar geographical distributions can serve as natural units in conservation planning. Here, we assess the extent to which temporal, methodological and taxonomical differences in the source of species’ distribution data can affect the relationships that are found. Location Western Europe. Methods We used two data sets – the Atlas of European mammals and polygon range maps from the IUCN Global Mammal Assessment – both as presence–absence data for UTM 50 km × 50 km squares. We performed pairwise comparisons among 156 species for each data set to build matrices of the similarity in distribution across species, using both Jaccard’s and Baroni‐Urbani & Buser’s indices. We then compared these similarity matrices (chorological relationships), as well as the species richness and occurrence patterns from the two data sets. Results As expected, range maps increased both the mean prevalence per species and mean species richness per grid cell in comparison to atlas data, reflecting the general view that these data types respectively over‐ and underestimate species occurrence. However, species richness and occurrence patterns in atlas and range map data were positively associated and, most importantly, the chorological relationships underlying the two data sets were highly similar. Main conclusions Despite many methodological, temporal and taxonomical differences between atlas data and range maps, the chorological relationships encountered between species were similar for both data sets. Chorological analyses can thus be robust to the data source used and provide a solid basis for analytical biogeographical studies, even over broad spatial scales.     

Physiological state control of fermentation processes

In this article a novel approach to the control of fermentation processes is introduced. A "physiological state control approach" has been developed using the concept of representing fermentation processes through the current physiological state of the cell culture. No conventional mathematical model is required for the synthesis of such a control system.The main idea is based on the fact that during batch, feed-batch, or even continuous cultivation the physiological characteristics of the cell population, jointly expressed by the term "physiological state", are not constant but rather variable, which is reflected in expected or unexpected changes in the behavior of the control plant, and which requires flexible alteration of the current control strategy. The proposed approach involves decomposition of the physiological state space into several subspaces called "physiological situations." In every physiological situation the cell population expresses stable characteristics, and therefore an invariant control strategy can be effectively applied. The on-line functions of the physiological state control system consist of the calculation of physiological state variables, determination of the current physiological situation as an element of a previously defined set of known physiological situations, switching of the relevant control strategy, and calculation of the control action. Attention is focused on the synthesis of the novel and nonstandard part of the control system - the algorithm for online recognition of the current physiological state. To this end an effective approach, based on artificial intelligence methods, particularly fuzzy sets theory and pattern recognition theory, was developed. Its practical realization is demonstrated using data from a continuous fermentation process for single cell protein production.     

obitools: a unix‐inspired software package for DNA metabarcoding

DNA metabarcoding offers new perspectives in biodiversity research. This recently developed approach to ecosystem study relies heavily on the use of next‐generation sequencing (NGS) and thus calls upon the ability to deal with huge sequence data sets. The obitools package satisfies this requirement thanks to a set of programs specifically designed for analysing NGS data in a DNA metabarcoding context. Their capacity to filter and edit sequences while taking into account taxonomic annotation helps to set up tailor‐made analysis pipelines for a broad range of DNA metabarcoding applications, including biodiversity surveys or diet analyses. The obitools package is distributed as an open source software available on the following website: http://metabarcoding.org/obitools . A Galaxy wrapper is available on the GenOuest core facility toolshed: http://toolshed.genouest.org .     

Stochastic model for analysis of longitudinal data on aging and mortality

Aging-related changes in a human organism follow dynamic regularities, which contribute to the observed age patterns of incidence and mortality curves. An organism's 'optimal' (normal) physiological state changes with age, affecting the values of risks of disease and death. The resistance to stresses, as well as adaptive capacity, declines with age. An exposure to improper environment results in persisting deviation of individuals' physiological (and biological) indices from their normal state (due to allostatic adaptation), which, in turn, increases chances of disease and death. Despite numerous studies investigating these effects, there is no conceptual framework, which would allow for putting all these findings together, and analyze longitudinal data taking all these dynamic connections into account. In this paper we suggest such a framework, using a new version of stochastic process model of aging and mortality. Using this model, we elaborated a statistical method for analyses of longitudinal data on aging, health and longevity and tested it using different simulated data sets. The results show that the model may characterize complicated interplay among different components of aging-related changes in humans and that the model parameters are identifiable from the data.     

Functional Morphology and Neural Control of Neck Muscles in Mammals

SYNOPSIS. Although the data described in this paper are froma traditional experimental animal, the cat, the problem addressedis applicable to many forms of motor control in a broad rangeof species. In general, we are trying to understand how themusculo-skeletal structure of a body component imposes constraintson how that component is controlled by the central nervous system.Our approach to this problem has been to develop a theoreticalmodel of sensory-motor integration in complex systems wheremovement is produced by a large number of muscles and then totest whether that model can predict the patterns of neck muscleactivity that underlie head movements. The resulting theoreticalinterpretation of physiological data sheds light on the organizationof head movement control in the cat and also reveals a numberof fundamental principles that must be considered in tryingto characterize and understand any sensory-motor system.     

Predicting leaf gravimetric water content from foliar reflectance across a range of plant species using continuous wavelet analysis

Leaf water content is an important variable for understanding plant physiological properties. This study evaluates a spectral analysis approach, continuous wavelet analysis (CWA), for the spectroscopic estimation of leaf gravimetric water content (GWC, %) and determines robust spectral indicators of GWC across a wide range of plant species from different ecosystems. CWA is both applied to the Leaf Optical Properties Experiment (LOPEX) data set and a synthetic data set consisting of leaf reflectance spectra simulated using the leaf optical properties spectra (PROSPECT) model. The results for the two data sets, including wavelet feature selection and GWC prediction derived using those features, are compared to the results obtained from a previous study for leaf samples collected in the Republic of Panamá (PANAMA), to assess the predictive capabilities and robustness of CWA across species. Furthermore, predictive models of GWC using wavelet features derived from PROSPECT simulations are examined to assess their applicability to measured data. The two measured data sets (LOPEX and PANAMA) reveal five common wavelet feature regions that correlate well with leaf GWC. All three data sets display common wavelet features in three wavelength regions that span 1732-1736 nm at scale 4, 1874-1878 nm at scale 6, and 1338-1341 nm at scale 7 and produce accurate estimates of leaf GWC. This confirms the applicability of the wavelet-based methodology for estimating leaf GWC for leaves representative of various ecosystems. The PROSPECT-derived predictive models perform well on the LOPEX data set but are less successful on the PANAMA data set. The selection of high-scale and low-scale features emphasizes significant changes in both overall amplitude over broad spectral regions and local spectral shape over narrower regions in response to changes in leaf GWC. The wavelet-based spectral analysis tool adds a new dimension to the modeling of plant physiological properties with spectroscopy data.