- Proceedings of the National Academy of Sciences of the United States of America
- Published almost 5 years ago
A cornerstone of modern biomedical research is the use of mouse models to explore basic pathophysiological mechanisms, evaluate new therapeutic approaches, and make go or no-go decisions to carry new drug candidates forward into clinical trials. Systematic studies evaluating how well murine models mimic human inflammatory diseases are nonexistent. Here, we show that, although acute inflammatory stresses from different etiologies result in highly similar genomic responses in humans, the responses in corresponding mouse models correlate poorly with the human conditions and also, one another. Among genes changed significantly in humans, the murine orthologs are close to random in matching their human counterparts (e.g., R(2) between 0.0 and 0.1). In addition to improvements in the current animal model systems, our study supports higher priority for translational medical research to focus on the more complex human conditions rather than relying on mouse models to study human inflammatory diseases.
A complex relationship exists between the psychosocial environment and the perception and experience of pain, and the mechanisms of the social communication of pain have yet to be elucidated. The present study examined the social communication of pain and demonstrates that “bystander” mice housed and tested in the same room as mice subjected to inflammatory pain or withdrawal from morphine or alcohol develop corresponding hyperalgesia. Olfactory cues mediate the transfer of hyperalgesia to the bystander mice, which can be measured using mechanical, thermal, and chemical tests. Hyperalgesia in bystanders does not co-occur with anxiety or changes in corticosterone and cannot be explained by visually dependent emotional contagion or stress-induced hyperalgesia. These experiments reveal the multifaceted relationship between the social environment and pain behavior and support the use of mice as a model system for investigating these factors. In addition, these experiments highlight the need for proper consideration of how experimental animals are housed and tested.
Recent simulations have indicated that vinyl cyanide is the best candidate molecule for the formation of cell membranes/vesicle structures in Titan’s hydrocarbon-rich lakes and seas. Although the existence of vinyl cyanide (C2H3CN) on Titan was previously inferred using Cassini mass spectrometry, a definitive detection has been lacking until now. We report the first spectroscopic detection of vinyl cyanide in Titan’s atmosphere, obtained using archival data from the Atacama Large Millimeter/submillimeter Array (ALMA), collected from February to May 2014. We detect the three strongest rotational lines of C2H3CN in the frequency range of 230 to 232 GHz, each with >4σ confidence. Radiative transfer modeling suggests that most of the C2H3CN emission originates at altitudes of ≳200 km, in agreement with recent photochemical models. The vertical column densities implied by our best-fitting models lie in the range of 3.7 × 10(13) to 1.4 × 10(14) cm(-2). The corresponding production rate of vinyl cyanide and its saturation mole fraction imply the availability of sufficient dissolved material to form ~10(7) cell membranes/cm(3) in Titan’s sea Ligeia Mare.
Finite element modelling versus classic beam theory: comparing methods for stress estimation in a morphologically diverse sample of vertebrate long bones
- Journal of the Royal Society, Interface / the Royal Society
- Published about 5 years ago
Classic beam theory is frequently used in biomechanics to model the stress behaviour of vertebrate long bones, particularly when creating intraspecific scaling models. Although methodologically straightforward, classic beam theory requires complex irregular bones to be approximated as slender beams, and the errors associated with simplifying complex organic structures to such an extent are unknown. Alternative approaches, such as finite element analysis (FEA), while much more time-consuming to perform, require no such assumptions. This study compares the results obtained using classic beam theory with those from FEA to quantify the beam theory errors and to provide recommendations about when a full FEA is essential for reasonable biomechanical predictions. High-resolution computed tomographic scans of eight vertebrate long bones were used to calculate diaphyseal stress owing to various loading regimes. Under compression, FEA values of minimum principal stress (σ(min)) were on average 142 per cent (±28% s.e.) larger than those predicted by beam theory, with deviation between the two models correlated to shaft curvature (two-tailed p = 0.03, r(2) = 0.56). Under bending, FEA values of maximum principal stress (σ(max)) and beam theory values differed on average by 12 per cent (±4% s.e.), with deviation between the models significantly correlated to cross-sectional asymmetry at midshaft (two-tailed p = 0.02, r(2) = 0.62). In torsion, assuming maximum stress values occurred at the location of minimum cortical thickness brought beam theory and FEA values closest in line, and in this case FEA values of τ(torsion) were on average 14 per cent (±5% s.e.) higher than beam theory. Therefore, FEA is the preferred modelling solution when estimates of absolute diaphyseal stress are required, although values calculated by beam theory for bending may be acceptable in some situations.
Recent years have witnessed much progress in computational modelling for protein subcellular localization. However, the existing sequence-based predictive models demonstrate moderate or unsatisfactory performance, and the gene ontology (GO) based models may take the risk of performance overestimation for novel proteins. Furthermore, many human proteins have multiple subcellular locations, which renders the computational modelling more complicated. Up to the present, there are far few researches specialized for predicting the subcellular localization of human proteins that may reside in multiple cellular compartments. In this paper, we propose a multi-label multi-kernel transfer learning model for human protein subcellular localization (MLMK-TLM). MLMK-TLM proposes a multi-label confusion matrix, formally formulates three multi-labelling performance measures and adapts one-against-all multi-class probabilistic outputs to multi-label learning scenario, based on which to further extends our published work GO-TLM (gene ontology based transfer learning model for protein subcellular localization) and MK-TLM (multi-kernel transfer learning based on Chou’s PseAAC formulation for protein submitochondria localization) for multiplex human protein subcellular localization. With the advantages of proper homolog knowledge transfer, comprehensive survey of model performance for novel protein and multi-labelling capability, MLMK-TLM will gain more practical applicability. The experiments on human protein benchmark dataset show that MLMK-TLM significantly outperforms the baseline model and demonstrates good multi-labelling ability for novel human proteins. Some findings (predictions) are validated by the latest Swiss-Prot database. The software can be freely downloaded at http://soft.synu.edu.cn/upload/msy.rar.
MOTIVATION: Biochemical reaction networks in the form of coupled ordinary differential equations (ODEs) provide a powerful modeling tool for understanding the dynamics of biochemical processes. During the early phase of modeling, scientists have to deal with a large pool of competing nonlinear models. At this point, discrimination experiments can be designed and conducted to obtain optimal data for selecting the most plausible model. Since biological ODE models have widely distributed parameters due to, e.g., biologic variability or experimental variations, model responses become distributed. Therefore, a robust optimal experimental design (OED) for model discrimination can be used to discriminate models based on their response probability distribution functions (PDFs). RESULTS: In this work we present an optimal control based methodology for designing optimal stimulus experiments aimed at robust model discrimination. For estimating the time-varying model response PDF, which results from the nonlinear propagation of the parameter PDF under the ODE dynamics, we suggest using the Sigma-Point approach. Using the model overlap (expected likelihood) as a robust discrimination criterion to measure dissimilarities between expected model response PDFs, we benchmark the proposed nonlinear design approach against linearization with respect to prediction accuracy and design quality for two nonlinear biological reaction networks. As we show, the Sigma-Point outperforms the linearization approach in the case of widely distributed parameter sets and/or existing multiple steady states. Since the Sigma-Point approach scales linearly with the number of model parameter, it can be applied to large systems for robust experimental planing. AVAILABILITY: An implementation of the method in MATLAB/AMPL is available at http://www.uni-magdeburg.de/ivt/svt/person/rf/roed.html. CONTACT: email@example.com SUPPLEMENTARY INFORMATION: Supplementary details and discussions are available at Bioinformatics online.
Climate change is expected to alter biotic interactions, and may lead to temporal and spatial mismatches of interacting species. Although the importance of interactions for climate change risk assessments is increasingly acknowledged in observational and experimental studies, biotic interactions are still rarely incorporated in species distribution models. We assessed the potential impacts of climate change on the obligate interaction between Aeshna viridis and its egg-laying plant Stratiotes aloides in Europe, based on an ensemble modelling technique. We compared three different approaches for incorporating biotic interactions in distribution models: (1) We separately modelled each species based on climatic information, and intersected the future range overlap (‘overlap approach’). (2) We modelled the potential future distribution of A. viridis with the projected occurrence probability of S. aloides as further predictor in addition to climate (‘explanatory variable approach’). (3) We calibrated the model of A. viridis in the current range of S. aloides and multiplied the future occurrence probabilities of both species (‘reference area approach’). Subsequently, all approaches were compared to a single species model of A. viridis without interactions. All approaches projected a range expansion for A. viridis. Model performance on test data and amount of range gain differed depending on the biotic interaction approach. All interaction approaches yielded lower range gains (up to 667% lower) than the model without interaction. Regarding the contribution of algorithm and approach to the overall uncertainty, the main part of explained variation stems from the modelling algorithm, and only a small part is attributed to the modelling approach. The comparison of the no-interaction model with the three interaction approaches emphasizes the importance of including obligate biotic interactions in projective species distribution modelling. We recommend the use of the ‘reference area approach’ as this method allows a separation of the effect of climate and occurrence of host plant.
BACKGROUND: Recursive partitioning is a non-parametric modeling technique, widely used in regression and classification problems. Model-based recursive partitioning is used to identify groups of observations with similar values of parameters of the model of interest. The mob() function in the party package in R implements model-based recursive partitioning method. This method produces predictions based on single tree models. Predictions obtained through single tree models are very sensitive to small changes to the learning sample. We extend the model-based recursive partition method to produce predictions based on multiple tree models constructed on random samples achieved either through bootstrapping (random sampling with replacement) or subsampling (random sampling without replacement) on learning data. RESULTS: Here we present an R package called “mobForest” that implements bagging and random forests methodology for model-based recursive partitioning. The mobForest package constructs large number of model-based trees and the predictions are aggregated across these trees resulting in more stable predictions. The package also includes functions for computing predictive accuracy estimates and plots, residuals plot, and variable importance plot. CONCLUSION: The mobForest package implements a random forest type approach for model-based recursive partitioning. The R package along with it source code is available at http://CRAN.R-project.org/package=mobForest.
Substance use is a risk behavior that tends to increase during adolescence, a time when part of the personality is still in development. Traditionally, personality psychopathology has been measured in terms of categories, although dimensional models have demonstrated better consistency. This study aimed to analyze differences in personality profiles between adolescents with substance use disorders (SUD n = 74) and matched community controls (MCC n = 74) using the Personality Psychopathology Five (PSY-5) dimensional model. Additionally, we compared age at first drug use, level of drug use and internalizing and externalizing symptoms between the groups. In this study, the PSY-5 model has proved to be useful for differentiating specific personality disturbances in adolescents with SUD and community adolescents. The Disconstraint scale was particularly useful for discriminating adolescents with substance use problems and the Delinquent Attitudes facet offered the best differentiation.
Zebrafish is fast becoming a species of choice in biomedical research for the investigation of functional and dysfunctional processes coupled with their genetic and pharmacological modulation. As with mammals, experimentation with zebrafish constitutes a complicated ethical issue that calls for the exploration of alternative testing methods to reduce the number of subjects, refine experimental designs, and replace live animals. Inspired by the demonstrated advantages of computational studies in other life science domains, we establish an authentic data-driven modelling framework to simulate zebrafish swimming in three dimensions. The model encapsulates burst-and-coast swimming style, speed modulation, and wall interaction, laying the foundations for in-silico experiments of zebrafish behaviour. Through computational studies, we demonstrate the ability of the model to replicate common ethological observables such as speed and spatial preference, and anticipate experimental observations on the correlation between tank dimensions on zebrafish behaviour. Reaching to other experimental paradigms, our framework is expected to contribute to a reduction in animal use and suffering.