A simplified approach to quasi-linear viscoelastic modeling

The fitting of quasi-linear viscoelastic (QLV) constitutive models to material data often involves somewhat cumbersome numerical convolution. A new approach to treating quasi-linearity in 1-D is described and applied to characterize the behavior of reconstituted collagen. This approach is based on a new principle for including nonlinearity and requires considerably less computation than other comparable models for both model calibration and response prediction, especially for smoothly applied stretching. Additionally, the approach allows relaxation to adapt with the strain history. The modeling approach is demonstrated through tests on pure reconstituted collagen. Sequences of "ramp-and-hold" stretching tests were applied to rectangular collagen specimens. The relaxation force data from the "hold" was used to calibrate a new "adaptive QLV model" and several models from literature, and the force data from the "ramp" was used to check the accuracy of model predictions. Additionally, the ability of the models to predict the force response on a reloading of the specimen was assessed. The "adaptive QLV model" based on this new approach predicts collagen behavior comparably to or better than existing models, with much less computation.     

Critical evaluation of mathematical models for the amplification of chirality

The main types of models related to the origin of biological asymmetry are reviewed and new models are proposed. It is shown that in polymerization (in contrast to Yamagata's hypothesis) only a temporary amplification of asymmetry occurs. Models have been constructed in which always the same enantiomer survives, independently of any fluctuations or asymmetric initial conditions. Therefore, the question of the by chance or the causal origin of biological asymmetry remains still open, although with a slight preference for a causal origin.     

Dynamics of heat destruction of spores: A new view

Summary Discrepancies between actual, viable spore populations and those predicted by a classical model during heat sterilization of food and pharmaceutical products have long concerned food engineers and scientists as they pursue new sterilization techniques, including ultra-high temperature processes. Among potential causes of those discrepancies, activation of dormant spores is significant, and models addressing that factor were developed recently. This paper reviews historic and current views on the biology and models of microbial spore populations during heat sterilization. Activation and inactivation of viable spores are emphasized, with each viewed as a first-order reaction. Rate constants of those reactions may differ significantly, inactivation rates of dormant and activated spores may differ, and variations of all rate constants with temperature appear to be well described by Arrhenius equations. Model-based analyses show how categories of survivor response curves observed during isothermal heat treatments can arise from simultaneous activation and inactivation of spores in an overall population. Effects of different distributions of initial subpopulations, different distributions of rate constants, and heat shock for homogenizing an indicator population are shown. The complexity of new, multiple process models has not increased greatly, but the potential for accurate, dynamic prediction of product safety after prescribed sterilization has. The relevant biology is understood and accounted for more thoroughly, and it is anticipated that the new models will aid design and evaluation of new and improved sterilization processes for food and pharmaceuticals.Mention of brand or firm names does not constitute an endorsement by the US Department of Agriculture over others of a similar nature not mentioned.     

Modeling neurodegenerative disorders in adult somatic cells: A critical review

Development of new therapeutic targets for neurodegenerative disorders has been hampered by a reliance on post mortem tissue that is representative of end-stage disease, or on animal models that fail to provide faithful analogs. However, rapid advances in cellular genetic reprogramming, in particular the induction of somatic cells into stem cells, or directly into neurons, has led to intense interest in modeling of human neurodegeneration in vitro. Here, we critically review current methods and recent progress in cellular models of Alzheimer’s disease, Parkinson’s disease and amyotrophic lateral sclerosis. Several challenges are identified, including technical variability, lack of degenerative phenotypes, neurodevelopmental age and establishing ground truths for models of sporadic disease. Recommendations for evaluating neurodegenerative cellular models are proposed along with suggestions for future research.     

Why is revitalizing clinical research so important, yet so difficult?

We believe that support for academic clinical research has greatly declined in recent decades. Here we discuss our views on why this has happened. We define clinical or patient-oriented research as limited to the study of human beings or populations of individuals, and argue that its eclipse in favor of basic and "translational" research is the result of inappropriate conceptual paradigms or "models" for medical advances. We believe that medical history shows that the "bench-to-bedside" model is inadequate to explain most recent progress and that clinical advances themselves often lead to new basic research. Discussion of alternate conceptual frameworks for biomedical research should help lead to changes in funding and organizational structures that might finally revitalize clinical research.     

Comparison of classification methods that combine clinical data and high-dimensional mass spectrometry data

Background

The identification of new diagnostic or prognostic biomarkers is one of the main aims of clinical cancer research. Technologies like mass spectrometry are commonly being used in proteomic research. Mass spectrometry signals show the proteomic profiles of the individuals under study at a given time. These profiles correspond to the recording of a large number of proteins, much larger than the number of individuals. These variables come in addition to or to complete classical clinical variables. The objective of this study is to evaluate and compare the predictive ability of new and existing models combining mass spectrometry data and classical clinical variables. This study was conducted in the context of binary prediction.

Results

To achieve this goal, simulated data as well as a real dataset dedicated to the selection of proteomic markers of steatosis were used to evaluate the methods. The proposed methods meet the challenge of high-dimensional data and the selection of predictive markers by using penalization methods (Ridge, Lasso) and dimension reduction techniques (PLS), as well as a combination of both strategies through sparse PLS in the context of a binary class prediction. The methods were compared in terms of mean classification rate and their ability to select the true predictive values. These comparisons were done on clinical-only models, mass-spectrometry-only models and combined models.

Conclusions

It was shown that models which combine both types of data can be more efficient than models that use only clinical or mass spectrometry data when the sample size of the dataset is large enough.     

1.25 A resolution crystal structures of human haemoglobin in the oxy, deoxy and carbonmonoxy forms

The most recent refinement of the crystallographic structure of oxyhaemoglobin (oxyHb) was completed in 1983, and differences between this real-space refined model and later R state models have been interpreted as evidence of crystallisation artefacts, or numerous sub-states. We have refined models of deoxy, oxy and carbonmonoxy Hb to 1.25 A resolution each, and compare them with other Hb structures. It is shown that the older structures reflect the software used in refinement, and many differences with newer structures are unlikely to be physiologically relevant. The improved accuracy of our models clarifies the disagreement between NMR and X-ray studies of oxyHb, the NMR experiments suggesting a hydrogen bond to exist between the distal histidine and oxygen ligand of both the alpha and beta-subunits. The high-resolution crystal structure also reveals a hydrogen bond in both subunit types, but with subtly different geometry which may explain the very different behaviour when this residue is mutated to glycine in alpha or beta globin. We also propose a new set of relatively fixed residues to act as a frame of reference; this set contains a similar number of atoms to the well-known "BGH" frame yet shows a much smaller rmsd value between R and T state models of HbA.     

Spontaneous tumours of pet dog as models for human cancers: searching for adequate guidelines.

Despite the ongoing search for "replacement" alternatives, animal models continue to play a crucial role in bio-medical research. However, both in vivo and in vitro models usually employed, such as rodents and/or cell lines, display intrinsic limits related to the specific characteristics of the biological systems used, whose management is very complex, and whose pathology, usually induced under artificial laboratory conditions, is frequently dissimilar to the studied human spontaneous disease. It has been suggested that carrying out clinical trials based on pre-clinical data obtained after a screening on animal models developing the neoplastic disease in a more similar way to human beings, as represented by spontaneous canine tumours, could accelerate the entry of new effective drugs into the clinical practice. In this paper the authors discuss the scientific foundation as well as the ethic and legal concerns related to the use of this "pet model" in comparative oncology, suggesting some "key words" as the necessary starting point for a correct approach to the matter.     

Current Developments in Dementia Risk Prediction Modelling: An Updated Systematic Review

Background

Accurate identification of individuals at high risk of dementia influences clinical care, inclusion criteria for clinical trials and development of preventative strategies. Numerous models have been developed for predicting dementia. To evaluate these models we undertook a systematic review in 2010 and updated this in 2014 due to the increase in research published in this area. Here we include a critique of the variables selected for inclusion and an assessment of model prognostic performance.

Methods

Our previous systematic review was updated with a search from January 2009 to March 2014 in electronic databases (MEDLINE, Embase, Scopus, Web of Science). Articles examining risk of dementia in non-demented individuals and including measures of sensitivity, specificity or the area under the curve (AUC) or c-statistic were included.

Findings

In total, 1,234 articles were identified from the search; 21 articles met inclusion criteria. New developments in dementia risk prediction include the testing of non-APOE genes, use of non-traditional dementia risk factors, incorporation of diet, physical function and ethnicity, and model development in specific subgroups of the population including individuals with diabetes and those with different educational levels. Four models have been externally validated. Three studies considered time or cost implications of computing the model.

Interpretation

There is no one model that is recommended for dementia risk prediction in population-based settings. Further, it is unlikely that one model will fit all. Consideration of the optimal features of new models should focus on methodology (setting/sample, model development and testing in a replication cohort) and the acceptability and cost of attaining the risk variables included in the prediction score. Further work is required to validate existing models or develop new ones in different populations as well as determine the ethical implications of dementia risk prediction, before applying the particular models in population or clinical settings.     

SysFinder:A customized platform for search,comparison and assisted design of appropriate animal models based on systematic similarity

Animal models are increasingly gaining values by cross-comparisons of response or resistance to clinical agents used for patients.However,many disease mechanisms and drug effects generated from animal models are not transferable to human.To address these issues,we developed SysFinder(http://lifecenter.sgst.cn/SysFinder),a platform for scientists to find appropriate animal models for translational research.SysFinder offers a "topic-centered" approach for systematic comparisons of human genes,whose functions are involved in a specific scientific topic,to the corresponding homologous genes of animal models.Scientific topic can be a certain disease,drug,gene function or biological pathway.SysFinder calculates multi-level similarity indexes to evaluate the similarities between human and animal models in specified scientific topics.Meanwhile,SysFinder offers species-specific information to investigate the differences in molecular mechanisms between humans and animal models.Furthermore,SysFinder provides a userfriendly platform for determination of short guide RNAs(sgRNAs) and homology arms to design a new animal model.Case studies illustrate the ability of SysFinder in helping experimental scientists.SysFinder is a useful platform for experimental scientists to carry out their research in the human molecular mechanisms.     

A maximum-likelihood approach to fitting equilibrium models of microsatellite evolution

Here, we develop a new approach to Markov chain modeling of microsatellite evolution through polymerase slippage and introduce new models: a "constant-slippage-rate" model, in which there is no dependence of slippage rate on microsatellite length, as envisaged by Moran; and a "linear-with-constant" model, in which slippage rate increases linearly with microsatellite length, but the line of best fit is not constrained to go through the origin. We show how these and a linear no-constant model can be fitted to data hierarchically using maximum likelihood. This has advantages over previous methods in allowing statistical comparisons between models. When applied to a previously analyzed data set, the method allowed us to statistically establish that slippage rate increases with microsatellite length for dinucleotide microsatellites in humans, mice, and fruit flies, and suggested that no slippage occurs in very short microsatellites of one to four repeats. The suggestion that slippage rates are zero or close to zero for very short microsatellites of one to four repeats has important implications for understanding the mechanism of polymerase slippage.     

Toward the active conformations of rhodopsin and the beta2-adrenergic receptor

Using sets of experimental distance restraints, which characterize active or inactive receptor conformations, and the X-ray crystal structure of the inactive form of bovine rhodopsin as a starting point, we have constructed models of both the active and inactive forms of rhodopsin and the beta2-adrenergic G-protein coupled receptors (GPCRs). The distance restraints were obtained from published data for site-directed crosslinking, engineered zinc binding, site-directed spin-labeling, IR spectroscopy, and cysteine accessibility studies conducted on class A GPCRs. Molecular dynamics simulations in the presence of either "active" or "inactive" restraints were used to generate two distinguishable receptor models. The process for generating the inactive and active models was validated by the hit rates, yields, and enrichment factors determined for the selection of antagonists in the inactive model and for the selection of agonists in the active model from a set of nonadrenergic GPCR drug-like ligands in a virtual screen using ligand docking software. The simulation results provide new insights into the relationships observed between selected biochemical data, the crystal structure of rhodopsin, and the structural rearrangements that occur during activation.     

Toxin or medicine? Explanatory models of radon in Montana health mines

Environmental protection and public health agencies in the United States and elsewhere label radioactive radon gas a toxic environmental hazard and a major cause of lung cancer. Paradoxically, in Europe and Japan radon gas is also used as an analgesic and anti-inflammatory, as one choice in the spectrum of conventional medical care. Although it is possible to find radon therapy in the United States, it exists only as an unconventional practice in Montana "radon health mines." In this article, I examine the use of radon therapy by Americans despite intensive public health education media campaigns. Using the notion of explanatory models as an analytical framework, I argue that American health mine clients adjust or replace "toxic models" of radon with new kinds of explanatory models that allow radon to be redefined as a healing substance. The manner of this adjustment varies according to peoples' individual needs, their own preexisting cultural models and experiences, and their individual personalities; the source of authoritative knowledge accepted by each person is a strong influence. Through these altered explanatory models, mine clients are able to view their use of radon therapy as a rational course of action.     

A rank-based mixed model approach to multisite clinical trials.

New rank-based methods for analyzing data from multisite clinical trials are presented in the context of "mixed" linear models. In contrast to current rank methods, the new procedures test for a drug main effect in the presence of a random drug by site interaction (or drug by investigator interaction when there is only one investigator per site). Analogous procedures are also provided for the "fixed-effects" situation, and comparisons are made with current methods. The rationale for an analysis that assumes random investigator effects is described.     

A habitat-based model for the spread of hantavirus between reservoir and spillover species

New habitat-based models for spread of hantavirus are developed which account for interspecies interaction. Existing habitat-based models do not consider interspecies pathogen transmission, a primary route for emergence of new infectious diseases and reservoirs in wildlife and man. The modeling of interspecies transmission has the potential to provide more accurate predictions of disease persistence and emergence dynamics. The new models are motivated by our recent work on hantavirus in rodent communities in Paraguay. Our Paraguayan data illustrate the spatial and temporal overlaps among rodent species, one of which is the reservoir species for Jabora virus and others which are spillover species. Disease transmission occurs when their habitats overlap. Two mathematical models, a system of ordinary differential equations (ODE) and a continuous-time Markov chain (CTMC) model, are developed for spread of hantavirus between a reservoir and a spillover species. Analysis of a special case of the ODE model provides an explicit expression for the basic reproduction number, , such that if , then the pathogen does not persist in either population but if , pathogen outbreaks or persistence may occur. Numerical simulations of the CTMC model display sporadic disease incidence, a new behavior of our habitat-based model, not present in other models, but which is a prominent feature of the seroprevalence data from Paraguay. Environmental changes that result in greater habitat overlap result in more encounters among various species that may lead to pathogen outbreaks and pathogen establishment in a new host.     

Arrhenius and Gleason revisited: new hybrid models resolve an old controversy

Aim Studies have typically employed species–area relationships (SARs) from sample areas to fit either the power relationship or the logarithmic (exponential) relationship. However, the plots from empirical data often fall between these models. This article proposes two complementary and hybrid models as solutions to the controversy regarding which model best fits sample‐area SARs. Methods The two models are and , where S_A is number of species in an area, A, where z, b, c₁ and c₂ are predetermined parameters found by calculation, and where d and n are parameters to be fitted. The number of parameters is reduced from six to two by fixing the model at either end of the scale window of the data set, a step that is justified by the condition that the error or the bias, or both, in the first and the last data points is negligible. The new hybrid models as well as the power model and the logarithmic model are fitted to 10 data sets. Results The two proposed models fit well not only to Arrhenius’ and Gleason’s data sets, but also to the other six data sets. They also provide a good fit to data sets that follow a sigmoid (or triphasic) shape in log–log space and to data sets that do not fall between the power model and the logarithmic model. The log‐transformation of the dependent variable, S, does not affect the curve fit appreciably, although it enhances the performance of the new models somewhat. Main conclusions Sample‐area SARs have previously been shown to be convex upward, convex downward (concave), sigmoid and inverted sigmoid in log–log space. The new hybrid models describe successfully data sets with all these curve shapes, and should therefore produce good fits also to what are termed triphasic SARs.     

Life, self-reproduction and information: beyond the machine metaphor

The problem of representing information in automation models of self-replication is considered. It is shown that, unlike in the natural reproduction process, in a computable model the reproduced entities do not contain all the information necessary for guiding the process. Current theoretical understanding of life and its replication, based on such models, is argued to be essentially inadequate. The solution to this problem is claimed to require recognition of the theoretical fact that information in living systems is different from that subsumed under the category of "knowledge", which is representable as computer programs or triggers of state transitions. A discussion of fundamentals of a new theory of information and its relationship to replication models is given and a new direction of further developments of biological theories is envisioned.     

Protein-protein docking with backbone flexibility

Computational protein-protein docking methods currently can create models with atomic accuracy for protein complexes provided that the conformational changes upon association are restricted to the side chains. However, it remains very challenging to account for backbone conformational changes during docking, and most current methods inherently keep monomer backbones rigid for algorithmic simplicity and computational efficiency. Here we present a reformulation of the Rosetta docking method that incorporates explicit backbone flexibility in protein-protein docking. The new method is based on a "fold-tree" representation of the molecular system, which seamlessly integrates internal torsional degrees of freedom and rigid-body degrees of freedom. Problems with internal flexible regions ranging from one or more loops or hinge regions to all of one or both partners can be readily treated using appropriately constructed fold trees. The explicit treatment of backbone flexibility improves both sampling in the vicinity of the native docked conformation and the energetic discrimination between near-native and incorrect models.