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Segmental flexibility of immunoglobulin G antibody molecules in solution: a new interpretation.

We propose a new model for the segmental flexibility of immunoglobulin G (IgG). The flexibility of native and mildly reduced anti-5-(dimethylamino)naphthalene-1-sulfonyl (anti-dansyl) antibody was reexamined by nanosecond fluorescence spectroscopy using deconvolution and lamp-shift corrections. The rabbit antibodies used for this study were purified of dimers and other aggregates. The original results indicated that the decay of fluorescence anisotropy involved two rotational correlation times. It was suggested that the short rotational correlation time, phi s, represented a flexible Fab arm motion over a restricted angle and that the long correlation time, phi L, represented global tumbling of the molecule [Yguerabide, J., Epstein, H. F., & Stryer, L. (1970) J. Mol. Biol. 51, 573--590]. Our new data indicate that the long correlation time primarily represents motions of the Fab segments and not global tumbling of the entire molecule. This interpretation implies a more flexible model for IgG. Thus, in solution the antibody arms appear to move over a wide angle and are not restricted to 33 degrees as was suggested in the earlier model. Simple diffusion calculations and other evidence suggest that phi s may represent V-module flexibility about the switch peptides or Fab twisting around its long axis, whereas phi L may represent wagging or wobbling motions of the Fab arms about the hinge region. The faster motions appear to occur over small angles whereas the slower wagging or wobbling motions responsible for most of the decay of anisotropy appear to be much less restricted. The biological function of IgG and anisotropy changes resulting from hinge disulfide cleavage are interpreted in terms of the proposed model. We also demonstrate a useful method for comparison of time-dependent and steady-state fluorescence polarization data.

Animals↗

Genetic algorithms and self-organizing maps: a powerful combination for modeling complex QSAR and QSPR problems.

Modeling non-linear descriptor-target activity/property relationships with many dependent descriptors has been a long-standing challenge in the design of biologically active molecules. In an effort to address this problem, we couple the supervised self-organizing map with the genetic algorithm. Although self-organizing maps are non-linear and topology-preserving techniques that hold great potential for modeling and decoding relationships, the large number of descriptors in typical quantitative structure-activity relationship or quantitative structure-property relationship analysis may lead to spurious correlation(s) and/or difficulty in the interpretation of resulting models. To reduce the number of descriptors to a manageable size, we chose the genetic algorithm for descriptor selection because of its flexibility and efficiency in solving complex problems. Feasibility studies were conducted using six different datasets, of moderate-to-large size and moderate-to-great diversity; each with a different biological endpoint. Since favorable training set statistics do not necessarily indicate a highly predictive model, the quality of all models was confirmed by withholding a portion of each dataset for external validation. We also address the variability introduced onto modeling through dataset partitioning and through the stochastic nature of the combined genetic algorithm supervised self-organizing map method using the z-score and other tests. Experiments show that the combined method provides comparable accuracy to the supervised self-organizing map alone, but using significantly fewer descriptors in the models generated. We observed consistently better results than partial least squares models. We conclude that the combination of genetic algorithms with the supervised self-organizing map shows great potential as a quantitative structure-activity/property relationship modeling tool.

Algorithms↗

An interphase model for mitotic chromosome organization in eukaryota.

A model for the spatial relationship of the arrangement of the chromosomes in the nucleus in eukaryota is presented. Evidence is derived from light and electron microscopic studies, application of autoradiographic and banding techniques; on the organization, structure and behaviour of chromosomes during interphase and other stages of cell cycle. This model visualizes the entire chromosomal DNA as a single uninemic multirepliconic continuum where the chromosomes with their centromeres and telomeres have a predetermined arrangement among themselves as well as in relation to the nucleolus and nuclear membrane. This orderly arrangement is presumably maintained through interchromosomal connections. The impact of this model on the interpretation of various cytogenetic phenomena is discussed.

Animals↗

The value of ejection fraction as a predictor of early and late survival following aortocoronary bypass surgery in patients with moderate to severe depression of the left ventricular function.

Predictability analysis of early and late survival of coronary bypass patients with reduced left ventricular function was performed using stepwise logistic regression based on preoperative and operative data. The total perioperative mortality was 3.4% and the total actuarial survival of the whole group at 42 months was 87.7%. Thirty preoperative and operative parameters of 149 patients were entered into a first mathematical model. Four parameters were selected as a consequence: ejection fraction, anginal class, obesity, septal infarct. A prediction model based on these parameters, gave a correct prediction of the operative outcome in 99.3% of the patients. However due to the only five perioperative deaths, this model should be interpreted with caution. The accuracy of ejection fraction alone was 97.3%, the sensitivity was 100% but the specificity was only 20%. The same parameters of 97 patients were entered into a second mathematical model. Four parameters were selected in this case: ejection fraction, NYHA functional classification, septal infarct, anterior infarct. A second prediction model, based on these parameters, gave a correct prediction of the late outcome at 24 months in 93.8% of the patients. The accuracy of the discrimination on the basis of ejection fraction alone was 89.7%, its sensitivity was 96.5% but its specificity was only 41.7%. Ejection fraction was the best predictor out of the studied variables but due to its low specificity not good enough as a sole parameter.

Adult↗

Molecular evolution modeled as a fractal Poisson process in agreement with mammalian sequence comparisons.

The fractal doubly stochastic Poisson process (FDSPP) model of molecular evolution, like other doubly stochastic Poisson models, agrees with the high estimates for the index of dispersion found from sequence comparisons. Unlike certain previous models, the FDSPP also predicts a positive geometric correlation between the index of dispersion and the mean number of substitutions. Such a relationship is statistically proven herein using comparisons between 49 mammalian genes. There is no characteristic rate associated with molecular evolution according to this model, but there is a scaling relationship in rates according to a fractal dimension of evolution. The FDSPP is a suitable replacement for the homogeneous Poisson process in tests of the lineage dependence of rates and in estimating confidence intervals for divergence times. As opposed to other fractal models, this model can be interpreted in terms of Darwinian selection and drift.

Animals↗

Relation of observer agreement to accuracy according to a two-receiver signal detection model of diagnosis.

A long-standing problem of medical diagnosis is why rates of observer disagreement are so high. To study this question a model of test interpretation is defined which accounts for disagreement when observers use identical diagnostic strategies. Test subjects are assigned to one of two disease classes on the basis of two parameters: the test score and lambda, the diagnostic certainty, a parameter analogous to signal-to-noise ratio. The rate of disagreement and the mean error rate of independent ideal observers are derived as a function of diagnostic strategy. It follows from the model that: 1) Maximization of the ratio of disagreement to error defines an optimal diagnostic strategy with advantages over maximum likelihood and Bayes diagnostic strategies. 2) Disagreement exceeds error if the sensitivity and specificity of independent observers exceed one-half for all lambda. 3) The ratio of disagreement to error can be no greater than two if test interpretation is dichotomous but can approach infinity for trichotomous interpretation, i.e., if observers are allowed to interpret cases as indeterminate.

Bayes Theorem↗

Magnetization transfer fast imaging of implanted glioma in the rat brain at 4.7 T: interpretation using a binary spin-bath model.

C6 glioma cells were implanted in the left caudate nucleus of the rat brain. Histologic studies confirmed the presence of neoplastic tissue surrounded by a thin edematous region. Proton magnetization transfer contrast (MTC) fast imaging, using continuous wave off-resonance irradiation, was performed in vivo at 4.7 T with the rapid acquisition with relaxation enhancement (RARE) sequence. The observed MTC allowed very clear distinction of the tumoral region, in which magnetization transfer (MT) ratios were lower than in healthy tissues. Contrasts were analyzed as a function of the offset frequency and the amplitude of the radiofrequency (RF) irradiation. The contrast was higher between the contralateral basal ganglia and the tumor and lower between the tumor and the temporal lobe. Modeling of MT in the three brain regions was performed using a system including free water and a pool of protons with restricted motions. The rate of exchange between the two pools exhibited a decreasing hierarchy from the basal ganglia to the tumor. T2B values for the immobile protons ranged from 9.3 microsec in the basal ganglia to 7.5 microsec for the glioma. The acquisition conditions leading to the highest contrasts between the tumor and the healthy tissues correspond to 3,000 Hz offset frequency and 300 to 700 Hz RF irradiation amplitude.

Animals↗

Structure and composition of influenza virus. A small-angle neutron scattering study.

A detailed analysis is presented of the small-angle neutron scattering curves of homogeneous solutions of influenza B virus, both intact and after treatment with bromelain, which removes the external glycoprotein spikes. The two sets of data are consistent with the following low-resolution structure: the virus particles are spherical, about 1200 A in diameter and of Mr about 180 X 10(6). The lipid bilayer is centred at a radius of 425 A, is 40 A to 50 A thick and constitutes 25% to 28% of the virus mass. The surface glycoproteins, predominantly haemagglutinin, contribute 40% to 46% of the total mass. Surprisingly little protein is found in the interior of the virus. It is suggested that the reason for this is that many particles do not contain the full complement of ribonucleoprotein complexes. These results are in good agreement with recent scanning transmission electron microscopic measurements of molecular mass and cryo-electron microscopic observations of the same preparations. Appendix 1 describes a new method of deriving spherical shell models from contrast variation neutron scattering data on viruses, in which scattering curves from all measured contrasts are used simultaneously. There is also a discussion of the assumptions and limitations implicit in the structural interpretation of such models, with emphasis on viruses containing lipid bilayers. Appendix 2 examines the effect on the scattering curves of various arrangements of the surface glycoproteins.

Bromelains↗

'Sick people' and 'trolls': a contribution to the understanding of the dynamics of physician explanatory models.

This paper describes and interprets a case in which the mutual influences of countertransference and physician explanatory model (EM) are identified, as are their roles in negotiating the clinical relationship and in clinical decision-making (assessment, diagnosis, treatment, expectations of outcome). The author argues that physicians' EMs do not derive exclusively from the official and professionally ideal biomedical model, but are shaped by situational, historical, and subjective influences. Patient-typing (e.g., the widespread distinction between "sick people" and "trolls"), together with its associated emotions (e.g., empathy and disdain, respectively), pervades medical education and practice, and often supersedes the biomedical model. Physicians' subjective responses to patients can affect biomedical diagnosis. Failed empathy can lead to missed diagnosis. Finally, the case study illustrates the validity and vitality of the ethnographic approach in clinical teaching and supervision, both in eliciting physicians' own complex EMs, and in facilitating the process of reinterpretation as well.

Attitude of Health Personnel↗

Polynuclear growth model with external source and random matrix model with deterministic source.

We present a random matrix interpretation of the distribution functions which have appeared in the study of the one-dimensional polynuclear growth (PNG) model with external sources. It is shown that the distribution, GOE2 , which is defined as the square of the Gaussian orthogonal ensemble (GOE) Tracy-Widom distribution, can be obtained as the scaled largest eigenvalue distribution of a special case of a random matrix model with a deterministic source, which have been studied in a different context previously. Compared to the original interpretation of the GOE2 as "the square of GOE," ours has an advantage in that it can also describe the transition from the Gaussian unitary ensemble (GUE) Tracy-Widom distribution to the GOE2 . We further demonstrate that our random matrix interpretation can be obtained naturally by noting the similarity of the topology between a certain noncolliding Brownian motion model and the multilayer PNG model with an external source. This provides us with a multimatrix model interpretation of the multipoint height distributions of the PNG model with an external source.

Journal Article↗

On specifying the null model for incremental fit indices in structural equation modeling.

In structural equation modeling, incremental fit indices are based on the comparison of the fit of a substantive model to that of a null model. The standard null model yields unconstrained estimates of the variance (and mean, if included) of each manifest variable. For many models, however, the standard null model is an improper comparison model. In these cases, incremental fit index values reported automatically by structural modeling software have no interpretation and should be disregarded. The authors explain how to formulate an acceptable, modified null model, predict changes in fit index values accompanying its use, provide examples illustrating effects on fit index values when using such a model, and discuss implications for theory and practice of structural equation modeling.

Data Interpretation, Statistical↗

Benchmarking large language models for extracting biobank-derived insights into health and disease.

Biobank-scale datasets such as the UK Biobank have become foundational resources for advancing biomedical discovery. Yet the complexity and heterogeneity of these resources, spanning genomics, imaging, clinical records, and metadata, pose substantial barriers to access and interpretation. Large Language Models (LLMs) offer a promising avenue for making such datasets more navigable through natural language interfaces. However, the extent to which current general-purpose LLMs can retrieve and synthesize biobank-specific insights has not yet been systematically evaluated. In this study, we present a reproducible, multi-metric evaluation framework to benchmark the capabilities of leading LLMs. We evaluated six leading large language models: Gemini 3 Pro, Claude Opus 4.5, Claude Sonnet 4.5, GPT-5.2, Mistral Large 2, and DeepSeek V3, on four benchmark tasks designed to assess biobank-related knowledge retrieval. We evaluate model performance across six dimensions (semantic accuracy, factual correctness, domain knowledge, reasoning quality, response depth, and biobank specificity) and assessed output consistency using curated UK Biobank references and a robust random baseline. All models outperformed the baseline by 2&#xd7; to 3&#xd7;&#x2009;, with strong statistical separation (p&#x2009;<&#x2009;0.001), confirming meaningful biobank-specific knowledge retrieval. Gemini 3 Pro achieved the highest overall accuracy across tasks such as keyword synthesis, institution recognition, and topic inference, while Claude Sonnet 4.5 demonstrated the most uniform performance across evaluation dimensions. Our benchmark provides a rigorous framework for evaluating LLMs in biomedical settings. Using the UK Biobank as a real-world testbed, we highlight both the capabilities and limitations of current models, measuring their capacity to recall structured biomedical knowledge consistent with authoritative biobank metadata.

Large Language Models↗

Mathematical model of compartmentalized energy transfer: its use for analysis and interpretation of 31P-NMR studies of isolated heart of creatine kinase deficient mice.

A mathematical model of the compartmentalized energy transfer in cardiac cells is described and used for interpretation of novel experimental data obtained by using phosphorus NMR for determination of the energy fluxes in the isolated hearts of transgenic mice with knocked out creatine kinase isoenzymes. These experiments were designed to study the meaning and importance of compartmentation of creatine kinase isoenzymes in the cells in vivo. The model was constructed to describe quantitatively the processes of energy production, transfer, utilization, and feedback between these processes. It describes the production of ATP in mitochondrial matrix space by ATP synthase, use of this ATP for phosphocreatine production in the mitochondrial creatine kinase reaction coupled to the adenine nucleotide translocation, diffusional exchange of metabolites in the cytoplasmic space, and use of phosphocreatine for resynthesis of ATP in the myoplasmic creatine kinase reaction. It accounts also for the recently discovered phenomenon of restricted diffusion of adenine nucleotides through mitochondrial outer membrane porin pores (VDAC). Practically all parameters of the model were determined experimentally. The analysis of energy fluxes between different cellular compartments shows that in all cellular compartments of working heart cells the creatine kinase reaction is far from equilibrium in the systolic phase of the contraction cycle and approaches equilibrium only in cytoplasm and only in the end-diastolic phase of the contraction cycle. Experimental determination of the relationship between energy fluxes by a 31P-NMR saturation transfer method and workload in isolated and perfused heart of transgenic mice deficient in MM isoenzyme of the creatine kinase, MM-/-showed that in the hearts from wild mice, containing all creatine kinase isoenzymes, the energy fluxes determined increased 3-4 times with elevation of the workload. By contrast, in the hearts in which only the mitochondrial creatine kinase was active, the energy fluxes became practically independent of the workload in spite of the preservation of 26% of normal creatine kinase activity. These results cannot be explained on the basis of the conventional near-equilibrium theory of creatine kinase in the cells, which excludes any difference between creatine kinase isoenzymes. However, these apparently paradoxical experimental results are quantitatively described by a mathematical model of the compartmentalized energy transfer based on the steady state kinetics of coupled creatine kinase reactions, compartmentation of creatine kinase isoenzymes in the cells, and the kinetics of ATP production and utilization reactions. The use of this model shows that: (1) in the wild type heart cells a major part of energy is transported out of mitochondria via phosphocreatine, which is used for complete regeneration of ATP locally in the myofibrils--this is the quantitative estimate for PCr pathway; (2) however, in the absence of MM-creatine kinase in the myofibrils in transgenic mice the contraction results in a very rapid rise of ADP in cytoplasmic space, that reverses the mitochondrial creatine kinase reaction in the direction of ATP production. In this way, because of increasing concentrations of cytoplasmic ADP, mitochondrial creatine kinase is switched off functionally due to the absence of its counterpart in PCr pathway, MM-creatine kinase. This may explain why the creatine kinase flux becomes practically independent from the workload in the hearts of transgenic mouse without MM-CK. Thus, the analysis of the results of studies of hearts of creatine kinase-deficient transgenic mice, based on the use of a mathematical model of compartmentalized energy transfer, show that in the PCr pathway of intracellular energy transport two isoenzymes of creatine kinase always function in a coordinated manner out of equilibrium, in the steady state, and disturbances in functioning of one of them inevitably result

Adenosine Triphosphate↗

A statistical approach for array CGH data analysis.

BACKGROUND: Microarray-CGH experiments are used to detect and map chromosomal imbalances, by hybridizing targets of genomic DNA from a test and a reference sample to sequences immobilized on a slide. These probes are genomic DNA sequences (BACs) that are mapped on the genome. The signal has a spatial coherence that can be handled by specific statistical tools. Segmentation methods seem to be a natural framework for this purpose. A CGH profile can be viewed as a succession of segments that represent homogeneous regions in the genome whose BACs share the same relative copy number on average. We model a CGH profile by a random Gaussian process whose distribution parameters are affected by abrupt changes at unknown coordinates. Two major problems arise: to determine which parameters are affected by the abrupt changes (the mean and the variance, or the mean only), and the selection of the number of segments in the profile. RESULTS: We demonstrate that existing methods for estimating the number of segments are not well adapted in the case of array CGH data, and we propose an adaptive criterion that detects previously mapped chromosomal aberrations. The performances of this method are discussed based on simulations and publicly available data sets. Then we discuss the choice of modeling for array CGH data and show that the model with a homogeneous variance is adapted to this context. CONCLUSIONS: Array CGH data analysis is an emerging field that needs appropriate statistical tools. Process segmentation and model selection provide a theoretical framework that allows precise biological interpretations. Adaptive methods for model selection give promising results concerning the estimation of the number of altered regions on the genome.

Algorithms↗

Small-angle X-ray scattering and light scattering on lysozyme and sodium glycocholate micelles.

Small-angle X-ray scattering (SAXS) together with static (SLS) and dynamic light scattering (DLS) measurements were carried out on aqueous solutions of lysozyme (LY) and of the ionic biological detergent sodium glycocholate (NaGC). Apparent diffusion coefficients (D app), excess Rayleigh ratio, and SAXS spectra were measured for 0.1 M NaGC solutions at different ionic strengths (0.05-0.30 M NaCl). The same data were collected for LY in sodium acetate buffer 50 mM without and with 92 mM NaCl as a function of protein concentration (10-80 g L(-1)). A correlated analysis of SLS data and SAXS spectra was first tested on the LY samples and then extended to the interpretation of the NaGC data to infer information on particle structure and interaction potential. A hard-core (HC) interaction shell of uniform thickness, a screened Coulomb potential of the electric double layer (EDL) or the complete DLVO potential were alternatively used to represent the long-range tail of the interaction potential. Whenever an essentially repulsive tail is expected, all the representations give reasonable results, but the data analysis does not allow the discrimination between the oblate and the prolate symmetries of the NaGC aggregates. The DLVO model allows the interpretation of the data even when the attractive component determines the tail character. With this model an overall fit of the micelle data at all the NaCl concentrations was successfully performed by assuming a simple spherical symmetry of the micelles and invariant values of their ionization degree and Hamaker constant, thus considering just the screening effect of the added electrolyte. Whatever model is used, the results point out that the aggregates are quite hydrated (26-38 water molecules per monomer) and very slightly grow by increasing the NaCl concentration. When spherical symmetry is assumed the aggregate radii for all the samples fall in the range 15-16 A. From the SAXS and SLS, best fitting geometrical parameters, and interparticle structure factor, a D app value was calculated for each sample. An excellent consistence is achieved for LY results. On the contrary, calculated D app values systematically lower than the experimental values are always obtained for the NaGC micelles. Micelle polydispersity and internal dynamics seem to be the most probable reasons of the bad agreement.

Glycocholic Acid↗

Modelling techniques and their application for monitoring in high dependency environments--learning models.

This paper reviews the use of learning models including Bayesian classifiers and artificial neural networks in monitoring and interpreting biosignals. Generally learning models applied for analysis of biosignals are "black-box' types trained on the basis of measured signals. It is illustrated that the training and application of learning models more or less follow the same sequences. The main focus is the interpretation of electrical signals from the brain (electroencephalogram (EEG) and evoked potentials (EP)). Current analysis of these signals often reveals sudden changes in the EEG or evoked potentials to be the earliest discernible signs of inadequate perfusion of the brain. They may reflect problems such as systemic arterial oxygen desaturation or hypotension arising from other body system failures during critical illness. It is suggested that these brain signals should be recorded in the critical care unit, and that they should form part of the annotated database of biosignals established during the IMPROVE project. This would allow for the development of new methods for on-line warning of impending damage to the central nervous system, such that corrective actions could be taken before permanent damage occurred.

Auscultation↗

Allosteric interpretation of the oxygen-binding reaction of human hemoglobin tetramers.

An allosteric model is presented that provides a simple explanation for the low population of triply ligated species, relative to the other species, in the oxygenation of human hemoglobin tetramers as found in high-concentration studies [Gill, S. J., Di Cera, E., Doyle, M. L., Bishop, G. A., & Robert, C. H. (1987) Biochemistry (preceding paper in this issue)]. The model is a quantitative interpretation of the Perutz mechanism [Perutz, M. F. (1970) Nature (London) 228, 726-739] and is based on a number of structural and thermodynamic findings so far reported in the analysis of hemoglobin properties. Human hemoglobin is assumed to exist in two quaternary states: the T or low-affinity state and the R or high-affinity state. An extreme chain heterogeneity in the T state is postulated so that oxygen binds only to the alpha chains. Nearest-neighbor interactions between the alpha chains may lead to cooperativity within the T state. The R state is noncooperative, and both the alpha and beta chains have equal oxygen affinity.

Hemoglobins↗

The Conductivity Confined Temperature Dependence of Water-Free Electrorheological Fluids

The shear stresses and the current densities of four sorts of water-free ER fluids, two oxidized polyacrylonitriles-based and two aluminosilicates-based, were investigated at elevated and reduced temperatures in order to elucidate the operating mechanism. The complicated relationships among the mechanical properties, the current density, and temperature were especially addressed. The Wagner model was introduced to interpret our observations, instead of the commonly used conductivity model. It is found that the conductivity of the dispersed particles determines the temperature dependence of the shear stress and the current density of an ER fluid. Our findings have direct implication for the design of industrially required ER fluids and would be significant for ER applications.

Journal Article↗