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An extensive comparison between measured and computed heat absorption distributions in several phantoms inside the 'coaxial TEM' applicator is presented. The influence of the width of the aperture and of the presence of a PVC tube is especially considered.
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Skin expansion is the principal technique used in plastic surgery to repair large cutaneous defects, typically after tumour removal, burn care, craniofacial surgery and post-mastectomy breast reconstruction. It allows a gain of new tissue by means of gradual expansion of a prosthesis, surgically implanted beneath the patient's skin. Nevertheless, wide clinical use is not supported by a deep quantitative knowledge of the phenomena occurring during the expansion. A finite element model of the skin expansion was developed to evaluate the stresses and the strains of the skin due to the expander inflation and validated by proper in vitro experiments; furthermore, a growth model based on the mechanical stimulus was implemented to estimate the skin area gain. The developed computational approach, composed of the skin expansion model interaction and the growth law, proved its validity to investigate skin expansion phenomena: its use suggests a new predictive tool to optimize clinical procedures and the expander devices' design.
Mouse models represent a powerful platform to study genes and variants associated with human diseases. While genome editing technologies have increased the rate and precision of model development, predicting and installing specific types of mutations in mice that mimic the native human genetic context is complicated. Computational tools can identify and align orthologous wild-type genetic sequences from different species; however, predictive modeling and engineering of equivalent mouse variants that mirror the nucleotide and/or polypeptide change effects of human variants remains challenging. Here, we present H2M (human-to-mouse), a computational pipeline to analyze human genetic variation data to systematically model and predict the functional consequences of equivalent mouse variants. We show that H2M can integrate mouse-to-human and paralog-to-paralog variant mapping analyses with precision genome editing pipelines to devise strategies tailored to model specific variants in mice. We leveraged these analyses to establish a database containing > 3 million human-mouse equivalent mutation pairs, as well as in silico-designed base and prime editing libraries to engineer 4,944 recurrent variant pairs. Using H2M, we also found that predicted pathogenicity and immunogenicity scores were highly correlated between human-mouse variant pairs, suggesting that variants with similar sequence change effects may also exhibit broad interspecies functional conservation. Overall, H2M fills a gap in the field by establishing a robust and versatile computational framework to identify and model homologous variants across species while providing key experimental resources to augment functional genetics and precision medicine applications. The H2M database (including software package and documentation) can be accessed at https://human2mouse.com.
OBJECTIVE: To explore the use of photodynamic therapy (PDT) as a minimally invasive form of treatment for organ-confined prostate cancer, for although there are several therapies, ablative treatments are associated with significant morbidity. MATERIALS AND METHODS: Using the photosensitizer tin etiopurpurin, dogs were treated with interstitially placed laser fibres in an effort to validate PDT for treating prostate cancer. Earlier models assumed a uniform distribution of light output from a cylindrical fibre and a uniform attenuation coefficient throughout the prostate. Subsequent observations show that this model was too simple and that light radiance is not linear. To overcome under-treatment, a computer program to complement real-time fibre placement was developed. RESULTS: As light radiance from interstitially placed laser fibres varies significantly from the commonly assumed ideal cylindrical emission, a predictive mathematical model of prostate PDT needs to consider the real emission. Also, the optical properties of the prostate, e.g. absorption and scattering of light, are anisotropic. Differences in the attenuation coefficient (combining absorption and scattering of light) also varied among different animals. Incorporating all these variables into a computer program produced a virtual model of the photo-ablated zone within +/- 2 mm of that observed in animals. CONCLUSION: PDT of the prostate is not trivial and should benefit from computer-aided methods as it is developed for clinical use.
We use a vocal-cord/vocal-tract model devised earlier to study acoustic effects of the air volume displaced by the vibrating vocal cords. We consider both lateral and longitudinal motion of the cords. We make computer simulations to establish quantitatively the contribution of the displacement current to the total glottal volume velocity (and hence to the total sound excitation for voiced sounds). The displacement current from both lateral and longitudinal motion is found to be second-order by comparison to the total glottal flow. The displacement contribution can, however, be identified in the time waveforms and spectra of the glottal and mouth volume velocities. We use the computer formulation to synthesize speech sounds with and without the displacement current. Auditory observations and spectral calculations on the synthetic output show the displacement current to be discriminable only in close differential comparisons.
The planar distribution of intramembranous particles on the P faces of freeze-fractured human erythrocyte membranes is characterized by radial distribution, angular distribution and differential density distribution analysis. Various degrees of intramembranous particle aggregation induced by spectrin removal and low pH are differentiated through computation. Random hard disk models with various disk diameters are built for comparison studies. In all samples, the 80 +/- 10 A particles are found to have a preferred neighboring distance of 100 +/- 10 A, but no preferred angular relation is found between neighboring particles. A pattern recognition process using both radial and density distribution analyses reveals that none of the particle distributions observed may be regarded as random. The fact that the particle distributions observed are neither even nor random suggests that factors other than long range electrostatic force alone are involved in determining the particle distribution.
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The cyclin-dependent protein kinases are important targets in drug discovery because of their role in cell cycle regulation. In this computational study, we have applied a continuum solvent model to study the interactions between cyclin-dependent kinase 2 (CDK2) and analogues of the clinically tested anticancer agent flavopiridol. The continuum solvent model uses Coulomb's law to account for direct electrostatic interactions, solves the Poisson equation to obtain the electrostatic contributions to solvation energy, and calculates scaled solvent-accessible surface area to account for hydrophobic interactions. The computed free energy of binding gauges the strength of protein-ligand interactions. Our model was first validated through a study on the binding of a number of flavopiridol derivatives to CDK2, and its ability to identify potent inhibitors was observed. The model was then used to aid in the design of novel CDK2 inhibitors with the aid of a computational sensitivity analysis. Some of these hypothetical structures could be significantly more potent than the lead compound flavopiridol. We applied two approaches to gain insights into designing selective inhibitors. One relied on the comparative analysis of the binding pocket for several hundred protein kinases to identify the parts of a lead compound whose modifications might lead to selective compounds. The other was based on building and using homology models for energy calculations. The homology models appear to be able to classify ligand potency into groups but cannot yet give reliable quantitative results.
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Brief descriptions are provided for commonly used computer codes for estimating the consequences of atmospheric releases of radionuclides and chemicals. Different levels of sophistication and input detail are necessary for emergency response versus emergency planning.
Over recent years, neural network models of several cognitive neuropsychological disorders have been developed. These include word recognition difficulties, face recognition difficulties, attentional deficits, visual processing impairments, semantic deficits, and aphasia. These models are useful in various ways. Firstly, they require detailed specifications of theories, and can focus attention on critical assumptions. Secondly, they can query alternative theories, and provide predictions which can be verified by testing patients. In this paper, issues relating both to the methodology and validity of attempts to model cognitive deficits using neural networks will be discussed, providing examples from several studies. Issues discussed will include the requirement for models to perform normally prior to damage, and to show potential effects of rehabilitation or partial recovery following damage. A single model should be able to incorporate multiple symptoms of a deficit and ideally also multiple syndromes when different lesions are introduced. The model must also be able to handle variability between patients with the same syndrome, and even with the same patient at different test sessions.
We present a biologically plausible model of processing intrinsic to the basal ganglia based on the computational premise that action selection is a primary role of these central brain structures. By encoding the propensity for selecting a given action in a scalar value (the salience), it is shown that action selection may be recast in terms of signal selection. The generic properties of signal selection are defined and neural networks for this type of computation examined. A comparison between these networks and basal ganglia anatomy leads to a novel functional decomposition of the basal ganglia architecture into 'selection' and 'control' pathways. The former pathway performs the selection per se via a feedforward off-centre on-surround network. The control pathway regulates the action of the selection pathway to ensure its effective operation, and synergistically complements its dopaminergic modulation. The model contrasts with the prevailing functional segregation of basal ganglia into 'direct' and 'indirect' pathways.
We have derived a model of the nicotinic acetylcholine binding site. This was accomplished by using three known agonists (acetylcholine, nicotine and epibatidine) as templates around which polypeptide side chains, found to be part of the receptor cavity from published molecular biology studies, are allowed to flow freely in molecular dynamics simulations and mold themselves around these templates. The resulting supramolecular complex should thus be a complement, both in terms of steric effects as well as electronic effects, to the agonists and it should be a good estimation of the true receptor cavity structure. The shapes of those minireceptor cavities equilibrated rapidly on the simulation time scale and their structural congruence is very high, implying that a satisfactory model of the nicotinic acetylcholine binding site has been achieved. The computational methodology was internally tested against two rigid and specific antagonists (dihydro-beta-erytroidine and erysoidine), that are expected to give rise to a somewhat differently shaped binding site compared to that derived from the agonists. Using these antagonists as templates there were structural reorganizations of the initial receptor cavities leading to distinctly different cavities compared to agonists. This indicates that adequate times and temperatures were used in our computational protocols to achieve equilibrium structures for the agonists. Overall, both minireceptor geometries for agonists and antagonists are similar with the exception of one amino acid (ARG209).
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The present paper compares current mathematical striated sphincter models. Current models are subdivided in four categories: (1) simple models, (2) implementations of the urethral resistance relation, (3) models with realistic muscle dynamics and (4) finite element models. In our research group a neural network model, representing Onuf's nucleus, the spinal motor nucleus that innervates the external urethral and anal sphincters, was developed. A realistic sphincter model is needed to test the neural network. To decide whether or not a model is applicable in our research two requirements should be fulfilled: (1) the presence of realistic muscle dynamics preferably by implementation of a Huxley type muscle model and (2) the model should consist of more than one muscle unit to form a more dimensional model. Reviewing the literature, if a myogenic sphincter is modelled, mainly the Hill-equation is applied. Moreover, single muscle unit models are published. In general a multi-unit muscle model of the sphincter is lacking, prohibiting the study of the inherent properties of sphincter muscles, which could give information on the realistic behaviour of elements in circular muscles. It is concluded that the functionality of current sphincter models is limited for our purpose.
A dynamic model for studying man's movements is proposed. Lagrange equations of the second order are used. Differential equations of the model are presented in the matrix form, and all the coefficients involved are calculated from recurrent formulae. The dynamic model described is easily algorythmized. Differentiating operations can be thus avoided which are realized on electron computers with difficulities.
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