Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “computational frameworks”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 811 records · Page 45Linked to original sources
PubMed1990

[Deviation and rotation of the larynx in computer tomography].

Many authors described the clinical importance of asymmetry of the laryngeal framework. However, its pathogenesis is generally unknown. In this study, CT images of 315 Japanese subjects were investigated to define the laryngeal position relative to the midline of the cervical vertebra. The CT slice of each subject within 5 mm cephalad of the cricoarytenoid joint was traced. Then, the deviation and rotation angles were measured using our method. Seventy one percent of the subjects' larynges deviated and/or rotated to the right side, while 17% to the left side. Six percent showed neither deviation nor rotation. As to the rest of 6%, deviation and rotation were in opposite directions. Besides, the length of the thyroid alae were measured in 282 subjects. Left ala was longer in 55%, and right was in 23%, and almost equal in 22%. The conclusions are as follows, 1. The majority of the subjects' CT images showed deviation and/or rotation of the laryngeal framework to the right side. 2. So called idiopathic laryngeal deviation is a case which observed in those cases with remarkable deviation and/or rotation of the laryngeal framework. 3. Aging seemed to be an important factor in acceleration of the laryngeal deviation and rotation. 4. The type of diseases and the side of mass lesions had no statistical significance in deviation and rotation of the larynx.

Adult↗
PubMed2005

A general framework for nonlinear multigrid inversion.

A variety of new imaging modalities, such as optical diffusion tomography, require the inversion of a forward problem that is modeled by the solution to a three-dimensional partial differential equation. For these applications, image reconstruction is particularly difficult because the forward problem is both nonlinear and computationally expensive to evaluate. In this paper, we propose a general framework for nonlinear multigrid inversion that is applicable to a wide variety of inverse problems. The multigrid inversion algorithm results from the application of recursive multigrid techniques to the solution of optimization problems arising from inverse problems. The method works by dynamically adjusting the cost functionals at different scales so that they are consistent with, and ultimately reduce, the finest scale cost functional. In this way, the multigrid inversion algorithm efficiently computes the solution to the desired fine-scale inversion problem. Importantly, the new algorithm can greatly reduce computation because both the forward and inverse problems are more coarsely discretized at lower resolutions. An application of our method to Bayesian optical diffusion tomography with a generalized Gaussian Markov random-field image prior model shows the potential for very large computational savings. Numerical data also indicates robust convergence with a range of initialization conditions for this nonconvex optimization problem.

Algorithms↗
PubMed2004

Quantum computation, non-demolition measurements, and reflective control in living systems.

Internal computation underlies robust non-equilibrium living process. The smallest details of living systems are molecular devices that realize non-demolition quantum measurements. These smaller devices form larger devices (macromolecular complexes), up to living body. The quantum device possesses its own potential internal quantum state (IQS), which is maintained for a prolonged time via reflective error-correction. Decoherence-free IQS can exhibit itself by a creative generation of iteration limits in the real world. It resembles the properties of a quasi-particle, which interacts with the surround, applying decoherence commands to it. In this framework, enzymes are molecular automata of the extremal quantum computer, the set of which maintains highly ordered robust coherent state, and genome represents a concatenation of error-correcting codes into a single reflective set. The biological evolution can be viewed as a functional evolution of measurement constraints in which limits of iteration are established, possessing criteria of perfection and having selective values.

Adaptation, Physiological↗
PubMed2026

A scalable HPC framework for bioinformatics in resource-limited settings: design principles, implementation, and sustainability from the UVRI experience.

MOTIVATION: Building and sustaining High-Performance Computing (HPC) infrastructure for bioinformatics research in resource-limited settings presents significant technical, financial and operational challenges. Institutions in low-and middle-income regions often face constraints such as limited technical expertise, unstable infrastructure and restricted funding which can hinder the deployment of large-scale computational platforms necessary for modern genomics and bioinformatics analyses. RESULTS: We present a scalable and modular HPC framework developed at the Uganda Virus Research Institute (UVRI) to support large-scale genomics and other omics data analyses in resource-limited settings. The framework integrates open-source HPC management tools, infrastructure automation, and reproducible configuration management to enable reliable deployment and maintenance. Optimized storage and networking configurations combined with a phased capacity-building strategy support high-throughput genomic workflows while strengthening local technical expertise. From our implementation experience, we derive ten practical design and operational rules that provide a transferable methodology for establishing and sustaining in-house HPC infrastructure. These rules emphasize strategic investment in human capacity, structured planning, leveraging collaborations, adoption of open-source technologies and service management practices to improve operational resilience and long-term sustainability. AVAILABILITY: The design principles, automation strategies and implementation guidelines described in this work are applicable to institutions seeking to establish sustainable HPC resources for bioinformatics research in resource-constrained environments.

Computational Biology↗
PubMed2002

[Computer-assisted system for analysis of heart rate variability in clinical studies].

This paper describes an adaptable framework that facilitates exploratory analysis, interpretation and classification of beat-to-beat data extracted from the electrocardiogram (ECG). The system supports a variety of user-defined annotations and allows the definition of analysis programs. Special care is taken on the correct treatment of corrupted and missing data, ubiquitously found in real world problems. Besides the computation, the performance of single features can be inspected using different kinds of diagrams provided by the system. Combinations of features can be evaluated using a polynomial classifier. Both the computation and combination of features are defined as tasks that can be dispatched by a server to various clients. The framework is easily adaptable to different problem structures and has been used successfully in three studies.

Atrial Fibrillation↗
PubMed1996

The dynamics of discrete-time computation, with application to recurrent neural networks and finite state machine extraction.

Recurrent neural networks (RNNs) can learn to perform finite state computations. It is shown that an RNN performing a finite state computation must organize its state space to mimic the states in the minimal deterministic finite state machine that can perform that computation, and a precise description of the attractor structure of such systems is given. This knowledge effectively predicts activation space dynamics, which allows one to understand RNN computation dynamics in spite of complexity in activation dynamics. This theory provides a theoretical framework for understanding finite state machine (FSM) extraction techniques and can be used to improve training methods for RNNs performing FSM computations. This provides an example of a successful approach to understanding a general class of complex systems that has not been explicitly designed, e.g., systems that have evolved or learned their internal structure.

Neural Networks, Computer↗
PubMed2004

RecA mediated initial alignment of homologous DNA molecules displays apparent first order kinetics with little effect of heterology.

The mechanism and determinants of RecA mediated initial alignment of homologous DNA molecules were studied by performing Monte Carlo simulations of the dynamics of DNA molecules. The simulation procedure was used to assess the effect of heterologous DNA and dilution on the rate of formation and yield of homologous alignments. The results show that the apparent first order kinetic behavior and the impact of heterologous DNA, reported in literature [J. Biol. Chem. 261 (1986) 1025], can be observed even if the conversion of the initially aligned molecules into a stable joint is not rate-determining. The present study is the first step towards developing rigorous computational models to describe the process of homologous recombination, and theoretical frameworks to retrieve biophysical parameters of strand pairing and exchange proteins from in vitro assays of joint molecule formation.

Computer Simulation↗
PubMed2006

Numbers and space: a computational model of the SNARC effect.

The SNARC (spatial numerical associations of response codes) effect reflects the tendency to respond faster with the left hand to relatively small numbers and with the right hand to relatively large numbers (S. Dehaene, S. Bossini, & P. Giraux, 1993). Using computational modeling, the present article aims to provide a framework for conceptualizing the SNARC effect. In line with models of spatial stimulus-response congruency, the authors modeled the SNARC effect as the result of parallel activation of preexisting links between magnitude and spatial representation and short-term links created on the basis of task instructions. This basic dual-route model simulated all characteristics associated with the SNARC effect. In addition, 2 experiments tested and confirmed new predictions derived from the model.

Adult↗
PubMed2003

Models to predict emissions of health-damaging pollutants and global warming contributions of residential fuel/stove combinations in China.

Residential energy use in developing countries has traditionally been associated with combustion devices of poor energy efficiency, which have been shown to produce substantial health-damaging pollution, contributing significantly to the global burden of disease, and greenhouse gas (GHG) emissions. Precision of these estimates in China has been hampered by limited data on stove use and fuel consumption in residences. In addition limited information is available on variability of emissions of pollutants from different stove/fuel combinations in typical use, as measurement of emission factors requires measurement of multiple chemical species in complex burn cycle tests. Such measurements are too costly and time consuming for application in conjunction with national surveys. Emissions of most of the major health-damaging pollutants (HDP) and many of the gases that contribute to GHG emissions from cooking stoves are the result of the significant portion of fuel carbon that is diverted to products of incomplete combustion (PIC) as a result of poor combustion efficiencies. The approximately linear increase in emissions of PIC with decreasing combustion efficiencies allows development of linear models to predict emissions of GHG and HDP intrinsically linked to CO2 and PIC production, and ultimately allows the prediction of global warming contributions from residential stove emissions. A comprehensive emissions database of three burn cycles of 23 typical fuel/stove combinations tested in a simulated village house in China has been used to develop models to predict emissions of HDP and global warming commitment (GWC) from cooking stoves in China, that rely on simple survey information on stove and fuel use that may be incorporated into national surveys. Stepwise regression models predicted 66% of the variance in global warming commitment (CO2, CO, CH4, NOx, TNMHC) per 1 MJ delivered energy due to emissions from these stoves if survey information on fuel type was available. Subsequently if stove type is known, stepwise regression models predicted 73% of the variance. Integrated assessment of policies to change stove or fuel type requires that implications for environmental impacts, energy efficiency, global warming and human exposures to HDP emissions can be evaluated. Frequently, this involves measurement of TSP or CO as the major HDPs. Incorporation of this information into models to predict GWC predicted 79% and 78% of the variance respectively. Clearly, however, the complexity of making multiple measurements in conjunction with a national survey would be both expensive and time consuming. Thus, models to predict HDP using simple survey information, and with measurement of either CO/CO2 or TSP/CO2 to predict emission factors for the other HDP have been derived. Stepwise regression models predicted 65% of the variance in emissions of total suspended particulate as grams of carbon (TSPC) per 1 MJ delivered if survey information on fuel and stove type was available and 74% if the CO/CO2 ratio was measured. Similarly stepwise regression models predicted 76% of the variance in COC emissions per MJ delivered with survey information on stove and fuel type and 85% if the TSPC/CO2 ratio was measured. Ultimately, with international agreements on emissions trading frameworks, similar models based on extensive databases of the fate of fuel carbon during combustion from representative household stoves would provide a mechanism for computing greenhouse credits in the residential sector as part of clean development mechanism frameworks and monitoring compliance to control regimes.

Air Pollutants↗
PubMed1998

Learning with technology: use of case-based physical and computer simulations in professional education.

This paper describes a multimedia technology project in midwifery education and how it is being developed to improve student learning experiences and outcomes. The role of the university providing quality education relevant to today's world and professional practice is emphasised. A collaborative (Royal Melbourne Institute of Technology and Australian Catholic University) interdisciplinary project 'Pregnancy Simulator: Developing and Enhancing Student Learning of Pregnancy Assessment Skills' was developed with university and direct Commonwealth support. This project consists of a physical simulation of a pregnant woman at term and case-based multimedia computer simulations designed to develop and enhance student learning of abdominal assessment skills. A key feature of the development has been to design a learning experience explicitly on an authoritative theory-based view of teaching, in this case Diana Laurillard's 'Conversational Framework'.

Computer Simulation↗
PubMedDate not supplied

Information technology and computer-based decision support in diabetic management.

This paper describes the application of computer-based techniques within an intelligent, knowledge-based framework to the management of diabetes. The objectives are to structure data collection and storage so that the relevant patient-specific data are collected and made accessible as needed, and to provide clinical decision support on either a day-by-day or longer timescale as appropriate; these objectives relating to both hospital clinic and general practice. For longer-term management, a prototype rule set (greater than 500 rules) has been developed (coded in Sigma PROLOG), validated and tested on patient data. The data collection programs (written in SCULPTOR) to feed the ruleset have been tested in the hospital clinic and compared with the resident data collection system for usability, and impact on the running of the clinic. Links between the data collection programs and the ruleset program have been written and tested. The computer system will also incorporate a module, combining knowledge-based advisory system and glucose/insulin model as patient simulator, that can be tested as a potential decision aid for adjusting insulin dosage on a daily basis.

Data Collection↗
PubMed2006

Neuronal computations with stochastic network states.

Neuronal networks in vivo are characterized by considerable spontaneous activity, which is highly complex and intrinsically generated by a combination of single-cell electrophysiological properties and recurrent circuits. As seen, for example, during waking compared with being asleep or under anesthesia, neuronal responsiveness differs, concomitant with the pattern of spontaneous brain activity. This pattern, which defines the state of the network, has a dramatic influence on how local networks are engaged by inputs and, therefore, on how information is represented. We review here experimental and theoretical evidence of the decisive role played by stochastic network states in sensory responsiveness with emphasis on activated states such as waking. From single cells to networks, experiments and computational models have addressed the relation between neuronal responsiveness and the complex spatiotemporal patterns of network activity. The understanding of the relation between network state dynamics and information representation is a major challenge that will require developing, in conjunction, specific experimental paradigms and theoretical frameworks.

Anesthesia↗
PubMed2004

Computational models of the basal ganglia: from robots to membranes.

With the rapid accumulation of neuroscientific data comes a pressing need to develop models that can explain the computational processes performed by the basal ganglia. Relevant biological information spans a range of structural levels, from the activity of neuronal membranes to the role of the basal ganglia in overt behavioural control. This viewpoint presents a framework for understanding the aims, limitations and methods for testing of computational models across all structural levels. We identify distinct modelling strategies that can deliver important and complementary insights into the nature of problems the basal ganglia have evolved to solve, and describe methods that are used to solve them.

Animals↗
PubMed2026

Collective posterior inference from highly variable empirical replicates.

High-throughput experimental platforms now routinely generate data from dozens or hundreds of independent observations. Simulation-based inference (SBI) offers a powerful framework for estimating model parameters from such complex datasets, but standard methods struggle to scale to the noisy multiple-replicates regime without incurring prohibitive computational costs or careful hyperparameter tuning. Here, we introduce a new method for fast and robust collective posterior inference from multiple independent replicates using a robust product-of-experts aggregation scheme that automatically mitigates the influence of outliers. Evaluating it on synthetic and empirical evolutionary datasets, we find it achieves state-of-the-art estimation accuracy and computational efficiency, including inference from noisy observations. Our method is compatible with any SBI framework, providing a scalable, plug-and-play solution for inference from noisy multiple-replicate datasets.

Computational Biology↗