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PubMed1999

The suprahyoid neck: normal and pathological anatomy.

The suprahyoid neck can be divided into fascia-bound spaces. These spaces, which are readily demonstrated on computed tomography (CT) and magnetic resonance imaging (MRI), form the anatomical framework for generating differential diagnosis and assessing disease extent. By correlating the radiological features with clinical information, the diagnostic possibilities of demonstrated lesions could be narrowed down considerably. Multiple space involvement is common in inflammatory and neoplastic processes and the full extent of these lesions should be outlined to facilitate surgical or radiotherapy planning.

Carotid Arteries↗

PubMed2000

Neither occlusion constraint nor binocular disparity accounts for the perceived depth in the 'sieve effect'.

Current notions of binocular depth perception include (1) neural computations that solve the correspondence problem and calculate retinal positional disparity, and (2) recovery of ecologically valid occlusion relationships. The former framework works well for stimuli with unambiguous interocular correspondence, but less so for stimuli without well-defined disparity cues. The latter framework has been proposed to account for the phenomenon of perceived depth in stimuli without interocular correspondence, but its mechanism remains unclear. In order to obtain more insight into the mechanism, we studied the depth percept elicited by a family of stereograms - 'sieve' stimuli, adapted from Howard (1995) [Perception, 24, 67-74] - with interocular differences but no well-defined positional disparity cue. The perceived depth was measured by comparison to references at various depths established by standard retinal disparity and was consistently found to lie behind the fixation plane. Moreover, the magnitude of the depth percept depended on both the horizontal and vertical spatial characteristics of the stimulus in ways that were at odds with constraints of occlusion geometry. In comparison to the depth percept elicited by stimuli with well-defined disparity cues, the precision of the percept from the sieve stimuli was 10-20 times worse, suggesting that a different underlying computation was involved. Thus, neither of the above frameworks accounts for the depth percept arising from these stimuli. We discuss implications of our results for physiologically based computations underlying binocular depth perception.

Convergence, Ocular↗

PubMed2001

Mathematical modeling for functional divergence after gene duplication.

In this paper, I present a statistical framework for modeling the functional divergence after gene duplication. A rate-component model to describe the rate covariation among homologous genes of a gene family is implemented when a phylogenetic tree is known. The Markov chain model is rigorous but may require a huge amount of computational time when the number of sequences is large. On the other hand, the Poisson-based model is mathematically analytical so that computation is very fast even for a large dataset. Moreover, under the posterior framework, we have developed a site-specific profile for predicting important amino acid residues responsible for these functional differences between member genes of a gene family. Our study may have great potential for functional genomics because it is cost-effective, and these predictions can be further tested by biological experimentation.

Biological Evolution↗

PubMed2003

On the heritability of job satisfaction: the mediating role of personality.

In this article the authors investigate the extent to which traits reflecting individual differences in personality and affectivity explain or mediate genetic influences on job satisfaction. Using estimates of the dispositional source of job satisfaction according to 2 dispositional frameworks--the five-factor model and positive affectivity-negative affectivity (PA-NA)--and behavioral-genetic estimates of the heritabilities of job satisfaction and the dispositional factors, the authors computed the proportion of genetic variance in job satisfaction that is explained by these trait frameworks. Results indicate that the affectivity model is a stronger mediator of genetic effects on job satisfaction than the five-factor model. PA and NA mediate about 45% of the genetic influences on job satisfaction, whereas the five-factor model mediates approximately 24% of these genetic effects.

Affect↗

PubMed2006

Computational intelligence in earth sciences and environmental applications: issues and challenges.

This paper introduces a generic theoretical framework for predictive learning, and relates it to data-driven and learning applications in earth and environmental sciences. The issues of data quality, selection of the error function, incorporation of the predictive learning methods into the existing modeling frameworks, expert knowledge, model uncertainty, and other application-domain specific problems are discussed. A brief overview of the papers in the Special Issue is provided, followed by discussion of open issues and directions for future research.

Artificial Intelligence↗

PubMed2003

Repeated-measures contrasts for "multiple-pattern" hypotheses.

Contrast analysis of repeated-measures data generally focuses on hypotheses when only 1 pattern of results is of theoretical interest. This article articulates a framework for contrast analysis in repeated-measures contexts in which researchers have hypotheses relevant to 1 potential pattern or multiple potential patterns of results. For example, a researcher might ask whether participants exhibit a pattern of (a) immediate symptom reduction or (b) delayed symptom reduction. Alternatively, the researcher might ask whether 2 or more groups exhibit 2 or more patterns to differing degrees. Building on the familiar logic and computational procedures for 1-pattern hypotheses, the authors present a contrast analysis framework that integrates analysis of 1-pattern and multiple-pattern hypotheses and accommodates 1 group or multiple groups of participants.

Humans↗

PubMed2005

TOM software toolbox: acquisition and analysis for electron tomography.

Automated data acquisition procedures have changed the perspectives of electron tomography (ET) in a profound manner. Elaborate data acquisition schemes with autotuning functions minimize exposure of the specimen to the electron beam and sophisticated image analysis routines retrieve a maximum of information from noisy data sets. "TOM software toolbox" integrates established algorithms and new concepts tailored to the special needs of low dose ET. It provides a user-friendly unified platform for all processing steps: acquisition, alignment, reconstruction, and analysis. Designed as a collection of computational procedures it is a complete software solution within a highly flexible framework. TOM represents a new way of working with the electron microscope and can serve as the basis for future high-throughput applications.

Algorithms↗

PubMed2003

Design and performance frameworks for constructing problem-solving simulations.

Rapid advancements in hardware, software, and connectivity are helping to shorten the times needed to develop computer simulations for science education. These advancements, however, have not been accompanied by corresponding theories of how best to design and use these technologies for teaching, learning, and testing. Such design frameworks ideally would be guided less by the strengths/limitations of the presentation media and more by cognitive analyses detailing the goals of the tasks, the needs and abilities of students, and the resulting decision outcomes needed by different audiences. This article describes a problem-solving environment and associated theoretical framework for investigating how students select and use strategies as they solve complex science problems. A framework is first described for designing on-line problem spaces that highlights issues of content, scale, cognitive complexity, and constraints. While this framework was originally designed for medical education, it has proven robust and has been successfully applied to learning environments from elementary school through medical school. Next, a similar framework is detailed for collecting student performance and progress data that can provide evidence of students' strategic thinking and that could potentially be used to accelerate student progress. Finally, experimental validation data are presented that link strategy selection and use with other metrics of scientific reasoning and student achievement.

Computer Simulation↗

PubMed1991

Designing an outcome-oriented computer decision-support system for cardiovascular ICU--a preliminary report.

This paper describes the conceptual framework and preliminary results of an outcome-oriented decision-support system prototype for the cardiovascular intensive care unit (CVICU). The major characteristics of this design include: (1) its problem-based approach to solving clinical problems; (2) an integrated structure with the hospital information system in terms of its data, model and knowledge bases; (3) proposed alternative modes of interaction that include monitoring and critiquing; (4) and research modules that design, manage, and analyze outcome-based clinical studies. At present, an initial prototype has been implemented on a PC as a set of modules accessible from a main menu. The structural framework of the overall system is fairly well defined but only limited quantitative, statistical and expert knowledge has been captured. The second phase of the project involves porting the prototype to a Unix workstation environment, refining and adding models to the model base, expanding its knowledge bases, reasoning capability, and testing the prototype with actual clinical cases in a real-time fashion.

Alberta↗

PubMed2026

Scalable, generalizable and uncertainty-aware integration of spatial multiomics across diverse modalities and platforms with SCIGMA.

Recent advances in spatial omics technologies have enabled simultaneous profiling of transcriptomic, proteomic, epigenomic, metabolomic and imaging data at high spatial resolution, offering unprecedented opportunities to dissect tissue complexity. However, integrating these diverse and large-scale spatial multimodal datasets remains a major computational challenge. We present SCIGMA, a scalable and generalizable deep learning framework for spatial multiomics integration. SCIGMA introduces an uncertainty-aware contrastive learning objective and multiview graph neural networks to preserve modality-specific signals while learning biologically meaningful joint representations. Unlike previous methods, SCIGMA provides spatially resolved uncertainty estimates, interpretably identifying regions of biological or technical heterogeneity. SCIGMA supports integration of up to five modalities, and its modular framework is extensible to future technologies with even more modalities. It also scales to more than 1 million spatial locations, enabling analysis of high-resolution datasets such as Visium HD and Xenium Prime. We evaluated SCIGMA across 19 datasets spanning 8 modalities, 10 tissues and 9 platforms. On benchmarkable datasets, SCIGMA outperformed other methods in spatial domain detection, modality preservation, feature reconstruction and reproducibility. SCIGMA identifies biologically meaningful structures, refined spatial domains and modality-specific regulatory programs, providing a robust, flexible and future-ready solution for scalable spatial multimodal integration.

Multiomics↗

PubMed2026

Phylogenomic subsampling and upsampling for efficient evolutionary analyses of big data.

Long runtimes, high memory demands, and reliance on high-performance computing impede phylogenomic analyses. We review a scalable phylogenomic subsampling with upsampling (PSU) framework to address this challenge, which reduces runtime and memory requirements by orders of magnitude. In PSU, small subsamples of sites from a concatenated alignment are analyzed, which are expanded by upsampling before inference, and the resulting inferences are aggregated to obtain evolutionary estimates. PSU harnesses the fact that the computational cost of maximum likelihood analysis is strongly influenced by the number of distinct site patterns in the concatenated alignment, whereas statistical power depends primarily on the amount of evolutionary information represented by the total number of sites and substitutions. By reducing the former while restoring the latter through upsampling, PSU can approximate many full-alignment analyses at substantially lower computational cost. Analysis of simulated and empirical datasets shows that PSU can accurately estimate bootstrap support values, select the optimal substitution model, test evolutionary hypotheses, and infer branch lengths, divergence times, and associated uncertainty measures. PSU also provides distributions of inferred clade support across independent subsamples, enabling detection of conflicting phylogenetic signals that may remain hidden in conventional bootstrap analysis of concatenated alignments. Automated tuning of subsample size, the number of subsamples, and the number of upsampling replicates make PSU practical. We suggest that PSU is a general approach for scalable phylogenomic inference using a broad range of statistical methods. By enabling analyses of genome-scale alignments on commodity hardware, PSU broadens research access and reduces environmental and infrastructural costs of big-data phylogenomics.

Phylogeny↗

PubMed2002

A Bayesian framework for combining gene predictions.

MOTIVATION: Gene identification and gene discovery in new genomic sequences is one of the most timely computational questions addressed by bioinformatics scientists. This computational research has resulted in several systems that have been used successfully in many whole-genome analysis projects. As the number of such systems grows the need for a rigorous way to combine the predictions becomes more essential. RESULTS: In this paper we provide a Bayesian network framework for combining gene predictions from multiple systems. The framework allows us to treat the problem as combining the advice of multiple experts. Previous work in the area used relatively simple ideas such as majority voting. We introduce, for the first time, the use of hidden input/output Markov models for combining gene predictions. We apply the framework to the analysis of the Adh region in Drosophila that has been carefully studied in the context of gene finding and used as a basis for the GASP competition. The main challenge in combination of gene prediction programs is the fact that the systems are relying on similar features such as cod on usage and as a result the predictions are often correlated. We show that our approach is promising to improve the prediction accuracy and provides a systematic and flexible framework for incorporating multiple sources of evidence into gene prediction systems.

Algorithms↗

PubMed1997

[Computer networks in hospital information systems].

The author explains the term computer network in health institutions, incl. links with the hospital information system. He describes basic technical principles necessary for the understanding of medical network applications in the framework of the information system. The author mentions the importance of connecting the hospital network to international computer networks.

Computer Communication Networks↗

PubMed2025

CSGL: chemical synthesis graph learning for molecule representation.

MOTIVATION: Molecule representation learning (MRL) translates molecules into a real vector space, serving as input to downstream tasks in biology, chemistry, and computer science. This article introduces a chemical synthesis graph learning (CSGL) framework, which enhances MRL by considering both the atomic structures of molecules and their roles in chemical reactions through a hierarchical graph representation. Specifically, molecules are first modeled based on their molecular graphs, which capture atomic-level structural information. They are then further refined using a chemical synthesis graph, where nodes represent reactant and product molecule sets, and edges encode chemical transformations between reactants and products (e.g. changes in molecular structures). CSGL optimizes molecular embeddings of reactant and product nodes in a fashion that ensures the embeddings conform to a chemical balance constraint. RESULTS: Experimental results show that our method CSGL achieves strong performance on a variety of tasks, including product prediction, reaction classification, and molecular property prediction. AVAILABILITY AND IMPLEMENTATION: https://github.com/li-2023/CSGL.

Machine Learning↗

PubMed2004

RNA 3D structure prediction: (1) assessing rna 3D structure similarity from 2D structure similarity.

Computational techniques for 3D structure prediction of proteins, the holy grail of bioinformatics, have undergone major developments in recent years, geared by international cooperation and competition with CASP (Critical Assessment of Structure Prediction Techniques) like contests to improve and refine them. Although straightforward extrapolation of these methodologies for the prediction of the 3D structures of other similarly relevant bio macromolecules may not be too compelling due mostly to the intrinsic differences in constitution, nature, and function between them, the conceptual framework underlying most of those techniques applied to the development of similar computational techniques in structural biology can lead to efficient systems for prediction of the 3D structure of other bio-macromolecules. One of them is the development of rational methodologies to model RNA 3D structures from the sequence of nucleotides composing them. In this paper we establish the fundamentals of a methodology to thread a sequence of nucleotides into a set of 3D fragments extracted from a data base expressly developed for this purpose. The technique is based on a newly implemented algorithm for extraction of 3D fragments by comparison of secondary structures of RNA. The result is a highly efficient system to produce a set of fragments from which entire RNA structure for the given nucleotide sequence can be built.

Algorithms↗

PubMed2005

Computational models of working memory: putting long-term memory into context.

Detailed computational modeling of human memory has typically been aimed at either short-term (working) memory or long-term memory in isolation. However, recent research highlights the importance of interactions between these systems for both item and order information. At the same time, computational models of both systems are beginning to converge onto a common framework in which items are associated with an evolving "context" signal and subsequently compete with one another at recall. We review some of these models, and discuss a common mechanism capable of modelling working memory and its interaction with long-term memory, focussing on memory for verbal sequences.

Attention↗

PubMed2006

On the modeling of small sample distributions with generalized Gaussian density in a maximum likelihood framework.

The modeling of sample distributions with generalized Gaussian density (GGD) has received a lot of interest. Most papers justify the existence of GGD parameters through the asymptotic behavior of some mathematical expressions (i.e., the sample is supposed to be large). In this paper, we show that the computation of GGD parameters on small samples is not the same as on larger ones. In a maximum likelihood framework, we exhibit a necessary and sufficient Condition for the existence of the parameters. We derive an algorithm to compute them and then compare it to some existing methods on random images of different sizes.

Algorithms↗

PubMed1998

Evolution of virulence: a unified framework for coinfection and superinfection.

Models of the evolution of parasite virulence have focused on computing the evolutionarily stable level of virulence favored by tradeoffs within a host and by competition for hosts, and deriving conditions under which strains with different virulence levels can coexist. The results depend on the type of interaction between disease strains, such as single infection (immunity of infected individuals to other strains), coinfection (simultaneous infection by two strains), and superinfection (instantaneous takeover of host by the more virulent strain). We present a coinfection model with two strains and derive the superinfection model as the limit where individuals are rapidly removed from the doubly-infectious class. When derived in this way, the superinfection model includes not only the takeover of hosts infected by the less virulent strain, but new terms which take into account the possibility of increased mortality of doubly-infected individuals. Coinfection tends to favor higher virulence and support more coexistence than the single infection model, but the detailed results depend sensitively on two factors: (1) whether and how the model is near the superinfection limit, and (2) the shape of the coinfection function (the function describing the rate at which a more virulent strain can infect a host). If the superinfection limit arises due to rapid mortality of doubly-infected hosts, there is a region of uninvadable virulence levels rather than coexistence. When the coinfection function is discontinuous, as in many previous models, neither the coinfection model nor the superinfection limit can support an evolutionarily stable virulence level. Piecewise differentiable and differentiable coinfection functions produce qualitatively different results, and we propose that these more general cases should be used to study evolution of virulence when other mechanisms like space, population dynamics, and stochasticity interact.

Animals↗