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Results for “computational frameworks”

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PubMed2004

Integration of microbial ecology and statistics: a test to compare gene libraries.

Libraries of 16S rRNA genes provide insight into the membership of microbial communities. Statistical methods help to determine whether differences in library composition are artifacts of sampling or are due to underlying differences in the communities from which they are derived. To contribute to a growing statistical framework for comparing 16S rRNA libraries, we present a computer program, integral -LIBSHUFF, which calculates the integral form of the Cramér-von Mises statistic. This implementation builds upon the LIBSHUFF program, which uses an approximation of the statistic and makes a number of modifications that improve precision and accuracy. Once integral -LIBSHUFF calculates the P values, when pairwise comparisons are tested at the 0.05 level, the probability of falsely identifying a significant P value is 0.098 for a study with two libraries, 0.265 for three libraries, and 0.460 for four libraries. The potential negative effects of making the multiple pairwise comparisons necessitate correcting for the increased likelihood that differences between treatments are due to chance and do not reflect biological differences. Using integral -LIBSHUFF, we found that previously published 16S rRNA gene libraries constructed from Scottish and Wisconsin soils contained different bacterial lineages. We also analyzed the published libraries constructed for the zebrafish gut microflora and found statistically significant changes in the community during development of the host. These analyses illustrate the power of integral -LIBSHUFF to detect differences between communities, providing the basis for ecological inference about the association of soil productivity or host gene expression and microbial community composition.

Aeromonas↗
PubMedDate not supplied

Spectrum of imaging findings in hyperextension injuries of the neck.

Nonphysiologic hyperextension and lateral forces acting on the cervical spine and soft-tissue structures of the neck can result in a wide spectrum of injury patterns. Multiple factors (eg, patient age; the underlying morphologic features of the cervical spine; the magnitude, vector, and maximal focus of the force) all influence the observed patterns and the severity of injury. A review of the 5-year trauma database in two trauma centers revealed various injury patterns that were frequently recognized in patients with clinical evidence or historical documentation of a predominant hyperextension mechanism. Injuries included anterior arch avulsion and posterior arch compression fractures of the atlas, odontoid fractures, traumatic spondylolisthesis and teardrop fracture of C2, laminar and articular pillar fractures, and hyperextension dislocation injuries. More severe injuries were observed in patients with underlying predisposing conditions (eg, degenerative spondylosis, ankylosing spondylitis, diffuse idiopathic skeletal hyperostosis). Knowledge of the involved biomechanical factors provides a framework for understanding these injury patterns. Diagnostic imaging, especially computed tomography and magnetic resonance imaging, plays a fundamental role in the assessment of patients with suspected cervical injury. Furthermore, cross-sectional imaging facilitates the recognition of accompanying injuries to the face, the head, and the vascular structures of the neck.

Biomechanical Phenomena↗
PubMed2000

(Coarse coding of shape fragments) + (retinotopy) approximately = representation of structure.

The ability to deal with object structure--to determine what is where in a given object, rather than merely to categorize or identify it--has been hitherto considered the prerogative of 'structural description' approaches, which represent shapes as categorical compositions of generic parts taken from a small alphabet. In this note, we propose a simple extension to a theoretically motivated and extensively tested appearance-based model of recognition and categorization, which should make it capable of representing object structure. We describe a pilot implementation of the extended model, survey independent evidence supporting its modus operandi, and outline a research program focused on achieving a range of object processing capabilities, including reasoning about structure, within a unified appearance-based framework.

Animals↗
PubMed1991

Surgical pathology of cancer of the oral cavity and oropharynx.

A study was designed to determine the influence of certain surgical pathologic findings on tumor spread and survival in patients with cancer of the oral cavity and oropharynx. All patients with the histopathological diagnosis of carcinoma of the oral cavity or oropharynx from 1955 to 1983 were included in the study. Using the Head and Neck Tumor Registry of the department of otolaryngology of the Washington University School of Medicine, information was obtained regarding preoperative evaluation, staging, classification, diagnosis, treatment, surgical pathology parameters, and outcome results. The patient populations consisted of 545 patients with oral cavity cancer and 224 patients with oropharynx cancer, all of whom were eligible for 3-year follow-up. Information from a retrospective analysis of the pretreatment examination records regarding site and size of the primary tumor and neck dissection, and specific treatment, and from surgical pathology reports regarding site, size, tumor spread and resection margins, was correlated with treatment outcome. The database file was analyzed using dbase III and its companion program Framework, and SAS PC (Statistical Analysis Systems for personal computers).

Cause of Death↗
PubMed2001

Sleep architecture and its relationship to insomnia.

The methods used to obtain and depict sleep data shape our understanding of sleep as a phenomenon. The standard criteria for describing sleep were developed in the late 1960s. These criteria, which were established on the basis of the polysomnographic equipment available at that time, called for the division of sleep into stages according to depth; the visual depiction of these stages led to the now widely accepted concept of "sleep architecture." Although the sleep architecture model remains useful, the technology that provided the model's framework for understanding sleep has been superseded by computer-assisted systems for recording and analyzing sleep that may allow us to acquire data on sleep that were unobtainable with older equipment. Future gathering and depiction of sleep data, regardless of the recording and assessment methods used, should minimize disruption of sleep during study, allow for computerized analysis of sleep parameters, and describe the data from the perspective of the effect that sleep and the problems surrounding it have on daytime functioning.

Aging↗
PubMed1992

Optimizing staff scheduling by Monte-Carlo simulation.

DOCS is a computer program which generates the staff schedule. An accounting framework is combined with an optimization technique that searches for a schedule in which all accounts are simultaneously in balance. The search is accomplished using a Monte-Carlo process which shuffles staff within the schedule. The shuffling is biased according to each staffer's account balance: the staffer who owes the most is most likely to be scheduled.

Algorithms↗
PubMedDate not supplied

[Taking account of macromolecule conformation dynamics in the analysis of luminescent probe signals].

A mathematical model of electron excitation energy transport between molecular probes sorbed on the polymeric chain in solution was proposed. The kinetics of the process was described in terms of the conception of stochastic changes in macromolecule conformation. The results of computer simulation and the analytical expressions obtained in the framework of the perturbation theory for the cases of low transfer rate and/or fast conformation motion of the macrochain are presented. A channel of nonlinear deactivation as a result of binary annihilation of closely-spaced excited centers was considered. Expressions for the effective rate of mutual quenching and delayed annihilation fluorescence of the probe were obtained. Time dependencies of typical luminescent signals and parameteric curves of relative fluorescence quantum yield are presented.

Electron Transport↗
PubMed1995

Expert systems in the health administration curriculum.

With the growing use of computers and on-line hospital information systems, a need exists for health care managers trained in computing and computer applications. This article reviews the advances in health care computing and reports on the design and implementation of a new course in a specific computer application, known as expert systems, in a health administration curriculum. The specific course framework is described and several student projects are presented and discussed.

Computer Communication Networks↗
PubMed1991

Energy filters, motion uncertainty, and motion sensitive cells in the visual cortex: a mathematical analysis.

Energy filters are tuned to space-time frequency orientations. In order to compute velocity it is necessary to use a collection of filters, each tuned to a different space-time frequency. Here we analyze, in a probabilistic framework, the properties of the motion uncertainty. Its lower bound, which can be explicitly computed through the Cramér-Rao inequality, will have different values depending on the filter parameters. We show for the Gabor filter that, in order to minimize the motion uncertainty, the spatial and temporal filter sizes cannot be arbitrarily chosen; they are only allowed to vary over a limited range of values such that the temporal filter bandwidth is larger than the spatial bandwidth. This property is shared by motion sensitive cells in the primary visual cortex of the cat, which are known to be direction selective and are tuned to space-time frequency orientations. We conjecture that these cells have larger temporal bandwidth relative to their spatial bandwidth because they compute velocity with maximum efficiency, that is, with a minimum motion uncertainty.

Animals↗
PubMed1986

The evaluation of artificial intelligence systems in medicine.

This paper discusses the underlying issues in the evaluation of computer systems which apply artificial intelligence in medicine. Three different levels of evaluation are described: the subjective evaluation of the research contribution of a developmental prototype, the validation of a system's knowledge and performance, and the evaluation of the clinical efficacy of an operational system. The paper outlines a number of evaluation issues at each level, and discusses how previous artificial intelligence in medicine evaluations fit into this framework.

Artificial Intelligence↗
PubMed2003

Cytoskeletal architecture and mechanical behavior of living cells.

Conventional continuum mechanics models considering living cells as viscous fluid balloons are unable to explain some recent experimental observations. In contrast, new microstructural models provide the desirable explanations. These models emphasize the role of the cell cytoskeleton built of struts-microtubules and cables-microfilaments. A specific architectural model of the cytoskeletal framework called "tensegrity" deserved wide attention recently. Tensegrity models particularly account for the phenomenon of linear stiffening of living cells. These models are discussed from the structural mechanics perspective. Classification of structural assemblies is given and the meaning of "tensegrity" is pinpointed. Possible sources of non-linearity leading to cell stiffening are emphasized. The role of local buckling of microtubules and overall stability of the cytoskeleton is stressed. Computational studies play a central role in the development of the microstructural theoretical framework allowing for the prediction of the cell behavior from "first principles". Algorithms of computer analysis of the cytoskeleton that consider unilateral response of microfilaments and deep postbuckling of microtubules are addressed.

Actin Cytoskeleton↗
PubMed1998

Medical imaging and registration in computer assisted surgery.

Imaging, sensing, and computing technologies that are being introduced to aid in the planning and execution of surgical procedures are providing orthopaedic surgeons with a powerful new set of tools for improving clinical accuracy, reliability, and patient outcomes while reducing costs and operating times. Current computer assisted surgery systems typically include a measurement process for collecting patient specific medical data, a decision making process for generating a surgical plan, a registration process for aligning the surgical plan to the patient, and an action process for accurately achieving the goals specified in the plan. Some of the key concepts in computer assisted surgery applied to orthopaedics with a focus on the basic framework and underlying technologies is outlined. In addition, technical challenges and future trends in the field are discussed.

Bone and Bones↗
PubMed2005

A general framework for characterizing studies of brain interface technology.

The development of brain interface (BI) technology continues to attract researchers with a wide range of backgrounds and expertise. Though the BI community is committed to accurate and objective evaluation of methods, systems, and technology, the very diversity of the methods and terminology used in the field hinders understanding and impairs technology cross-fertilization and cross-group validation of findings. Underlying this dilemma is a lack of common perspective and language. As seen in our previous works in this area, our approach to remedy this problem is to propose language in the form of taxonomy and functional models. Our intent is to document and validate our best thinking in this area and publish a perspective that will stimulate discussion. We encourage others to do the same with the belief that focused discussion on language issues will accelerate the inherently slow natural evolution of language selection and thus alleviate related problems. In this work, we propose a theoretical framework for describing BI-technology-related studies. The proposed framework is based on the theoretical concepts and terminology from classical science, assistive technology development, human-computer interaction, and previous BI-related works. Using a representative set of studies from the literature, the proposed BI study framework was shown to be complete and appropriate perspective for thoroughly characterizing a BI study. We have also demonstrated that this BI study framework is useful for (1) objectively reviewing existing BI study designs and results, (2) comparing designs and results of multiple BI studies, (3) designing new studies or objectively reporting BI study results, and (4) facilitating intra- and inter-group communication and the education of new researchers. As such, it forms a sound and appropriate basis for community discussion.

Brain↗
PubMed2006

A factor analysis model for functional genomics.

BACKGROUND: Expression array data are used to predict biological functions of uncharacterized genes by comparing their expression profiles to those of characterized genes. While biologically plausible, this is both statistically and computationally challenging. Typical approaches are computationally expensive and ignore correlations among expression profiles and functional categories. RESULTS: We propose a factor analysis model (FAM) for functional genomics and give a two-step algorithm, using genome-wide expression data for yeast and a subset of Gene-Ontology Biological Process functional annotations. We show that the predictive performance of our method is comparable to the current best approach while our total computation time was faster by a factor of 4000. We discuss the unique challenges in performance evaluation of algorithms used for genome-wide functions genomics. Finally, we discuss extensions to our method that can incorporate the inherent correlation structure of the functional categories to further improve predictive performance. CONCLUSION: Our factor analysis model is a computationally efficient technique for functional genomics and provides a clear and unified statistical framework with potential for incorporating important gene ontology information to improve predictions.

Algorithms↗
PubMed1997

Fine-scale genetic mapping based on linkage disequilibrium: theory and applications.

Linkage-disequilibrium mapping (LDM) recently has been hailed as a powerful statistical method for fine-scale mapping of disease genes. After reviewing its historical background and methodological development, we present a general, mathematical, and conceptually coherent framework for LDM that incorporates multilocus and multiallelic markers and mutational processes at the marker and disease loci. With this framework, we address several issues relevant to fine-scale mapping and propose some efficient computational methods for LDM. We implement various LDM methods that incorporate population growth, recurrent mutation, and marker mutations, on the basis of a general framework. We demonstrate these methods by applying them to published data on cystic fibrosis, Huntington disease, Friedreich ataxia, and progressive myoclonus epilepsy. Since the genes responsible for these diseases all have been cloned, we can evaluate the performance of our methods and can compare ours with that of other methods. Using the proposed methods, we successfully and accurately predicted the locations of genes responsible for these diseases, on the basis of published data only.

Chromosome Mapping↗
PubMed2026

Proteomics at scale: Bottlenecks and opportunities for early-career researchers in a fast developing field.

The field of proteomics has rapidly evolved over the last five years enabled by rapid advances in instrumentation and computation. At the same time, the proteomics community is also growing. This is reflected by the increasing participation in international conferences such as those organized by the European Proteomics Association and the Human Proteome Organization. These events provide early-career researchers with unique opportunities to exchange ideas, develop collaborations, and build networks that support professional development. One such network is the Young Proteomics Investigators Club, a European initiative supported by European Proteomics Association and led by early-career researchers. In this Community-Driven project, we investigate recent trends in proteomics by screening conference abstracts and evaluating the session attendance at Human Proteome Organization Congresses and European Proteomics Association conferences. Based on these analyses, we identified five areas that, from our perspective, are shaping the current trends in proteomics: clinical proteomics, proteomics of post-translational modifications, single-cell proteomics, systems biology and multi-omics, and computational proteomics. For each area, we highlight both unique challenges and identify a common theme: a shift from exploratory studies with manageable sample numbers towards large screenings and cohorts and the generation of big data, which often comes with the lack of computational support, organizational networks, and infrastructure. In this light, we describe the unique challenges and opportunities faced by early-career researchers. We point to actionable directions for enabling reproducible and transparent proteomics as well as community-driven projects and initiatives, which are often providing training and support. SIGNIFICANCE: In this perspective, the Young Proteomics Investigators Club (YPIC) discusses advances in analytical developments and computational approaches in proteomics research. Based on empirical analysis of recent European Proteomics Association conference and Human Proteome Organization congresses contributions, we identify clinical, single-cell, post-translational and systems-level proteomics as the research areas that have gained most momentum in the last three to five years. What makes this work distinctive is that it is written by and for early-career researchers, thereby uniquely identifying where momentum, challenges, and unmet needs converge for the newest generation of proteomics researchers. Rather than cataloguing advances, we examine the widening gap between what modern proteomics can generate and what individual researchers can realistically process, validate, and interpret. We describe specific structural barriers including access to high performance computing, limited formal training in scalable data analysis, the need for unified benchmarking standards and navigating clinical collaboration frameworks. We then highlight opportunities for the field, such as community-curated benchmarks, interdisciplinary mentorship models, and shared computational infrastructure. By making these challenges explicit from an early-career researchers standpoint, we aim to inform how training, funding, and community initiatives can be shaped to support the next generation of proteomics researchers.

Proteomics↗
PubMed1995

Computational properties of self-reproducing growing automata.

Living organisms perform much better than computers at solving complex, irregular computational tasks, like search and adaptation. Key features of living organisms, identified in the paper as a basis for their success in solving complex problems, are: self-reproduction of cells, flexible framework, and modification. These key features of living organisms are abstracted into a computational model, called growing automata. Growing automata are suited for extremely large computational problems, such as search problems. Growing automata are representatives of soft machines. Soft machines can change their physical structure as opposed to hard machines which have fixed structure. An example of a soft machine is a living organism, an example of a hard machine is an electronic computer. The computational properties of soft and hard machines are analyzed and compared. An analysis of growing automata demonstrates their advantages, as well as their limitations as compared to hard machines.

Computer Simulation↗
PubMed2027

Inferring Gene Regulatory Networks in Stem Cells: Methods and Applications.

Gene regulatory networks (GRNs) represent the complex interplay of transcription factors, regulatory elements, and target genes that orchestrate cellular identity and function, playing a crucial role in the differentiation and maintenance of stem cells. This chapter provides an overview of experimental and computational methodologies for inferring GRNs, with particular emphasis on single-cell approaches. We first review key experimental techniques for detecting transcription factor binding sites, chromatin accessibility, and DNA motifs, alongside essential databases that support GRN reconstruction. We then introduce computational inference methods that can be categorized into four principal frameworks: correlation-based approaches, regression and machine learning models, probabilistic and deep learning methods, and integrative or message-passing frameworks. To illustrate practical application, we present a case study applying the pySCENIC workflow to a peripheral blood mononuclear cell single-cell RNA sequencing dataset from mouse, demonstrating how regulon-based analysis can reveal cell-type-specific regulatory programs. This chapter aims to serve as a practical guide for researchers seeking to understand and implement GRN inference methodologies in stem cell biology and related fields.

Gene Regulatory Networks↗