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At least 1,621 records · Page 90Linked to original sources
PubMed1980

Potassium currents and conductance. Comparison between motor and sensory myelinated fibers.

The potassium conductance system of sensory and motor fibers from the frog Rana esculenta were studied and compared by means of the voltage clamp. The potassium ion accumulation was first estimated from the currents and reversal potentials within the framework of both a three-compartment model and diffusion-in-an-unstirred-layer model. The potassium conductance parameters were then computed using the measured currents and corrected ionic driving forces. It was found that the potassium accumulation is faster and more pronounced in sensory fibers, the voltage dependency of the potassium conductance is steeper in sensory fibers, the maximal potassium conductance, corrected for accumulation, is approximately 1.1 S/cm2 in sensory and 0.55 S/cm2 in motor fibers, and that the conductance time constants, tau n, are smaller in sensory than in motor fibers. These differences, which increase progressively with depolarization, are not detectable for depolarization of 50 mV or smaller. The interpretation of these findings in terms of different types of potassium channels as well as their implications with regard to the differences between the excitability phenomena in motor and sensory fibers are discussed.

Animals↗
PubMed2003

Rich structure chemistry in the aluminophosphate family.

This Account describes a family of aluminophosphates ranging from neutral open-frameworks to anionic frameworks with fascinating structural architectures. In contrast to aluminosilicate zeolites, these aluminophosphates exhibit wealthy structural features, including extra-large micropores, mixed bonding, different structural dimensionalities, rich compositional diversities, and various stacking sequences of their 2-D sheets. The low-dimensional networks, acting as building units, can be assembled to 3-D open-frameworks via transition metal cations through a building-up process. New stoichiometries and hypothetical structural topologies can be predicted and enumerated. Computational and combinatorial approaches will greatly help to access the range of the diverse structures.

Journal Article↗
PubMed2006

Contextual guidance of eye movements and attention in real-world scenes: the role of global features in object search.

Many experiments have shown that the human visual system makes extensive use of contextual information for facilitating object search in natural scenes. However, the question of how to formally model contextual influences is still open. On the basis of a Bayesian framework, the authors present an original approach of attentional guidance by global scene context. The model comprises 2 parallel pathways; one pathway computes local features (saliency) and the other computes global (scene-centered) features. The contextual guidance model of attention combines bottom-up saliency, scene context, and top-down mechanisms at an early stage of visual processing and predicts the image regions likely to be fixated by human observers performing natural search tasks in real-world scenes.

Attention↗
PubMed2002

More After Hours Medical Service: 'pillars' of success.

This study aimed to identify and explore the factors that are crucial to the successful operation of rural after-hours medical services. It sought to determine the attributes that contribute toward the successful operation of after-hours medical services in rural towns. It drew on computer-assisted telephone interviews with stakeholders, and operational, demographic and financial data. The findings were brought together and analysed within an integrated framework for the guidance of policy makers. In a rural setting, the most important factors for a successful after-hours medical service are related to, 'place', 'process', 'people' and 'time'. These need to be integrated through effective management of relationships.

Adolescent↗
PubMed2006

Analysis of visual search patterns with EMD metric in normalized anatomical space.

Eye movements provide important insight into the cognitive processes underlying the visual search tasks. For image understanding, although the visual search patterns of different observers while studying the same scene bear some common characteristics, the idiosyncrasy associated with individual observers provides both research opportunities and challenges. The aim of this paper is to study the spatial characteristics of visual search, together with the intrinsic visual features of the fixation points for comparing different visual search strategies. An analysis framework based on earth mover's distance (EMD) in normalized anatomical space is proposed, and the results are demonstrated with high resolution computed tomography (HRCT) images of the lungs. The study shows that through the effective use of both spatial and feature space representation, it is possible to untangle what appear to be uncorrelated fixation distribution patterns to reveal common visual search behaviors.

Artificial Intelligence↗
PubMed1992

On multidimensional ultrasonic scattering in an inhomogeneous elastic background.

This work is concerned with the modeling of elastic wave scattering by solid or fluid-filled objects embedded in an inhomogeneous elastic background. The medium is probed by a monochromatic force and the scattered field is computed (forward problem) or observed (inverse problem) at some known receiver locations. Based on vector integral equations for elastic scattering, a general framework is developed, independent of both the problem geometry and the transmitter-receiver characteristics. This framework encompasses both forward and inverse modeling. In the forward model, a Born approximation for an inhomogeneous background is applied to obtain a closed form expression for the scattered field. In the inverse model, this approximation is also invoked to linearize for the multiparameter characteristic of the object. Finally, an iterative inversion scheme alternating forward and inverse modeling is proposed to improve the resolution and accuracy of the reconstruction algorithm.

Elasticity↗
PubMed2003

cDNA2Genome: a tool for mapping and annotating cDNAs.

BACKGROUND: In the last years several high-throughput cDNA sequencing projects have been funded worldwide with the aim of identifying and characterizing the structure of complete novel human transcripts. However some of these cDNAs are error prone due to frameshifts and stop codon errors caused by low sequence quality, or to cloning of truncated inserts, among other reasons. Therefore, accurate CDS prediction from these sequences first require the identification of potentially problematic cDNAs in order to speed up the posterior annotation process. RESULTS: cDNA2Genome is an application for the automatic high-throughput mapping and characterization of cDNAs. It utilizes current annotation data and the most up to date databases, especially in the case of ESTs and mRNAs in conjunction with a vast number of approaches to gene prediction in order to perform a comprehensive assessment of the cDNA exon-intron structure. The final result of cDNA2Genome is an XML file containing all relevant information obtained in the process. This XML output can easily be used for further analysis such us program pipelines, or the integration of results into databases. The web interface to cDNA2Genome also presents this data in HTML, where the annotation is additionally shown in a graphical form. cDNA2Genome has been implemented under the W3H task framework which allows the combination of bioinformatics tools in tailor-made analysis task flows as well as the sequential or parallel computation of many sequences for large-scale analysis. CONCLUSIONS: cDNA2Genome represents a new versatile and easily extensible approach to the automated mapping and annotation of human cDNAs. The underlying approach allows sequential or parallel computation of sequences for high-throughput analysis of cDNAs.

Chromosome Mapping↗
PubMed2026

Leveraging single-cell and spatial omics for brain tumour insights to improve therapeutic strategies.

Single-cell and spatial omics (SPOs) technologies have advanced how healthcare physicians characterise brain tumours by enabling detailed understanding of their cellular architecture, functional states, and microenvironmental dynamics. These approaches provide high-resolution detection of tumour heterogeneity and allow precise analysis of the brain tumour microenvironment. Their application has also led to the discovery of novel biomarkers used for early brain tumour detection, prognosis, and improved tumour stratification. Furthermore, integrative multi-omic analyses have revealed new therapeutic targets, clarified mechanisms of drug resistance, and uncovered molecular pathways underpinning treatment failure. By bridging cellular-level insights with spatial context, SPOs hold significant promise for advancing personalised diagnostics, predicting therapeutic response, and guiding the development of targeted interventions for brain tumours. Despite these advances, several limitations constrain the full translational potential of SPOs, including high experimental costs, substantial computational demands, lack of standardised protocols, and challenges in data integration and reproducibility. Addressing these barriers through scalable bioinformatic pipelines, consensus experimental frameworks, and cost-effective platforms remains critical for broadening accessibility and enabling clinical adoption.

Brain Neoplasms↗
PubMed1995

Effect of VK framework-1 glycosylation on the binding affinity of lymphoma-specific murine and chimeric LL2 antibodies and its potential use as a novel conjugation site.

A potential asparagine (Asn)-linked glycosylation site was identified in the VK FRI sequence of an anti-B lymphoma monoclonal antibody (MAb), LL2.SDS-PAGE analysis and endo-F treatment of both murine and chimeric LL2 antibodies indicated that this site was glycosylated; however, no differences in the binding affinity to Raji cells were observed between the native murine LL2 and the endo-F-deglycosylated murine LL2 antibodies. Elimination of the glycosylation site from the chimeric LL2 antibody was accomplished by an Asn to Gln mutation in the tri-acceptor site found in the light chain. The resultant aglycosylated chimeric LL2 exhibited a similar Raji cell binding affinity to that of the glycosylated form. The results are in agreement with computer modeling studies which suggested the lack of interactions between the oligosaccharide moiety and the CDRs. The finding is interesting because it enables a wider choice of human framework sequences, which in most cases do not have a corresponding glycosylation site, for the humanization of the LL2 VK domain, as well as a greater latitude of host expression systems. Most importantly, the LL2 VK carbohydrate moiety might be used as a novel conjugation site for drugs and radionuclides without compromising the immunoreactivity of the antibody.

Amino Acid Sequence↗
PubMed2004

Implicit brain imaging.

We describe how implicit surface representations can be used to solve fundamental problems in brain imaging. This kind of representation is not only natural following the state-of-the-art segmentation algorithms reported in the literature to extract the different brain tissues, but it is also, as shown in this paper, the most appropriate one from the computational point of view. Examples are provided for finding constrained special curves on the cortex, such as sulcal beds, regularizing surface-based measures, such as cortical thickness, and for computing warping fields between surfaces such as the brain cortex. All these result from efficiently solving partial differential equations (PDEs) and variational problems on surfaces represented in implicit form. The implicit framework avoids the need to construct intermediate mappings between 3-D anatomical surfaces and parametric objects such planes or spheres, a complex step that introduces errors and is required by many other cortical processing approaches.

Algorithms↗
PubMed2001

Visual filling-in for computing perceptual surface properties.

The visual system is constantly confronted with the problem of integrating local signals into more global arrangements. This arises from the nature of early cell responses, whether they signal localized measures of luminance, motion, retinal position differences, or discontinuities. Consequently, from sparse, local measurements, the visual system must somehow generate the most likely hypothesis that is consistent with them. In this paper, we study the problem of determining achromatic surface properties, namely brightness. Mechanisms of brightness filling-in have been described by qualitative as well as quantitative models, such as by the one proposed by Cohen and Grossberg. We demonstrate that filling-in from contrast estimates leads to a regularized solution for the computational problem of generating brightness representations from sparse estimates. This provides deeper insights into the nature of filling-in processes and the underlying objective function one wishes to compute. This particularly guided the proposal of a new modified version of filling-in, namely confidence-based filling-in which generates more robust brightness representations. Our investigation relates the modeling of perceptual data for biological vision to the mathematical frameworks of regularization theory and linear spatially variant diffusion. It therefore unifies different research directions that have so far coexisted in different scientific communities.

Animals↗
PubMedDate not supplied

The medical information bus: overview of the medical device data language.

The Medical Information Bus (MIB) reference model defines a new, object-oriented Medical Device Data Language (MDDL), under development by the Institute of Electrical and Electronic Engineers Society (IEEE) P1073 MIB Standard Committee. The MDDL treats medical devices, host computers, humans and device parameters as objects, and provides a flexible and extensible language for describing and passing messages between objects. This paper describes the MDDL semantic reference model and presents an overview of the MDDL structure, within the framework of the International Standards Organization (ISO) System Management Overview (SMO) model. A simple example of how the MDDL can be used to construct a device event report is also described.

Computer Communication Networks↗
PubMed2006

Three-dimensional surface model analysis in the gastrointestinal tract.

The biomechanical changes during functional loading and unloading of the human gastrointestinal (GI) tract are not fully understood. GI function is usually studied by introducing probes in the GI lumen. Computer modeling offers a promising alternative approach in this regard, with the additional ability to predict regional stresses and strains in inaccessible locations. The tension and stress distributions in the GI tract are related to distensibility (tension-strain relationship) and smooth muscle tone. More knowledge on the tension and stress on the GI tract are needed to improve diagnosis of patients with gastrointestinal disorders. A modeling framework that can be used to integrate the physiological, anatomical and medical knowledge of the GI system has recently been developed. The 3-D anatomical model was constructed from digital images using ultrasonography, computer tomography (CT) or magnetic resonance imaging (MRI). Different mathematical algorithms were developed for surface analysis based on thin-walled structure and the finite element method was applied for the mucosa-folded three layered esophageal model analysis. The tools may be useful for studying the geometry and biomechanical properties of these organs in health and disease. These studies will serve to test the structure-function hypothesis of geometrically complex organs.

Algorithms↗
PubMed1998

Recursive MUSIC: a framework for EEG and MEG source localization.

The multiple signal classification (MUSIC) algorithm can be used to locate multiple asynchronous dipolar sources from electroencephalography (EEG) and magnetoencephalography (MEG) data. The algorithm scans a single-dipole model through a three-dimensional (3-D) head volume and computes projections onto an estimated signal subspace. To locate the sources, the user must search the head volume for multiple local peaks in the projection metric. This task is time consuming and subjective. Here, we describe an extension of this approach which we refer to as recursive MUSIC (R-MUSIC). This new procedure automatically extracts the locations of the sources through a recursive use of subspace projections. The new method is also able to locate synchronous sources through the use of a spatio-temporal independent topographies (IT) model. This model defines a source as one or more nonrotating dipoles with a single time course. Within this framework, we are able to locate fixed, rotating, and synchronous dipoles. The recursive subspace projection procedure that we introduce here uses the metric of canonical or subspace correlations as a multidimensional form of correlation analysis between the model subspace and the data subspace. By recursively computing subspace correlations, we build up a model for the sources which account for a given set of data. We demonstrate here how R-MUSIC can easily extract multiple asynchronous dipolar sources that are difficult to find using the original MUSIC scan. We then demonstrate R-MUSIC applied to the more general IT model and show results for combinations of fixed, rotating, and synchronous dipoles.

Algorithms↗
PubMed2025

Accurate prediction of toxicity peptide and its function using multi-view tensor learning and latent semantic learning framework.

MOTIVATION: Therapeutic peptide is an important ingredient in the treatment of various diseases and drug discovery. The toxicity of peptides is one of the major challenges in peptide drug therapy. With the abundance of therapeutic peptides generated in the post-genomics era, it is a challenge to promptly identify toxicity peptides using computational methods. Although several efforts have been made, few algorithms are designed to identify whether a query peptide exhibits toxicity. Considering the varied levels of biological activities, the toxicity peptides should be further classified into multi-functional peptides. RESULTS: This study introduces a two-level predictor, ToxPre-2L, developed using the multi-view tensor learning and latent semantic learning framework. The proposed method utilized multi-label learning with feature induced labels to avoid the redundancy of information from each view. Then the multi-view tensor learning was employed to establish the latent semantic information among different views, while low-rank constraint learning was leveraged to exploit the correlation information among multi-labels. Finally, we constructed an updated toxicity peptide benchmark dataset to assess the effectiveness of the proposed method. Experimental results demonstrated that ToxPre-2L achieves a better performance than alternative computational methods in the prediction of toxicity peptides and their multi-functional types. AVAILABILITY AND IMPLEMENTATION: The source code and data of ToxPre-2L can be accessed at http://bliulab.net/ToxPre-2L.

Peptides↗
PubMed2004

DBMap: a space-conscious data visualization and knowledge discovery framework for biomedical data warehouse.

Advances in digital imaging modalities as well as other diagnosis and therapeutic techniques have generated a massive amount of diverse data for clinical research. The purpose of this study is to investigate and implement a new intuitive and space-conscious visualization framework, called DBMap, to facilitate efficient multidimensional data visualization and knowledge discovery against the large-scale data warehouses of integrated image and nonimage data. The DBMap framework is built upon the TreeMap concept. TreeMap is a space constrained graphical representation of large hierarchical data sets, mapped to a matrix of rectangles, whose size and color represent interested database fields. It allows the display of a large amount of numerical and categorical information in limited real estate of the computer screen with an intuitive user interface. DBMap has been implemented and integrated into a large brain research data warehouse to support neurologic and neuroradiologic research at the University of California, San Francisco Medical Center. For imaging specialists and clinical researchers, this novel DBMap framework facilitates another way to better explore and classify the hidden knowledge embedded in medical image data warehouses.

Algorithms↗
PubMed2002

The ERATO Systems Biology Workbench: enabling interaction and exchange between software tools for computational biology.

Researchers in computational biology today make use of a large number of different software packages for modeling, analysis, and data manipulation and visualization. In this paper, we describe the ERATO Systems Biology Workbench (SBW), a software framework that allows these heterogeneous application components--written in diverse programming languages and running on different platforms--to communicate and use each others' data and algorithmic capabilities. Our goal is to create a simple, open-source software infrastructure which is effective, easy to implement and easy to understand. SBW uses a broker-based architecture and enables applications (potentially running on separate, distributed computers) to communicate via a simple network protocol. The interfaces to the system are encapsulated in client-side libraries that we provide for different programming languages. We describe the SBW architecture and the current set of modules, as well as alternative implementation technologies.

Computational Biology↗
PubMed2026

MetaCCI: meta cell-cell interaction inference and its application to CCIs characteristics of MDS.

MOTIVATION: Cell-cell interactions (CCIs) are fundamental to multicellular organisms and play crucial roles in diverse biological processes and disease mechanisms. Understanding CCIs is vital for deciphering disease pathogenesis and developing therapeutic strategies. Although numerous computational methods have been developed to infer CCIs from complex biological data, most existing approaches rely primarily on single-gene expression levels and ligand-receptor databases, often failing to capture the nuanced network-wide changes characteristic of disease states. RESULT: We propose MetaCCI, a novel computational strategy that integrates meta-information into CCI inference by extending the traditional gene expression-based analysis to a gene regulatory network framework. MetaCCI meticulously combines established ligand-receptor pairs with quantitative insights into gene behavior within complex gene networks, enabling the precise extraction of relevant targets for CCI inference. Subsequently, CCI inference was performed using an eigen cell co-expression network, providing a more holistic view of cell-cell communication. Monte Carlo simulations demonstrated that MetaCCI consistently outperforms existing methods in CCI inference. We applied MetaCCI to characterize cell-cell communication in Myelodysplastic Syndromes (MDS). Our results identified distinct interaction patterns in MDS compared with normal cell populations, specifically highlighting the loss of CCIs between "Dendritic cells and Hematopoietic precursor cells" and between "Dendritic cells and Hematopoietic multipotent progenitor cells" as characteristic features of MDS. Furthermore, FABP5, CD63, and HMGB1 were identified as MDS-specific markers. These findings suggest that diminished CCIs involving dendritic cells, hematopoietic precursor cells, and multipotent progenitor cells are pivotal to MDS pathogenesis. AVAILABILITY AND IMPLEMENTATION: The MetaCCI software is freely available at https://github.com/HeewonGitHub/MetaCCI. An archived version of the software and example datasets used in this study is available at Zenodo: https://doi.org/10.5281/zenodo.20101527.

Myelodysplastic Syndromes↗