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PubMed1991

A framework for risk characterization of environmental pollutants.

Risk characterization is defined by both the U.S. National Academy of Sciences and the U.S. EPA as the estimation of human health risk due to harmful (i.e., toxic or carcinogenic) substances or organisms. Risk characterization studies are accomplished by integrating quantitative exposure estimates and dose-response relationships with the qualitative results of hazard identification. A Risk Characterization Framework has been developed to encourage a systematic approach for analysis and presentation of risk estimates. This methodology subdivides the four common components of the risk assessment process into ten elements. Each of these elements is based on a term in a predictive risk equation. The equation allows independent computations of exposure, dose, lifetime individual risk, and risk to affected populations. All key assumptions in the predictive risk equation can be explicitly shown. This is important to understand the basis and inherent uncertainties of the risk estimation process. The systematic treatment of each of the ten elements in this framework aids in the difficult job of comparing risk estimates by different researchers using different methodologies. The Risk Characterization Framework has been applied to various indoor and outdoor air pollutants of a carcinogenic nature. With further development, it also promises to be applicable to noncarcinogenic effects.

Air Pollution, Indoor↗

PubMed2006

Developments in CORG: a gene-centric comparative genomics resource.

The CORG resource (Comparative Regulatory Genomics, http://corg.eb.tuebingen.mpg.de) provides extensive cross-species comparisons of promoter regions in particular and whole gene loci in general. Pairwise as well as multiple alignments of 10 vertebrate species form the key component of CORG. We implemented a rapid alignment approach based on weight matrix motif anchors to ensure efficient computation and biologically informative alignments. All CORG workbench components have been enhanced towards more flexibility and interactivity. Reference sequence based data presentation and analysis was put into the well-known and modular Generic Genome Browser framework. Herein, various plugins facilitate online data analysis and integration with static conservation data. Main emphasis was put on the design of a new JAVA WebStart application for comparative data display. Flexible data import and export options for standard formats complete the provided services.

Animals↗

PubMed2005

A framework to analyze multiple time series data: a case study with Streptomyces coelicolor.

Transcriptional regulation in differentiating microorganisms is highly dynamic involving multiple and interwinding circuits consisted of many regulatory genes. Elucidation of these networks may provide the key to harness the full capacity of many organisms that produce natural products. A powerful tool evolved in the past decade is global transcriptional study of mutants in which one or more key regulatory genes of interest have been deleted. To study regulatory mutants of Streptomyces coelicolor, we developed a framework of systematic analysis of gene expression dynamics. Instead of pair-wise comparison of samples in different combinations, genomic DNA was used as a common reference for all samples in microarray assays, thus, enabling direct comparison of gene transcription dynamics across different isogenic mutants. As growth and various differentiation events may unfold at different rates in different mutants, the global transcription profiles of each mutant were first aligned computationally to those of the wild type, with respect to the corresponding growth and differentiation stages, prior to identification of kinetically differentially expressed genes. The genome scale transcriptome data from wild type and a DeltaabsA1 mutant of Streptomyces coelicolor were analyzed within this framework, and the regulatory elements affected by the gene knockout were identified. This methodology should find general applications in the analysis of other mutants in our repertoire and in other biological systems.

Anthraquinones↗

PubMed2003

Next generation simulation tools: the Systems Biology Workbench and BioSPICE integration.

Researchers in quantitative systems biology make use of a large number of different software packages for modelling, analysis, visualization, and general data manipulation. In this paper, we describe the Systems Biology Workbench (SBW), a software framework that allows heterogeneous application components--written in diverse programming languages and running on different platforms--to communicate and use each others' capabilities via a fast binary encoded-message system. Our goal was to create a simple, high performance, opensource software infrastructure which is easy to implement and understand. SBW 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 in this paper the SBW architecture, a selection of current modules, including Jarnac, JDesigner, and SBWMeta-tool, and the close integration of SBW into BioSPICE, which enables both frameworks to share tools and compliment and strengthen each others capabilities.

Biochemical Phenomena↗

PubMed2026

Artificial Intelligence-Driven Multi-Omics Analysis Reveals Hydroxytyrosol Targeting of the TXNIP-NLRP3 Inflammasome Axis in Traumatic Brain Injury.

Traumatic brain injury (TBI) induces secondary neuroinflammation driven by oxidative stress, inflammasome activation, and immune remodeling, yet specific mechanism-guided pharmacological interventions remain limited. This study established an artificial intelligence (AI)-integrated network pharmacology and multi-omics framework to evaluate whether hydroxytyrosol (HT), an olive-derived natural polyphenol, may regulate TBI-related neuroinflammatory targets centered on the TXNIP/NLRP3 inflammasome axis. Starting from the SMILES structure of HT, potential targets were predicted using PharmMapper, SwissTargetPrediction, and the Similarity Ensemble Approach and were standardized to UniProt identifiers. TBI-associated genes were integrated from GeneCards, DisGeNET, OMIM, and the Therapeutic Target Database. The overlapping target set was analyzed using STRING-based protein-protein interaction (PPI) networks, MCODE, CytoHubba, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. Public GEO transcriptomic datasets (GSE123831 and GSE104687) were used for cross-platform expression validation, differential expression analysis, and exploratory CIBERSORT-based immune infiltration estimation. Random forest (RF), multilayer perceptron (MLP), graph convolutional network (GCN), graph attention network (GAT), SHAP/LIME explainability analysis, LASSO inflammatory-risk scoring, and two-sample Mendelian randomization (MR) were further applied for target prioritization, immune phenotype mapping, and genetic association analysis. Seventy-three overlapping HT-TBI targets were identified. PPI and topology analyses prioritized TXNIP, NLRP3, CASP1, MAPK1, and TP53 as key hubs enriched in inflammasome activation, oxidative stress, apoptosis, and NOD-like receptor signaling. TXNIP, NLRP3, and CASP1 were consistently upregulated in both TBI transcriptomic datasets. LM22-based immune deconvolution suggested increased pro-inflammatory immune signatures and a positive TXNIP-M1 macrophage association (r = 0.63, p < 0.001), which should be interpreted as a transcriptome-derived hypothesis rather than validated murine immune-cell proportions. AI-based models consistently ranked TXNIP/NLRP3 as high-contribution features under internal validation, and removal of these targets reduced model performance. A five-gene inflammatory score achieved an internally evaluated AUC of 0.87, while two-sample MR supported positive genetic associations involving TXNIP expression, TBI risk, NLRP3 and IL-1β expression. Collectively, these findings prioritize the TXNIP/NLRP3/CASP1 module as a computationally supported candidate mechanism through which HT may influence oxidative stress-inflammasome-immune coupling in TBI. This study provides an interpretable drug-target-pathway-phenotype framework and identifies TXNIP, NLRP3, and CASP1 as priority nodes for future experimental validation.

Artificial Intelligence↗

PubMed2001

Towards NeuroML: model description methods for collaborative modelling in neuroscience.

Biological nervous systems and the mechanisms underlying their operation exhibit astonishing complexity. Computational models of these systems have been correspondingly complex. As these models become ever more sophisticated, they become increasingly difficult to define, comprehend, manage and communicate. Consequently, for scientific understanding of biological nervous systems to progress, it is crucial for modellers to have software tools that support discussion, development and exchange of computational models. We describe methodologies that focus on these tasks, improving the ability of neuroscientists to engage in the modelling process. We report our findings on the requirements for these tools and discuss the use of declarative forms of model description--equivalent to object-oriented classes and database schema--which we call templates. We introduce NeuroML, a mark-up language for the neurosciences which is defined syntactically using templates, and its specific component intended as a common format for communication between modelling-related tools. Finally, we propose a template hierarchy for this modelling component of NeuroML, sufficient for describing models ranging in structural levels from neuron cell membranes to neural networks. These templates support both a framework for user-level interaction with models, and a high-performance framework for efficient simulation of the models.

Animals↗

PubMed2006

Shapeme histogram projection and matching for partial object recognition.

Histograms of shape signature or prototypical shapes, called shapemes, have been used effectively in previous work for 2D/3D shape matching and recognition. We extend the idea of shapeme histogram to recognize partially observed query objects from a database of complete model objects. We propose representing each model object as a collection of shapeme histograms and match the query histogram to this representation in two steps: 1) compute a constrained projection of the query histogram onto the subspace spanned by all the shapeme histograms of the model and 2) compute a match measure between the query histogram and the projection. The first step is formulated as a constrained optimization problem that is solved by a sampling algorithm. The second step is formulated under a Bayesian framework, where an implicit feature selection process is conducted to improve the discrimination capability of shapeme histograms. Results of matching partially viewed range objects with a 243 model database demonstrate better performance than the original shapeme histogram matching algorithm and other approaches.

Algorithms↗

PubMed2025

NextVir: Enabling classification of tumor-causing viruses with genomic foundation models.

MOTIVATION: Oncoviruses, pathogens known to cause or increase the risk of cancer, include both common viruses such as human papillomaviruses and rarer pathogens such as human T-lymphotropic viruses. Computational methods for detecting viral DNA from data acquired by modern DNA sequencing technologies have enabled studies of the association between oncoviruses and cancers. Those studies are rendered particularly challenging when multiple species of oncovirus are present in a tumor sample. In such scenarios, merely detecting the presence of a sequencing read of viral origin is insufficiently informative-instead, a more precise characterization of the viral content in the sample is required. RESULTS: We address this need with NextVir, to our knowledge the first multi-class viral classification framework that adapts genomic foundation models to detecting and classifying sequencing reads of oncoviral origin. Specifically, NextVir explores several foundation models-DNABERT-S, Nucelotide Transformer, and HyenaDNA-and efficiently fine-tunes them to enable accurate identification of the sequencing reads' origin. The results demonstrate superior performance of the proposed framework over existing deep learning methods and suggest downstream potential for foundational models in genomics.

Humans↗

PubMed2000

Towards symbolization using data-driven extraction of local trends for ICU monitoring.

We propose a methodology for the extraction of local trends from a stream of data. It has been designed to suit the needs of interpretation-oriented visualization and symbolization from ICU monitoring data. After giving implementation details for efficient computation of local trends, we propose the use of a characteristic analysis span for each variable. This characteristic span is obtained from a set of criteria that we compare and evaluate in regard of analysis of ICU monitoring data gathered within the Aiddaig project. The processing results in a rich visual representation and a framework for the local symbolization of the data stream based on its dynamics.

Artificial Intelligence↗

PubMed1993

From the Canadian Dietetic Association. Concept of dietetic practice and framework for undergraduate education for the 21st century.

Rapid change and marked diversity are expected to characterize the 21st century. If dietitians are to serve as change facilitators in this environment they will have to demonstrate greater flexibility and creativity, practise critical analysis and problem solving and employ creative thinking. Although provision of quality nutrition care will remain the unique contribution of dietitians, practitioners in the future will require a greater understanding of the impact of social, economic and political systems on food availability and food consumption and, in turn, health and well-being. Critical to the future practice of dietetics will be a greater understanding of research methodology, computer technology, quality improvement processes and risk management, principles governing learning and behaviour, personnel management and organizational behaviour, family and group dynamics, interpersonal communication and their application to dietetic practice. The Canadian Dietetic Association recently adopted a framework for the development of baccalaureate programs in dietetics designed to enable the dietetic practitioner to continue to make a unique contribution in the 21st century. The framework allows individual institutions the freedom and flexibility to plan programs that are compatible with their philosophy and organizational structure. In addition, it is predicted on the principle that a career in dietetics entails a lifetime commitment to education, of which the baccalaureate program is only the beginning.

Canada↗

PubMed2002

Relativistic electronic structure theory.

The theoretical and technical foundations are presented for the efficient relativistic electronic structure theories to treat heavy-atomic molecular systems. This review contains two surveys of four-component and two-component quasi-relativistic approaches. First, we review our highly efficient computational scheme for four-component relativistic ab initio molecular orbital (MO) methods over generally contracted spherical harmonic Gaussian-type spinors (GTSs). Illustrative calculations, which are performed with a new four-component relativistic ab initio molecular orbital program package REL4D, clearly show the efficiency of our computational scheme by the Dirac-Hartree-Fock (DHF) and Dirac-Hartree-Fock (DKS) methods. Next, in the two-component quasi-relativistic framework, two relativistic Hamiltonians, RESC and higher order Douglas-Kroll (DK) Hamiltonians, are introduced, and several illustrative calculations are shown. Numerical results for several systems show that good accuracy can be obtained with our third-order DK (DK3) Hamiltonian.

Journal Article↗

PubMed2004

Sum-over-states based multireference ab initio calculation of EPR spin Hamiltonian parameters for transition metal complexes. A case study.

The recently developed spectroscopy oriented multireference configuration interaction variant (SORCI) is applied to the problem of the prediction of electron paramagnetic resonance parameters in transition metal complexes within a sum-over-states (SOS) framework. The prototypical complex [Cu(NH(3))(4)](2+) is taken as an example in order to test the validity and convergence properties of several computational approximations. The results show that the SORCI method affords accurate results for the g-tensor, the metal dipolar hyperfine coupling (HFC), the spin-orbit coupling contribution to the metal HFC and the isotropic and dipolar ligand HFCs. Accurate prediction of the isotropic metal HFC is challenging and requires flexible basis sets in the core region together with explicit inclusion of the core electrons and high-lying core-like virtual orbitals in the CI. Comparison of SORCI and density functional theory (DFT) results reveals that the latter are far off experiment (g-tensor) or achieve reasonable results only through a fortunate cancellation of large errors (metal HFC). The ligand HFC is overestimated by DFT owing to overly covalent bonding which leads to too much spin transfer on to the ligands.

Journal Article↗

PubMed1985

Studies of some new phenomena of motion perception.

Contrast modulation of a set of features in a Glass pattern invokes, under certain circumstances, the perception of motion in the absence of any physical displacements. The illusion is spatially limited to within about 10' and is governed by contrast relationships. We find that the detected motion in these situations is due to local computations the results of which can be integrated over retinal areas of more than 1 degree. The possible motion detection mechanisms are discussed within the framework of the Reichardt/Hassenstein and Marr/Ullman type of motion detectors.

Animals↗

PubMed1996

Analysis of cognitive function in animals, the value of SDT.

The search for a better understanding of cognitive decline in man has lead to the use of increasingly complex procedures in animal research. The analysis of the data generated in such experiments has been greatly facilitated by the wider use of computer assisted techniques. These techniques can only be as good as the hypotheses they are used to test. Signal detection theory (SDT) provides a rational framework within which to work. The procedures are derived from human cognitive neuropsychology and are already used to some extent in primate but to a lesser degree in rodent research. The use of SDT offers two main advantages: first, a testable hypothesis as to the manner in which competing processes arrive at choice between various courses of action; second, the statistical procedures offer clear advantages over more traditional approaches by reducing the chances of misinterpretation. Though relatively easy to apply some care must be exercised in the protocol design and the choice of SDT indices if the full value of the approach is to be achieved. If experimental designs can be developed to include the appropriate use of SDT analysis; both the power of such protocols, and their value in the understanding of cognitive function, will provide a major step forward for animal-based research.

Animals↗

PubMed2006

Modeling adolescent drug-use patterns in cluster-unit trials with multiple sources of correlation using robust latent class regressions.

PURPOSE: The purpose of the study is to examine variation in adolescent drug-use patterns by using latent class regression analysis and evaluate the properties of an estimating-equations approach under different cluster-unit trial designs. METHODS: A set of second-order estimating equations for latent class models under the cluster-unit trial design are proposed. This approach models the correlation within subclusters (drug-use behaviors), but ignores the correlation within clusters (communities). A robust covariance estimator is proposed that accounts for within-cluster correlation. Performance of this approach is addressed through a Monte Carlo simulation study, and practical implications are illustrated by using data from the National Evaluation of the Enforcing Underage Drinking Laws Randomized Community Trial. RESULTS: The example shows that the proposed method provides useful information about the heterogeneous nature of drug use by identifying two subtypes of adolescent problem drinkers. A Monte Carlo simulation study supports the proposed estimation method by suggesting that the latent class model parameters were unbiased for 30 or more clusters. Consistent with other studies of generalized estimating equation (GEE) estimators, the robust covariance estimator tended to underestimate the true variance of regression parameters, but the degree of inflation in the test size was relatively small for 70 clusters and only slightly inflated for 30 clusters. CONCLUSIONS: The proposed model for studying adolescent drug use provides an alternative to standard diagnostic criteria, focusing on the nature of the drug-use profile, rather than relying on univariate symptom counts. The second-order GEE-type estimation procedure provided a computationally feasible approach that performed well for a moderate number of clusters and was consistent with prior studies of GEE under the generalized linear model framework.

Adolescent↗

PubMed1986

Scanning fluorescence correlation spectroscopy. I. Theory and simulation of aggregation measurements.

Scanning Fluorescence Correlation Spectroscopy (S-FCS) is introduced as an adaptation of Fluorescence Correlation Spectroscopy (FCS) to measure aggregation in systems, such as biological cell membranes, where diffusion or flow is slow. The theoretical framework for interpretation of S-FCS measurements are discussed in this paper with emphasis on the limitations arising from the sample size and shape. Computer simulations of the experiment demonstrate the potential of the technique and illustrate how some of the limitations may be overcome.

Cell Membrane↗

PubMed1998

Similarity as an explanatory construct.

Theories can be found throughout cognitive science that give an explanatory role to similarity. Such theories can be contrasted with those that model thought using abstract rules. We lay out four possible explanatory roles for similarity. We then review the computational pros and cons of similarity- and rule-based models and outline the empirical work that speaks to the psychological plausibility of the two frameworks. We conclude that an adequate model of human thought must take advantage of both the flexibility of similarity-based inference and the compositionality and certainty associated with rule-based inference.

Attention↗

PubMed2002

Surfing a spike wave down the ventral stream.

Numerous theories of neural processing, often motivated by experimental observations, have explored the computational properties of neural codes based on the absolute or relative timing of spikes in spike trains. Spiking neuron models and theories however, as well as their experimental counterparts, have generally been limited to the simulation or observation of isolated neurons, isolated spike trains, or reduced neural populations. Such theories would therefore seem inappropriate to capture the properties of a neural code relying on temporal spike patterns distributed across large neuronal populations. Here we report a range of computer simulations and theoretical considerations that were designed to explore the possibilities of one such code and its relevance for visual processing. In a unified framework where the relation between stimulus saliency and spike relative timing plays the central role, we describe how the ventral stream of the visual system could process natural input scenes and extract meaningful information, both rapidly and reliably. The first wave of spikes generated in the retina in response to a visual stimulation carries information explicitly in its spatio-temporal structure: the most salient information is represented by the first spikes over the population. This spike wave, propagating through a hierarchy of visual areas, is regenerated at each processing stage, where its temporal structure can be modified by (i). the selectivity of the cortical neurons, (ii). lateral interactions and (iii). top-down attentional influences from higher order cortical areas. The resulting model could account for the remarkable efficiency and rapidity of processing observed in the primate visual system.

Action Potentials↗