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PubMed2026

Decoding regional keratinization in human oral mucosa through high-resolution spatial transcriptomics.

Oral mucosa exhibits region-specific keratinization, essential for periodontal health, yet the spatial and molecular mechanisms driving these differences remain poorly understood. This study aimed to generate a high-resolution spatial transcriptomic atlas of the human oral mucosa around the mucogingival junction, to reveal stromal-epithelial interactions, that distinguish keratinized from non-keratinized programs. Formalin-fixed paraffin-embedded specimens from the mucogingival junction area of two healthy donors were analyzed with the 10 × Genomics Visium HD platform, yielding two keratinized and two non-keratinized regions. Spatial clustering, pseudotime trajectory inference, cell-type integration with a single-cell reference, and ligand-receptor network analysis were applied to delineate epithelial and stromal compartments. Sixteen reproducible clusters, recapitulating tissue architecture, were identified and revealed distinct transcriptional signatures, distinguishing gingiva from lining mucosa. Pseudotime analysis revealed bifurcating epithelial lineages, originating from a shared basal progenitor layer into keratinized and non-keratinized programs. Gingival keratinization was driven by stromal collagen ligands (COL1A1, COL1A2, COL6A1, COL6A2) engaging epithelial receptors (CD44, SDC1), further reinforced within the epithelium by desmosomal adhesion via DSG1-DSC2/3. Gingival keratinization emerges from integrated stromal collagen signaling and epithelial adhesion. This spatially resolved framework advances understanding of oral mucosal specialization and provides a foundation for biologically guided regenerative therapies.

Humans↗

PubMed2004

Haplotypes histories as pathways of recombinations.

MOTIVATION: The diversity of a haplotype, represented as a string of polymorphic sites along a DNA sequence, increases exponentially with the number of sites if recombinations are taking place. Reconstructing the history of recombinations compared with that of the polymorphic sites is thus extremely difficult. However, in the human genome, because of the relatively simple pattern of haplotype diversity dominated by a few ancestral haplotypes, the complexity of the recombinational network can be reduced, thus making its reconstruction feasible. We focus on the problem of inferring the recombination pathways starting with putative ancestral haplotypes and leading to new rare recombinant haplotypes. RESULTS: We describe classes of recombinations that represent the whole set of minimal recombination pathways leading to a new haplotype. We present an O(n(2)) algorithm that outputs such representative recombination pathways. We apply it to haplotypes of the 8 kb dystrophin gene segment dys44. AVAILABILITY: A software implementing the algorithm and some other extentions has been developed on a Java platform (JDK 1.3.1). It is freely available at http://www.iro.umontreal.ca/~mabrouk/

Algorithms↗

PubMed2006

Constructing quantitative models from qualitative mutant phenotypes: preferences in selecting sensory organ precursors.

MOTIVATION: To study biology from the systems level, mathematical models that describe the time-evolution of the system offer useful insights. Quantitative information is required for constructing such models, but such information is rarely provided. RESULTS: We propose a scheme-based on random searches over a parameter space, according to criteria set by qualitative experimental observations-for inferring quantitative parameters from qualitative experimental results. We used five mutant constraints to construct genetic network models for sensory organ precursor formation in Drosophila development. Most of the models were capable of generating expression patterns for the gene Enhancer of split that were compatible with experimental observations for wild type and two Notch mutants. We further examined factors differentiating the neural fate among cells in a proneural cluster, and found two opposite driving forces that bias the choice between middle cells and the peripheral cells. Therefore, it is possible to build numerical models from mutant screening and to study mechanisms behind the complicated network.

Animals↗

PubMed2006

Quantifying the influence of sociality on population structure in bottlenose dolphins.

1. The social structure of a population plays a key role in many aspects of its ecology and biology. It influences its genetic make-up, the way diseases spread through it and the way animals exploit their environment. However, the description of social structure in nonprimate animals is receiving little attention because of the difficulty in abstracting social structure from the description of association patterns between individuals. 2. Here we focus on recently developed analytical techniques that facilitate inference about social structure from association patterns. We apply them to the population of bottlenose dolphins residing along the Scottish east coast, to detect the presence of communities within this population and infer its social structure from the temporal variation in association patterns between individuals. 3. Using network analytical techniques, we show that the population is composed of two social units with restricted interactions. These two units seem to be related to known differences in the ranging pattern of individuals. By examining social structuring at different spatial scales, we confirm that the identification of these two units is the result of genuine social affiliation and is not an artefact of their spatial distribution. 4. We also show that the structure of this fission-fusion society relies principally on short-term casual acquaintances lasting a few days with a smaller proportion of associations lasting several years. These findings highlight how network analyses can be used to detect and understand the forces driving social organization of bottlenose dolphins and other social species.

Animals↗

PubMed1982

Generation and maintenance of the respiratory rhythm.

Activities of the phrenic and internal intercostal nerves show that the central nervous rhythm of respiration consists of 3 phases: inspiratory, postinspiratory and expiratory. The discharge patterns of medullary respiratory neurones of the anaesthetized, paralysed cat can be correlated with these phases of the central respiratory cycle, and the postsynaptic activity of individual cells can be analysed to obtain information about the populations of neurones converging upon them. Inferences are drawn about respiratory neurone connectivity and a theory is developed that the respiratory network primarily employs inspiratory-related neurones and that medullary expiratory neurones are less important for the rhythmogenesis of respiration. It is suggested that the inspiratory network consists of a ramp generating excitatory loop network of interneurones whose discharge is brought to an end ('off-switched') by inhibitory late-inspiratory interneurones. The discharge pattern of the latter type of neurone is explained by inhibition arriving from early-inspiratory interneurones. Subsequent to 'off-switching' the ramp generator is assumed to be immediately gated by a very powerful postinspiratory inhibition whereas expiratory activity seems to be disfacilitated at this time. This is the period when 'passive' (stage 1) expiration occurs. Following this interposed postinspiratory phase 'active' (stage 2) expiration may begin, depending on the amount of excitatory inflow to the inspiratory ramp generator. When expiratory neurones are activated the inspiratory system is again synaptically inhibited and the frequency of ventilation is markedly slowed.

Animals↗

PubMed2002

Automated sleep stage detection with a classical and a neural learning algorithm--methodological aspects.

For classification tasks in biosignal processing, several strategies and algorithms can be used. Knowledge-based systems allow prior knowledge about the decision process to be integrated, both by the developer and by self-learning capabilities. For the classification stages in a sleep stage detection framework, three inference strategies were compared regarding their specific strengths: a classical signal processing approach, artificial neural networks and neuro-fuzzy systems. Methodological aspects were assessed to attain optimum performance and maximum transparency for the user. Due to their effective and robust learning behavior, artificial neural networks could be recommended for pattern recognition, while neuro-fuzzy systems performed best for the processing of contextual information.

Algorithms↗

PubMed2003

Role of cytokines in hyperoxia mediated inflammation in the developing lung.

The development of Chronic Lung Disease of Prematurity (CLD) has been associated with the use of hyperoxic conditions during ventilation. Inflammation has been demonstrated to contribute to the development of this disease, both on histological examination of diseased lungs, and by the use of bronchoalveolar lavage. Hyperoxia is believed to contribute to this inflammatory process by causing direct injury to epithelial and endothelial cells. The formation of reactive oxygen species is thought to result in production of cytokines. These act within a complex network, orchestrating an inflammatory response. Evidence for a role of cytokines in CLD has been inferred by studies in human infants showing increased concentrations of cytokines, growth factors and inflammatory cells at early stages in infants destined to develop CLD. These findings have been supported by the use of animal models of hyperoxic lung injury. The treatment of CLD is currently centered on the suppression of cytokine production. As understanding of this disease increases, more specific targets are being developed which aim to reduce the oxidative load on the lung, and prevent recruitment of inflammatory cells that are responsible for the tissue damage underlying this disease.

Animals↗

PubMed2001

Development of river ecosystem models for Flemish watercourses: case studies in the Zwalm river basin.

Only recently, modelling has been accepted as an interesting and powerful tool to support river quality assessment and management. The 'River Invertebrate Prediction and Classification System' (RIVPACS), based on statistical modelling, was one of the first and best known systems in this context. RIVPACS was developed to classify macroinvertebrate community types and to predict the fauna expected to occur in different types of watercourses, based on a small number of environmental variables. The prediction is essentially a static 'target' of the fauna to be expected at a site with stated environmental features, in the absence of environmental stress. Therefore this system is rather limited to apply in river assessment and management. Models that offer a prediction of faunal responses to changes in environmental features (e.g. changes in discharge regime, dissolved oxygen level, ...) would be of considerable value for river management. In this context, models based on classification trees, artificial neural networks and fuzzy logic were developed and applied to predict macro-invertebrate communities in the Zwalm river basin located in Flanders, Belgium. Structural characteristics (meandering, substrate type, flow velocity, ...) and physical-chemical variables (dissolved oxygen, pH, ...) were used as inputs to predict the presence or absence of macroinvertebrate taxa in the headwaters and brooks of the Zwalm river basin. In total, data from 60 measurement sites were available. Reliability and particular strengths and weaknesses of these techniques were compared and evaluated. Classification trees performed in general well to predict the absence or presence of the different macroinvertebrate taxa and allowed also to deduct general relations from the dataset. Models based on artificial neural networks (ANNS) were also good in predicting the macroinvertebrate communities at the different sites. Sensitivity analyses related to ANNs allowed to study the impact of the input variables on the presence or absence of macroinvertebrate taxa and to determine the major variables that affect the ecosystem quality and should be taken under direct consideration in the management of river basins. Performance of the fuzzy logic models was significantly related to the methods that were used to set up the membership functions and the reliability of the information that was available. Fuzzy logic did not perform as well as the other two techniques with regard to short term predictions. Fuzzy logic appeared to be better and more robust for long term predictions, because of the easy and pragmatic integration of general expert knowledge and data derived rules in the transparent inference engine. The overall conclusion of our study is that all three techniques, classification trees, artificial neural networks and fuzzy logic appeared to be reliable to predict macroinvertebrate communities in polluted streams.

Animals↗

PubMed2006

A distributed environment for the integration of multiple high-performance decision support systems into clinical workflow.

Current studies conclude that clinical decision support systems can help reduce serious medical errors. The importance of Causal Probabilistic Networks (CPNs) for constructing such systems is already well-known. However, the computational complexity of probabilistic inference, which results in unacceptably high response times, can hinder acceptance and integration into clinician workflow. This paper investigates the optimization and parallelization potential of complex CPN-based medical decision support systems and evaluates the results of implementing a parallel, high performance version of an existing decision support system concerning proper antibiotic treatment therapy. Furthermore, it discusses distributed computing techniques for making multiple high performance decision support systems available at the time and location of decision making, by exploiting computing resources residing inside, as well as outside the hospital walls optimally.

Algorithms↗

PubMed2004

Neural networks for protein classification.

This paper describes a biomolecular classification methodology based on multilayer perceptron neural networks. The system developed is used to classify enzymes found in the Protein Data Bank. The primary goal of classification, here, is to infer the function of an (unknown) enzyme by analysing its structural similarity to a given family of enzymes. A new codification scheme was devised to convert the primary structure of enzymes into a real-valued vector. The system was tested with a different number of neural networks, training set sizes and training epochs. For all experiments, the proposed system achieved a higher accuracy rate when compared with profile hidden Markov models. Results demonstrated the robustness of this approach and the possibility of implementing fast and efficient biomolecular classification using neural networks.

Algorithms↗

PubMed2000

Functional connectivity among ventrolateral medullary respiratory neurones and responses during fictive cough in the cat.

This study tested predictions from a network model of ventrolateral medullary respiratory neurone interactions for the generation of the cough motor pattern observed in inspiratory and expiratory pump muscles. Data were from 34 mid-collicularly decerebrated, paralysed, artificially ventilated cats. Cough-like patterns (fictive cough) in efferent phrenic and lumbar nerve activities were elicited by mechanical stimulation of the intrathoracic trachea. Neurones in the ventral respiratory group, including the Botzinger and pre-Botzinger complexes, were monitored simultaneously with microelectrode arrays. Spike trains were analysed for evidence of functional connectivity and responses during fictive cough with cycle-triggered histograms, autocorrelograms, cross-correlograms, and spike-triggered averages of phrenic and recurrent laryngeal nerve activities. Significant cross-correlogram features were detected in 151 of 1988 pairs of respiratory modulated neurones. There were 59 central peaks, 5 central troughs, 11 offset peaks and 2 offset troughs among inspiratory neurone pairs. Among expiratory neurones there were 23 central peaks, 8 offset peaks and 4 offset troughs. Correlations between inspiratory and expiratory neurones included 20 central peaks, 10 central troughs and 9 offset troughs. Spike-triggered averages of phrenic motoneurone activity had 51 offset peaks and 5 offset troughs. The concurrent responses and multiple short time scale correlations support parallel and serial network interactions proposed in our model for the generation of the cough motor pattern in the respiratory pump muscles. Inferred associations included the following. (a) Excitation of augmenting inspiratory (I-Aug) neurones and phrenic motoneurones by I-Aug neurones. (b) Inhibition of augmenting expiratory (E-Aug) neurones by decrementing inspiratory (I-Dec) neurones. (c) Inhibition of I-Aug, I-Dec and E-Aug neurones by E-Dec neurones. (d) Inhibition of I-Aug and I-Dec neurones and phrenic motoneurones by E-Aug neurones. The data also confirm previous results and support hypotheses in current network models for the generation of the eupnoeic pattern.

Action Potentials↗

PubMed2001

Development of inferential measurements using neural networks.

In many industrial processes, the most desirable variables to control are measured infrequently off-line in a quality control laboratory. In these situations, use of advanced control or optimization techniques requires use of inferred measurements generated from correlations. For well-understood processes, the structure of the correlation as well as the choice of inputs may be known a priori. However, many industrial processes are too complex and the appropriate form of the correlation and choice of input measurements are not obvious. Here, process knowledge, operating experience, and statistical methods play an important role in development of correlations. This paper describes a systematic approach to the development of nonlinear correlations for inferential measurements using neural networks. A three-step procedure is proposed. The first step consists of data collection and preprocessing. Next, the process variables are subjected to simple statistical analyses to identify a subset of measurements to be used in the inferential scheme. The third step involves generation of the inferential scheme. We demonstrate the methodology by inferring the ASTM 95% endpoint of a petroleum product using data from a domestic US refinery.

Algorithms↗

PubMed2004

Knowledge sharing and information integration in healthcare using ontologies and deductive databases.

This paper describes a method for using Semantic Web technologies for sharing knowledge in healthcare. It combines deductive databases and ontologies, so that it is possible to extract knowledge that has not been explicitly declared within the database. A representation of the UMLS (Unified Medical Language System) Semantic Network and Metathesaurus was created using the RDF standard, in order to represent the basic medical ontology. The inference over the knowledge base is done by the TRI-DEDALO System, a deductive data-base created to query and update RDF based knowledge sources as well as conventional relational databases. Finally, an ontology was created for the Brazilian National Health Card data interchange format, a standard to capture and transmit health encounter information throughout the country. This paper demonstrates how this approach can be used to integrate heterogeneous information and to answer complex queries in a real world environment.

Brazil↗

PubMedDate not supplied

Measuring solar magnetic fields with artificial neural networks.

The quantification of the solar magnetic field is a crucial step in modern solar physics to understand the dynamics, activity and variability of our star. Presently, a reliable inference of these fields is only possible by means of a computer-intensive process that has so far limited scientists to the analysis of observations from small regions of the solar disk, and/or very crude spatial and temporal resolution. This work presents a different approach to the problem, in which a multilayer perceptron, trained with known synthetic profiles, is able to recognize the profiles and return the magnetic field used to synthesize them. The network is then confronted with real observations of a sunspot which had been previously inverted using traditional inversion techniques. A quantitative comparison between these two procedures shows the reliability of the network when applied to points having magnetic filling factors larger than approximately 70%. The dramatic decrease in the required computing time presents an opportunity for the routine analysis of large-scale, high-resolution solar observations.

Electromagnetic Fields↗

PubMed1976

Extreme vestibulo-ocular adaptation induced by prolonged optical reversal of vision.

1. These experiments investigated plastic changes in the vestibulo-ocular reflex (VOR) of human subjects consequent to long-term optical reversal of vision during free head movement. Horizontal vision-reversal was produced by head-mounted dove prisms. Four normal adults were continuously exposed to these conditions during 2, 6, 7 and 27 days respectively.2. A sinusoidal rotational stimulus, previously shown to be nonhabituating (1/6 Hz; 60 degrees /sec amplitude), was used to test the VOR in the dark at frequent intervals both during the period of vision-reversal and an equal period after return to normal vision. D.c. electro-oculography (EOG) was used to record eye movement, taking care to avoid changes of EOG gain due to light/dark adaptation of the retina.3. All subjects showed substantial reduction of VOR gain (eye velocity/head velocity) during the first 2 days of vision-reversal. The 6-, 7- and 27-day subjects showed further reduction of gain which reached a low plateau at about 25% the normal value by the end of one week. At this time the attenuation of some EOG records was so marked as to defy extraction of a meaningful sinusoidal signal.4. After removal of the prisms VOR gain recovered along a time course which approximated that of the original adaptive attenuation.5. In the 27-day experiment large changes of phase developed in the VOR during the second week of vision-reversal. These changes generally progressed in a lagging sense, to reach 130 degrees phase lag relative to normal by the beginning of the third week. Accompanying this was a considerable restoration of gain from 25 to 50% the normal value. These adapted conditions, which approximate functional reversal of the reflex, were then maintained steady, even overnight, until return to normal vision on the 28th day.6. Thereafter, whereas VOR phase returned to near-normal in 2 hr, restoration of gain occupied a further 2-3 weeks.7. There was a highly systematic relation between instantaneous gain and phase, even during periods of widely fluctuating change associated with transition from one steady state to another. During such transition there was a tendency for directional preponderance to occur in the VOR.8. All the observed changes were highly specific to the plane of vision-reversal, no VOR changes being observed in the sagittal plane.9. VOR changes were adaptive, in the sense that they were always goal-directed towards the requirements of retinal image stabilization during head movement. They were plastic to the extent that there was extensive and retained remodelling of the reflex towards this goal.10. It is inferred that all the observed changes in gain and phase are compatible with a simple neural network employing known vestibulo-ocular projections via brainstem and cerebellar pathways, providing that the reversed visual tracking task can produce plastic modulation of efficacy in the cerebellar pathway and that this pathway exhibits a dynamic characteristic producing moderate phase lead in a sinusoidal signal at 1/6 Hz.

Journal Article↗

PubMed2006

Brain activation during input from mechanoinsensitive versus polymodal C-nociceptors.

C-nociceptors mediating cutaneous pain in humans can be distinguished in mechano-heat-responsive units (CMH) and mechano-insensitive units (CMi). However, if sensitized in damaged tissue, CMi play an important role in inflammatory pain. CMi differ from CMH by higher electrical thresholds and by mediating the axon reflex. Using these properties, we established two stimulation paradigms: (1) transcutaneous stimulation (TCS) of low current density below the CMi threshold and (2) intracutaneous stimulation (ICS) of high current density that excites CMi. This was proven by the quantification of the axon-reflex flare. Applying these stimulation paradigms during functional magnetic resonance imaging, we investigated whether nociceptor stimulation that recruits CMi leads to different cerebral activation than stimuli that do not recruit CMi. Brain activation by CMi was inferred by subtraction. Both stimuli recruited multiple afferents other than CMi, and we expected a common network of regions involved in different aspects of pain perception and motor nocifensive reactions in both stimuli. ICS that additionally recruited CMi should activate regions with low acuity that are involved in pain memory and emotional attribution. Besides a common network of pain in both stimuli, TCS activated the supplementary motor area, motor thalamic nuclei, the ipsilateral insula, and the medial cingulate cortex. These regions contribute to a pain processing loop that coordinates the nocifensive motor reaction. CMi nociceptor activation did not cause relevant activation in this loop and does not seem to play a role in withdrawal. The posterior cingulate cortex was selectively activated by ICS and is apparently important for the processing of inflammatory pain.

Adult↗

PubMed1999

A pathway finding system for the cell signaling networks database.

We report on a knowledge-based pathway-finding system that builds on the cell-signaling networks database, CSNDB, which we developed previously. This new system, PaF-CSNDB, uses a general inference engine to apply rules for finding and coupling pathways between or around specific biomolecules from the CSNDB database. We show how PaF-CSNDB finds relationships in a large but fragmented collection of cell-signaling knowledge by filtering out and composing together those sections of pathways specified from an extensive and complex set of binary or pair-wise cell-signaling reactions.

Animals↗

PubMed2004

Uniform sampling of steady-state flux spaces: means to design experiments and to interpret enzymopathies.

Reconstruction of genome-scale metabolic networks is now possible using multiple different data types. Constraint-based modeling is an approach to interrogate capabilities of reconstructed networks by constraining possible cellular behavior through the imposition of physicochemical laws. As a result, a steady-state flux space is defined that contains all possible functional states of the network. Uniform random sampling of the steady-state flux space allows for the unbiased appraisal of its contents. Monte Carlo sampling of the steady-state flux space of the reconstructed human red blood cell metabolic network under simulated physiologic conditions yielded the following key results: 1), probability distributions for the values of individual metabolic fluxes showed a wide variety of shapes that could not have been inferred without computation; 2), pairwise correlation coefficients were calculated between all fluxes, determining the level of independence between the measurement of any two fluxes, and identifying highly correlated reaction sets; and 3), the network-wide effects of the change in one (or a few) variables (i.e., a simulated enzymopathy or fixing a flux range based on measurements) were computed. Mathematical models provide the most compact and informative representation of a hypothesis of how a cell works. Thus, understanding model predictions clearly is vital to driving forward the iterative model-building procedure that is at the heart of systems biology. Taken together, the Monte Carlo sampling procedure provides a broadening of the constraint-based approach by allowing for the unbiased and detailed assessment of the impact of the applied physicochemical constraints on a reconstructed network.

Animals↗