Heavy metal contamination from gold mining recorded in Porites lobata skeletons, Buyat-Ratototok district, North Sulawesi, Indonesia.

Shallow marine sediments and fringing coral reefs of the Buyat-Ratototok district of North Sulawesi, Indonesia, are affected by submarine disposal of tailings from industrial gold mining and by small-scale gold mining using mercury amalgamation. Between-site variation in heavy metal concentrations in shallow marine sediments was partially reflected by trace element concentrations in reef coral skeletons from adjacent reefs. Corals skeletons recorded silicon, manganese, iron, copper, chromium, cobalt, antimony, thallium, and lead in different concentrations according to proximity to sources, but arsenic concentrations in corals were not significantly different among sites. Temporal analysis found that peak concentrations of arsenic and chromium generally coincided with peak concentrations of silica and/or copper, suggesting that most trace elements in the coral skeleton were incorporated into detrital siliciclastic sediments, rather than impurities within skeletal aragonite.

Lowland forest loss in protected areas of Indonesian Borneo.

The ecology of Bornean rainforests is driven by El Niño-induced droughts that trigger synchronous fruiting among trees and bursts of faunal reproduction that sustain vertebrate populations. However, many of these species- and carbon-rich ecosystems have been destroyed by logging and conversion, which increasingly threaten protected areas. Our satellite, Geographic Information System, and field-based analyses show that from 1985 to 2001, Kalimantan's protected lowland forests declined by more than 56% (>29,000 square kilometers). Even uninhabited frontier parks are logged to supply international markets. "Protected" forests have become increasingly isolated and deforested and their buffer zones degraded. Preserving the ecological integrity of Kalimantan's rainforests requires immediate transnational management.

Computational integrative physiology: at the convergence of the life and computational sciences.

OBJECTIVE: In this paper we outline how Computational Integrative Physiology (CIP) can help unravel the mechanisms of normal and pathological biological processes. Our objective is to illustrate how CIP is firmly grounded on the life and computational sciences. METHOD: After describing a general theoretical frame-work for CIP, we will center our discussion on cardiac rhythmic disorders with a particular focus on the Long QT syndrome that will serve as a case example. Within this context, we will describe multi-scale processes in biological, medical and in general mathematical terms, starting from the control of gene expression to the electrical activity of the entire heart. We will therefore proceed from the smaller microscopic scales to the larger macroscopic ones. In doing so, we will illustrate, at least in a qualitative sense, how CIP can be accomplished by showing some of the relations that can exist between mathematical variables characterizing models of different space-scales. CONCLUSION: We will conclude by putting forth how CIP and the related fields of bioinformatics and medical informatics are necessary to derive meaningful knowledge from the huge and exponentially growing biological and medical data.

An interactive 3D anisotropic cellular automata model of the heart.

A 3D cellular anisotropic automata model with modifiable geometry is described. The modeling parameters include grain size, fiber orientation, and free-wall and septal thickness. From this modifiable model, three specific models corresponding to normal heart, left ventricular hypertrophy, and ventricular dilatation were generated. Each model is a conduction and propagation model in which the atria, the major atrial vessel bases, the ventricles, and the specialized conduction system are represented. Muscle tissues are modeled as bundles of fibers with anisotropic conduction speed of the activation wavefronts. Regional variations of conduction, refractory gradients, and regional potential gradients can also be specified before each simulation. Each element has adaptive properties with respect to cycle length and to the prematurity of incoming impulses. Action potentials can be specified for each cell and an equivalent source formulation is carried out to simulate the vectorcardiogram and the corresponding 12-standard-lead electrocardiogram.

A qualitative model for computer-assisted instruction in cardiology.

CARDIOLAB is an interactive computational framework dedicated to teaching and computer-aided diagnosis in cardiology. The framework embodies models that simulate the heart's electrical activity. They constitute the core of a Computer-Assisted Instruction (CAI) program intended to teach, in a multimedia environment, the concepts underlying rhythmic disorders and cardiac diseases. The framework includes a qualitative model (QM) which is described in this paper. During simulation using QM, dynamic sequences representing impulse formation and conduction processes are produced along with the corresponding qualitative descriptions. The corresponding electrocardiogram (ECG) and ladder diagram are also produced, and thus, both qualitative notions and quantitative facts can be taught via the model. We discuss how qualitative models in particular, and computational models in general can enhance the teaching capability of CAI programs.

Medical Informatics Training and Research at Rennes University School of Medicine.

The University of Rennes Medical School has offered Medical informatics training since 1988; at the same time, research in medical informatics was started. This paper describes the teaching programs at both the undergraduate level and at the graduate level. Research topics comprise fundamental research on biomedical models, ontologies for medical knowledge representations, natural language processing, and research for diagnosis and therapeutic aids. We have developed a local area network which communicates between the School of Medicine and the University Hospital, both internally (Intranet) and externally by access to the National Research network (RENATER) in France, which is connected to the internet.

Spatio-temporal reasoning for multi-scale modeling in cardiology.

In this paper, we present an overview of the CARDIOLAB environment where the heart's electrical activity is modeled at distinct space and time scales. CARDIOLAB uses three models, two of which are based on cellular automata, and the third on qualitative simulation. They are combined around a blackboard for multi-scale quantitative and qualitative modeling of cardiac electrical activity. A general account on how spatio-temporal representation and reasoning methods are applied to produce heuristic associations is also presented.

A cellular automata model of the heart and its coupling with a qualitative model.

Cellular Automata (CA) models offer a good compromise between computational complexity and biological plausibility while qualitative models have expressive power for explicitly describing dynamic processes. In this paper we present a 2D CA model and its coupling with a qualitative model. The CA model includes elements characterizing muscle, nodal tissue, and bypass conduction. Each element exhibits adaptive properties to cycle length and to the prematurity of incoming impulses. A crude electrocardiogram is also simulated via an equivalent source formulation. Arrhythmias such as the Wenckebach phenomenon, atrial flutter, or extrasystole-triggered tachyarrhythmias can be simulated using relatively simple models when they incorporate the fast conduction system with muscle tissue and when the model elements exhibit adaptive properties. We then illustrate how a CA model can be coupled to a qualitative model to produce a system that combines the fine grained description of CA models with the high level interpretative role of qualitative models.

Introducing spatio-temporal reasoning into the inverse problem in electroencephalography.

Studying the Brain's Electrical and Magnetic Signals (BEMS) requires the contribution of many area of research that include anatomy, neurophysiology and electromagnetic theory. NEUROLAB is a framework dedicated to the study of brain disorders. Upon completion, it should provide users with an intelligent computational environment that incorporates qualitative and quantitative models of the brain and head, and a model for representing and reasoning about time and space. Spatio-temporal knowledge of a given problem is represented as a constraint network where to each node of the network are attached temporal and spatial variables that must satisfy the constraints defined by the arch labels connecting the nodes. In this paper, we show how temporal reasoning can be combined with spatial descriptions to produce different scenarios of possible seizure spread. These scenarios can provide a priori information for the inverse problem the role of which is to localise the sources of the observed BEMS.

An interactive qualitative model in cardiology.

Qualitative modeling is a generic term that involves explicit and qualitative representations of the physical world. It can extend the realm of pure mathematical modeling in the sense that qualitative descriptions can, on one hand, simulate complex physical systems and processes and, on the other, produce linguistic descriptions and summaries of simulated system behavior. These summaries should be an essential element of the human/machine interface if truly interactive computational environments are to be developed. In the context of cardiac arrhythmias, a thorough understanding of the underlying processes that lead to the different pathological states is a first step toward optimizing diagnosis and therapy. The CARDIOLAB project is dedicated to cardiology and is aimed at providing a theoretical framework composed of computational models of different grain size and based on different formalisms. One of the intended roles of the framework is to assist researchers, clinicians, and pharmacologists in their quest for a better understanding of rhythmic disorders and ischemic events. In this paper, we present the first element of the framework. It is a cardiac simulator conceptualized in terms of a research field known as qualitative physics. As a simulator, the model's role is to produce fairly detailed descriptions, at different levels of abstraction, of cardiac electrical events when initial tissue-state conditions are given. A crude simulated ECG is also produced as a visual aid. At the end of each simulation session, and upon user request, the system can memorize the initial conditions and the descriptions into an arrhythmia knowledge base. As such, the model can be used as an interactive tool, to grossly delineate the regions in parameter space that correspond to causing or predisposing states leading to specific rhythmic disorders. More refined analysis can thereafter be performed using finer-grained models, the initial conditions of which will have been suggested by the qualitative model.

Model-based diagnosis of brain disorders: a prototype framework.

This paper describes a prototype framework, named NEUROLAB, dedicated to research and diagnosis in the area of brain disorders. The diagnostic task uses a blending of factual knowledge, formal knowledge, and experiential knowledge. The prototype's first target clinical application is partial seizures in epilepsy. Diagnosis is carried out using qualitative electroencephalographic descriptions, clinical attack pattern descriptions, and pre- and post-ictal observations. From this information, the system builds explanations in the form of candidate epileptogenic foci and trajectories of the seizure spread. Hypothesis-testing and discrimination is based on minimal set coverage, and consistency-checking is performed using the general background knowledge. Upon completion, NEUROLAB will provide specific physiological knowledge for solving the so-called inverse problems in electroencephalography (EEG) and magnetoencephalography (MEG).

Deep knowledge and computer-assisted instruction in cardiology.

In this paper, we describe a qualitative heart model that is part of a computing environment, CARDIOLAB, and whose role includes the diagnosis and Computer Assisted Instruction (CAI) in cardiology. The model is based on a "deep knowledge" approach to diagnosis. Deep knowledge representations model the inner works of complex physical systems. Explicit representations of system components, component functions, and behavior allow a principled form of reasoning that extends the classical rule-based, first-generation expert systems. One of the main advantage of model-based diagnosis resides in the possibility of providing explanations to observed facts or measured data. This feature can be incorporated into CAI programs with similar benefits.