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PubMed1975

Origins of anthropoid intelligence.

The development of the extrastriate visual system relative to the striate system was estimated indirectly by measuring the volumes of the lateral posteriorpulvinar complex and lateral geniculate nucleus in six varieties of mammals selected on the basis of their propinquity with Anthropoidea [oppossums, hedgehogs, rats, squirrels, tree shrews and bushbabies]. The same animals were tested on two related behavioral tasks [spatial and visual reversal learning] whose successful achievement requires a simple sort of abstraction. The results show that the ability to learn visual reversal, but not spatial reversal, corresponds closely to the relative degree of development of the extrastriate system. Since the variation in both these behavioral and morphological characteristics also parallels the phylogenetic dimension, the recency of common ancestry to anthropoids, the evolutionary origin of the anthropoid capacity for visual abstraction is suggested.

Animals↗
PubMed2004

Marine litter prediction by artificial intelligence.

Artificial intelligence techniques of neural network and fuzzy systems were applied as alternative methods to determine beach litter grading, based on litter surveys of the Antalya coastline (the Turkish Riviera). Litter measurements were categorized and assessed by artificial intelligence techniques, which lead to a new litter categorization system. The constructed neural network satisfactorily predicted the grading of the Antalya beaches and litter categories based on the number of litter items in the general litter category. It has been concluded that, neural networks could be used for high-speed predictions of litter items and beach grading, when the characteristics of the main litter category was determined by field studies. This can save on field effort when fast and reliable estimations of litter categories are required for management or research studies of beaches--especially those concerned with health and safety, and it has economic implications. The main advantages in using fuzzy systems are that they consider linguistic adjectival definitions, e.g. many/few, etc. As a result, additional information inherent in linguistic comments/refinements and judgments made during field studies can be incorporated in grading systems.

Ecosystem↗
PubMed2000

Case-based reasoning for medical knowledge-based systems.

In many domains Case-based Reasoning (CBR) has become a successful technique for knowledge-based systems. In medical domains, attempts to apply the complete CBR cycle are rather exceptional. Some systems have recently been developed, which on the one hand use only parts of the CBR method, mainly the retrieval, and on the other hand enrich the method by a generalisation step to fill the knowledge gap between the specificity of single cases and general rules. So, in this paper we discuss the appropriateness of CBR for medical knowledge-based systems, point out problems, limitations and possibilities how they can partly be overcome.

Artificial Intelligence↗
PubMed2001

AI in medicine on its way from knowledge-intensive to data-intensive systems.

The last 20 years of research and development in the field of artificial intelligence in medicine (AIM) show a path from knowledge-intensive systems, which try to capture the essential knowledge of experts in a knowledge-based system, to data-intensive systems available today. Nowadays enormous amounts of information is accessible electronically. Large datasets are collected continuously monitoring physiological parameters of patients. Knowledge-based systems are needed to make use of all these data available and to help us to cope with the information explosion. In addition, temporal data analysis and intelligent information visualization can help us to get a summarized view of the change over time of clinical parameters. Integrating AIM modules into the daily-routine software environment of our care providers gives us a great chance for maintaining and improving quality of care.

Artificial Intelligence↗
PubMed2003

Self-programming machines (II): Network of self-programming machines driving an Ashby homeostat.

The progress in artificial intelligence enables us to conceive adaptive systems whose characteristics are nearer and nearer to those of living beings. These characteristics though depend on ingenious choices by the designer of these systems: Initial conditions, parameters, optimisation functions, gradient and measure of fitness within the environment. Nevertheless, in living systems which are non-finalist, there are no programmers or designers to conceive of such ingenious choices. Our paper "Self-Programming Machines (I)" presents a non-finalist model since initial states and functions are randomly chosen at the beginning and once and for all. In spite of the fact that they are non-finalist, these machines always stabilise at fixed points when they are connected to an external process. This paper studies the dynamics of a mono-layered network of self-programming machines driving a real device. "the Ashby homeostat", and shows the striking properties of such networks. This system stabilises only at fixed points even if it is subjected to small perturbations or intentional breakdowns such as a reversal of power supply or disconnection of one or several motors. Real and simulated experiences are compared and theoretical results are demonstrated.

Artificial Intelligence↗
PubMed1994

A case-based assistant for clinical psychiatry expertise.

Case-based reasoning is an artificial intelligence methodology for the processing of empirical knowledge. Recent case-based reasoning systems also use theoretic knowledge about the domain to constrain the case-based reasoning. The organization of the memory is the key issue in case-based reasoning. The case-based assistant presented here has two structures in memory: cases and concepts. These memory structures permit it to be as skilled in problem-solving tasks, such as diagnosis and treatment planning, as in interpretive tasks, such as clinical research. A prototype applied to clinical work about eating disorders in psychiatry, reasoning from the alimentary questionnaires of these patients, is presented as an example of the system abilities.

Anorexia↗
PubMed1988

Perspectives on clinical decision-making: the application of pattern analysis.

It is the thesis of this presentation that more precise diagnoses and prognostications result when clinical information is analyzed as data-sets or patterns rather than collections of discrete data. Two examples are given in support of the thesis: the classification by computer of QRS complexes in the ambulatory electrocardiogram and the prediction of risk for recurrent ventricular tachycardia. The advantages to be gained from pattern analysis are: (1) significant variables are not preselected and the data are, therefore, unbiased, and (2) nuances in clinical patterns become evident when patients are presented as data-sets. It is proposed that competent physicians undoubtedly use pattern analysis in their decision-making and that expert systems designed to simulate physician behavior might be more accurate if based in pattern analysis.

Artificial Intelligence↗
PubMed1997

Creating an environment for linking knowledge-based systems to a clinical database: a suite of tools.

A difficulty in using knowledge-based systems has been linking them to clinical databases. The challenge is in making a correct mapping from the data in the knowledge base to the data in the database. At Columbia-Presbyterian Medical Center, we have built a suite of tools developed to create queries that address this challenge. The tools were designed to allow users to easily retrieve data from the database without requiring the users have extensive database and vocabulary knowledge. The tools help users write correct queries (Query Builder), find correct terms in the clinical database (MED Browser), aggregate the resulting data into a useful form (Clinical Database Browser), and allow the user to test the query within the environment of the knowledge-based system (Event Playback). The tools have been in use for one year.

Artificial Intelligence↗
PubMed1989

Robotics in biomedical chromatography and electrophoresis.

The ideal laboratory robot can be viewed as "an indefatigable assistant capable of working continuously for 24 h a day with constant efficiency". The development of a system approaching that promise requires considerable skill and time commitment, a thorough understanding of the capabilities and limitations of the robot and its specialized modules and an intimate knowledge of the functions to be automated. The robot need not emulate every manual step. Effective substitutes for difficult steps must be devised. The future of laboratory robots depends not only on technological advances in other fields, but also on the skill and creativity of chromatographers and other scientists. The robot has been applied to automate numerous biomedical chromatography and electrophoresis methods. The quality of its data can approach, and in some cases exceed, that of manual methods. Maintaining high data quality during continuous operation requires frequent maintenance and validation. Well designed robotic systems can yield substantial increase in the laboratory productivity without a corresponding increase in manpower. They can free skilled personnel from mundane tasks and can enhance the safety of the laboratory environment. The integration of robotics, chromatography systems and laboratory information management systems permits full automation and affords opportunities for unattended method development and for future incorporation of artificial intelligence techniques and the evolution of expert systems. Finally, humanoid attributes aside, robotic utilization in the laboratory should not be an end in itself. The robot is a useful tool that should be utilized only when it is prudent and cost-effective to do so.

Chemistry, Clinical↗
PubMed2001

Extracting clinical cases from XML-based electronic patient records for use in web-based medical case based reasoning systems.

Development and usage of Case Based Reasoning (CBR) driven medical diagnostic system requires a large volume of clinical cases that depict the problem-solving methodology of medical experts. Successful usage of CBR based systems in healthcare is constrained by the need for a continuous supply of current and correct clinical cases (in an electronic medium) from medical experts. To address this constraint we present a strategy to pro-actively transform generic Electronic Patient Records (EPR) to Operable CBR-oriented Cases (OCC) that are compliant to specialised CBR-based medical systems. EPR-OCC transformation methodology is based on XML parse-trees, Unified Medical Language Source (UMLS) meta-thesauri and medical knowledge ontologies. The featured work involves the implementation of a Java-based computer system for the automatic transformation of XML-based EPR-originating from heterogeneous EPR repositories accessible over the Internet/WWW-to specialised OCC that can then be seamlessly incorporated within Intelligent CBR-based Medical Diagnostic Systems.

Diagnosis, Computer-Assisted↗
PubMed1996

Validation of the Applied Biosystems Prism 377 automated sequencer for the forensic short tandem repeat analysis.

The Applied Biosystems (ABI) Prism 377 DNA sequencer has been evaluated in an attempt to increase the throughput of samples for short tandem repeat (STR) analysis, in both forensic casework and the UK National Criminal Intelligence DNA Database. The gel system assessed consisted of 0.2 mm, 4% acrylamide 6 M urea gels, with a well-to-read distance of 36 cm. Gels were run at a constant voltage of 3 kV and constant temperature of 51 degrees C. The run time of our second generation multiplex (SGM) STR system was achieved in less than 2 h. Rigorous validation has been performed on the instrument hardware and software. Complete resolution of 1 base differences was obtained, up to and beyond 350 bases; sizing precision across gels was more than 2-fold higher than the 373A and the sensitivity was increased by one third.

Autoanalysis↗
PubMed2005

Metabolic engineering.

Metabolic engineering is a powerful methodology aimed at intelligently designing new biological pathways, systems, and ultimately phenotypes through the use of recombinant DNA technology. Built largely on the theoretical and computational analysis of chemical systems, the field has evolved to incorporate a growing number of genome scale experimental tools. This combination of rigorous analysis and quantitative molecular biology methods has endowed metabolic engineering with an effective synergism that crosses traditional disciplinary bounds. As such, there are a growing number of applications for the effective employment of metabolic engineering, ranging from the initial industrial fermentation applications to more recent medical diagnosis applications. In this review we highlight many of the contributions metabolic engineering has provided through its history, as well as give an overview of new tools and applications that promise to have a large impact on the field's future.

Biotechnology↗
PubMed2026

Emerging hantavirus risks in mass gatherings: epidemiology, diagnostic challenges, and outbreak preparedness.

Hantaviruses are emerging rodent borne zoonotic pathogens of increasing global public health concern because of their high mortality, expanding ecological distribution, and potential for international dissemination. Although traditionally associated with sporadic rural outbreaks, recent ecological disruption, climate variability, urbanization, and increased global mobility have heightened concerns regarding hantavirus risks in mass gathering settings. This review critically examines the epidemiology, transmission uncertainty, diagnostic and surveillance challenges, and preparedness strategies related to hantavirus infections in the context of mass gatherings, including religious events, refugee settlements, cruise tourism, sporting events, and temporary accommodations. Particular emphasis is placed on the 2026 multinational cruise ship associated outbreak linked to the MV Hondius, which highlighted vulnerabilities related to delayed diagnosis, international passenger dispersal, and uncertainties surrounding possible human to human transmission of Andes virus. Current evidence indicates that hantavirus transmission occurs primarily through inhalation of aerosolized rodent excreta; however, controversies regarding limited interpersonal transmission, environmental persistence, and asymptomatic infections continue to complicate risk assessment and outbreak preparedness. Diagnostic limitations, underreporting, insufficient environmental surveillance, and lack of mass gathering specific preparedness frameworks remain major public health challenges, especially in resource limited settings. Strengthening proactive preparedness through integrated One Health approaches, ecological surveillance, genomic monitoring, AI driven epidemic intelligence, and coordinated international response systems is essential for mitigating future risks. The review emphasizes the urgent need for multidisciplinary research and evidence based policy development to improve global preparedness against emerging hantavirus associated threats in increasingly interconnected mass gathering environments.

Humans↗
PubMed2003

Artificial life: organization, adaptation and complexity from the bottom up.

Artificial life attempts to understand the essential general properties of living systems by synthesizing life-like behavior in software, hardware and biochemicals. As many of the essential abstract properties of living systems (e.g. autonomous adaptive and intelligent behavior) are also studied by cognitive science, artificial life and cognitive science have an essential overlap. This review highlights the state of the art in artificial life with respect to dynamical hierarchies, molecular self-organization, evolutionary robotics, the evolution of complexity and language, and other practical applications. It also speculates about future connections between artificial life and cognitive science.

Journal Article↗
PubMed1997

Progressive dystonia with optic atrophy in a Jewish-Iraqi family.

The combination of progressive dystonia and optic atrophy is extremely rare and its morphological, metabolic and genetic basis is unknown. In a family of 9 children (8 males) born to consanguineous Israeli-Jewish-Iraqi parents, we identified four brothers who developed the syndrome at the end of the first decade. Patients had hemi or bilateral dystonia associated with striatal, mainly putaminal, atrophy on CT and MRI, various degrees of optic atrophy, minimal corticospinal tract involvement, normal intelligence and no peripheral nervous system or systemic abnormalities. No causative metabolic defect was identified. None of the several known mitochondrial DNA mutations associated with Leber's hereditary optic neuropathy (LHON) or with LHON with dystonia were detected. Likewise, linkage to the idiopathic torsion dystonia region on chromosome 9q34 was excluded. It is suggested that this in our patients might be due to a yet unidentified genomic, autosomal recessive mutation.

Adolescent↗