Diagnostic experiments for transport mechanisms of suspended sediment discharged from the Yellow River in the Bohai Sea

Five diagnostic experiments with a 3D baroclinic hydrodynamic and sediment transport model ECOMSED in couple with the third generation wave model SWAN and the Grant-Madsen bottom boundary layer model driven by the monthly sediment load of the Yellow River, were conducted to separately diagnose effects of different hydrodynamic factors on transport of suspended sediment discharged from the Yellow River in the Bohai Sea. Both transport and spatio-temporal distribution of suspended sediment concentration in the Bohai Sea were numerially simulated. It could be concluded that suspended sediment discharged from the Yellow River cannot be delivered in long distance under the condition of tidal current. Almost all of sediments from the Yellow River are deposited outside the delta under the condition of wind-driven current, and only very small of them are transported faraway. On the basis of wind forcing, sediments from the Yellow River are mainly transported north-northwestward, and others which are first delivered to the Laizhou Bay are continuously moved northward. An obvious 3D structure characteristic of sediment transport is produced in the wind-driven and tide-induced residual circulation condition. Transport patterns at all layers are generally consistent with circulation structure, but there is apparent deviation between the depth-averaged sediment flux and the circulation structure. The phase of temporal variation of sediment concentration is consistent with that of the bottom shear stress, both of which are proved to have a ten-day cycle in wave and current condition.

Evaluation of a combined wavelet and a combined principal component analysis classification system for BCG diagnostic problem

Heart disease is one of the main factor causing death in the developed countries. Over several decades, variety of electronic and computer technology have been developed to assist clinical practices for cardiac performance monitoring and heart disease diagnosis. Among these methods, Ballistocardiography (BCG) has an interesting feature that no electrodes are needed to be attached to the body during the measurement. Thus, it is provides a potential application to asses the patients heart condition in the home. In this paper, a comparison is made for two neural networks based BCG signal classification models. One system uses a principal component analysis (PCA) method, and the other a discrete wavelet transform, to reduce the input dimensionality. It is indicated that the combined wavelet transform and neural network has a more reliable performance than the combined PCA and neural network system. Moreover, the wavelet transform requires no prior knowledge of the statistical distribution of data samples and the computation complexity and training time are reduced.

Using Structural and Functional Information in Diagnostic Design

We wish to design a diagnostic for a device from knowledge of its structure and function. the diagnostic should achieve both coverage of the faults that can occur in the device, and should strive to achieve specificity in its diagnosis when it detects a fault. A system is described that uses a simple model of hardware structure and function, representing the device in terms of its internal primitive functions and connections. The system designs a diagnostic in three steps. First, an extension of path sensitization is used to design a test for each of the connections in teh device. Next, the resulting tests are improved by increasing their specificity. Finally the tests are ordered so that each relies on the fewest possible connections. We describe an implementation of this system and show examples of the results for some simple devices.

Computer-Based Diagnostic Systems: Computer-Based Troubleshooting

ISBN: 3-540-76198-5 (out of print)

The Use of Graph Grammars for Model-based Reasoning in Diagnostic Expert Systems

Flasinski M. and Lee M.H., The Use of Graph Grammars for Model-based Reasoning in Diagnostic Expert Systems, Prace Informatyczne, Zeszyty Naukowe Uniwersytetu Jagiellonskiego, 9, 1999, pp147-165.

Computer technology in detection and staging of prostate carcinoma: a review

Y. Zhu, S. Williams and R. Zwiggelaar, 'Computer technology in detection and staging of prostate carcinoma: a review', Medical Image Analysis 10 (2), 178-199 (2006)

Perda de dimensão vertical de oclusão e estruturas dentárias remanescentes

Projeto de Pós-Graduação/Dissertação apresentado à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Mestre em Medicina Dentária

Artefact detection and removal algorithms for EEG diagnostic systems

The electroencephalogram (EEG) is a medical technology that is used in the monitoring of the brain and in the diagnosis of many neurological illnesses. Although coarse in its precision, the EEG is a non-invasive tool that requires minimal set-up times, and is suitably unobtrusive and mobile to allow continuous monitoring of the patient, either in clinical or domestic environments. Consequently, the EEG is the current tool-of-choice with which to continuously monitor the brain where temporal resolution, ease-of- use and mobility are important. Traditionally, EEG data are examined by a trained clinician who identifies neurological events of interest. However, recent advances in signal processing and machine learning techniques have allowed the automated detection of neurological events for many medical applications. In doing so, the burden of work on the clinician has been significantly reduced, improving the response time to illness, and allowing the relevant medical treatment to be administered within minutes rather than hours. However, as typical EEG signals are of the order of microvolts (μV ), contamination by signals arising from sources other than the brain is frequent. These extra-cerebral sources, known as artefacts, can significantly distort the EEG signal, making its interpretation difficult, and can dramatically disimprove automatic neurological event detection classification performance. This thesis therefore, contributes to the further improvement of auto- mated neurological event detection systems, by identifying some of the major obstacles in deploying these EEG systems in ambulatory and clinical environments so that the EEG technologies can emerge from the laboratory towards real-world settings, where they can have a real-impact on the lives of patients. In this context, the thesis tackles three major problems in EEG monitoring, namely: (i) the problem of head-movement artefacts in ambulatory EEG, (ii) the high numbers of false detections in state-of-the-art, automated, epileptiform activity detection systems and (iii) false detections in state-of-the-art, automated neonatal seizure detection systems. To accomplish this, the thesis employs a wide range of statistical, signal processing and machine learning techniques drawn from mathematics, engineering and computer science. The first body of work outlined in this thesis proposes a system to automatically detect head-movement artefacts in ambulatory EEG and utilises supervised machine learning classifiers to do so. The resulting head-movement artefact detection system is the first of its kind and offers accurate detection of head-movement artefacts in ambulatory EEG. Subsequently, addtional physiological signals, in the form of gyroscopes, are used to detect head-movements and in doing so, bring additional information to the head- movement artefact detection task. A framework for combining EEG and gyroscope signals is then developed, offering improved head-movement arte- fact detection. The artefact detection methods developed for ambulatory EEG are subsequently adapted for use in an automated epileptiform activity detection system. Information from support vector machines classifiers used to detect epileptiform activity is fused with information from artefact-specific detection classifiers in order to significantly reduce the number of false detections in the epileptiform activity detection system. By this means, epileptiform activity detection which compares favourably with other state-of-the-art systems is achieved. Finally, the problem of false detections in automated neonatal seizure detection is approached in an alternative manner; blind source separation techniques, complimented with information from additional physiological signals are used to remove respiration artefact from the EEG. In utilising these methods, some encouraging advances have been made in detecting and removing respiration artefacts from the neonatal EEG, and in doing so, the performance of the underlying diagnostic technology is improved, bringing its deployment in the real-world, clinical domain one step closer.

Patient-centred screening for primary immunodeficiency: a multi-stage diagnostic protocol designed for non-immunologists.

Efficient early identification of primary immunodeficiency disease (PID) is important for prognosis, but is not an easy task for non-immunologists. The Clinical Working Party of the European Society for Immunodeficiencies (ESID) has composed a multi-stage diagnostic protocol that is based on expert opinion, in order to increase the awareness of PID among doctors working in different fields. The protocol starts from the clinical presentation of the patient; immunological skills are not needed for its use. The multi-stage design allows cost-effective screening for PID within the large pool of potential cases in all hospitals in the early phases, while more expensive tests are reserved for definitive classification in collaboration with an immunologist at a later stage. Although many PIDs present in childhood, others may present at any age. The protocols presented here are therefore aimed at both adult physicians and paediatricians. While designed for use throughout Europe, there will be national differences which may make modification of this generic algorithm necessary.

Latent tuberculosis in a newborn: diagnostic challenges.

In countries where the incidence of tuberculosis is low, perinatal tuberculosis is seldom diagnosed. With increasing numbers of human immunodeficiency virus-infected people and increasing immigrant population from high tuberculosis incidence countries, one might expect perinatal tuberculosis to become more frequent. Early recognition of newborns at risk for perinatal tuberculosis infection is of utmost importance to prevent disease by chemoprophylaxis. We describe a case of latent perinatal tuberculosis infection in a newborn infected from a mother with extrapulmonary primary tuberculosis. Tuberculin skin test was negative, and latent tuberculosis infection was eventually diagnosed by specific immunological tests. We discuss the difficulties in diagnosis of recent tuberculosis infection in neonates and infants, and the risk factors for vertical transmission of tuberculosis, which need to be taken into account in considering the need for chemoprophylaxis in the newborn. Although perinatal TB infection is a rare condition and diagnosis is difficult due to poor diagnostic testing in pregnancy and newborns, a high index of suspicion is needed to limit the diagnostic delay and to avoid progression to perinatal TB disease.

Les anticorps anti-cytoplasme des neutrophiles (ANCAs): un progrès majeur dans le diagnostic des vasculites.

SCOPUS: ar.j

Déficits immunitaires primaires: diagnostic, prise en charge, quelques perspectives.

Severe primary immunodeficiencies (PID) are rare; their global incidence is comparable to that of childhood leukemia; they include more than 100 different entities. Clinical manifestations are: unusually severe or frequent infections or infections that do not respond to adequate treatment; an increased risk of certain malignancies; sometimes auto-immune manifestations. Delayed diagnosis and management of PID can lead to severe and irreversible complications or to death. PID can become manifest only in the adult; in common variable immune deficiency, the median age at diagnosis is between the 2nd and the 3rd decade of life. PID are often transmitted genetically; recent progresses in molecular biology have allowed more precise and earlier, including antenatal, diagnosis. Molecular treatment of 3 infants with a severe immunodeficiency has recently been achieved in April 2000. Those progresses were mostly based on the study of immunodeficiency databases. We present here the work of a Belgian group specialized in PID; meetings have started in June 1997. This group establishes guidelines for the diagnosis and treatment of PID, adapted to the local situation. The elaboration of a national register of PID is also underway; this has to provide all guaranties of anonymity to patients and families. Such a register already exists at the European level; it has provided the basis for new diagnostic and therapeutic possibilities. The inclusion of Belgian data in this register should allow essential progresses essential for our patients.