Chip 1999 June/Chip_1999-06_cd.bin/tema/Protec/_setup.1/Entrez.pmd

Text File | 1997-03-17 | 21KB | 435 lines

UI  - 97205277
PM  - 9052781
AU  - Smirnakis SM
AU  - Berry MJ
AU  - Warland DK
AU  - Bialek W
AU  - Meister M
TI  - Adaptation of retinal processing to image contrast and spatial scale
DP  - 1997 Mar 6
TA  - Nature
IS  - 0028-0836
JC  - NSC
PG  - 69-73
IP  - 6620
VI  - 385
AB  - Owing to the limited dynamic range of a neuron's output, neural
      circuits are faced with a trade-off between encoding the full range of
      their inputs and resolving gradations among those inputs. For example,
      the ambient light level varies daily over more than nine orders of
      magnitude, whereas the firing rate of optic nerve fibres spans less
      than two. This discrepancy is alleviated by light adaptation: as the
      mean intensity increases, the retina becomes proportionately less
      sensitive. However, image statistics other than the mean intensity also
      vary drastically during routine visual processing. Theory predicts that
      an efficient visual encoder should adapt its strategy not only to the
      mean, but to the full shape of the intensity distribution. Here we
      report that retinal ganglion cells, the output neurons of the retina,
      adapt to both image contrast-the range of light intensities-and to
      spatial correlations within the scene, even at constant mean intensity.
      The adaptation occurs on a scale of seconds, one hundred times more
      slowly than the immediate light response, and involves 2-5-fold changes
      in the firing rate. It is mediated within the retinal network: two
      independent sites of modulation after the photoreceptor cells appear to
      be involved. Our results demonstrate a remarkable plasticity in retinal
      processing that may contribute to the contrast adaptation of human
      vision.
AD  - Department of Physics, Harvard University, Cambridge, Massachusetts
      02138, USA.
SO  - Nature 1997 Mar 6;385(6620):69-73

UI  - 97188314
PM  - 9036899
AU  - Izenberg SD
AU  - Williams MD
AU  - Luterman A
TI  - Prediction of trauma mortality using a neural network.
MH  - Age Factors
MH  - Artificial Intelligence
MH  - Female
MH  - Hospital Mortality
MH  - Human
MH  - Injury Severity Score
MH  - Length of Stay
MH  - Male
MH  - *Neural Networks (Computer)
MH  - Prognosis
MH  - Wounds and Injuries/*MORTALITY/CLASSIFICATION
DP  - 1997 Mar
TA  - Am Surg
IS  - 0003-1348
JC  - 43E
PG  - 275-281
IP  - 3
VI  - 63
AB  - A neural network is a computerized construct consisting of input
      neurons (which process input data) connected to hidden neurons (to
      mathematically manipulate values they receive from all the input
      neurons) connected to output neurons (to output a prediction). Neural
      networks are created and trained via multiple iterations over data with
      known results. In 1993, 897 trauma patients were either declared dead
      in the emergency room (ER; 76 cases), admitted to the intensive care
      unit (427 cases, 36 deaths), or taken directly to the operating room
      (394 cases, 29 deaths). Using only data available from the ER, a neural
      network was created, and 628 cases were randomly selected for training.
      After 268 iterations, the network was trained to correctly predict
      death or survival in all 628 cases. This trained network was then
      tested on the other 269 cases without our providing the death or
      survival result. Its overall accuracy was 91 per cent (244 of 269
      cases). It was able to predict correctly 60 per cent (12 of 20 cases)
      of the postoperative or post-intensive care unit admission deaths and
      90 per cent (26 of 29 cases) of the deaths in the ER. Computerized
      neural networks can accurately predict a trauma patient's fate based on
      inital ER presentation. The theory and use of neural networks in
      predicting clinical outcome will be presented.
AD  - Department of Surgery, University of South Alabama Medical Center,
      Mobile, USA.
SO  - Am Surg 1997 Mar;63(3):275-281

UI  - 97203769
PM  - 9051341
AU  - Handels H
AU  - Busch C
AU  - Encarnacao J
AU  - Hahn C
AU  - Kuhn V
AU  - Miehe J
AU  - Poppl SI
AU  - Rinast E
AU  - Rossmanith C
AU  - Seibert F
AU  - Will A
TI  - KAMEDIN: a telemedicine system for computer supported cooperative work
      and remote image analysis in radiology
DP  - 1997 Mar
TA  - Comput Methods Programs Biomed
IS  - 0169-2607
JC  - DOH
PG  - 175-183
IP  - 3
VI  - 52
AB  - The software system KAMEDIN (Kooperatives Arbeiten und MEdizinische
      Diagnostik auf Innovativen Netzen) is a multimedia telemedioine system
      for exchange, cooperative diagnostics, and remote analysis of digital
      medical image data. It provides components for visualisation,
      processing, and synchronised audio-visual discussion of medical images.
      Techniques of computer supported cooperative work (CSCW) synchronise
      user interactions during a teleconference. Visibility of both local and
      remote cursor on the conference workstations facilitates telepointing
      and reinforces the conference partner's telepresence. Audio
      communication during teleconferences is supported by an integrated
      audio component. Furthermore, brain tissue segmentation with artificial
      neural networks can be performed on an external supercomputer as a
      remote image analysis procedure. KAMEDIN is designed as a low cost CSCW
      tool for ISDN based telecommunication. However it can be used on any
      TCP/IP supporting network. In a field test, KAMEDIN was installed in 15
      clinics and medical departments to validate the systems' usability. The
      telemedicine system KAMEDIN has been developed, tested, and evaluated
      within a research project sponsored by German Telekom.
AD  - Institut fur Medizinische Informatik, Medizinische Universitat zu
      Lubeck, Germany. [e-mail]
SO  - Comput Methods Programs Biomed 1997 Mar;52(3):175-183

UI  - 97197312
PM  - 9044372
AU  - Luckman SM
AU  - Huckett L
AU  - Bicknell RJ
AU  - Voisin DL
AU  - Herbison AE
TI  - Up-regulation of nitric oxide synthase messenger RNA in an integrated
      forebrain circuit involved in oxytocin secretion
DP  - 1997 Mar
TA  - Neuroscience
IS  - 0306-4522
JC  - NZR
PG  - 37-48
IP  - 1
VI  - 77
AB  - The hypothalamo-neurohypophysial system contains high levels of
      neuronal nitric oxide synthase and this increases further during times
      of neurohormone demand, such as that following osmotic stimulation.
      Using double in situ hybridization, we demonstrate here an increase in
      the expression of nitric oxide synthase messenger RNA by oxytocin
      neurons, but not vasopressin neurons, of the supraoptic nucleus at the
      time of lactation, when oxytocin is in demand due to another
      neuroendocrine stimulus, the milk-ejection reflex. In addition, using
      immunocytochemical retrograde tracing, we show that neurons of the
      subfornical organ, median preoptic nucleus and organum vasculosum of
      the lamina terminalis, which project to the supraoptic nucleus, contain
      nitric oxide synthase. These three structures of the lamina terminalis,
      together with the hypothalamo-neurohypophysial system, make up the
      forebrain osmoresponsive circuit that controls osmotically-stimulated
      release of oxytocin in the rat. The expression of nitric oxide synthase
      messenger RNA in the lamina terminalis was also shown to increase
      during lactation. The increases in nitric oxide synthase messenger RNA
      were not apparent during pregnancy. These results provide evidence for
      an integrated nitric oxide synthase-containing neural network involved
      in the regulation of the hypothalamo-neurohypophysial axis. The
      expression of nitric oxide synthase messenger RNA increases in this
      circuit during lactation and correlates with a reduction in the
      senstivity of the circuit to osmotic stimuli also present in lactation
      but not pregnancy. As nitric oxide is believed to attenuate
      neurohormone release, it seems that the increased nitric oxide synthase
      messenger RNA expression detected here during lactation at a time of
      high oxytocin demand may be involved in reducing the sensitivity of the
      whole forebrain circuit to osmotic stimuli.
AD  - Department of Neurobiology, Babrahain Institute, Cambridge, U.K.
SO  - Neuroscience 1997 Mar;77(1):37-48

UI  - 97181119
PM  - 9029280
AU  - Nussbaum MA
AU  - Martin BJ
AU  - Chaffin DB
TI  - A neural network model for simulation of torso muscle coordination
DP  - 1997 Mar
TA  - J Biomech
IS  - 0021-9290
JC  - HJF
PG  - 251-258
IP  - 3
VI  - 30
AB  - An artificial neural network (ANN) was created to simulate lumbar
      muscle response to static moment loads. The network model was based on
      an abstract representation of a motor control system in which muscle
      activity is driven primarily to maintain moment equilibrium. The
      network model parameters were obtained by an iterative method
      (trained), using a modification of the standard backpropagation
      algorithm and moment equilibrium constraints. In contrast to previous
      ANN models of muscle activity, patterns of muscle activity are not
      target (training) values, but rather emerge as a result of moment
      equilibrium constraints. Assumptions regarding the moment generating
      capacity muscles and competitive interactions between muscles were
      employed and enabled the prediction of realistic patterns of muscle
      activity upon comparison with experimental electromyographic (EMG) data
      sets (r2: 0.4-0.9). The success of the simulation model suggests that a
      motor recruitment plan can be mimicked with relatively simple systems
      and that 'competition' between responsive units (muscles) may be
      intrinsic to the learning process. Prediction of alternative
      recruitment patterns and differing magnitudes of co-contractile
      activity were achieved by varying competition parameters within and
      between units.
AD  - Industrial and Systems Engineering, Virginia Polytechnic Institute and
      State University, Blacksburg 24061-0118, USA. [e-mail]
SO  - J Biomech 1997 Mar;30(3):251-258

UI  - 97160651
PM  - 9006985
AU  - Strata F
AU  - Atzori M
AU  - Molnar M
AU  - Ugolini G
AU  - Tempia F
AU  - Cherubini E
TI  - A pacemaker current in dye-coupled hilar interneurons contributes to
      the generation of giant GABAergic potentials in developing hippocampus.
MH  - Aging/*PHYSIOLOGY
MH  - Animal
MH  - Animals, Newborn/*PHYSIOLOGY/GROWTH & DEVELOPMENT
MH  - Cations/METABOLISM
MH  - Electric Conductivity
MH  - Electrophysiology
MH  - GABA/*PHYSIOLOGY
MH  - Hippocampus/*PHYSIOLOGY/GROWTH & DEVELOPMENT/CYTOLOGY
MH  - Interneurons/*PHYSIOLOGY
MH  - Neurons/PHYSIOLOGY
MH  - *Periodicity
MH  - Rats
MH  - Rats, Wistar
MH  - Receptors, GABA-A/PHYSIOLOGY
MH  - Support, Non-U.S. Gov't
RN  - 56-12-2 (GABA)
RN  - 0 (Receptors, GABA-A)
RN  - 0 (Cations)
DP  - 1997 Feb 15
TA  - J Neurosci
IS  - 0270-6474
JC  - JDF
PG  - 1435-1446
IP  - 4
VI  - 17
AB  - The establishment of synaptic connections and their refinement during
      development require neural activity. Increasing evidence suggests that
      spontaneous bursts of neural activity within an immature network are
      mediated by gamma-aminobutyric acid via a paradoxical excitatory
      action. Our data show that in the developing hippocampus such
      synchronous burst activity is generated in the hilar region by
      transiently coupled cells. These cells have been identified as neuronal
      elements because they fire action potentials and they are not positive
      for the glial fibrillary acidic protein staining. Oscillations in hilar
      cells are "paced" by a hyperpolarization-activated current, with
      properties of Ih. Coactivated interneurons synchronously release GABA,
      which via its excitatory action may serve a neurotrophic function
      during the refinement of hippocampal circuitry.
AD  - Biophysics Laboratory, International School for Advanced Studies
      (SISSA), 34014 Trieste, Italy.
SO  - J Neurosci 1997 Feb 15;17(4):1435-1446

UI  - 97204834
PM  - 9052342
AU  - Heston TF
AU  - Norman DJ
AU  - Barry JM
AU  - Bennett WM
AU  - Wilson RA
TI  - Cardiac risk stratification in renal transplantation using a form of
      artificial intelligence
DP  - 1997 Feb 15
TA  - Am J Cardiol
IS  - 0002-9149
JC  - 3DQ
PG  - 415-417
IP  - 4
VI  - 79
AB  - The purpose of this study was to determine if an expert network, a form
      of artificial intelligence, could effectively stratify cardiac risk in
      candidates for renal transplant. Input into the expert network
      consisted of clinical risk factors and thallium-201 stress test data.
      Clinical risk factor screening alone identified 95 of 189 patients as
      high risk. These 95 patients underwent thallium-201 stress testing, and
      53 had either reversible or fixed defects. The other 42 patients were
      classified as low risk. This algorithm made up the "expert system," and
      during the 4-year follow-up period had a sensitivity of 82%,
      specificity of 77%, and accuracy of 78%. An artificial neural network
      was added to the expert system, creating an expert network. Input into
      the neural network consisted of both clinical variables and thallium-
      201 stress test data. There were 5 hidden nodes and the output (end
      point) was cardiac death. The expert network increased the specificity
      of the expert system alone from 77% to 90% (p < 0.001), the accuracy
      from 78% to 89% (p < 0.005), and maintained the overall sensitivity at
      88%. An expert network based on clinical risk factor screening and
      thallium-201 stress testing had an accuracy of 89% in predicting the 4-
      year cardiac mortality among 189 renal transplant candidates.
AD  - Department of Radiology, Oregon Health Sciences University, Portland
      97201, USA.
SO  - Am J Cardiol 1997 Feb 15;79(4):415-417

UI  - 97172539
PM  - 9020183
AU  - Le Gall AH
AU  - Powell SK
AU  - Yeaman CA
AU  - Rodriguez-Boulan E
TI  - The neural cell adhesion molecule expresses a tyrosine-independent
      basolateral sorting signal
DP  - 1997 Feb 14
TA  - J Biol Chem
IS  - 0021-9258
JC  - HIV
PG  - 4559-4567
IP  - 7
VI  - 272
AB  - Transmembrane isoforms of the neural cell adhesion molecule, N-CAM (N-
      CAM-140 and N-CAM-180), are vectorially targeted from the trans-Golgi
      network to the basolateral domain upon expression in transfected Madin-
      Darby canine kidney cells (Powell, S. K., Cunningham, B. A., Edelman,
      G. M., and Rodriguez-Boulan, E. (1991) Nature 353, 76-77). To localize
      basolateral targeting information, mutant forms of N-CAM-140 were
      constructed and their surface distribution analyzed in Madin-Darby
      canine kidney cells. N-CAM-140 deleted of its cytoplasmic domain shows
      a non-polar steady state distribution, resulting from delivery from the
      trans-Golgi network to both the apical and basolateral surfaces. This
      result suggests that entrance into the basolateral pathway may occur
      without cytoplasmic signals, implying that apical targeting from the
      trans-Golgi network is not a default mechanism but, rather, requires
      positive sorting information. Subsequent construction and analysis of a
      nested set of C-terminal deletion mutants identified a region of 40
      amino acids (amino acids 749-788) lacking tyrosine residues required
      for basolateral targeting. Addition of these 40 amino acids is
      sufficient to restore basolateral targeting to both the non-polar
      cytoplasmic deletion mutant of N-CAM as well as to the apically
      expressed cytoplasmic deletion mutant of the p75 low affinity
      neurotrophin receptor (p75(NTR)), indicating that this tyrosine-free
      sequence is capable of functioning independently as a basolateral
      sorting signal. Deletion of both cytoplasmic and transmembrane domains
      resulted in apical secretion of N-CAM, demonstrating that the
      ectodomain of this molecule carries recessive apical sorting
      information.
AD  - Cornell University Medical College, Dyson Vision Research Institute,
      Department of Ophthalmology, New York, New York 10021, USA.
SO  - J Biol Chem 1997 Feb 14;272(7):4559-4567

UI  - 97196387
PM  - 9050408
AU  - Zhu Y
AU  - Yan H
TI  - Computerized tumor boundary detection using a Hopfield neural network
DP  - 1997 Feb
TA  - IEEE Trans Med Imaging
IS  - 0278-0062
JC  - COX
PG  - 55-67
IP  - 1
VI  - 16
AB  - In this paper, we present a new approach for detection of brain tumor
      boundaries in medical images using a Hopfield neural network. The
      boundary detection problem is formulated as an optimization process
      that seeks the boundary points to minimize an energy functional based
      on an active contour model. A modified Hopfield network is constructed
      to solve the optimization problem. Taking advantage of the collective
      computational ability and energy convergence capability of the Hopfield
      network, our method produces the results comparable to those of
      standard "snakes"-based algorithms, but it requires less computing
      time. With the parallel processing potential of the Hopfield network,
      the proposed boundary detection can be implemented for real time
      processing. Experiments on different magnetic resonance imaging (MRI)
      data sets show the effectiveness of our approach.
AD  - Department of Electrical Engineering, University of Sydney, NSW,
      Australia. [e-mail]
SO  - IEEE Trans Med Imaging 1997 Feb;16(1):55-67

UI  - 97187221
PM  - 9034674
AU  - Kennedy RL
AU  - Harrison RF
AU  - Burton AM
AU  - Fraser HS
AU  - Hamer WG
AU  - MacArthur D
AU  - McAllum R
AU  - Steedman DJ
TI  - An artificial neural network system for diagnosis of acute myocardial
      infarction (AMI) in the accident and emergency department: evaluation
      and comparison with serum myoglobin measurements
DP  - 1997 Feb
TA  - Comput Methods Programs Biomed
IS  - 0169-2607
JC  - DOH
PG  - 93-103
IP  - 2
VI  - 52
AB  - Recent studies have confirmed that artificial neural networks (ANNs)
      are adept at recognising patterns in sets of clinical data. The
      diagnosis of acute myocardial infarction (AMI) in patients presenting
      with chest pain remains one of the greatest challenges in emergency
      medicine. The aim of this study was to evaluate the performance of an
      ANN trained to analyse clinical data from chest pain patients. The ANN
      was compared with serum myoglobin measurements-cardiac damage is
      associated with increased circulating myoglobin levels, and this is
      widely used as an early marker for evolving AMI. We used 39 items of
      clinical and ECG data from the time of presentation to derive 53 binary
      inputs to a back propagation network. On test data (200 cases), overall
      accuracy, sensitivity, specificity and positive predictive value (PPV)
      of the ANN were 91.8, 91.2, 90.2 and 84.9% respectively. Corresponding
      figures using linear discriminant analysis were 81.0, 77.9, 82.6 and
      69.7% (P < 0.01). Using a further test set from a different centre (91
      cases), the accuracy, sensitivity, specificity and PPV for the
      admitting physicians were 65.1, 28.5, 76.9 and 28.6% respectively
      compared with 73.6, 52.4, 80.0 and 44.0% for the ANN. Although
      myoglobin at presentation was highly specific, it was only 38.0%
      sensitive, compared with 85.7% at 3 h. Simple strategies to combine
      clinical opinion, ANN output and myoglobin at presentation could
      greatly improve sensitivity and specificity of AMI diagnosis. The ideal
      support for emergency room physicians may come from a combination of
      computer-aided analysis of clinical factors and biochemical markers
      such as myoglobin. This study demonstrates that the two approaches
      could be usefully combined, the major benefit of the decision support
      system being in the first 3 h before biochemical markers have become
      abnormal.
AD  - City Hospitals Sunderland, Department of Medicine, UK.
SO  - Comput Methods Programs Biomed 1997 Feb;52(2):93-103