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PubMed2003

The internal structure of the phenomenology of auditory verbal hallucinations.

BACKGROUND: Auditory verbal hallucinations (AVH) do not have uniform pathological significance. They affect patients with different brain disorders, and vary along multiple phenomenological dimensions. Evidence indicates that some of the phenomenological variables have specific neural substrates. Therefore, a comprehensive characterization of the phenomenological variations of AVH and the interrelationship between these variables was undertaken. METHOD: Twenty phenomenological variables were identified; on each AVH had a binary value (present or absent). Information about 11 of these variables were obtained from 30 patients. Hierarchical cluster (HC) and multidimensional scaling (MDS) analyses were performed to investigate the hidden structure and dimensions of these variables. RESULTS: HC yielded two main clusters with further sub-clusters in each. The first cluster included hallucinations with low linguistic complexity, repetitive content, attributed to self, located in outer space, and associated with different kinds of control strategies. The second cluster included hallucinations with high linguistic complexity, systematized content, multiple voices, attributed to others, and located in inner space. In MDS, three dimensions were identified: linguistic complexity, self-other attribution, and inner-outer space location. CONCLUSION: The patterns of clustering and dimensional configuration of AVH characteristics were in accord with intuitive expectation and validated the patients' descriptions of their experiences. These findings could reflect aspects of the neural mechanisms of AVH. For example, the presence of neural specificity for each phenomenological variable, intermediate neural commonality for groups of variables, and a final common pathway for all subtypes of AVH. Another example is a differential level of language dysfunction according to the linguistic complexity of AVH.

Adult↗
PubMed1991

On the use of hidden Markov modelling for recognition of dysarthric speech.

Recognition of the speech of severely dysarthric individuals requires a technique which is robust to extraordinary conditions of high variability and very little training data. A hidden Markov model approach to isolated word recognition is used in an attempt to automatically model the enormous variability of the speech, while signal preprocessing measures and model modifications are employed to make better use of the existing data. Two findings are contrary to general experience with normal speech recognition. The first is that an ergodic model is found to outperform a standard left-to-right (Bakis) model structure. The second is that automated clipping of transitional acoustics in the speech is found to significantly enhance recognition. Experimental results using utterances of cerebral palsied persons with an array of articulatory abilities are presented.

Adult↗
PubMedDate not supplied

Limits of Couinaud's liver segment classification: a quantitative computer-based three-dimensional analysis.

PURPOSE: Traditionally, liver surgery relies on Couinaud's liver segment classification. As the position and shape of these segments are variable and their borders are hidden within the homogeneous liver mass, the accuracy of segment identification methods needs computer-aided reevaluation. METHOD: The segmental liver anatomy of 23 patients receiving diagnostic helical CT scans because of suspected intrahepatic lesion was analyzed with the aid of a computer-based operation-planning system. We compared the standard Couinaud classification, which depends particularly on the main stems of portal and hepatic veins, with a method that calculates the segment borders by analyzing the complete portal venous tree. Volume, shape, and position of the liver segments found by each method were compared. RESULTS: With reference to the portal vein-based method, segmental volumes were overestimated by the classic Couinaud method by up to 24% and underestimated to 13%. Volumes of Couinaud segments 4a, 7, and 8 were generally larger compared with those obtained by the portal vein-based method, whereas segments 3 and 6 were smaller. Gross variations were found in segments 5, 7, and 8. When shape and position were considered, poor correlation was found for five segments (median kappa = 0.35-0.45). Only segments 2, 7, and 8 had kappa values clearly above 0.45 in the majority of cases. The plane that divides the two hemilivers along the middle hepatic vein and the border between the left sector (segments 2 and 3) and the medial sector (4a and 4b) were found in both methods with very good conformity (kappa > 0.75). CONCLUSION: Couinaud's method of dividing the liver into eight autonomous liver segments has to be accepted as a good approximation. Nevertheless, the volume, position, and shape of these segments and their segmental borders show significant variability.

Adult↗
PubMed1998

Detection of spikes with artificial neural networks using raw EEG.

Artificial neural networks (ANN) using raw electroencephalogram (EEG) data were developed and tested off-line to detect transient epileptiform discharges (spike and spike/wave) and EMG activity in an ongoing EEG. In the present study, a feedforward ANN with a variable number of input and hidden layer units and two output units was used to optimize the detection system. The ANN system was trained and tested with the backpropagation algorithm using a large data set of exemplars. The effects of different EEG time windows and the number of hidden layer neurons were examined using rigorous statistical tests for optimum detection sensitivity and selectivity. The best ANN configuration occurred with an input time window of 150 msec (30 input units) and six hidden layer neurons. This input interval contained information on the wave component of the epileptiform discharge which improved detection. Two-dimensional receiver operating curves were developed to define the optimum threshold parameters for best detection. Comparison with previous networks using raw EEG showed improvement in both sensitivity and selectivity. This study showed that raw EEG can be successfully used to train ANNs to detect epileptogenic discharges with a high success rate without resorting to experimenter-selected parameters which may limit the efficiency of the system.

Adolescent↗
PubMed1996

Hidden clasps versus C clasps and I bars: a comparison of retention.

The Hidden clasp system overcomes the problem of poor esthetics of partial denture clasps, but it is not known how well it works as a retainer. This study tested the retention of Hidden clasps compared with that of C clasps and I bars when they function as part of a tooth-supported partial denture framework. Nine standardized frameworks that incorporated one of the three clasp designs on a premolar abutment were tested on a laboratory model. Their retention was recorded on a tensile testing machine. Specimens carrying the Hidden clasp had the greatest variability in retention among the three groups. Statistically significant differences were found between the C clasp and I bar groups.

Analysis of Variance↗
PubMed2000

Large-scale predictions of secretory proteins from mammalian genomic and EST sequences.

Machine learning techniques have improved predictions of secretory proteins from protein, genomic and expressed sequence tag (EST) sequences. Artificial neural networks, physical sequence analysis using high-performance optimization, and hidden Markov models identify extremely variable signal peptides (the vehicles of protein transport across the endoplasmic reticulum membrane), transmembrane segments, and specific extracellular and intracellular domains as indicators of possible roles in the intercellular and intracellular chemical signaling pathways. The major role of peptide hormones, blood coagulation factors, carcinogenesis agents, and other secretory proteins in orchestrating multicellular life indicates pharmacological potential in the cure of major diseases and numerous biotechnological applications.

Animals↗
PubMed2004

Threshold extraction in metabolite concentration data.

MOTIVATION: Continued development of analytical techniques based on gas chromatography and mass spectrometry now facilitates the generation of larger sets of metabolite concentration data. An important step towards the understanding of metabolite dynamics is the recognition of stable states where metabolite concentrations exhibit a simple behaviour. Such states can be characterized through the identification of significant thresholds in the concentrations. But general techniques for finding discretization thresholds in continuous data prove to be practically insufficient for detecting states due to the weak conditional dependences in concentration data. RESULTS: We introduce a method of recognizing states in the framework of decision tree induction. It is based upon a global analysis of decision forests where stability and quality are evaluated. It leads to the detection of thresholds that are both comprehensible and robust. Applied to metabolite concentration data, this method has led to the discovery of hidden states in the corresponding variables. Some of these reflect known properties of the biological experiments, and others point to putative new states. AVAILABILITY: An implementation of this approach can be obtained from the authors upon request.

Algorithms↗
PubMed2005

Discussion of "A Bayesian approach to DNA sequence segmentation".

This article discusses the results in Boys and Henderson (2004, Biometrics 60, 573-581) in which the authors propose a new approach to the classification of genomic DNA into a number of hidden Markov states with a variable order of dependency, potentially allowing for the high-throughput detection of structure within genomic DNA. This article is likely to be an important point of departure for further modeling of this type. We question whether the genome of the bacteriophage lambda is the most appropriate example with which to demonstrate the method's effectiveness, whether it can be expected that the method will carry over to genomes where there is only one direction of transcription and no operon structure, and suggest a graphical display that seems to offer insight into the results. It would be interesting to see an analysis that uses the codon alphabet.

Algorithms↗
PubMed1989

Cross-reactive antiidiotypic antibodies against human rheumatoid factors from patients with juvenile rheumatoid arthritis.

We prepared antiidiotypic (anti-Id) antibody to 2 polyclonal IgM rheumatoid factors (IgM-RF) and 2 polyclonal "hidden" IgM-RF. The anti-Id antibodies were isolated by chromatography on Sepharose 4B, to which was bound rabbit anti-human IgG Fc fragments. F(ab')2 fragments from the anti-Id antibodies were generated by pepsin digestion and isolated by gel filtration. The anti-Id antibodies directed against RF from 4 patients with juvenile rheumatoid arthritis (JRA) were tested by an inhibition hemolytic assay for cross-reactivity with IgM-RF from 4 adult patients with rheumatoid arthritis, 6 patients with JRA, and 13 JRA patients with hidden RF. The 4 anti-Id antibodies had variable cross-reactivity with the isolated adult RA RF, JRA RF, and JRA hidden RF. Similar results were obtained by a direct-binding enzyme-linked immunosorbent assay for the anti-Id antibodies. The broad pattern of cross-reactivity was apparently unrelated to a particular amino acid sequence, but was associated with the antigen-binding site of IgM-RF. These results suggest the possibility that the anti-Id antibodies prepared against isolated RF obtained from JRA patients bear the "internal image" of antigen; that is, the Fc region of human IgG. These anti-Id antibodies may be generated in JRA patients and may possess specific immunomodulatory properties.

Animals↗
PubMed2001

Response variability to analgesics: a role for non-specific activation of endogenous opioids.

Individual differences in pharmacokinetics and pharmacodynamics, the type of pain and the method of drug administration can account for the response variability to analgesics. By integrating a clinical and an experimental approach, we report here that another important source of variability is represented by individual differences in non-specific (placebo) activation of endogenous opioid systems. In the first part of this study, we analyzed the effectiveness of buprenorphine, tramadol, ketorolac and metamizol in the clinical setting, where the placebo effect was completely eliminated by means of hidden infusions. We found that the hidden injections were significantly less effective and less variable compared with open injections (in full view of the subject), suggesting that part of the response variability was due to non-specific factors (placebo). Since we could not administer the opioid antagonist, naloxone, to these patients, in the second part of this study, we induced experimental ischemic arm pain in healthy volunteers and found that, as occurred in clinical pain, the analgesic response to a hidden injection of the non-opioid ketorolac was less effective and less variable than an open injection. Most importantly, we obtained the same effects by adding naloxone to an open injection of ketorolac, thus blocking the opioid-mediated placebo component of analgesia. These findings indicate that both the psychological (hidden injection) and pharmacological (naloxone) blockade of the placebo response reduce the effectiveness of, and the response variability to, analgesic drugs. Therefore, an important source of response variability to analgesics appears to be due to differences in non-specific activation of endogenous opioid systems.

Adult↗
PubMed2007

Quantitative analysis of the phenotypic variability of shoot architecture in two grapevine (Vitis vinifera) cultivars.

BACKGROUND AND AIMS: Plant architecture and its interaction with agronomic practices and environmental constraints are determinants of the structure of the canopy, which is involved in carbon acquisition and fruit quality development. A framework for the quantitative analysis of grapevine (Vitis vinifera) shoot architecture, based on a set of topological and geometrical parameters, was developed for the identification of differences between cultivars and the origins of phenotypic variability. METHODS: Two commercial cultivars ('Grenache N', 'Syrah') with different shoot architectures were grown in pots, in well-irrigated conditions. Shoot topology was analysed, using a hidden semi-Markov chain and variable-order Markov chains to identify deviations from the normal pattern of succession of phytomer types (P0-P1-P2), together with kinematic analysis of shoot axis development. Shoot geometry was characterized by final internode and individual leaf area measurements. KEY RESULTS: Shoot architecture differed significantly between cultivars. Secondary leaf area and axis length were greater for 'Syrah'. Secondary leaf area distribution along the main axis also differed between cultivars, with secondary leaves preferentially located towards the basal part of the shoot in 'Syrah'. The main factors leading to differences in leaf area between the cultivars were: (a) slight differences in main shoot structure, with the supplementary P0 phytomer on the lower part of the shoot in 'Grenache N', which bears a short branch; and (b) an higher rate and duration of development of branches bearing by P1-P2 phytomers related to P0 ones at the bottom of the shoot in 'Syrah'. Differences in axis length were accounted for principally by differences in individual internode morphology, with 'Syrah' having significantly longer internodes. This trait, together with a smaller shoot diameter, may account for the characteristic 'droopy' habit of 'Syrah' shoots. CONCLUSIONS: This study highlights the architectural parameters involved in the phenotypic variability of shoot architecture in two grapevine cultivars. Differences in primary shoot structure and in branch development potential accounted for the main differences in leaf area distribution between the two cultivars. By contrast, shoot shape seemed to be controlled by differences in axis length due principally to differences in internode length.

Kinetics↗
PubMed1991

Search for a putative scrapie genome in purified prion fractions reveals a paucity of nucleic acids.

Scrapie can be transmitted by novel infectious pathogens termed prions. No evidence for a scrapie-specific nucleic acid has been detected to date. To investigate amounts, types and sizes of nucleic acid molecules associated with prions in purified preparations, aliquots were deproteinized, and the nucleic acids analysed by PAGE and silver staining. Digestion with nucleases and exposure to Zn2+ prior to analysis substantially diminished the content of nucleic acids, but did not alter the prion titre indicating that those nucleic acids which were removed are not essential for infectivity. Since a single species of scrapie-specific nucleic acid could not be identified, we explored the unprecedented possibility of scrapie-specific nucleic acids of variable length which are biologically active. If such molecules of variable length exist then they might be hidden within the background smear on silver-stained gels after PAGE. A new procedure designated return refocusing gel electrophoresis (RRGE) was developed to identify heterogeneous nucleic acids in purified prion fractions. The content of variable length nucleic acids was reduced by a factor of 10 by exhaustive Bal 31 exonuclease digestion after dispersion of purified prions into detergent-lipid-protein complexes. For example, a typical sample after Bal 31 digestion contained approximately 4 ng of nucleic acid of variable length and 10(8.7) ID50 units of scrapie prion infectivity. Consideration of different models for a hypothetical scrapie-specific nucleic acid suggests that such a molecule would have to be: (i) quite small (less than 100 nucleotides), (ii) possess a particle-to-infectivity ratio near unity or (iii) heterogeneous in size. Although our results do not eliminate the possibility that prions possess a scrapie-specific nucleic acid of variable length, they narrow considerably the spectrum of features specifying such a candidate molecule.

Animals↗
PubMed2002

Multiple injections of thyrotropin releasing hormone fail to reverse learning and memory deficits in rats with lesions of the nucleus basalis of meynert.

The learning and memory enhancing effects of thyrotropin releasing hormone (TRH) was examined in an animal model of Alzheimer's disease. Adult rats were prepared with either sham surgeries or cholinergic lesions of the nucleus basalis of Meynert (nbM). Subjects were injected (ip) with one of three doses of TRH (0, 5, 10 mg/kg) starting on the day of surgery and continuing once every other day for a total of four injections. Performance (four trials/day for 4 days, 30 m inter-trial interval) in a Morris water maze was assessed one week after the last TRH injection (i.e., 2 weeks postoperatively). Latency to find the hidden platform served as the dependent variable. Results indicated that damage to the nbM impaired task performance in that animals with nbM lesions generally required more time to find the platform and showed less trial-to-trial improvement. Treatment with TRH failed to reverse this lesion-induced deficit. These results suggest that multiple injections of TRH do not provide residual protection against the deleterious effects on learning and memory produced by cholinergic lesions of the basal forebrain. Other doses and administration parameters, however, need to be studied in order to determine the generalizability of these findings.

Alzheimer Disease↗
PubMed1996

Statistical approach to neural network model building for gentamicin peak predictions.

Feed forward neural networks are flexible, nonlinear modeling tools that are an extension of traditional statistical techniques. The hypothesis that feed forward neural network models can be built in a similar fashion as a statistical model was tested. Feed forward neural network models were built using forward and backward variable selection, and zero to five hidden nodes, and tanh and linear transfer functions were used. Gentamicin serum concentrations were predicted as a model drug for testing these methods. Peak observations from 392 patients were used to train, test, and validate the feed forward neural network. Inputs were demographic and drug dosing information. Model selection was performed using the Akaike information criteria (AIC), Bayesian information criteria (BIC), and a method of stopped training. The models with lowest root mean square (rms) error were those with all 10 inputs and five hidden nodes. Average rms error in the validation set was lowest for stopped training (1.46), then AIC (1.51), and finally BIC (1.56). Larger models tended to result in the best predictions. Overfitting can occur in models that are too large, either by using too many nodes in the hidden layer (rms = 1.49) or by using too many inputs with little information associated with them (rms = 1.70). We conclude that neural networks can be built using a large number of parameters that have good predictive performance. Care must be used during training to avoid overfitting the data. A stopped training method resulted in the network with the lowest rms error.

Adult↗
PubMed2006

Hidden Markov models based on symbolic dynamics for statistical modeling of cardiovascular control in hypertensive pregnancy disorders.

Discrete hidden Markov models (HMMs) were applied to classify pregnancy disorders. The observation sequence was generated by transforming RR and systolic blood pressure time series using symbolic dynamics. Time series were recorded from 15 women with pregnancy-induced hypertension, 34 with preeclampsia and 41 controls beyond 30th gestational week. HMMs with five to ten hidden states were found to be sufficient to characterize different blood pressure variability, whereas significant classification in RR-based HMMs was found using fifteen hidden states. Pregnancy disorders preeclampsia and pregnancy induced hypertension revealed different patho-physiological autonomous regulation supposing different etiology of both disorders.

Algorithms↗
PubMedDate not supplied

Toward an optimal procedure for variable selection and QSAR model building.

In this work, we report the development of a novel QSAR technique combining genetic algorithms and neural networks for selecting a subset of relevant descriptors and building the optimal neural network architecture for QSAR studies. This technique uses a neural network to map the dependent property of interest with the descriptors preselected by the genetic algorithm. This technique differs from other variable selection techniques combining genetic algorithms to neural networks by two main features: (1) The variable selection search performed by the genetic algorithm is not constrained to a defined number of descriptors. (2) The optimal neural network architecture is explored in parallel with the variable selection by dynamically modifying the size of the hidden layer. By using both artificial data and real biological data, we show that this technique can be used to build both classification and regression models and outperforms simpler variable selection techniques mainly for nonlinear data sets. The results obtained on real data are compared to previous work using other modeling techniques. We also discuss some important issues in building QSAR models and good practices for QSAR studies.

Algorithms↗
PubMed1997

Probabilistic independence networks for hidden Markov probability models.

Graphical techniques for modeling the dependencies of random variables have been explored in a variety of different areas, including statistics, statistical physics, artificial intelligence, speech recognition, image processing, and genetics. Formalisms for manipulating these models have been developed relatively independently in these research communities. In this paper we explore hidden Markov models (HMMs) and related structures within the general framework of probabilistic independence networks (PINs). The paper presents a self-contained review of the basic principles of PINs. It is shown that the well-known forward-backward (F-B) and Viterbi algorithms for HMMs are special cases of more general inference algorithms for arbitrary PINs. Furthermore, the existence of inference and estimation algorithms for more general graphical models provides a set of analysis tools for HMM practitioners who wish to explore a richer class of HMM structures. Examples of relatively complex models to handle sensor fusion and coarticulation in speech recognition are introduced and treated within the graphical model framework to illustrate the advantages of the general approach.

Algorithms↗
PubMed1989

Dinoflagellates in evolution. A molecular phylogenetic analysis of large subunit ribosomal RNA.

The sequence of the large subunit ribosomal RNA (LsuRNA) gene of the dinoflagellate Prorocentrum micans has been determined. The inferred rRNA sequence [3408 nucleotides (nt)] is presented in its most probable secondary structure based on compensatory mutations, energy, and conservation criteria. No introns have been found but a hidden break is present in the second variable domain, 690 nt from the 5' end, as judged by agarose gel electrophoresis and primer extension experiments. Prorocentrum micans LsuRNA length and G+C content are close to those of ciliates and yeast. The conserved portions of the molecule (1900 nt) have been aligned with corresponding sequences from various eukaryotes, including five protista, one metaphyta, and three metazoa. An extensive phylogenetic study was performed, comparing two phenetic methods (neighbor joining on difference matrix, and Fitch and Margoliash on Knuc values matrix) and one cladistic (parsimony). The three methods led to similar tree topologies, except for the emergence of yeast that groups with ciliates and dinoflagellates when phenetic methods are used, but emerges later in the most parsimonious tree. This discrepancy was checked by statistical analyses on reduced trees (limited to four species) inferred using parsimony and evolutionary parsimony methods. The data support the phenetic tree topologies and a close relationship between dinoflagellates, ciliates, and yeast.

Animals↗