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At least 73 records · Page 4Linked to original sources
PubMed2003

MRI definition of target volumes using fuzzy logic method for three-dimensional conformal radiation therapy.

PURPOSE: Three-dimensional (3D) volume determination is one of the most important problems in conformal radiation therapy. Techniques of volume determination from tomographic medical imaging are usually based on two-dimensional (2D) contour definition with the result dependent on the segmentation method used, as well as on the user's manual procedure. The goal of this work is to describe and evaluate a new method that reduces the inaccuracies generally observed in the 2D contour definition and 3D volume reconstruction process. METHODS AND MATERIALS: This new method has been developed by integrating the fuzziness in the 3D volume definition. It first defines semiautomatically a minimal 2D contour on each slice that definitely contains the volume and a maximal 2D contour that definitely does not contain the volume. The fuzziness region in between is processed using possibility functions in possibility theory. A volume of voxels, including the membership degree to the target volume, is then created on each slice axis, taking into account the slice position and slice profile. A resulting fuzzy volume is obtained after data fusion between multiorientation slices. Different studies have been designed to evaluate and compare this new method of target volume reconstruction and a classical reconstruction method. First, target definition accuracy and robustness were studied on phantom targets. Second, intra- and interobserver variations were studied on radiosurgery clinical cases. RESULTS: The absolute volume errors are less than or equal to 1.5% for phantom volumes calculated by the fuzzy logic method, whereas the values obtained with the classical method are much larger than the actual volumes (absolute volume errors up to 72%). With increasing MRI slice thickness (1 mm to 8 mm), the phantom volumes calculated by the classical method are increasing exponentially with a maximum absolute error up to 300%. In contrast, the absolute volume errors are less than 12% for phantom volumes calculated by the fuzzy logic method. On radiosurgery clinical cases, target volumes defined by the fuzzy logic method are about half of the size of volumes defined by the classical method. Also, intra- and interobserver variations slightly decrease with the fuzzy logic method, resulting in the definition of a better common volume fraction. CONCLUSION: Our fuzzy logic method shows accurate, robust, and reproducible results on phantoms and clinical targets imaged on MRI.

Algorithms↗
PubMed2006

Alternate forms of logical memory and verbal fluency tasks for repeated testing in early cognitive changes.

BACKGROUND: Repeat cognitive testing is an essential diagnostic strategy to measure changes in cognition over time when following people with memory problems. Alternate forms may avert practice effects that can mimic improvements in cognition. We evaluated alternate forms of verbal fluency and logical memory (paragraph recall) tasks to evaluate their equivalence for clinical use. METHODS: Participants with mild cognitive impairment (MCI) and dementia were recruited from five outpatient memory clinics and one nursing home. Participants with normal cognition (NC) were recruited from family members or friends. Verbal fluency categories of animals, cities & towns, fruits & vegetables and first names were used. Scores were recorded for 0-30 seconds, 31-60 seconds and errors. For the logical memory task, participants were read one of three different paragraphs and then were asked to recall the story. Immediate recall and delayed recall scores were recorded. The Standardized Mini-mental State Examination, the AB Cognitive Screen and the 15-point Geriatric Depression Scale were administered as part of the assessment. Analyses were performed using means, frequency distributions, t-tests, receiver-operating characteristic curves and effect sizes. RESULTS: There were 46 NC participants, 45 with MCI and 55 with dementia. For verbal fluency, the mean number of animals, cities & towns, names or fruits & vegetables named in 60 seconds did not differ significantly within each cognitive group. First names was an easier category than the others: NC named 16.9-22.3 items, MCI named 11.6-14.4 items and dementia named 8.1-11.4 items. The mean number of items immediately recalled in logical memory was not significantly different for the three paragraphs. The verbal fluency task (in 60 seconds) and logical memory immediate recall were highly sensitive and specific to differences between NC and MCI (areas under the curves 0.87 and 0.76, respectively). CONCLUSIONS: Alternate forms allow serial testing without learning bias. Verbal fluency and logical memory tasks are sensitive to early cognitive changes.

Age of Onset↗
PubMed1995

On-line evidence for elaborative logical inferences in text.

A model of propositional-logic reasoning proposed by M. D. S. Braine, B. J. Reiser, and B. Rumain (1984) claims that inferences such as "p or q; not p/therefore q" are made spontaneously by readers at the moment both premises are available. This claim is inconsistent with some evidence in the text-processing literature that suggests that only those inferences necessary for textual coherence are made spontaneously. In the present study, participants read stories in which a logical inference was not necessary to maintain textual coherence, and inference making was assessed with on-line probes. Two experiments tested logical forms central to Braine et al.'s model, and both indicated that participants were making the logical inferences. Two further experiments replicated this result with stories that did not begin with thematic titles. These findings support Braine et al.'s prediction that some propositional-logic inferences are made routinely in texts that do not require them for coherence.

Form Perception↗
PubMed2005

Multi/infinite dimensional neural networks, multi/infinite dimensional logic theory.

A mathematical model of an arbitrary multi-dimensional neural network is developed and a convergence theorem for an arbitrary multi-dimensional neural network represented by a fully symmetric tensor is stated and proved. The input and output signal states of a multi-dimensional neural network/logic gate are related through an energy function, defined over the fully symmetric tensor (representing the connection structure of a multi-dimensional neural network). The inputs and outputs are related such that the minimum/maximum energy states correspond to the output states of the logic gate/neural network realizing a logic function. Similarly, a logic circuit consisting of the interconnection of logic gates, represented by a block symmetric tensor, is associated with a quadratic/higher degree energy function. Infinite dimensional logic theory is discussed through the utilization of infinite dimension/order tensors.

Algorithms↗
PubMed2005

Logic model use for breast health in rural communities.

PURPOSE/OBJECTIVES: To describe the use of a logic model methodology in the development, implementation, and evaluation of a regionally based cancer health network. DATA SOURCES: Published articles; online references; published reports from government, state, and private organizations; and regional breast health project results. DATA SYNTHESIS: Through the use of the logic model, the program objectives and outcomes were identified and actualized. CONCLUSIONS: The logic model served as a framework for developing the key components of the program: infrastructure, implementation, and sustainability. Supportive structures, such as the timeline, process evaluation, and outcome evaluation plan, enhanced the use of the logic model by adding clarity to program development and program evaluation. IMPLICATIONS FOR NURSING: Nurses, particularly advanced practice nurses and nurse managers, play a key role in leading program development. A logic model can be used to guide program development, implementation, and evaluation. It serves as an excellent framework for developing a program that integrates service, practice, and research.

Breast Neoplasms↗
PubMed2002

The prediction of attitudes from beliefs and evaluations: the logic of the double negative.

One of the most intriguing aspects of Fishbein's (e.g. 1980) theory of reasoned action is the logic of the double negative: if a behaviour is thought to be unlikely to result in a negatively evaluated consequence, then the product of the two negatives (an unlikely consequence that is negatively valued) is thought to provide an impetus for the formation of a positive attitude towards the behaviour. The present experiment tested two derivations from the logic of the double negative. First, according to this logic, whether beliefs and evaluations are positively or negatively framed should not affect their ability to predict attitudes. Second, the multiplicative assumption of this logic (a negative x negative = a positive) suggests that a multiplicative model should be a superior predictor of attitudes compared to an additive model that does not allow for the logic of the double negative. The obtained findings contradicted both of these predictions.

Adult↗
PubMedDate not supplied

Fuzzy logic-based tumor marker profiles including a new marker tumor M2-PK improved sensitivity to the detection of progression in lung cancer patients.

In lung cancer patients tumor markers are used for disease monitoring. The goal of this study was to improve diagnostic efficiency in the detection of tumor progression in lung cancer patients by using fuzzy logic modeling in combination with a tumor marker panel (Tumor M2-PK, CYFRA 21-1, CEA, NSE and SCC). Thirty-three small cell lung cancers (SCLC) and 69 consecutive inoperable patients (40 squamous and 29 adenocarcinomas) were included in a prospective study. The changes of blood levels of tumor markers as well as their analysis by fuzzy logic modelling were compared to the clinical evaluation of response vs. non-response to therapy. Clinical monitoring was evaluated according to the standard criteria of the WHO. Tumor M2-PK was measured in plasma with an ELISA (ScheBo Biotech, Germany) and all other markers in sera (Roche, Germany). At a 90% specificity, the respective best single marker found the following fraction of all patients who had tumor progression clinically detected: in SCLC with NSE 52%, in adenocarcinoma with CYFRA 21-1 89% and in squamous carcinoma with SCC 65%. A fuzzy logic rule-based system employing a tumor marker panel increased the sensitivity in small cell carcinomas to 73% with the marker combination NSE/CEA and to 63% with the marker combination NSE/Tumor M2-PK, respectively. In squamous carcinomas an improvement of sensitivity is also observed using the marker combination of SCC/Tumor M2-PK (Sensitivity: 81%) or SCC/CEA (Sensitivity: 71%). By using the fuzzy logic method and the marker combination CYFRA 21-1/CEA as well as CYFRA 21-1/Tumor M2-PK, the detection of lung cancer progression was possible in all adenocarcinomas. With the fuzzy logic method and a tumor marker panel (including the new marker Tumor M2-PK), a useful diagnostic tool for the detection of progression in lung cancer patients is available.

Adenocarcinoma↗
PubMed1993

Protein topology prediction through parallel constraint logic programming.

In this paper, two programs are described (CBS1e and CBS2e). These are implemented in the parallel constraint logic programming language ElipSys. These predict protein alpha/beta-sheet and beta-sheet topologies from secondary structure assignments and topological folding rules (constraints). These programs illustrate how recent developments in logic programming environments can be applied to solve large-scale combinatorial problems in molecular biology. We demonstrate that parallel constraint logic programming is able to overcome some of the important limitations of more established logic programming languages i.e. Prolog. This is particularly the case in providing features that enhance the declarative nature of the program and also in addressing directly the problems of scaling-up logic programs to solve scientifically realistic problems. Moreover, we show that for large topological problems CBS1e was approximately 60 times faster than an equivalent Prolog implementation (CBS1) on a sequential device with further performance enhancements possible on parallel computer architectures. CBS2e is an extension of CBS1e that addresses the important problem of integrating the use of uncertain (weighted) protein folding constraints with categorical ones, through the use of a cost function that is minimized. CBS2e achieves this with a relatively minor reduction of performance. These results significantly extend the range and complexity of protein structure prediction methods that can reasonably be addressed using AI languages.

Artificial Intelligence↗
PubMed1993

Deterministic logic versus software-based artificial neural networks in the diagnosis of atrial fibrillation.

An investigation into the use of software-based neural networks for the detection of atrial fibrillation was made. At a specific point in the Glasgow 12-lead electrocardiographic interpretation program, a decision has to be made as to whether atrial fibrillation or sinus rhythm with supraventricular or ventricular extrasystoles is present. The same input parameters used for the deterministic logic at that point were also utilized to train a variety of neural networks. Results from a separate test set showed that the sensitivity of detecting atrial fibrillation could be improved using the best of the neural networks. On the other hand, it was felt that the original deterministic logic could be improved by considering adjustments in order that the presence of certain combinations of findings not previously regarded as representing atrial fibrillation would now do so. When the deterministic logic was upgraded in this way, it was found, again using a separate test set, that the revised logic was improved compared to the original, and also gave a performance similar to that of the neural network. It is concluded that the use of a neural network at a specific diagnostic decision point in a rhythm analysis program can be as effective as deterministic logic, which may take several years to perfect.

Atrial Fibrillation↗
PubMed2005

Effective PET and ICT switching of boradiazaindacene emission: a unimolecular, emission-mode, molecular half-subtractor with reconfigurable logic gates.

[reaction, structure: see text] We report a unimolecular system functioning as a combinatorial logic circuit for half-subtractor. The emission characteristics can be modulated by chemical inputs, and when followed at two different wavelengths, two functionally integrated logic gates XOR and INHIBIT are obtained. Both logic gates function in the emission mode, and with very large differences in the signal intensity allowing unequivocal assignment of logic-0 and logic-1.

Journal Article↗
PubMed2005

The logical repertoire of ligand-binding proteins.

Proteins whose conformation can be altered by the equilibrium binding of a regulatory ligand are one of the main building blocks of signal-processing networks in cells. Typically, such proteins switch between an 'inactive' and an 'active' state, as the concentration of the regulator varies. We investigate the properties of proteins that can bind two different ligands and show that these proteins can individually act as logical elements: their 'output', quantified by their average level of activity, depends on the two 'inputs', the concentrations of both regulators. In the case where the two ligands can bind simultaneously, we show that all of the elementary logical functions can be implemented by appropriate tuning of the ligand-binding energies. If the ligands bind exclusively, the logical repertoire is more limited. When such proteins cluster together, cooperative interactions can greatly enhance the sharpness of the response. Protein clusters can therefore act as digital logical elements whose activity can be abruptly switched from fully inactive to fully active, as the concentrations of the regulators pass threshold values. We discuss a particular instance in which this type of protein logic appears to be used in signal transduction-the chemotaxis receptors of E. coli.

Chemotaxis↗
PubMed2004

Regulatory motif finding by logic regression.

MOTIVATION: Multiple transcription factors coordinately control transcriptional regulation of genes in eukaryotes. Although many computational methods consider the identification of individual transcription factor binding sites (TFBSs), very few focus on the interactions between these sites. We consider finding TFBSs and their context specific interactions using microarray gene expression data. We devise a hybrid approach called LogicMotif composed of a TFBS identification method combined with the new regression methodology logic regression. LogicMotif has two steps: First, potential binding sites are identified from transcription control regions of genes of interest. Various available methods can be used in this step when the genes of interest can be divided into groups such as up-and downregulated. For this step, we also develop a simple univariate regression and extension method MFURE to extract candidate TFBSs from a large number of genes in the availability of microarray gene expression data. MFURE provides an alternative method for this step when partitioning of the genes into disjoint groups is not preferred. This first step aims to identify individual sites within gene groups of interest or sites that are correlated with the gene expression outcome. In the second step, logic regression is used to build a predictive model of outcome of interest (either gene expression or up- and down-regulation) using these potential sites. This 2-fold approach creates a rich diverse set of potential binding sites in the first step and builds regression or classification models in the second step using logic regression that is particularly good at identifying complex interactions. RESULTS: LogicMotif is applied to two publicly available datasets. A genome-wide gene expression data set of Saccharomyces cerevisiae is used for validation. The regression models obtained are interpretable and the biological implications are in agreement with the known resuts. This analysis suggests that LogicMotif provides biologically more reasonable regression models than previous analysis of this dataset with standard linear regression methods. Another dataset of S.cerevisiae illustrates the use of LogicMotif in classification questions by building a model that discriminates between up- and down-regulated genes in iron copper deficiency. LogicMotif identifies an inductive and two repressor motifs in this dataset. The inductive motif matches the binding site of the transcription factor Aft1p that has a key role in regulation of the uptake process. One of the novel repressor sites is highly present in transcription control regions of FeS genes. This site could represent a TFBS for an unknown transcription factor involved in repression of genes encoding FeS proteins in iron deficiency. We establish the robustness of the method to the type of outcome variable used by considering both continuous and binary outcome variables for this dataset. Our results indicate that logic regression used in combination with cluster/group operating binding site identification methods or with our proposed method MFURE is a powerful and flexible alternative to linear regression based motif finding methods. AVAILABILITY: Source code for logic regression is freely available as a package of the R programming language by Ruczinski et al. (2003) and can be downloaded at http://bear.fhcrc.org/~ingor/logic/download/download.html an R package for MFURE is available at http://www.stat.berkeley.edu/~sunduz/software.html

Algorithms↗
PubMedDate not supplied

Improving the human readability of Arden Syntax medical logic modules using a concept-oriented terminology and object-oriented programming expressions.

Medical logic modules are a procedural representation for sharing task-specific knowledge for decision support systems. Based on the premise that clinicians may perceive object-oriented expressions as easier to read than procedural rules in Arden Syntax-based medical logic modules, we developed a method for improving the readability of medical logic modules. Two approaches were applied: exploiting the concept-oriented features of the Medical Entities Dictionary and building an executable Java program to replace Arden Syntax procedural expressions. The usability evaluation showed that 66% of participants successfully mapped all Arden Syntax rules to Java methods. These findings suggest that these approaches can play an essential role in the creation of human readable medical logic modules and can potentially increase the number of clinical experts who are able to participate in the creation of medical logic modules. Although our approaches are broadly applicable, we specifically discuss the relevance to concept-oriented nursing terminologies and automated processing of task-specific nursing knowledge.

Attitude of Health Personnel↗
PubMed2003

Use of program logic models in the Southern Rural Access Program evaluation.

The Southern Rural Access Program (SRAP) evaluation team used program logic models to clarify grantees' activities, objectives, and timelines. This information was used to benchmark data from grantees' progress reports to assess the program's successes. This article presents a brief background on the use of program logic models--essentially charts or diagrams specifying a program's planned activities, objectives, and goals--for evaluating and managing a program. It discusses the structure of the logic models chosen for the SRAP and how the model concept was introduced to the grantees to promote acceptance and use of the models. The article describes how the models helped clarify the program's objectives and helped lead agencies plan and manage the many program initiatives and subcontractors in their states. Models also provided a framework for grantees to report their progress to the National Program Office and evaluators and promoted the evaluators' visibility and acceptance by the grantees. Program logics, however, increased grantees' reporting requirements and demanded substantial time of the evaluators. Program logic models, on balance, proved their merit in the SRAP through their contributions to its management and evaluation and by providing a better understanding of the program's initiatives, successes, and potential impact.

Benchmarking↗
PubMed2005

Magnetic domain-wall logic.

"Spintronics," in which both the spin and charge of electrons are used for logic and memory operations, promises an alternate route to traditional semiconductor electronics. A complete logic architecture can be constructed, which uses planar magnetic wires that are less than a micrometer in width. Logical NOT, logical AND, signal fan-out, and signal cross-over elements each have a simple geometric design, and they can be integrated together into one circuit. An additional element for data input allows information to be written to domain-wall logic circuits.

Journal Article↗
PubMed2002

Use of the logical analysis of data method for assessing long-term mortality risk after exercise electrocardiography.

BACKGROUND: Logical Analysis of Data is a methodology of mathematical optimization on the basis of the systematic identification of patterns or "syndromes." In this study, we used Logical Analysis of Data for risk stratification and compared it to regression techniques. METHODS AND RESULTS: Using a cohort of 9454 patients referred for exercise testing, Logical Analysis of Data was applied to identify syndromes based on 20 variables. High-risk syndromes were patterns of up to 3 findings associated with >5-fold increase in risk of death, whereas low-risk syndromes were associated with >5-fold decrease. Syndromes were derived on a randomly derived training set of 4722 patients and validated in 4732 others. There were 15 high-risk and 26 low-risk syndromes. A risk score was derived based on the proportion of possible high risk and low risk syndromes present. A value > or =0, meaning the same or a greater proportion of high-risk syndromes, was noted in 979 patients (21%) in the validation set and was predictive of 5-year death (11% versus 1%, hazard ratio 8.3, 95% CI 5.9 to 11.6, P<0.0001), accounting for 67% of events. Calibration of expected versus observed death rates based on Logical Analysis of Data and Cox regression showed that both methods performed very well. CONCLUSION: Using the Logical Analysis of Data method, we identified subsets of patients who had an increased risk and who also accounted for the majority of deaths. Future research is needed to determine how best to use this technique for risk stratification.

Cardiovascular Diseases↗
PubMed2004

Constraint Logic Programming approach to protein structure prediction.

BACKGROUND: The protein structure prediction problem is one of the most challenging problems in biological sciences. Many approaches have been proposed using database information and/or simplified protein models. The protein structure prediction problem can be cast in the form of an optimization problem. Notwithstanding its importance, the problem has very seldom been tackled by Constraint Logic Programming, a declarative programming paradigm suitable for solving combinatorial optimization problems. RESULTS: Constraint Logic Programming techniques have been applied to the protein structure prediction problem on the face-centered cube lattice model. Molecular dynamics techniques, endowed with the notion of constraint, have been also exploited. Even using a very simplified model, Constraint Logic Programming on the face-centered cube lattice model allowed us to obtain acceptable results for a few small proteins. As a test implementation their (known) secondary structure and the presence of disulfide bridges are used as constraints. Simplified structures obtained in this way have been converted to all atom models with plausible structure. Results have been compared with a similar approach using a well-established technique as molecular dynamics. CONCLUSIONS: The results obtained on small proteins show that Constraint Logic Programming techniques can be employed for studying protein simplified models, which can be converted into realistic all atom models. The advantage of Constraint Logic Programming over other, much more explored, methodologies, resides in the rapid software prototyping, in the easy way of encoding heuristics, and in exploiting all the advances made in this research area, e.g. in constraint propagation and its use for pruning the huge search space.

Computational Biology↗
PubMed1999

Relative interference on Logical Memory I Story A versus Story B of the Wechsler Memory Scale-revised in a clinical sample.

Prior research found Logical Memory I Story A and Story B of the Wechsler Memory Scale-Revised to be equivalent in difficulty, with no effect of administration sequence. Following clinical impression of many patients "freezing" on Story A and recovering on Story B, it was hypothesized that the presence of performance or test anxiety contributed to this phenomenon. Logical Memory scores and concurrent Minnesota Multiphasic Personality Inventory (MMPI) data from 85 clinical cases were examined. Participants were divided into 3 groups based on performance on the Logical Memory I stories: A < B (67%), A = B (15%), A > B (18%). There were no significant differences on Logical Memory I performance related to medical or neuropsychiatric history, age, or education. In support of the hypothesis, those patients who showed poorer performance on Logical Memory I Story A than on Story B had significantly higher T scores on the MMPI Social Anxiety scale as compared to the A > B group. No other MMPI differences were found.

Adult↗