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PubMed1989

Attitudes of psychiatrists toward diagnostic options and issues.

Debate regarding the control of mental health services has marked the mental health scene for several decades. The debate continued and broadened, however, with the publication of DSM-III. The ubiquitous use of that diagnostic system for third-party reimbursement led some mental health professionals to call attention to the likely possibility that whoever controls diagnosis will also control mental health (O'Keefe 1980). This study sought to gather attitudinal information on psychiatrists' views regarding DSM-III, alternative nosological models, selected adjunct issues related to the medical model and mental illness concepts, and other professional concerns.

Adult↗
PubMed2003

ANCOVA approach for shelf life analysis of stability study of multiple factor designs.

For a traditional multiple batch stability design with no other factor, the conventional analysis is analysis of covariance (ANCOVA) modeling using F-tests based on type I sum of squares to determine whether the batches may be pooled for a common estimate of the linear regression line(s). In the last decade, many multiple factor designs were proposed in stability studies. With the objective of model selection, the generalization of the conventional ANCOVA model using type I sum of squares to designs with multiple factors requires a prespecified hierarchical pooling test ordering to determine whether any of the factors may be eliminated. Different shelf life estimates may be derived using different hierarchical pooling test orderings. On the other hand, setting the hierarchical ordering can be subjective and controversial. The stepwise modeling based on F-tests using type III sum of squares for model determination and factor elimination is proposed to eliminate such difficulties.

Drug Stability↗
PubMed2004

Positive selection on multiple antique allelic lineages of transferrin in the polyploid Carassius auratus.

Transferrin polymorphism has been studied in the polyploid Carassius auratus by cloning and sequence analysis of cDNAs from its three subspecies C. auratus gibelio, C. auratus auratus, and C. auratus cuvieri. DNA polymorphism of extremely high extent was shown for the transferrin gene by the 248 segregation sites among coding region sequences of its alleles. The deduced amino acid sequences of the transferrin alleles showed variable theoretical physicochemical parameters, which might constitute molecular basis for their electrophoretic heterogeneity. Positive selection was inferred by the replacement/synonymous ratios larger than 1 in partial allelic lineages which was subsequently confirmed by likelihood simulation under neutral or selection models. Furthermore, the correspondent sites to these selected codons were collectively located at two planes in the crystallographic structure of rabbit transferrin, which suggested that the rapid evolution of C. auratus transferrin might correlate to its adaptation to variable environmental elements such as oxygen pressure. The minimal 26 recombination events were detected among coding sequences of C. auratus transferrin, with partial mosaic sequences and breakpoints identified by identity scanning and information site analyses. Phylogenetic analyses revealed multiple antique allelic lineages of transferrin, which was estimated to diverge fifteen to twenty MYA. All these features strongly suggested the role of balancing selection in long persistence of high transferrin polymorphism in C. auratus. Furthermore, owing to its particular evolutionary backgrounds, the silver crucian carp might possess a distinctive balancing selection mechanism.

Alleles↗
PubMed2002

Monte Carlo simulation of an ethanol pharmacokinetic model.

BACKGROUND: One challenge of using even relatively simple pharmacokinetic models is valuation of model parameters. Unknown model parameter values can be determined by fitting the model to measured data. Goals of the present study were to (1) obtain ethanol pharmacokinetic data from a cohort of dogs, (2) propose a physiologic ethanol pharmacokinetic model, (3) and perform Monte Carlo simulation to determine model parameter values. The rationale for the particular model proposed here was to account for the interrelationship between blood ethanol concentration and gastrointestinal physiology. METHODS: To each of five fasted dogs, 1 g of ethanol/kg body weight was administered as a gavage of 20% w/v ethanol solution. Developed was an ethanol pharmacokinetic model that comprised a gastric emptying mechanism, a body water compartment, ethanol diffusion through the stomach mucosa, gastric alcohol dehydrogenase (GADH) oxidation of ethanol, diffusion through the small intestine epithelia to the villi, a countercurrent exchanger model of the villi, and liver alcohol dehydrogenase oxidation of ethanol. Monte Carlo simulation was used to estimate model parameter values and standard deviations by minimization of the chi function. RESULTS: Fitting the experimental data to the model using Monte Carlo simulation yielded reasonable values for model parameters. The model predicted that the capacity for ethanol absorption in the intestine was 6.79-fold greater than the ethanol absorption capacity in the stomach. The model indicated that 23.8 +/- 8.3% of the ethanol dose was actually absorbed in the stomach, and an insignificant amount of ethanol was metabolized by GADH. CONCLUSIONS: Ethanol metabolism by GADH is insignificant in the present case. The blood ethanol profile was strongly determined by gastric emptying. Differences between experimental data and simulation results largely result from the gastric emptying model selected. Therefore, accuracy of the complete pharmacokinetic model can be improved significantly by improving the gastric emptying model.

Animals↗
PubMed1989

Electrical analogs for monitoring vascular properties in artificial heart studies.

The problem of choosing parametric descriptions of the systemic vascular bed suitable for monitoring beat-to-beat changes in peripheral vascular properties is considered. Three simple models with two, three, and five elements are compared, essentially exploiting the Akaike information criterion combined with reasonable requirements for estimate accuracy. Analysis of pressure data, which are either simulated by the five-element model or measured on a mock circulatory system during abrupt changes in peripheral resistance, suggests guidelines for model selection. In particular, the five-element model exhibits very close adherence to physical reality by allowing for reflection, while the classical windkessel model provides the most reproducible estimates.

Cardiovascular Physiological Phenomena↗
PubMed1989

Antigen-induced bronchopulmonary alterations in the guinea pig: a new model of passive sensitization mediated by mouse IgE antibodies.

A selective model to study the IgE-mediated anaphylactic bronchoconstriction (BC) in the guinea pig is needed, since human asthma involves mainly this class of antibody. However, most procedures presently available for passive homologous or active sensitization lead to responses which are mediated both by IgE and IgG antibodies. In this study, we developed an anaphylactic model in which guinea pigs are passively sensitized with mouse ascitic fluid containing dinitrophenol (DNP)-specific IgE antibodies. Challenge of sensitized animals with DNP coupled to bovine serum albumin evokes a bronchoconstrictor response that is maximal 5 h after sensitization. The resulting anaphylactic BC is not blocked by the H1 histamine antagonist mepyramine, by the peptido-leukotriene antagonist FLP 55712 nor by the platelet-activating factor antagonist BN 52021 alone. However, when the sensitized animals are pretreated with the three drugs in combination, significantly reduced BC was observed upon challenge with the antigen. This latter result indicates that IgE-dependent BC involves the participation of different mediators, a characteristic shared in common with allergic asthma in human.

Animals↗
PubMed2002

Identification and utilization of arbitrary correlations in models of recombination signal sequences.

BACKGROUND: A significant challenge in bioinformatics is to develop methods for detecting and modeling patterns in variable DNA sequence sites, such as protein-binding sites in regulatory DNA. Current approaches sometimes perform poorly when positions in the site do not independently affect protein binding. We developed a statistical technique for modeling the correlation structure in variable DNA sequence sites. The method places no restrictions on the number of correlated positions or on their spatial relationship within the site. No prior empirical evidence for the correlation structure is necessary. RESULTS: We applied our method to the recombination signal sequences (RSS) that direct assembly of B-cell and T-cell antigen-receptor genes via V(D)J recombination. The technique is based on model selection by cross-validation and produces models that allow computation of an information score for any signal-length sequence. We also modeled RSS using order zero and order one Markov chains. The scores from all models are highly correlated with measured recombination efficiencies, but the models arising from our technique are better than the Markov models at discriminating RSS from non-RSS. CONCLUSIONS: Our model-development procedure produces models that estimate well the recombinogenic potential of RSS and are better at RSS recognition than the order zero and order one Markov models. Our models are, therefore, valuable for studying the regulation of both physiologic and aberrant V(D)J recombination. The approach could be equally powerful for the study of promoter and enhancer elements, splice sites, and other DNA regulatory sites that are highly variable at the level of individual nucleotide positions.

Animals↗
PubMed2020

Exploration of predictive and prognostic alternative splicing signatures in lung adenocarcinoma using machine learning methods.

BACKGROUND: Alternative splicing (AS) plays critical roles in generating protein diversity and complexity. Dysregulation of AS underlies the initiation and progression of tumors. Machine learning approaches have emerged as efficient tools to identify promising biomarkers. It is meaningful to explore pivotal AS events (ASEs) to deepen understanding and improve prognostic assessments of lung adenocarcinoma (LUAD) via machine learning algorithms. METHOD: RNA sequencing data and AS data were extracted from The Cancer Genome Atlas (TCGA) database and TCGA SpliceSeq database. Using several machine learning methods, we identified 24 pairs of LUAD-related ASEs implicated in splicing switches and a random forest-based classifiers for identifying lymph node metastasis (LNM) consisting of 12 ASEs. Furthermore, we identified key prognosis-related ASEs and established a 16-ASE-based prognostic model to predict overall survival for LUAD patients using Cox regression model, random survival forest analysis, and forward selection model. Bioinformatics analyses were also applied to identify underlying mechanisms and associated upstream splicing factors (SFs). RESULTS: Each pair of ASEs was spliced from the same parent gene, and exhibited perfect inverse intrapair correlation (correlation coefficient = - 1). The 12-ASE-based classifier showed robust ability to evaluate LNM status of LUAD patients with the area under the receiver operating characteristic (ROC) curve (AUC) more than 0.7 in fivefold cross-validation. The prognostic model performed well at 1, 3, 5, and 10 years in both the training cohort and internal test cohort. Univariate and multivariate Cox regression indicated the prognostic model could be used as an independent prognostic factor for patients with LUAD. Further analysis revealed correlations between the prognostic model and American Joint Committee on Cancer stage, T stage, N stage, and living status. The splicing network constructed of survival-related SFs and ASEs depicts regulatory relationships between them. CONCLUSION: In summary, our study provides insight into LUAD researches and managements based on these AS biomarkers.

Adenocarcinoma of Lung↗
PubMed2005

Stochastic models for horizontal gene transfer: taking a random walk through tree space.

Horizontal gene transfer (HGT) plays a critical role in evolution across all domains of life with important biological and medical implications. I propose a simple class of stochastic models to examine HGT using multiple orthologous gene alignments. The models function in a hierarchical phylogenetic framework. The top level of the hierarchy is based on a random walk process in "tree space" that allows for the development of a joint probabilistic distribution over multiple gene trees and an unknown, but estimable species tree. I consider two general forms of random walks. The first form is derived from the subtree prune and regraft (SPR) operator that mirrors the observed effects that HGT has on inferred trees. The second form is based on walks over complete graphs and offers numerically tractable solutions for an increasing number of taxa. The bottom level of the hierarchy utilizes standard phylogenetic models to reconstruct gene trees given multiple gene alignments conditional on the random walk process. I develop a well-mixing Markov chain Monte Carlo algorithm to fit the models in a Bayesian framework. I demonstrate the flexibility of these stochastic models to test competing ideas about HGT by examining the complexity hypothesis. Using 144 orthologous gene alignments from six prokaryotes previously collected and analyzed, Bayesian model selection finds support for (1) the SPR model over the alternative form, (2) the 16S rRNA reconstruction as the most likely species tree, and (3) increased HGT of operational genes compared to informational genes.

Archaea↗
PubMed2006

Dynamics of prion disease transmission in mule deer.

Chronic wasting disease (CWD), a contagious prion disease of the deer family, has the potential to severely harm deer populations and disrupt ecosystems where deer occur in abundance. Consequently, understanding the dynamics of this emerging infectious disease, and particularly the dynamics of its transmission, has emerged as an important challenge for contemporary ecologists and wildlife managers. Although CWD is contagious among deer, the relative importance of pathways for its transmission remains unclear. We developed seven competing models, and then used data from two CWD outbreaks in captive mule deer and model selection to compare them. We found that models portraying indirect transmission through the environment had 3.8 times more support in the data than models representing transmission by direct contact between infected and susceptible deer. Model-averaged estimates of the basic reproductive number (R0) were 1.3 or greater, indicating likely local persistence of CWD in natural populations under conditions resembling those we studied. Our findings demonstrate the apparent importance of indirect, environmental transmission in CWD and the challenges this presents for controlling the disease.

Animals↗
PubMed2004

Homology-based modelling of targets for rational drug design.

The current status in rational drug design using homology-based models is discussed, with focus on template selection, model building, model verification and strategies for drug design based on model structures. A novel approach for identification of unique binding site features from homology-based models, Protein Alpha Shape Similarity Analysis (PASSA) is described.

Binding Sites↗
PubMedDate not supplied

A methodological model to study the effects of toxins on child development.

Human research on the effects of drugs, medications, pollutants, and other toxins on fetal and child development requires longitudinal studies. Such studies must recognize the heterogeneity of populations and individuals, the multiplicity of variables, and the dynamic interactional character of development. This requires the construction of an a priori theoretical model, selection of research tools to fit the model, and the use of appropriate multidimensional data analysis techniques that can identify profiles of variables in individuals and subgroups, and can trace development over time. A model, based upon the facet theory of Louis Guttman, is presented with examples from a study of infant offspring of methadone-maintained women. An introductory explanation of one non-metric multidimensional data analysis technique. Guttman's Multidimensional Scalogram Analysis (MSA), is presented. Examples of its use and its relation to the theoretical model are given in subsequent papers.

Child Development↗
PubMed2002

Toward a method of selecting among computational models of cognition.

The question of how one should decide among competing explanations of data is at the heart of the scientific enterprise. Computational models of cognition are increasingly being advanced as explanations of behavior. The success of this line of inquiry depends on the development of robust methods to guide the evaluation and selection of these models. This article introduces a method of selecting among mathematical models of cognition known as minimum description length, which provides an intuitive and theoretically well-grounded understanding of why one model should be chosen. A central but elusive concept in model selection, complexity, can also be derived with the method. The adequacy of the method is demonstrated in 3 areas of cognitive modeling: psychophysics, information integration, and categorization.

Cognition↗
PubMed1999

A computational model for visual selection.

We propose a computational model for detecting and localizing instances from an object class in static gray-level images. We divide detection into visual selection and final classification, concentrating on the former: drastically reducing the number of candidate regions that require further, usually more intensive, processing, but with a minimum of computation and missed detections. Bottom-up processing is based on local groupings of edge fragments constrained by loose geometrical relationships. They have no a priori semantic or geometric interpretation. The role of training is to select special groupings that are moderately likely at certain places on the object but rate in the background. We show that the statistics in both populations are stable. The candidate regions are those that contain global arrangements of several local groupings. Whereas our model was not conceived to explain brain functions, it does cohere with evidence about the functions of neurons in V1 and V2, such as responses to coarse or incomplete patterns (e.g., illusory contours) and to scale and translation invariance in IT. Finally, the algorithm is applied to face and symbol detection.

Algorithms↗
PubMed2003

Cluster, facets, and edges: site-dependent selective chemistry on model catalysts.

More than activity, selectivity of catalytic reactions is the focus of research in the 21(st) century. We review studies on model systems that address the issue of directing a catalytic reaction on disperse metal catalysts by controlling the specific surface site. Three examples are explored: methanol dehydrogenation over Pd/alumina, NO dissociation on Pd/alumina, and reaction studies for molecules relevant in a Fischer-Tropsch scenario on a bimetallic Pd/Co/alumina model catalyst. We show how surface science can be used by combining a variety of experimental techniques to study the chemistry of model catalysts at the atomic level.

Journal Article↗
PubMed2001

Computerized radiographic mass detection--part I: Lesion site selection by morphological enhancement and contextual segmentation.

This paper presents a statistical model supported approach for enhanced segmentation and extraction of suspicious mass areas from mammographic images. With an appropriate statistical description of various discriminate characteristics of both true and false candidates from the localized areas, an improved mass detection may be achieved in computer-assisted diagnosis (CAD). In this study, one type of morphological operation is derived to enhance disease patterns of suspected masses by cleaning up unrelated background clutters, and a model-based image segmentation is performed to localize the suspected mass areas using stochastic relaxation labeling scheme. We discuss the importance of model selection when a finite generalized Gaussian mixture is employed, and use the information theoretic criteria to determine the optimal model structure and parameters. Examples are presented to show the effectiveness of the proposed methods on mass lesion enhancement and segmentation when applied to mammographical images. Experimental results demonstrate that the proposed method achieves a very satisfactory performance as a preprocessing procedure for mass detection in CAD.

Breast Neoplasms↗
PubMed2006

Psychosis: a costly by-product of social brain evolution in Homo sapiens.

The plethora of varied and often conflictual research evidence on the functional psychoses calls for a unifying explanatory framework. An evolutionary framework is appropriate in view of the paradoxical epidemiology of the disorders. Evolutionary models that rely on balanced polymorphism or group selection models are not supported by the evidence. Rather, a hypothesis is presented arguing that the spectrum of psychoses should be regarded as a costly by-product of social brain evolution in Homo sapiens. Under social selective pressures, hominid ancestors evolved a sophisticated neural network supporting social cognition and adaptive interpersonal behaviour--this is termed the 'social brain'. The functional psychoses (and schizophrenia in particular) are characterised by functional and structural deficits in these fronto-temporal and fronto-parietal circuits; hence the epithet 'social brain disorders' is fitting. I argue that accumulating evidence for an evolved social brain calls for a new philosophy of mind; a philosophy focussed on the social and interpersonal nature of human experience and derived from the philosophies of Fromm, Heidegger and Merleau-Ponty. Such a paradigm shift would aid modern neuroscience in finally abandoning Cartesian dualism and would guide psychiatry towards an integrated and 'socio-neurologically' embedded understanding of mental disorders.

Biological Evolution↗
PubMed1987

Convergence to equilibrium in a genetic model with differential viability between the sexes.

A single locus, diallelic selection model with female and male viability differences is studied. If the variables are ratios of allele frequencies in each sex, a 2-dimensional difference equation describes the model. Because of the strong monotonicity of the resulting map, every initial genotypic structure converges to an equilibrium structure assuming that no equilibrium has eigenvalues on the unit circle.

Alleles↗