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Multimodality in the superior colliculus: an information theoretic analysis.

The deep superior colliculus (DSC) integrates multisensory input and triggers an orienting movement toward the source of stimulation (target). It would seem reasonable to suppose that input of an additional modality should always increase the amount of information received by a DSC neuron concerning a target. However, of all DSC neurons studied, only about one half in the cat and one-quarter in the monkey were multimodal. The rest received only unimodal input. Multimodal DSC neurons show the properties of multisensory enhancement, in which the neural response to an input of one modality is augmented by input of another modality, and of inverse effectiveness, in which weaker unimodal responses produce a higher percentage enhancement. Previously, we demonstrated that these properties are consistent with the hypothesis that DSC neurons use Bayes' rule to compute the posterior probability that a target is present given their stochastic sensory inputs. Here we use an information theoretic analysis of our Bayesian model to show that input of an additional modality may indeed increase target information, but only if input received from the initial modality does not completely reduce uncertainty concerning the presence of a target. Unimodal DSC neurons may be those whose unimodal input fully reduces target uncertainty and therefore have no need for input of another modality.

Bayes Theorem↗

The rat approximates an ideal detector of changes in rates of reward: implications for the law of effect.

Rats responded on 2 levers delivering brain stimulation reward on concurrent variable interval schedules. Following many successive sessions with unchanging relative rates of reward, subjects adjusted to an eventual change slowly and showed spontaneous reversions at the beginning of subsequent sessions. When changes in rates of reward occurred between and within every session, subjects adjusted to them about as rapidly as they could in principle do so, as shown by comparison to a Bayesian model of an ideal detector. This and other features of the adjustments to frequent changes imply that the behavioral effect of reinforcement depends on the subject's perception of incomes and changes in incomes rather than on the strengthening and weakening of behaviors in accord with their past effects or expected results. Models for the process by which perceived incomes determine stay durations and for the process that detects changes in rates are developed.

Animals↗

The subspecific origin of the inland breeding colonies of the cormorant Phalacrocorax carbo in Britain.

The establishment of cormorant breeding colonies inland within south-east Britain since 1981 is a matter of major conservation and pest management concern. This study was initiated to investigate the subspecific origin of two recently established breeding colonies. The analysis examined sequence variation of the control (D-loop) region of the mitochondrial genome. Samples of tissue were obtained from 334 individuals from across the species range in western Europe from both subspecies (Phalacrocorax carbo carbo and P. c. sinensis) and 84 birds from two inland breeding colonies in Britain. Single-strand conformation polymorphism (SSCP) was used to assess mitochondrial variation among samples, revealing four haplotypes. The samples from the traditional breeding colonies clustered into three distinct phylogeographic groupings: Norway-Scotland, Wales-England-Iles des Chausey and the rest of Continental Europe. These results only partly agree with the traditional subspecific taxonomic groupings and are slightly at variance with results using microsatellite DNA frequencies, and a hypothesis using results from both studies is advanced. The subspecific origin of the inland colonies was investigated using maximum likelihood and Bayesian models.

Animals↗

Ruling out acute myocardial infarction. A prospective multicenter validation of a 12-hour strategy for patients at low risk.

BACKGROUND: Although previous investigations have suggested that 24 hours is required to exclude acute myocardial infarction in patients who are admitted to a coronary care unit for the evaluation of acute chest pain, we hypothesized that a 12-hour period might be adequate for patients with a low probability of infarction at the time of admission. METHODS: Using a Bayesian model, we developed a strategy to identify candidates for a shorter period of observation from an analysis of a derivation set of 976 patients with acute chest pain who were admitted to three teaching and four community hospitals. In the derivation set, patients whose clinical characteristics in the emergency room predicted a low (less than or equal to 7 percent) probability of myocardial infarction had only a 0.4 percent risk of infarction if they had neither abnormal levels of cardiac enzymes nor recurrent ischemic pain during the first 12 hours of hospitalization. In an independent testing set of 2684 patients from the seven hospitals, 957 admitted patients (36 percent) were classified as candidates for this 12-hour period of observation according to a previously published multivariate algorithm. Few of these patients were actually transferred from a monitored setting at 12 hours. RESULTS: Of the 771 candidates for a 12-hour period of observation who did not have enzyme abnormalities or recurrent pain during the first 12 hours, 4 (0.5 percent) were subsequently found to have acute myocardial infarction, and only 3 (0.4 percent) died after primary cardiac arrests, all of which occurred three to five days after admission. Rates of other major cardiovascular complications were low in the patients who might have been transferred from the coronary care unit after 12 hours with this strategy. In patients with a higher initial risk of infarction, the standard strategy of 24-hour observation identified all but 11 of 739 acute myocardial infarctions (1 percent). CONCLUSIONS: Emergency room clinical data can be used to identify a large subgroup of patients for whom a 12-hour period of observation is normally sufficient to exclude acute myocardial infarction. Patient-specific evaluation and treatment can then proceed without the restrictions imposed by "rule-out" protocols for myocardial infarction.

Adult↗

Recombination and mutation during long-term gastric colonization by Helicobacter pylori: estimates of clock rates, recombination size, and minimal age.

The bacterium Helicobacter pylori colonizes the gastric mucosa of half of the human population, resulting in chronic gastritis, ulcers, and cancer. We sequenced ten gene fragments from pairs of strains isolated sequentially at a mean interval of 1.8 years from 26 individuals. Several isolates had acquired small mosaic segments from other H. pylori or point mutations. The maximal mutation rate, the import size, and the frequency of recombination were calculated by using a Bayesian model. The calculations indicate that the last common ancestor of H. pylori existed at least 2,500-11,000 years ago. Imported mosaics have a median size of 417 bp, much smaller than for other bacteria, and recombination occurs frequently (60 imports spanning 25,000 bp per genome per year). Thus, the panmictic population structure of H. pylori results from very frequent recombination during mixed colonization by unrelated strains.

Bayes Theorem↗

Assessment of response bias in mild head injury: beyond malingering tests.

The evaluation of response bias and malingering in the cases of mild head injury should not rely on a single test. Initial injury severity, typical neuropsychological test performance patterns, preexisting emotional stress or chronic social difficulties, history of previous neurological or psychiatric disorder, other system injuries sustained in the accident, preinjury alcohol abuse, and a propensity to attribute benign cognitive and somatic symptoms to a brain injury must be considered along with performances on specific measures of response bias. This article reviews empirically-supported tests and indices. Use of the likelihood ratio and other statistical indicators of diagnostic efficiency are demonstrated. Bayesian model averaging as a statistical technique to derive optimal prediction models is performed with a clinical data set.

Algorithms↗

Mutational signatures in blood-brain barrier: mechanisms, computational insights, and clinical applications in precision oncology.

The blood - brain barrier (BBB) plays a central role in maintaining central nervous system (CNS) homeostasis, and its disruption is a defining feature of malignant brain tumors such as glioblastoma. Emerging evidence indicates that BBB dysfunction not only alters the tumor microenvironment but also shapes the mutational processes that drive genomic instability in CNS malignancies. This review synthesizes current understanding of the biological mechanisms linking BBB breakdown with distinct mutational signatures, including those arising from oxidative stress, hypoxia-induced replication stress, lipid peroxidation, inflammation, and metabolic reprogramming. Advances in next-generation sequencing, coupled with computational tools such as non-negative matrix factorization, Bayesian modeling, and deep learning, have enabled precise extraction of these signatures and their integration with multi-omics data. Clinically, BBB-associated mutational signatures offer significant promise for therapeutic stratification, prediction of treatment response, and noninvasive monitoring through cerebrospinal fluid - derived circulating tumor DNA. Despite these advances, challenges persist due to limited tissue accessibility, low-yield CSF samples, incomplete mechanistic models, and the lack of CNS-specific analytical frameworks. A deeper understanding of BBB-driven mutational processes, supported by improved computational approaches and integrative datasets, holds potential to advance precision oncology in neuro-oncology.

Humans↗

Identifying multigenic modules under selection in the tumor genome.

MOTIVATION: Genomic alterations in cancer arise from selective pressures acting on hallmark molecular modules, layered over a background of random mutagenic events. Methods to detect selection at the level of modules, as opposed to genes or nucleotides, are relatively underdeveloped. RESULTS: Here we present CanSRMaPP (Cancer Selection Recovery by Maximum Posterior Probability), a Bayesian model of the cancer genome that infers mutational selection on single genes and multi-genic modules while simultaneously modeling background events. Applying CanSRMaPP to lung adenocarcinoma genomes, we identify positive selection on 63 modules, yielding a model that parsimoniously explains the observed pattern of genetic alterations observed in new cancer cohorts. We further show that CanSRMaPP is adaptable to more tumor types and to alternative module definitions. We show that these modules serve as an effective scaffold for translating the cancer genome to molecular states, with prediction of cancer biomarker status as demonstration. AVAILABILITY: CanSRMaPP is freely available on GitHub. SUPPLEMENTARY INFORMATION: Supplementary Figs. S1-5, Supplementary Tables S1-5, and Supplementary Notes 1 and 2 are available at Bioinformatics online.

Journal Article↗

Bayesian mapping of multiple quantitative trait loci from incomplete inbred line cross data.

A novel fine structure mapping method for quantitative traits is presented. It is based on Bayesian modeling and inference, treating the number of quantitative trait loci (QTLs) as an unobserved random variable and using ideas similar to composite interval mapping to account for the effects of QTLs in other chromosomes. The method is introduced for inbred lines and it can be applied also in situations involving frequent missing genotypes. We propose that two new probabilistic measures be used to summarize the results from the statistical analysis: (1) the (posterior) QTL intensity, for estimating the number of QTLs in a chromosome and for localizing them into some particular chromosomal regions, and (2) the locationwise (posterior) distributions of the phenotypic effects of the QTLs. Both these measures will be viewed as functions of the putative QTL locus, over the marker range in the linkage group. The method is tested and compared with standard interval and composite interval mapping techniques by using simulated backcross progeny data. It is implemented as a software package. Its initial version is freely available for research purposes under the name Multimapper at URL http://www.rni.helsinki.fi/mjs.

Bayes Theorem↗

Air quality and pediatric emergency room visits for asthma in Atlanta, Georgia, USA.

Pediatric emergency room visits for asthma were studied in relation to air quality indices in a spatio-temporal investigation of approximately 130,000 visits (approximately 6,000 for asthma) to the major emergency care centers in Atlanta, Georgia, during the summers of 1993-1995. Generalized estimating equations, logistic regression, and Bayesian models were fitted to the data. In logistic regression models comparing estimated exposures of asthma cases with those of the nonasthma patients, controlling for temporal and demographic covariates and using residential zip code to link patients to spatially resolved ozone levels, the estimated relative risk per 20 parts per billion (ppb) increase in the maximum 8-hour ozone level was 1.04 (p < 0.05). The estimated relative risk for particulate matter less than or equal to 10 microm in aerodynamic diameter (PM10) was 1.04 per 15 microg/m3 (p < 0.05). Exposure-response trends (p < 0.01) were observed for ozone (>100 ppb vs. <50 ppb: odds ratio = 1.23, p = 0.003) and PM10 (>60 microg/m3 vs. <20 microg/m3: odds ratio = 1.26, p = 0.004). In models with ozone and PM10, both terms became nonsignificant because of collinearity of the variables (r= 0.75). The other analytical approaches yielded consistent findings. This study supports accumulating evidence regarding the relation of air pollution to childhood asthma exacerbation.

Adolescent↗

Evaluation of a two-compartment Bayesian forecasting program for predicting vancomycin concentrations.

The application of a two-compartment Bayesian forecasting program for vancomycin was tested retrospectively in 45 adult patients with stable renal function. Serial blood samples from 25 of these patients were used to determine population-based parameter estimates. The predictive performance of the Bayesian program was assessed by using both non-steady-state and steady-state vancomycin concentrations as feedback information. Overall, the program tended to underpredict peak and trough steady-state vancomycin serum concentrations. A larger mean prediction error (ME) was seen when non-steady-state feedback serum concentrations were used compared with using population-based parameter estimates (no feedback). In contrast, a marked improvement in ME (peaks: -1.03 versus -2.61; troughs: -1.60 versus -2.07) was seen when steady-state feedback serum concentrations were used compared with no feedback data. Precision improved when either feedback serum concentrations were used to predict steady-state peak and trough vancomycin concentrations. The results from this clinical evaluation demonstrate that the initial pharmacokinetic parameter estimates for a two-compartment Bayesian model provided accurate prediction of steady-state vancomycin concentrations. Prediction bias and precision were improved when steady-state vancomycin concentrations were used to determine individualized pharmacokinetic parameters.

Adult↗

Predictive performance of Bayesian and nonlinear least-squares regression programs for lidocaine.

The predictive performance of two computer programs for lidocaine dosing were evaluated. Two-compartment Bayesian and nonlinear least-squares regression programs were used in two groups of patients (15 acute arrhythmia patients and 14 chronic arrhythmia patients). Lidocaine was given as a 1.5 mg/kg bolus and a 2.8 mg/min infusion for 48 h. A second bolus (0.5 mg/kg) was given 10 min after the first bolus over 2 min. Serum samples of the patients receiving lidocaine were drawn at 2, 15, 30 min and 1, 2, and 4 h and were used in forecasting the serum concentrations at 6, 8, 12, and 48 h. Predictive performance was assessed by mean error and mean-squared error. The results (mean +/- 95% confidence intervals) demonstrated the Bayesian program predicted a significant (p less than 0.05) difference at 12 h between the two arrhythmia groups (acute 0.52 [-0.95; -0.09] and chronic 0.28 [0.12; 0.44]). The results also demonstrated the Bayesian method was significantly more precise compared to the nonlinear least-squares regression program at 8, 12, and 48 h for the acute group. While caution is warranted, this study demonstrated that the predictive performance by a two-compartment Bayesian model is more accurate in predicting future lidocaine serum concentrations than that by nonlinear least-squares regression.

Acute Disease↗

Biases in three-dimensional structure-from-motion arise from noise in the early visual system.

The projected pattern of retinal-image motion supplies the human visual system with valuable information about properties of the three-dimensional environment. How well three-dimensional properties can be recovered depends both on the accuracy with which the early motion system estimates retinal motion, and on the way later processes interpret this retinal motion. Here we combine both early and late stages of the computational process to account for the hitherto puzzling phenomenon of systematic biases in three-dimensional shape perception. We present data showing how the perceived depth of a hinged plane ('an open book') can be systematically biased by the extent over which it rotates. We then present a Bayesian model that combines early measurement noise with geometric reconstruction of the three-dimensional scene. Although this model has no in-built bias towards particular three-dimensional shapes, it accounts for the data well. Our analysis suggests that the biases stem largely from the geometric constraints imposed on what three-dimensional scenes are compatible with the (noisy) early motion measurements. Given these findings, we suggest that the visual system may act as an optimal estimator of three-dimensional structure-from-motion.

Computer Simulation↗

Fusion of intelligence information: a Bayesian approach.

The attack that occurred on September 11, 2001 was, in the end, the result of a failure to detect and prevent the terrorist operations that hit the United States. The U.S. government thus faces at this time the daunting tasks of first, drastically increasing its ability to obtain and interpret different types of signals of impending terrorist attacks with sufficient lead time and accuracy, and second, improving its ability to react effectively. One of the main challenges is the fusion of information, from different sources (U.S. or foreign), and of different types (electronic signals, human intelligence. etc.). Fusion thus involves two very distinct and separate issues: communications, i.e., ensuring that the different U.S. and foreign intelligence agencies communicate all relevant and accurate information in a timely fashion and, perhaps more difficult, merging the content of signals, some "sharp" and some "fuzzy," some dependent and some independent into useful information. The focus of this article is on the latter issue, and on the use of the results. In this article, I present a classic probabilistic Bayesian model sometimes used in engineering risk analysis, which can be helpful in the fusion of information because it allows computation of the posterior probability of an event given its prior probability (before the signal is observed) and the quality of the signal characterized by the probabilities of false positive and false negative. Experience suggests that the nature of these errors has been sometimes misunderstood; therefore, I discuss the validity of several possible definitions.

Journal Article↗

Multivariate survival analysis with positive stable frailties.

In this paper, we describe Bayesian modeling of dependent multivariate survival data using positive stable frailty distributions. A flexible baseline hazard formulation using a piecewise exponential model with a correlated prior process is used. The estimation of the stable law parameter together with the parameters of the (conditional) proportional hazards model is facilitated by a modified Gibbs sampling procedure. The methodology is illustrated on kidney infection data (McGilchrist and Aisbett, 1991).

Algorithms↗

Chronic diffuse infiltrative lung disease: determination of the diagnostic value of clinical data, chest radiography, and CT and Bayesian analysis.

PURPOSE: To assess the value of clinical, chest radiographic, and computed tomographic (CT) findings in classifying chronic diffuse infiltrative lung disease (CDILD) MATERIALS AND METHODS: Two samples from the same population were consecutively studied: the training set (group A, n = 208) for the development of the decision aid and the test set (group B, n = 100) for validation. Computer-aided diagnoses were made with a Bayesian model that assigned to each patient diagnostic probabilities based on clinical, radiographic, or CT variables. RESULTS: In group A, a correct diagnosis based on clinical data was obtained in 29% of cases; radiography, 9%; and CT, 36%. This increased to 54% when clinical and radiographic variables were combined (P < .0001) and to 80% when data from all three were analyzed together (P < .0001). With prior and conditional probabilities determined from group A, the frequency of correct diagnosis in group B was 27% with clinical data, which increased to 53% (P < .0001) with radiographic findings and 61% after including CT data (P = .07). CONCLUSION: CT can help determine the specific diagnosis in patients with CDILD.

Bayes Theorem↗

Two-dimensional motion perception without feature tracking.

Feature-tracking explanations of 2D motion perception are fundamentally distinct from motion-energy, correlation, and gradient explanations, all of which can be implemented by applying spatiotemporal filters to raw image data. Filter-based explanations usually suffer from the aperture problem, but 2D motion predictions for moving plaids have been derived from the intersection of constraints (IOC) imposed by the outputs of such filters, and from the vector sum of signals generated by such filters. In most previous experiments, feature-tracking and IOC predictions are indistinguishable. By constructing plaids in apparent motion from missing-fundamental gratings, we set feature-tracking predictions in opposition to both IOC and vector-sum predictions. The perceived directions that result are inconsistent with feature tracking. Furthermore, we show that increasing size and spatial frequency in Type 2 missing-fundamental plaids drives perceived direction from vector-sum toward IOC directions. This reproduces results that have been used to support feature-tracking, but under experimental conditions that rule it out. We discuss our data in the context of a Bayesian model with a gradient-based likelihood and a prior favoring slow speeds. We conclude that filter-based explanations alone can explain both veridical and non-veridical 2D motion perception in such stimuli.

Adult↗

Ambulatory blood pressure monitoring and diagnostic errors in hypertension: a Bayesian approach.

Random variability of blood pressure complicates the diagnosis and subsequent treatment of hypertension. To evaluate the importance of the number of blood pressure measurements in the correct diagnosis and control of hypertension, the authors used a Bayesian model to estimate the true average blood pressure of a group of newly diagnosed hypertensives, then calculated the diagnostic error that would result from monitoring methods using 24 daytime measurements or from using only three random monitoring measurements. The study population consisted of 129 individuals with newly diagnosed mild hypertension according to standard criteria, who were also evaluated with an ambulatory blood pressure monitor. In true normotensives (daytime diastolic blood pressure <90 mm Hg), the negative predictive value with three measurements was 0.92, and it rose to 0.96 with monitoring methods. In mild hypertensives (90-104 mm Hg), the positive predictive value was 0.64 with three measurements and 0.84 with monitoring methods, thus reducing the rate of false mild hypertensives from 35% to 15%. Finally, in patients with moderate or severe hypertension (>104 mm Hg), the positive predictive value improved from 0.26 with three readings to 0.61 with monitoring methods. Similar results were observed with daytime systolic pressure measurements. As the number of measurements increased, the diagnostic error due to the random variability of blood pressure became progressively smaller. In cases of hypertension, the large improvement in predictive values may justify using monitoring methods to confirm standard diagnosis.

Adult↗