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PubMedDate not supplied

Opponent interactions between serotonin and dopamine.

Anatomical and pharmacological evidence suggests that the dorsal raphe serotonin system and the ventral tegmental and substantia nigra dopamine system may act as mutual opponents. In the light of the temporal difference model of the involvement of the dopamine system in reward learning, we consider three aspects of motivational opponency involving dopamine and serotonin. We suggest that a tonic serotonergic signal reports the long-run average reward rate as part of an average-case reinforcement learning model; that a tonic dopaminergic signal reports the long-run average punishment rate in a similar context; and finally speculate that a phasic serotonin signal might report an ongoing prediction error for future punishment.

Animals↗
PubMed2026

Metabolism pathway-based subtyping in pancreatic adenocarcinoma: an integrated study by bulk RNA-sequence and machine learning algorithms.

BACKGROUND: Pancreatic adenocarcinoma (PAAD) is highly aggressive, and its tumor microenvironment has significant metabolic and immune microenvironment complexity and genomic instability. In this study, by integrating the metabolic pathway activity score and clinical data, we constructed a novel risk assessment model to reveal the unique biological behavior and clinical significance behind different PAAD subtypes. METHODS: In this study, the transcriptome and clinical data of TCGA and GSE57495 databases were integrated to explore the interaction between metabolic pathways. Based on unsupervised clustering analysis of pathway activity and survival prognosis, patients with PAAD were classified into metabolic subtypes with significant prognostic differences. Subsequently, we assessed the heterogeneity of these subtypes in terms of clinical outcomes, genomic characteristics, and immune microenvironment composition. Based on the differentially expressed genes (DEGs) among metabolic subtypes, a clinical prognostic risk model and nomogram were constructed, which were double-validated by GSE57495-independent cohort and GSE57495 + TCGA-PAAD combined cohort. Finally, the correlations between risk scores (RSs) and signaling pathway activity and tumor immune microenvironment characteristics were evaluated. RESULTS: Based on metabolic pathway correlation and prognostic information, 240 patients in the TCGA-PAAD and GSE57495 datasets were divided into three subgroups. There were significant differences between subgroups in gene expression, pathway activity, clinical prognosis, and immune infiltration characteristics among the subtypes. Using machine learning algorithms, an RS model was constructed from DEGs among the subgroups, with the random forest method showing the best performance. A nomogram integrating the RS and clinical indicators demonstrated excellent predictive accuracy for 1-, 3-, and 5-year survival rates, confirming the RS as an independent prognostic factor. High- and low-risk groups exhibited significant differences in immune infiltration, pathway activity, and gene mutations. Drug sensitivity analysis showed that the high-risk group was more sensitive to AZD6244, ABT737, and other drugs. CONCLUSION: This study stratified patients with PAAD into three subgroups based on metabolic pathways and prognostic information, revealing significant differences in clinical outcomes, immune characteristics, and genetic mutations. The robust RS model developed from these findings demonstrated strong predictive power for patient survival and identified promising therapeutic strategies, providing valuable insights for advancing precision medicine in PAAD.

immune microenvironment↗
PubMed2002

Effects of fencamfamine on latent inhibition.

The effects of fencamfamine (FCF), an indirect dopamine (DA) agent, were investigated using the latent inhibition (LI) model of schizophrenia. In the LI procedure, rats preexposed (PE) to an unreinforced stimulus show difficulty in subsequent learning of an association in which that stimulus is predictive of an unconditioned stimulus (US). FCF (1.75, 3.5 and 7.0 mg/kg i.p.) yielded an inverse dose-response relationship regarding LI. At 3.5 mg/kg, LI was abolished and no effect was observed at 1.75 and 7.0 mg/kg. The effect of FCF (3.5 mg/kg) on LI was blocked by the antipsychotic risperidone (RIS; 4.0 mg/kg), a D2/5HT2 antagonist. These results confirm the similarity of the behavioral profile of FCF and amphetamine (AMPH). In addition, they provide a further validation of the LI model for psychosis, since RIS was shown to prevent a psychostimulant-induced disruption of LI.

Animals↗
PubMed2026

A Knowledge-Enhanced Multimodal Framework with Genomic Reconstruction for DLBCL Drug Response Prediction.

Diffuse large B-cell lymphoma (DLBCL) exhibits substantial biological heterogeneity, leading to pronounced variability in patient response to therapy. Accurate drug response prediction is therefore critical for precision treatment but remains challenging in clinical settings where genomic sequencing, a highly informative modality, is frequently incomplete. Existing methods, often developed from cell-line pharmacogenomic datasets or single-modality data, typically assume fully observed molecular profiles and thus show limited robustness under missing genomic data. To address this limitation, a knowledge-enhanced multimodal framework with genomic reconstruction (KeM-DRP) is proposed for individualized drug response prediction in DLBCL. The framework models the central role of genomics by integrating biological prior knowledge through a gene-pathway-biological process hierarchy, enabling robust representation learning from sparse observations. To compensate for missing genomic measurements, a cross-modal genomic compensation module reconstructs genomically informed latent features from routinely available clinical modalities. Furthermore, a genomics-guided adaptive fusion strategy dynamically integrates heterogeneous modalities conditioned on observed or reconstructed genomic representation. Experiments on a real-world DLBCL cohort demonstrate that KeM-DRP consistently outperforms competitive baselines. The reconstructed genomic representation represents most predictive utility, highlighting the robustness and practical value of the framework under incomplete genomic data.

Journal Article↗
PubMed2005

A method for predicting disease subtypes in presence of misclassification among training samples using gene expression: application to human breast cancer.

MOTIVATION: An accurate diagnostic and prediction will not be achieved unless the disease subtype status for every training sample used in the supervised learning step is accurately known. Such an assumption requires the existence of a perfect tool for disease diagnostic and classification, which is seldom available in the majority of the cases. Thus, the supervised learning step has to be conducted with a statistical model that contemplates and handles potential mislabeling in the input data. RESULTS: A procedure for handling potential mislabeling among training samples in the prediction of disease subtypes using gene expression data was proposed. A real data-based simulation study about the estrogen receptor status (ER+/ER-) of breast cancer patients was conducted. The results demonstrated that when 1-4 training samples (N = 30) were artificially mislabeled, the proposed method was able not only in correcting the ER status of mislabeled training samples but also more importantly in predicting the ER status of validation samples as well as using 'true' training data.

Algorithms↗
PubMed2005

Mechanism-based modeling of complex biomedical systems.

Mechanism-based modeling is an approach in which the physiological, pathological and pharmacological processes of relevance to a given problem are represented as directly as possible. This approach allows us (i) to test whether assumed hypotheses are consistent with observed behaviour, (ii) to examine the sensitivity of a system to parameter variation, (iii) to learn about processes not directly amenable to experimentation, and (iv) to predict system behavior under conditions not previously experienced. The paper illustrates different aspects of the application of mechanism-based modeling through three different examples of relevance to the treatment of diabetes and hypertension: subcutaneous absorption of insulin, pulsatile insulin secretion in normal young persons, and synchronization of the pressure and flow regulation in neighbouring nephrons. The underlying ideas are that each regulatory mechanism represents the target for intervention and that the development of new and more effective drugs must be based on a deeper understanding of the biological processes.

Blood Glucose↗
PubMed1990

Composite holographic associative recall model (CHARM) and blended memories in eyewitness testimony.

The idea that compositing or blending occurs in human episodic memory stems from two sources: (a) distributed memory models and (b) studies on the errors that occur in eyewitness testimony. These two traditions of research--theoretical and empirical--have been independent and distinct. Here, data from the eyewitness testimony paradigm are simulated by the distributed model CHARM (Composite Holographic Associative Recall Model). Of focal concern are Loftus's studies, which have been interpreted in favor of the blending hypothesis, and McCloskey and Zaragoza's studies, which have been interpreted as refuting Loftus's position. Both of these seemingly contradictory results, as well as recent findings with yes/no recognition, fall out of the model. Finally, the model predicts empirically found color shifts and provides specifications for when blends and memory impairments will and will not be expected.

Association Learning↗
PubMedDate not supplied

A neural network model for predicting aquifer water level elevations.

Artificial neural networks (ANNs) were developed for accurately predicting potentiometric surface elevations (monitoring well water level elevations) in a semiconfined glacial sand and gravel aquifer under variable state, pumping extraction, and climate conditions. ANNs "learn" the system behavior of interest by processing representative data patterns through a mathematical structure analogous to the human brain. In this study, the ANNs used the initial water level measurements, production well extractions, and climate conditions to predict the final water level elevations 30 d into the future at two monitoring wells. A sensitivity analysis was conducted with the ANNs that quantified the importance of the various input predictor variables on final water level elevations. Unlike traditional physical-based models, ANNs do not require explicit characterization of the physical system and related physical data. Accordingly, ANN predictions were made on the basis of more easily quantifiable, measured variables, rather than physical model input parameters and conditions. This study demonstrates that ANNs can provide both excellent prediction capability and valuable sensitivity analyses, which can result in more appropriate ground water management strategies.

Climate↗
PubMed1998

A learning model of binge eating: cue reactivity and cue exposure.

In the present article, a learning model of binge eating is presented. It has been hypothesized that, parallel to drug intake, the craving and excessive food intake of binge eaters is cue controlled. Research in support of the model is reviewed and a series of predictions about clinical and non-clinical issues is derived from the model. Amongst other things, the model predicts that binge eating might be successfully treated with cue exposure and response prevention. Practical issues are discussed and preliminary pilot studies on cue exposure for bingers are reviewed.

Animals↗
PubMedDate not supplied

Are there different predictors of distinct exercise components?

Rehabilitation nurses often recommend to aging adults that they exercise. Despite the recommendations and the widespread evidence of its benefits, few older adults exercise. This study examined the predictors of distinct components of exercise behavior: exercise intensity, frequency per week, duration of sessions, and months per year. Social cognitive theory predictors of exercise were measured with previously developed instruments. Interviews were conducted with community-dwelling elders (N = 147). The study constructs accounted for modest to moderate amounts of variance (41% of months per year, 35% of frequency, 35% of exercise intensity, and 20% of episode duration). Self-efficacy was the most important predictor of each construct, with Beta weights ranging from .23 to .53. Barriers significantly predicted months' per year, frequency per week, and exercise intensity, but not the duration of individual exercise sessions. Outcome expectancy predicted only exercise intensity. These findings support the importance of self-efficacy and perceived barriers for most components of exercise behavior.

Aged↗
PubMed2004

The change of the brain activation patterns as children learn algebra equation solving.

In a brain imaging study of children learning algebra, it is shown that the same regions are active in children solving equations as are active in experienced adults solving equations. As with adults, practice in symbol manipulation produces a reduced activation in prefrontal cortex area. However, unlike adults, practice seems also to produce a decrease in a parietal area that is holding an image of the equation. This finding suggests that adolescents' brain responses are more plastic and change more with practice. These results are integrated in a cognitive model that predicts both the behavioral and brain imaging results.

Adolescent↗
PubMed2026

Meta-PseU: A meta-classifier for robust prediction of RNA pseudouridine modification sites from long sequences.

BACKGROUND AND OBJECTIVES: Pseudouridine (Ψ) represents one of the most abundant and conserved RNA modifications. Ψ provides an additional hydrogen-bond donor that enhances RNA structural stability and modulates translation. It participates in diverse biological processes, including RNA-protein interactions, splicing, translational control, and stress responses. Aberrant pseudouridylation is implicated in cancer, neurodegenerative disorders, and autoimmune diseases. Despite its biological importance, experimental identification of Ψ sites remains time-consuming and costly, limiting the feasibility of transcriptome-wide profiling. Computational approaches have therefore become essential complements to experimental techniques. However, state-of-the-art machine-learning and deep-learning predictors often suffer from limited generalizability due to small training datasets. To overcome these issues, we aim at constructing new long-sequence datasets and developing a novel Ψ site predictor. METHODS: New long-sequence datasets were constructed as benchmarks for RNA Ψ-site prediction. The Ψ modification sites in RMBase 3.0 were mapped to the reference genomes across three species of human, mouse, and yeast, and the RNA sequences with a length of 201 were generated by extending the upstream and downstream from the mapped, central sites. To eliminate sequence redundancy, the sequences were clustered using CD-HIT with a 70% sequence identity threshold. We developed Meta-PseU, a logistic regression-based meta-classifier that considered 118 machine learning and deep learning classifiers. The datasets and programs are freely accessible at https://github.com/kuratahiroyuki/MetaPseU. RESULTS: By optimizing model configuration, we proposed the Meta-PseU model stacking 32 machine learning and deep learning classifiers out of 118 classifiers. Meta-PseU substantially improved model generalizability, overcoming a key limitation of existing approaches. It greatly outperformed state-of-the-art predictors and achieved increasing accuracy with increasing sequence length. CONCLUSIONS: Long-sequence datasets were newly constructed as benchmarks for RNA Ψ-site prediction. Meta-PseU offers a new framework for robust Ψ-site identification by using long sequences.

Pseudouridine↗
PubMed1998

The detection model of recognition using know and remember judgments.

The signal detection model for know and remember recognition judgments was tested in two experiments. In Experiment 1, two predictions of the model were tested: (1) that measures of memory sensitivity, A', are equivalent in value when based on either the recognition (know or remember) criterion or on the remember criterion; and (2) that there is a positive correlation between recognition bias and the proportion of know judgments that are hits, but no correlation between recognition bias and proportion of remember hits (Donaldson, 1996). Both predictions were supported by the data. In Experiment 2, the context of test items was manipulated to make it more or less similar to learning context. The detection model requires that memory sensitivity be the same for both recognition and remember judgments, regardless of test context. Alternatively, if remember judgments reflect only the retrieval of episodic information from memory, the two measures of memory sensitivity should become more disparate in value as learning and test context are made more similar. Memory sensitivity was generally the same in value for recognition and remember criteria but different across context conditions, thus supporting the detection model. The nature of the memory continuum used in detection theory is also discussed.

Adult↗
PubMed2004

Modeling of SEB-induced host gene expression to correlate in vitro to in vivo responses.

Detection of exposure to biological threat agents has relied on ever more sensitive methods for pathogen identification, but that usually requires pathogen proliferation to dangerous, near untreatable levels. Recent events have demonstrated that assessing exposure to a biological threat agent well in advance of onset of illness or at various stages post-exposure is invaluable among the diagnostic options. There is an urgent need for better diagnostic tools that will be sensitive, rapid, and unambiguous. Since human clinical cases of illness induced by biothreat agents are, fortunately, rare, use of animal models that closely mimic the human illness is the only in vivo option. Such studies can be very difficult and expensive; therefore, maximizing the information obtained from in vitro exposures to peripheral blood mononuclear cells (PBMCs) provide an opportunity to investigate dose/time variability in host responses. In our quest to study staphylococcal enterotoxin B (SEB) induced host gene expression patterns, we addressed two core issues using microarray analysis and predictive modeling. Our first objective was to determine gene expression patterns in human PBMCs exposed to SEB in vitro. Second, we compared the in vitro data with host responses gene expression patterns in vivo using PBMCs from an animal model of SEB intoxication that closely replicates the progression of illness in humans. We used cDNA microarrays to study global gene expression patterns in piglets intoxicated with SEB. We applied a supervised learning method for class prediction based on the k-nearest neighbor algorithm for the data obtained in piglets exposed to SEB in vivo against a training data set. This data set included gene expression profiles derived from in vitro exposures to eight different pathogens (Bacillus anthracis, Yersinia pestis, Brucella melitensis, SEB, cholera toxin, Clostridium botulinum toxin A, Venezuelan equine encephalitis, and Dengue-2) in PBMCs. We found that despite differences in gene expression profiles between in vitro and in vivo systems, there exists a subset of genes that show correlations between in vitro and in vivo exposures, which can be used as a predictor of exposure to SEB in vivo.

Algorithms↗
PubMed1993

Hippocampal mediation of stimulus representation: a computational theory.

The authors propose a computational theory of the hippocampal region's function in mediating stimulus representations. The theory assumes that the hippocampal region develops new stimulus representations that enhance the discriminability of differentially predictive cues while compressing the representation of redundant cues. Other brain regions, including cerebral and cerebellar cortices, are presumed to use these hippocampal representations to recode their own stimulus representations. In the absence of an intact hippocampal region, the theory implies that other brain regions will attempt to learn associations using previously established fixed representations. Instantiated as a connectionist network model, the theory provides a simple and unified interpretation of the functional role of the hippocampal region in a wide range of conditioning paradigms, including stimulus discrimination, reversal learning, stimulus generalization, latent inhibition, sensory preconditioning, and contextual sensitivity. The theory makes novel predictions regarding the effects of hippocampal lesions on easy-hard transfer and compound preexposure. Several prior qualitative characterizations of hippocampal function--including stimulus selection, chunking, cue configuration, and contextual coding--are identified as task-specific special cases derivable from this more general theory. The theory suggests that a profitable direction for future empirical and theoretical research will be the study of learning tasks in which both intact and lesioned animals exhibit similar initial learning behaviors but differ on subsequent transfer and generalization tasks.

Animals↗
PubMed2006

Prediction error as a linear function of reward probability is coded in human nucleus accumbens.

Reward probability has been shown to be coded by dopamine neurons in monkeys. Phasic neuronal activation not only increased linearly with reward probability upon expectation of reward, but also varied monotonically across the range of probabilities upon omission or receipt of rewards, therefore modeling discrepancies between expected and received rewards. Such a discrete coding of prediction error has been suggested to be one of the basic principles of learning. We used functional magnetic resonance imaging (fMRI) to show that the human dopamine system codes reward probability and prediction error in a similar way. We used a simple delayed incentive task with a discrete range of reward probabilities from 0%-100%. Activity in the nucleus accumbens of human subjects strongly resembled the phasic responses found in monkey neurons. First, during the expectation period of the task, the fMRI signal in the human nucleus accumbens (NAc) increased linearly with the probability of the reward. Second, during the outcome phase, activity in the NAc coded the prediction error as a linear function of reward probabilities. Third, we found that the Nac signal was correlated with individual differences in sensation seeking and novelty seeking, indicating a link between individual fMRI activation of the dopamine system in a probabilistic paradigm and personality traits previously suggested to be linked with reward processing. We therefore identify two different covariates that model activity in the Nac: specific properties of a psychological task and individual character traits.

Adult↗
PubMedDate not supplied

Alcohol consumption in university students: the role of reasons for drinking, coping strategies, expectancies, and personality traits.

Despite the popularity of the social learning perspective of alcohol abuse, there have been limited efforts devoted to developing comprehensive models that delineate the roles of the constituent components of this approach. In the present study, we determined whether reasons for drinking, coping strategies, alcohol expectancies, and personality traits predict binge drinking and alcohol consumption levels in university students. Escape drinking was the sole positive direct predictor of binge drinking. Social drinking predicted alcohol consumption and thereby exerted an indirect influence on binge drinking. Alcohol expectancies played a significant role in the model but only by influencing reasons for drinking. Although the use of alcohol and/or drugs to cope predicted alcohol consumption, none of a variety of other coping strategies exerted a significant influence in the model. Stress responsivity-related personality traits played a significant role, primarily via an influence on alcohol expectancies. These findings provide support for the social learning perspective of alcohol abuse and offer further insight into the factors that contribute to the development of risky alcohol consumption patterns.

Adaptation, Psychological↗