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PubMed2025

Machine Learning-Based Identification of Survival-Associated CpG Biomarkers in Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is an exceptionally aggressive cancer with a 5-year survival rate of less than 10%, driven by late-stage diagnosis, limited treatment options, and a lack of reliable biomarkers for early detection and prognosis. In this study, we integrated DNA methylation data from TCGA and ICGC cohorts, categorizing samples based on survival time, and identified 684 differentially methylated CpG sites, along with 224 CpG biomarkers significantly associated with patient survival through statistical and machine learning-based analyses. We developed a random forest model to predict patient survival, achieving 85.2% accuracy for short-survival patients and 70.0% for long-survival patients in the validation set. External dataset validation further confirmed the model's robustness and accuracy. De novo motif analysis of genomic regions surrounding the 224 CpG biomarkers identified TWIST1 and FOXA2 as key transcriptional regulators enriched in survival-associated CpG sites, linking their activity to patient survival outcomes. Collectively, our findings highlight valuable epigenetic biomarkers and provide a predictive model to assess PDAC risk levels post-surgery, offering the potential for improved patient stratification and personalized therapeutic strategies.

Journal Article↗

PubMedDate not supplied

A compromise for closed system anesthesia.

Closed system anesthesia is economical, minimally, polluting, and conserves a patient's airway heat and moisture. Yet this method of anesthesia is not widely used because it is considered dangerous by many clinicians. We review the origins of that belief and then test the application of 2 schemes for administering potent agents in a closed system with CO2 absorption. We 1st employed Lowe's square-root-of-time uptake model in 30 patients, using halothane or enflurane. We found that the model provided a good starting point for learning to use the closed system. However, anesthetic concentrations were not accurately predicted. Based on our experience with that model, we examined a simpler approach. We began each of 10 anesthetics using a semiclosed system, then closed the system. Only sufficient O2 for metabolic demand and halothane were added to the closed system. The rate of halothane administration was the same for each patient. This approach proved clinically satisfactory, and the measured halothane concentration remained relatively constant during 45 minutes using the closed system. Changing from a semiclosed to a closed system affords the advantages of the closed system 75 percent of the time, yet requires no extra tasks or equipment.

Anesthesia↗

PubMed2005

Rapid reshaping of human motor generalization.

People routinely learn how to manipulate new tools or make new movements. This learning requires the transformation of sensed movement error into updates of predictive neural control. Here, we demonstrate that the richness of motor training determines not only what we learn but how we learn. Human subjects made reaching movements while holding a robotic arm whose perturbing forces changed directions at the same rate, twice as fast, or four times as fast as the direction of movement, therefore exposing subjects to environments of increasing complexity across movement space. Subjects learned all three environments and learned the low- and medium-complexity environments equally well. We found that subjects lessened their movement-by-movement adaptation and narrowed the spatial extent of generalization to match the environmental complexity. This result demonstrated that people can rapidly reshape the transformation of sense into motor prediction to best learn a new movement task. We then modeled this adaptation using a neural network and found that, to mimic human behavior, the modeled neuronal tuning of movement space needed to narrow and reduce gain with increased environmental complexity. Prominent theories of neural computation have hypothesized that neuronal tuning of space, which determines generalization, should remained fixed during learning so that a combination of neuronal outputs can underlie adaptation simply and flexibly. Here, we challenge those theories with evidence that the neuronal tuning of movement space changed within minutes of training.

Adaptation, Physiological↗

PubMed1990

Nefazodone: preclinical pharmacology of a new antidepressant.

Recent pharmacologic studies suggest that nefazodone may possess antidepressant activity. Nefazodone is active in behavioral models predictive of antidepressant potential. It is active in reversing learned helplessness, prevents reserpine-induced ptosis, and enhances response efficiency in the differential reinforcement for low rates of response paradigm. In in vitro studies, nefazodone inhibits the binding of [3H]ketanserin to cortical serotonin2 (5-HT2) binding sites, whereas in vivo, it antagonizes the 5-HT2-mediated quipazine-induced head shake in rats. In ex vivo studies, acute oral administration of nefazodone inhibits cortical serotonin uptake and occupies frontal cortical 5-HT2 receptor binding sites. Chronic administration of nefazodone produces a reduction in 5-HT2-mediated behavior and decreases cortical 5-HT2 receptor binding site density. Further, a chronic high-dose nefazodone regimen significantly potentiates 5-HT1A-mediated behavioral responses in rats. Nefazodone exhibits decreased anticholinergic, alpha-adrenolytic, and sedative activity relative to other antidepressants.

Animals↗

PubMed2026

Dietary Polyphenol Acteoside-Related Molecular Signatures in Clear Cell Renal Cell Carcinoma: Multi-Omics Profiling and Functional Validation of IMPDH1.

Clear cell renal cell carcinoma (ccRCC) is characterized by substantial metabolic and molecular heterogeneity, but the disease-relevant programs associated with acteoside, a dietary polyphenol, remain poorly understood. We integrated predicted acteoside targets with bulk, single-cell, and spatial transcriptomic data from ccRCC and combined molecular subtyping with cross-cohort machine-learning analysis. Acteoside-related signatures were preferentially enriched in malignant compartments and increased with tumor grade and stage. Consensus clustering identified two molecular subtypes with distinct biological and clinical features. C1 was associated with immune activation, metabolic activity, and more favorable survival, whereas C2 showed greater genomic instability, reduced renal epithelial differentiation, and poorer outcomes. We further benchmarked multiple machine-learning strategies and established a 10-gene prognostic model that retained predictive performance across independent cohorts, with IMPDH1 emerging as the strongest risk-associated feature. Functional experiments confirmed the biological relevance of IMPDH1: its knockdown suppressed ccRCC cell proliferation, DNA synthesis, colony formation, and migration, whereas overexpression produced the opposite effects. Together, these findings indicate that acteoside-related molecular signatures capture clinically relevant heterogeneity in ccRCC and provide a framework for linking dietary-polyphenol-related molecular space with tumor biology. The identification and functional validation of IMPDH1 further highlight its potential importance in ccRCC progression.

IMPDH1↗

PubMed1988

A synergetic theory of environmentally-specified and learned patterns of movement coordination. I. Relative phase dynamics.

This paper outlines and applies a synergetic strategy to the coordination of human rhythmical movement. It extends earlier empirical and theoretical work to include the influence of specific environmental information and of memory on the dynamics of the collective variables (order parameters) that characterize the coordination patterns. Key ideas concern cooperative and competitive influences on the collective dynamics. Recent experiments on environmentally specified and learned rhythmic movement patterns are modeled explicitly on the level of the collective variable, relative phase. New predictions are presented and research directions proposed that follow directly from the present theoretical approach.

Animals↗

PubMed1988

An adaptive neural model for mapping invariant target position.

We perceive a constant target in space as constant even though the registration of that target on our senses is continuously shifting. This article derives and stimulates a neural network model that represents visual spot targets, invariant with respect to any combination of egocentric target measures. The model represents space in terms of signals used to move in that space. The model learns and maintains precise sensory-motor calibrations starting with only loosely defined relations. It is adaptive to physical changes of the eye and muscles as well as internal system parameters. Its performance is noise and fault tolerant. Computer simulations show that the average error in target orientation after learning is about 1% of the total visual field extent. The model maintains good accuracy with many different parameter choices. Its performance is most related to the function of the posterior parietal cortex. Testable predictions are made for the columnar topography and learning in that brain structure.

Animals↗

PubMed1991

Higher olfactory processes: perceptual learning and memory.

The past year has seen several important findings emerge from studies of higher olfactory processes. The identification of synaptic long-term potentiation in the olfactory cortex, induced via repetitive burst stimulation at the theta rhythm, and physiological activity patterns associated with learning, some of which mimic long-term potentiation induction patterns, have suggested relationships between rhythmic activity, behavioral learning and synaptic plasticity. In addition, the construction of computational models of the olfactory bulb and cortex have generated testable behavioral and physiological predictions which have been supported by experimental evidence.

Animals↗

PubMed2006

Computational prediction of the chromosome-damaging potential of chemicals.

We report on the generation of computer-based models for the prediction of the chromosome-damaging potential of chemicals as assessed in the in vitro chromosome aberration (CA) test. On the basis of publicly available CA-test results of more than 650 chemical substances, half of which are drug-like compounds, we generated two different computational models. The first model was realized using the (Q)SAR tool MCASE. Results obtained with this model indicate a limited performance (53%) for the assessment of a chromosome-damaging potential (sensitivity), whereas CA-test negative compounds were correctly predicted with a specificity of 75%. The low sensitivity of this model might be explained by the fact that the underlying 2D-structural descriptors only describe part of the molecular mechanism leading to the induction of chromosome aberrations, that is, direct drug-DNA interactions. The second model was constructed with a more sophisticated machine learning approach and generated a classification model based on 14 molecular descriptors, which were obtained after feature selection. The performance of this model was superior to the MCASE model, primarily because of an improved sensitivity, suggesting that the more complex molecular descriptors in combination with statistical learning approaches are better suited to model the complex nature of mechanisms leading to a positive effect in the CA-test. An analysis of misclassified pharmaceuticals by this model showed that a large part of the false-negative predicted compounds were uniquely positive in the CA-test but lacked a genotoxic potential in other mutagenicity tests of the regulatory testing battery, suggesting that biologically nonsignificant mechanisms could be responsible for the observed positive CA-test result. Since such mechanisms are not amenable to modeling approaches it is suggested that a positive prediction made by the model reflects a biologically significant genotoxic potential. An integration of the machine-learning model as a screening tool in early discovery phases of drug development is proposed.

Chromosomes↗

PubMed2006

Positive and negative recency effects in retirement savings decisions.

Retirement savings decisions can be influenced by the fund composition of the retirement savings plan. In 2 experiments, strong composition effects were observed, with a larger percentage of resources being invested in stock funds when more stock than bond funds were offered. Although participants changed their allocations repeatedly, the opportunity to learn did not alter the composition effects. Learning processes led to positive and negative recency effects as well, providing evidence that allocations were strongly influenced by the recent performance of the different allocation options. Two learning models were tested to explain these learning processes. The first, a local adaptation learning model, assumes that people change their behavior on the basis of recent experience, whereas the second, a reinforcement learning model, assumes that decisions are made on the basis of the totality of accumulated experience. The local adaptation model was more accurate in predicting allocation decisions, in explaining positive and negative recency effects, and in showing why composition effects are not overcome by learning.

Adult↗

PubMed2001

Learning the dynamics of reaching movements results in the modification of arm impedance and long-latency perturbation responses.

Some characteristics of arm movements that humans exhibit during learning the dynamics of reaching are consistent with a theoretical framework where training results in motor commands that are gradually modified to predict and compensate for novel forces that may act on the hand. As a first approximation, the motor control system behaves as an adapting controller that learns an internal model of the dynamics of the task. It approximates inverse dynamics and predicts motor commands that are appropriate for a desired limb trajectory. However, we had previously noted that subtle motion characteristics observed during changes in task dynamics challenged this simple model and raised the possibility that adaptation also involved sensory-motor feedback pathways. These pathways reacted to sensory feedback during the course of the movement. Here we hypothesize that adaptation to dynamics might also involve a modification of how the CNS responds to sensory feedback. We tested this through experiments that quantified how the motor system's response to errors during voluntary movements changed as it adapted to dynamics of a force field. We describe a nonlinear approach that approximates the impedance of the arm, i.e., force response as a function of arm displacement trajectory. We observe that after adaptation, the impedance function changes in a way that closely matches and counters the effect of the force field. This is particularly prominent in the long-latency (> 100 ms) component of response to perturbations. Therefore, it appears that practice not only modifies the internal model with which the brain generates motor commands that initiate a movement, but also the internal model with which sensory feedback is integrated with the ongoing descending commands in order to respond to error during the movement.

Adult↗

PubMed1992

The memory representation of motor skills: a test of schema theory.

Currently, a popular model for the central representation of motor skills is embodied in Schmidt's schema theory of discrete motor skill learning (Schmidt, 1975). Two experiments are reported here that contrast predictions from a schema abstraction model that is the basis for schema theory with those from an exemplar-based model of motor skill memory representation. In both experiments, subjects performed 300 trials per day of three variations of a three-segment timing task over 4 days of acquisition. The subjects then either immediately transferred to four novel variations of the same task (Experiment 1) that varied in degree of similarity to the exemplars experienced during acquisition; or performed two novel and two previously produced exemplars, following 24-h and 1-week retention intervals (Experiment 2). The results indicated that novel task transfer was not affected by the degree of similarity between the acquisition and transfer exemplars, and that there was no advantage for a previously produced exemplar over a novel exemplar after either a 24-hr or 1 week retention interval. Also, in both experiments, a consistent pattern of bias in responding was noted for novel task transfer and retention. These results are indicative of a schema abstraction model of memory representation for motor skills.

Journal Article↗

PubMed2002

Enriched behavioral prediction equation and its impact on structured leaning and the dynamic calculus.

This theoretical note describes an expansion of the behavioral prediction equation, in line with the greater complexity encountered in models of structured learning theory (R. B. Cattell, 1996a). This presents learning theory with a vector substitute for the simpler scalar quantities by which traditional Pavlovian-Skinnerian models have hitherto been represented. Structured learning can be demonstrated by vector changes across a range of intrapersonal psychological variables (ability, personality, motivation, and state constructs). Its use with motivational dynamic trait measures (R. B. Cattell, 1985) should reveal new theoretical possibilities for scientifically monitoring change processes (dynamic calculus model: R. B. Cattell, 1996b), such as encountered within psychotherapeutic settings (R. B. Cattell, 1987). The enhanced behavioral prediction equation suggests that static conceptualizations of personality structure such as the Big Five model are less than optimal.

Forecasting↗

PubMed2004

Configural representations in transverse patterning with a hippocampal model.

The hippocampus is necessary in both humans and rats for learning configural representations in tasks such as transverse patterning. The transverse patterning task, (A+B-, B+C-, C+A-), requires representing individual stimuli in the context of other stimuli. This paper extends a previous application to rat data [INNS World Congress on Neural Networks, 1995; Biol Cybern 6 (1998a) 203] by applying a model of the CA3 region of the hippocampus to human data. A decision function is also added that enables the system to choose among training items. Analysis of the simulations show that configural representations are formed by unique neural codes that depend on temporal and stimuli context. Based on the simulations, we hypothesize that configural representations in biological networks depend on a proper balance of input and context representations. Furthermore, the division of labor between functions in the model is a specific working hypothesis that in learning this task the hippocampus specializes in sequence prediction and the decision function evaluates the predictions.

Hippocampus↗

PubMed2004

Contribution of organizational strategy to verbal learning and memory in adults with attention-deficit/hyperactivity disorder.

Statistical mediation modeling was used to test the hypothesis that poor use of a semantic organizational strategy contributes to verbal learning and memory deficits in adults with attention-deficit/hyperactivity disorder (ADHD). Comparison of 28 adults with ADHD and 34 healthy controls revealed lower performance by the ADHD group on tests of verbal learning and memory, sustained attention, and use of semantic organization during encoding. Mediation modeling indicated that state anxiety, but not semantic organization, significantly contributed to the prediction of both learning and delayed recall in the ADHD group. The pattern of findings suggests that decreased verbal learning and memory in adult ADHD is due in part to situational anxiety and not to poor use of organizational strategies during encoding.

Adult↗

PubMed2000

A connectionist account of asymmetric category learning in early infancy.

Young infants show unexplained asymmetries in the exclusivity of categories formed on the basis of visually presented stimuli. A connectionist model is described that shows similar exclusivity asymmetries when categorizing the same stimuli presented to infants. The asymmetries can be explained in terms of an associative learning mechanism, distributed internal representations, and the statistics of the feature distributions in the stimuli. The model was used to explore the robustness of this asymmetry. The model predicts that the asymmetry will persist when a category is acquired in the presence of mixed category exemplars. An experiment with 3-4-month-olds showed that asymmetric exclusivity persisted in the presence of mixed-exemplar familiarization, thereby confirming the model's prediction.

Association Learning↗

PubMed2004

Generalization as a behavioral window to the neural mechanisms of learning internal models.

In generating motor commands, the brain seems to rely on internal models that predict physical dynamics of the limb and the external world. How does the brain compute an internal model? Which neural structures are involved? We consider a task where a force field is applied to the hand, altering the physical dynamics of reaching. Behavioral measures suggest that as the brain adapts to the field, it maps desired sensory states of the arm into estimates of force. If this neural computation is performed via a population code, i.e., via a set of bases, then activity fields of the bases dictate a generalization function that uses errors experienced in a given state to influence performance in any other state. The patterns of generalization suggest that the bases have activity fields that are directionally tuned, but directional tuning may be bimodal. Limb positions as well as contextual cues multiplicatively modulate the gain of tuning. These properties are consistent with the activity fields of cells in the motor cortex and the cerebellum. We suggest that activity fields of cells in these motor regions dictate the way we represent internal models of limb dynamics.

Biomechanical Phenomena↗

PubMedDate not supplied

A spike based learning rule for generation of invariant representations.

For biological realism, models of learning in neuronal networks often assume that synaptic plasticity solely depends on locally available signals, in particular only on the activity of the pre- and post-synaptic cells. As a consequence, synapses influence the plasticity of other synapses exclusively via the post-synaptic activity. Inspired by recent research on the properties of apical dendrites it has been suggested, that a second integration site in the apical dendrite may mediate specific global information. Here we explore this issue considering the example of learning invariant responses by examining a network of spiking neurones with two sites of synaptic integration. We demonstrate that results obtained in networks of units with continuous outputs transfer to the more realistic neuronal model. This allows a number of more specific experimental predictions, and is a necessary step to unified description of learning rules exploiting timing of action potentials.

Action Potentials↗