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Evaluation of DNA intercalation potential of pharmaceuticals and other chemicals by cell-based and three-dimensional computational approaches.

To what extent noncovalent chemical-DNA interactions, in particular weak nonbonded DNA intercalation, contribute to genotoxic responses in mammalian cells has not been fully elucidated. Moreover, with the exception of predominantly flat, multiple-fused-ring structures, our ability to predict intercalation ability of novel compounds is nearly completely lacking. Computational programs such as DEREK and MCASE recognize primarily those molecules that can form irreversible covalent adducts with DNA since their learning sets, for the most part, have not been populated by compounds for which a relationship between noncovalent interaction and genotoxicity exists. We describe here a novel three-dimensional (3D) computational DNA-docking model for prediction of DNA intercalative activity of molecules with both classical and nonclassical intercalating structures. The 3D docking results show a remarkable concordance with results obtained from testing these molecules directly in the Chinese hamster V79 cell-based bleomycin amplification system suggesting that either or both of these approaches may have utility in defining noncovalent chemical-DNA interactions. The ability to predict and/or demonstrate cellular DNA intercalation of novel molecules may well provide fresh insights into the nature and mechanistic basis of structurally unexpected genotoxicity observed during safety testing.

Animals↗

Predicting spontaneous recovery of memory.

Long after a new language has been learned and forgotten, relearning a few words seems to trigger the recall of other words. Neural-network models indicate that this form of spontaneous recovery may result from the storage of distributed representations, which are thought to mediate human memory. Here we use a psychomotor learning task to show that a corresponding effect of spontaneous memory recovery occurs in human subjects.

Humans↗

Blocking and the detection of odor components in blends.

Recent studies of olfactory blocking have revealed that binary odorant mixtures are not always processed as though they give rise to mixture-unique configural properties. When animals are conditioned to one odorant (A) and then conditioned to a mixture of that odorant with a second (X), the ability to learn or express the association of X with reinforcement appears to be reduced relative to animals that were not preconditioned to A. A recent model of odor-based response patterns in the insect antennal lobe predicts that the strength of the blocking effect will be related to the perceptual similarity between the two odorants, i.e. greater similarity should increase the blocking effect. Here, we test that model in the honeybee Apis mellifera by first establishing a generalization matrix for three odorants and then testing for blocking between all possible combinations of them. We confirm earlier findings demonstrating the occurrence of the blocking effect in olfactory learning of compound stimuli. We show that the occurrence and the strength of the blocking effect depend on the odorants used in the experiment. In addition, we find very good agreement between our results and the model, and less agreement between our results and an alternative model recently proposed to explain the effect.

1-Octanol↗

A modular learning environment for protein modeling.

We propose in this paper a modular learning environment for protein modeling. In this system, the protein modeling problem is tackled in two successive phases. First, partial structural informations are determined via numerical learning techniques. Then, in the second phase, the multiple available informations are combined in pattern matching searches via dynamic programming. It is shown on real problems that various protein structure predictions can be improved in this way, such as secondary structure prediction, alignment of weakly homologous protein sequences or protein model evaluations.

Amino Acid Sequence↗

Knowledge-driven interpretable neural networks provide mechanistic insight.

Analyzing omics data in the context of pathway knowledge is critical for understanding the molecular mechanisms underlying pathological changes. However, current pathway analysis methods do not model the detailed mechanistic nature of biological interactions, limiting the understanding of pathway behavior to a relatively shallow level. To address this issue, we present a knowledge-driven machine learning framework that embeds features into pathway graphs and models reactions analytically, producing interpretable feature hierarchies and subnetworks in which functional associations are estimated to model biological interactions. The approach is agnostic to feature selection, enabling the use of full omics data sets without discarding weak signals. Applications to breast cancer microRNA-gene regulation data and COVID-19 metabolomic data highlight immune and metabolic pathways relevant to disease progression. This framework bridges predictive modeling with mechanistic interpretation and offers a foundation for integrative pathway analysis.

Humans↗

Detection in fixed and random noise in foveal and parafoveal vision explained by template learning.

Foveal and parafoveal contrast detection thresholds for Gabor and checkerboard targets were measured in white noise by means of a two-interval forced-choice paradigm. Two white-noise conditions were used: fixed and twin. In the fixed noise condition a single noise sample was presented in both intervals of all the trials. In the twin noise condition the same noise sample was used in the two intervals of a trial, but a new sample was generated for each trial. Fixed noise conditions usually resulted in lower thresholds than twin noise. Template learning models are presented that attribute this advantage of fixed over twin noise either to fixed memory templates' reducing uncertainty by incorporation of the noise or to the introduction, by the learning process itself, of more variability in the twin noise condition. Quantitative predictions of the template learning process show that it contributes to the accelerating nonlinear increase in performance with signal amplitude at low signal-to-noise ratios.

Artifacts↗

An incremental EM-based learning approach for on-line prediction of hospital resource utilization.

OBJECTIVE: Inpatient length of stay (LOS) is an important measure of hospital activity, health care resource consumption, and patient acuity. This research work aims at developing an incremental expectation maximization (EM) based learning approach on mixture of experts (ME) system for on-line prediction of LOS. The use of a batch-mode learning process in most existing artificial neural networks to predict LOS is unrealistic, as the data become available over time and their pattern change dynamically. In contrast, an on-line process is capable of providing an output whenever a new datum becomes available. This on-the-spot information is therefore more useful and practical for making decisions, especially when one deals with a tremendous amount of data. METHODS AND MATERIAL: The proposed approach is illustrated using a real example of gastroenteritis LOS data. The data set was extracted from a retrospective cohort study on all infants born in 1995-1997 and their subsequent admissions for gastroenteritis. The total number of admissions in this data set was n = 692. Linked hospitalization records of the cohort were retrieved retrospectively to derive the outcome measure, patient demographics, and associated co-morbidities information. A comparative study of the incremental learning and the batch-mode learning algorithms is considered. The performances of the learning algorithms are compared based on the mean absolute difference (MAD) between the predictions and the actual LOS, and the proportion of predictions with MAD < or = 1 day (Prop(MAD < or = 1)). The significance of the comparison is assessed through a regression analysis. RESULTS: The incremental learning algorithm provides better on-line prediction of LOS when the system has gained sufficient training from more examples (MAD = 1.77 days and Prop(MAD < or = 1) = 54.3%), compared to that using the batch-mode learning. The regression analysis indicates a significant decrease of MAD (p-value = 0.063) and a significant (p-value = 0.044) increase of Prop(MAD < or = 1) with the incremental learning algorithm. CONCLUSIONS: The incremental learning feature and the self-adaptive model-selection ability of the ME network enhance its effective adaptation to non-stationary LOS data. It is demonstrated that the incremental learning algorithm outperforms the batch-mode algorithm in the on-line prediction of LOS.

Algorithms↗

Thirty categorization results in search of a model.

One category structure dominated in the shift toward exemplar-based theories of categorization. Given the theoretical burden on this category structure, the authors reanalyzed 30 of its uses over 20 years in 8 articles. The authors suggest 4 conclusions. (a) This category structure may encourage exemplar-memorization processes because of its poor structure, the learning difficulties it causes, and its small, memorizable exemplar sets. Its results may only generalize narrowly. (b) Exemplar models have an advantage in fitting these 30 data sets only because they reproduce a performance advantage for training items. Other models fit equally well if granted this capacity. (c) A simpler exemplar process than assumed by exemplar models suffices to explain these data sets. (d) An important qualitative result predicted by exemplar theory is not found overall and possibly should not even be expected. The authors conclude that the data produced by this category structure do not clearly support exemplar theory.

Concept Formation↗

Learning mechanisms in matching to sample.

A model system and an experiment on early learning and decision processes in matching-to-sample and oddity-from-sample tasks are presented. The model system is based, in part, on videotaped records of pigeons' looking responses before they chose 1 of 2 comparison stimuli. In order to see the wavelength stimuli recessed behind the pecking keys, the pigeons had to move in front of them. Although there were slight increases in the acceptance probability with switches between the stimuli before a choice response, the overall decision strategy was close to a Markov choice process in which choice proportions could be predicted by the product of each rejection probability and the final acceptance probability. Learning involved learning to discriminate rather than learning to adopt a stricter criterion for an acceptable sample match.

Animals↗

Photoperiod and tutor access affect the process of vocal learning.

Song learning in white-crowned sparrows, Zonotrichia leucophrys, involves three steps: memorization of external models, song practice and selection of a song from the practiced repertoire for crystallization. These three events occur in a sequential and predictable order during the first year of life in captive sparrows. To study the external regulation of these events, we raised nestling sparrows under conditions in which photoperiod and tutor exposure were manipulated. We measured plasma testosterone concentration twice a month to study its role in the mediation of vocal learning. We conclude that the timing of song memorization is relatively impervious to photoperiodic manipulation. Song practice and crystallization, however, were readily influenced by both photoperiod and tutor exposure. We suggest that low testosterone concentrations permit acquisition at an older age than would normally occur, and confirm that testosterone propels the transition to production of crystallized song, but is not required for the onset of song practice. Copyright 1998 The Association for the Study of Animal Behaviour.

Journal Article↗

Self-efficacy and cognitive achievement: implications for students with learning problems.

This article presents a self-efficacy model of achievement that comprises entry characteristics, self-efficacy for learning, task engagement variables, and efficacy cues. Students' sense of self-efficacy for learning is influenced as they work on tasks by cues that signal how well they are learning. Research is summarized on the effects of social and instructional variables on self-efficacy and achievement behaviors. Empirical evidence supports the idea that self-efficacy predicts student motivation and learning. Future research directions are provided, along with educational implications for students with learning problems.

Achievement↗

Song learning accelerates allopatric speciation.

The songs of many birds are unusual in that they serve a role in identifying conspecific mates, yet they are also culturally transmitted. Noting the apparently high rate of diversity in one avian taxon, the songbirds, in which song learning appears ubiquitous, it has often been speculated that cultural transmission may increase the rate of speciation. Here we examine the possibility that song learning affects the rate of allopatric speciation. We construct a population-genetic model of allopatric divergence that explores the evolution of genes that underlie learning preferences (predispositions to learn some songs over others). We compare this with a model in which mating signals are inherited only genetically. Models are constructed for the cases where songs and preferences are affected by the same or different loci, and we analyze them using analytical local stability analysis combined with simulations of drift and directional sexual selection. Under nearly all conditions examined, song divergence occurs more readily in the learning model than in the nonlearning model. This is a result of reduced frequency-dependent selection in the learning models. Cultural evolution causes males with unusual genotypes to tend to learn from the majority of males around them, and thus develop songs compatible with the majority of the females in the population. Unusual genotypes can therefore be masked by learning. Over a wide range of conditions, learning therefore reduces the waiting time for speciation to occur and can be predicted to accelerate the rate of speciation.

Animals↗

Predictive design of tissue-specific mammalian enhancers that function in the mouse embryo.

Enhancers control tissue-specific gene expression across animals1. Although deep learning2,3 has enabled enhancer prediction and design in mammalian cell lines and non-mammalian model organisms4-10 (reviewed in a previous publication11), it remains unclear whether such approaches can operate within the regulatory complexity of mammalian genomes and tissues in vivo. Here we present a general strategy for designing tissue-specific enhancers that function reliably in mice. We use deep learning to train compact convolutional neural networks on curated chromatin accessibility data and fine-tune them by transfer learning on validated human and mouse enhancers. Guided by these models, we design 15 synthetic enhancers for the heart, limb and central nervous system in mouse embryos, all of which are active in their intended target tissue. These results demonstrate that mammalian enhancer function can be reliably inferred from DNA sequence alone, enabling the predictive de novo design of tissue-specific synthetic enhancers from modest training sets. This work establishes a generalizable framework for programmable control of mammalian gene expression in vivo, opening new avenues in functional genomics, synthetic biology and gene therapy.

Animals↗

A neural network model of adaptively timed reinforcement learning and hippocampal dynamics.

A neural model is described of how adaptively timed reinforcement learning occurs. The adaptive timing circuit is suggested to exist in the hippocampus, and to involve convergence of dentate granule cells on CA3 pyramidal cells, and N-methyl-D-aspartate (NMDA) receptors. This circuit forms part of a model neural system for the coordinated control of recognition learning, reinforcement learning, and motor learning, whose properties clarify how an animal can learn to acquire a delayed reward. Behavioral and neural data are summarized in support of each processing stage of the system. The relevant anatomical sites are in thalamus, neocortex, hippocampus, hypothalamus, amygdala and cerebellum. Cerebellar influences on motor learning are distinguished from hippocampal influences on adaptive timing of reinforcement learning. The model simulates how damage to the hippocampal formation disrupts adaptive timing, eliminates attentional blocking and causes symptoms of medial temporal amnesia. Properties of learned expectations, attentional focussing, memory search and orienting reactions to novel events are used to analyze the blocking and amnesia data. The model also suggests how normal acquisition of subcortical emotional conditioning can occur after cortical ablation, even though extinction of emotional conditioning is retarded by cortical ablation. The model simulates how increasing the duration of an unconditioned stimulus increases the amplitude of emotional conditioning, but does not change adaptive timing; and how an increase in the intensity of a conditioned stimulus 'speeds up the clock', but an increase in the intensity of an unconditioned stimulus does not. Computer simulations of the model fit parametric conditioning data, including a Weber law property and an inverted U property. Both primary and secondary adaptively timed conditionings are simulated, as are data concerning conditioning using multiple interstimulus intervals (ISIs), gradually or abruptly changing ISIs, partial reinforcement and multiple stimuli that lead to time-averaging of responses. Neurobiologically testable predictions are made to facilitate further tests of the model.

Animals↗

Attitudes, norms, and self-efficacy: a model of adolescents' HIV-related sexual risk behavior.

Using data from a cross-sectional, statewide survey of 1,720 Texas ninth graders in 13 school districts, a model of psychosocial predictors of human immunodeficiency virus (HIV)-related sexual risk behavior was tested. Predictor variables in the model, based on variables from the Theory of Reasoned Action and Social Learning Theory, were attitudes, norms, self-efficacy, and behavioral intentions. Attitudes, norms, and self-efficacy predicted 36.4% of the variance in the intention to limit the number of sexual partners and the same variables plus intention predicted 24.6% of the variance in number of sexual partners in the past year. Attitudes, norms, and self-efficacy regarding condom use predicted 17.0% of the variance in condom use intentions; these variables plus intentions predicted 19.0% of the variance in condom use frequency. Attitudes, norms, and intentions were directly related to the number of sexual partners, while self-efficacy ad condom use intentions were directly related to frequency of condom use.

Adolescent↗

A general model for host plant selection in phytophagous insects.

We develop a general theoretical framework for exploring the host plant selection behaviour of herbivorous insects. This model can be used to address a number of questions, including the evolution of specialists, generalists, preference hierarchies, and learning. We use our model to: (i) demonstrate the consequences of the extent to which the reproductive success of a foraging female is limited by the rate at which they find host plants (host limitation) or the number of eggs they carry (egg limitation); (ii) emphasize the different consequences of variation in behaviour before and after landing on (locating) a host (termed pre- and post-alighting, respectively); (iii) show that, in contrast to previous predictions, learning can be favoured in post-alighting behaviour--in particular, individuals can be selected to concentrate oviposition on an abundant low-quality host, whilst ignoring a rare higher-quality host; (iv) emphasize the importance of interactions between mechanisms in favouring specialization or learning.

Animals↗

Social cognitive factors predicting the health of elders.

The social cognitive model of health is primarily concerned with influences on a person's decisions to perform health behaviors. In this study, the relationships among social cognitive factors reflecting enabling skills (learned resourcefulness), internal motivation for health (health self-determinism), and help responses (coping responses) and a measure of physical and psychosocial health were examined in 137 chronically ill elders. Gender, race, and number of chronic conditions were predictors of learned resourcefulness. Learned resourcefulness was associated with the use of informal help, whereas health self-determinism was related to self-help and formal help. None of the help responses were significant predictors of health. Model testing revealed that greater resourcefulness or skill in coping with stressful situations was the most important predictor of health. Strategies are recommended to help elders achieve optimal health.

Adaptation, Psychological↗

Neural networks in pharmacodynamic modeling. Is current modeling practice of complex kinetic systems at a dead end?

Neural networks (NN) are computational systems implemented in software or hardware that attempt to simulate the neurological processing abilities of biological systems, in particular the brain. Computational NN are classified as parallel distributed processing systems that for many tasks are recognized to have superior processing capability to the classical sequential Von Neuman computer model. NN are recognized mainly in terms of their adaptive learning and self-organization features and their nonlinear processing capability and are considered most suitable to deal with complex multivariate systems that are poorly understood and difficult to model by classical inductive, logically structured modeling techniques. A NN is applied to demonstrate one of the potentially many applications of NN for modeling complex kinetic systems. The NN was used to predict the effect of alfentanil on the heart rate resulting from a complex infusion scheme applied to six rabbits. Drug input-drug effect data resulting from a repeated, triple infusion rate scheme lasting from 30 to 180 min was used to train the NN to recognize and emulate the input-effect behavior of the system. With the NN memory fixed from the 30- to 180-min learning phase the NN was then tested for its ability to predict the effect resulting from a multiple infusion rate scheme applied in the subsequent 180 to 300 min of the experiment. The NN's ability to emulate the system (30-180 min) was excellent and its predictive extrapolation capability (180-300 min) was very good (mean relative prediction accuracy of 78%). The NN was best in predicting the higher intensity effect and was able to identify and predict an overshoot phenomenon likely caused by a withdrawal effect from acute tolerance. Current modeling philosophy and practice is discussed on the basis of the alternative offered by NN in the modeling of complex kinetic systems. In modeling such systems it is questioned whether traditional modeling practice that insists on structure relevance and conceptually pleasing structures has any practical advantages over the empirical NN approach that largely ignores structure relevance but concentrates on the emulation of the behavior of the kinetic system. The traditional searching for appropriate models of complex kinetic systems is a painstakingly slow process. In contrast, the search for empirical models using NN will continue to improve, limited only by technological advances supporting the very promising NN developments.

Alfentanil↗