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PubMed1998

Dissociable processes for learning the surface structure and abstract structure of sensorimotor sequences.

A sensorimotor sequence may contain information structure at several different levels. In this study, we investigated the hypothesis that two dissociable processes are required for the learning of surface structure and abstract structure, respectively, of sensorimotor sequences. Surface structure is the simple serial order of the sequence elements, whereas abstract structure is defined by relationships between repeating sequence elements. Thus, sequences ABCBAC and DEFEDF have different surface structures but share a common abstract structure, 123213, and are therefore isomorphic. Our simulations of sequence learning performance in serial reaction time (SRT) tasks demonstrated that (1) an existing model of the primate fronto-striatal system is capable of learning surface structure but fails to learn abstract structure, which requires an additional capability, (2) surface and abstract structure can be learned independently by these independent processes, and (3) only abstract structure transfers to isomorphic sequences. We tested these predictions in human subjects. For a sequence with predictable surface and abstract structure, subjects in either explicit or implicit conditions learn the surface structure, but only explicit subjects learn and transfer the abstract structure. For sequences with only abstract structure, learning and transfer of this structure occurs only in the explicit group. These results are parallel to those from the simulations and support our dissociable process hypothesis. Based on the synthesis of the current simulation and empirical results with our previous neuropsychological findings, we propose a neuro-physiological basis for these dissociable processes: Surface structure can be learned by processes that operate under implicit conditions and rely on the fronto-striatal system, whereas learning abstract structure requires a more explicit activation of dissociable processes that rely on a distributed network that includes the left anterior cortex.

Computer Simulation↗

PubMed1988

Aluminum induced encephalopathy in the rat.

Aluminum tartrate (AlT) but not sodium tartrate (NaT) produces a progressive encephalopathy when injected intracerebroventricularly in the rat. This syndrome, lethal within 30-35 days, is characterized by progressively deranged behavior. An early startle reaction (day 14), later joined by locomotor discoordination (day 19) is followed by locomotor and electrocorticographic (ECoG) seizures (day 21) in chronically instrumented AlT rats. There is early dissociation between ECoG and locomotor aspects. When tested in the shuttlebox for estimation of learning and memory function 7-8 days after AlT injection, marked impairment of both active and passive avoidance was observed. Glucose uptake capacity of synaptosomes from brain areas of AlT and NaT animals was indexed by the 2-deoxy-D-glucose method. Striatal and cortical synaptosomes showed reduced uptake activity 7 days following AlT injection. By day 14, hypothalamic areas also became affected, striatal uptake was further inhibited, and cortical uptake was reduced to 57% of control. The ECoG background rhythm remained unchanged until days 20-23, when the mean peak frequency was reduced. The model may be useful in the study of central aluminum toxicity and may have predictive validity in the testing of procedures to counter aluminum-associated encephalopathies in man.

Animals↗

PubMed1993

Recent advances in structure and function of food proteins: QSAR approach.

QSAR (quantitative structure-activity relationship), widely used in chemistry with hydrophobic, electronic, and steric parameters as structural factors, was found to be appropriate for use with food proteins, despite the difficulty, due to the complexity in macromolecular structure, in defining the steric terms. Emulsifying ability was closely related to hydrophobicity, and incorporation of solubility to hydrophobicity as factors improved the R2 of regression analysis. Foaming activity required both hydrophobicity and other factors pertaining to the adsorption of proteins at the interface in order to obtain adequate foam lamella strength. Hydrophobicity as well as other factors relating to the intermolecular interactions, for example, Ca and SH are involved in thermally induced gelation. For breadmaking, although no extensive QSAR work had been carried out, the important function of high molecular glutenin subunits was confirmed, and, notably, the critical function of hydrophobicity in breadmaking also was demonstrated. PLS (partial least-squares regression) and neural networks classify more correctly than other multivariate techniques, thereby yielding higher r2 values in modeling and prediction. However, multiple regression analysis and PCR (principal component regression) also were found to be effective for modeling because the information useful in elucidating the mechanism of protein function could be readily obtained. A characteristic property of unsupervised learning techniques, especially PCS (principal component similarity analysis), in identifying influential factors in the function mechanisms was demonstrated.

Bread↗

PubMed2006

Are sparse-coding simple cell receptive field models physiologically plausible?

Olshausen and Field (1996) developed a simple cell receptive field model for natural scene processing in V1, based on unsupervised learning and non-orthogonal basis function optimization of an overcomplete representation of visual space. The model was originally tested with an ensemble of whitened natural scenes, simulating pre-cortical filtering in the retinal ganglia and lateral geniculate nucleus, and the basis functions qualitatively resembled the orientation-specific responses of V1 simple cells in the spatial domain. In this study, the quantitative tuning responses of the basis functions in the spectral domain are estimated using a Gaussian model, to determine their goodness-of-fit to the known bandwidths of simple cells in primate V1. Five simulation experiments which examined key features of the model are reported: changing the size of the basis functions; using a complete versus over-complete representation; changing the sparseness factor; using a variable learning rate; and mapping the basis functions with a whitening spatial function. The key finding of this study is that across all image themes, basis function sizes, number of basis functions, sparseness factors and learning rates, the spatial-frequency tuning did not closely resemble that of primate area 17 -- the model results more closely resembled the unclassified cat neurones of area 19 with a single exception, and not area 17 as predicted.

Action Potentials↗

PubMed2004

The affirmation of self: a new perspective on the immune system.

The fundamental concepts of autopoiesis, which emphasize the circular organization underlying both living organisms and cognition, have been criticized on the grounds that since they are conceived as a tight logical chain of definitions and implications, it is often not clear whether they are indeed a scientific theory or rather just a potential scientific vocabulary of doubtful utility to working scientists. This article presents the deployment of the concepts of autopoiesis in the field of immunology, a discipline where working biologists themselves spontaneously have long had recourse to "cognitive" metaphors: "recognition"; a "repertoire" of recognized molecular shapes; "learning" and "memory"; and, most striking of all, a "self versus non-self" distinction. It is shown that in immunology, the concepts of autopoiesis can be employed to generate clear novel hypotheses, models demonstrating these ideas, testable predictions, and novel therapeutic procedures. Epistemologically, it is shown that the self-non-self distinction, while quite real, is misleadingly named. When a real mechanism for generating this distinction is identified, it appears that the actual operational distinction is between (a) a sufficiently numerous set of initial antigens, present from the start of ontogeny, in conditions that allow for their participation in the construction of the system's organization and operation, and (b) single antigens that are first presented to the system after two successive phases of maturation. To call this a self-non-self distinction obscures the issue by presupposing what it ought to be the job of scientific investigation to explain.

Artificial Intelligence↗

PubMed2006

Predicting protein subcellular locations using hierarchical ensemble of Bayesian classifiers based on Markov chains.

BACKGROUND: The subcellular location of a protein is closely related to its function. It would be worthwhile to develop a method to predict the subcellular location for a given protein when only the amino acid sequence of the protein is known. Although many efforts have been made to predict subcellular location from sequence information only, there is the need for further research to improve the accuracy of prediction. RESULTS: A novel method called HensBC is introduced to predict protein subcellular location. HensBC is a recursive algorithm which constructs a hierarchical ensemble of classifiers. The classifiers used are Bayesian classifiers based on Markov chain models. We tested our method on six various datasets; among them are Gram-negative bacteria dataset, data for discriminating outer membrane proteins and apoptosis proteins dataset. We observed that our method can predict the subcellular location with high accuracy. Another advantage of the proposed method is that it can improve the accuracy of the prediction of some classes with few sequences in training and is therefore useful for datasets with imbalanced distribution of classes. CONCLUSION: This study introduces an algorithm which uses only the primary sequence of a protein to predict its subcellular location. The proposed recursive scheme represents an interesting methodology for learning and combining classifiers. The method is computationally efficient and competitive with the previously reported approaches in terms of prediction accuracies as empirical results indicate. The code for the software is available upon request.

Amino Acid Sequence↗

PubMedDate not supplied

Defining the learning curve for paramedic student endotracheal intubation.

BACKGROUND: Proficiency in endotracheal intubation (ETI) is assumed to improve primarily with accumulated experience on live patients. While the National Standard Paramedic Curriculum recommends that paramedic students (PSs) perform at least five live ETIs, these training opportunities are limited. OBJECTIVE: To evaluate the effects of cumulative live ETI experience, elapsed duration of training, and clinical setting on PS ETI proficiency. METHODS: The authors used longitudinal, multicenter data from 60 paramedic training programs over a two-year period. The PSs reported outcomes (success/failure) for all live ETIs attempted in the operating room (OR), the emergency department (ED), the intensive care unit (ICU), and other hospital or prehospital settings. Fixed-effects logistic regression was used to model up to 30 consecutive ETI efforts by each PS, accounting for per-PS clustering. For each patient, the authors evaluated the association between ETI success and the PS's cumulative number of ETIs, adjusted for clinical setting, elapsed number of days from the first ETI encounter, and the interaction (cumulative ETIs x elapsed days). Predicted probability plots were constructed depicting the "learning curve" overall and for each clinical setting. Results. Between one and 74 ETIs (median 7; IQR 4-12) were performed by each of 802 PSs. Of 7,635 ETIs, 6,464 (87.4%) were successful. Stratified by clinical setting, 6,311 (82.7%) ETIs were performed in the OR, 271 (3.6%) in the ED, 64 (0.8%) in the ICU, 86 (1.1%) in other in-hospital settings, and 903 (11.8%) in the prehospital setting. For the 7,398 ETIs included in the multivariate analysis, cumulative number of ETI was associated with increased adjusted odds of ETI success (odds ratio 1.067 per ETI; 95% CI: 1.044-1.091). ETI learning curves were steepest for the ICU and prehospital settings but lower than for other clinical settings. CONCLUSIONS: Paramedic student ETI success improves with accumulated live experience but appears to vary across different clinical settings. Strategies for PS airway education must consider the volume of live ETIs as well as the clinical settings used for ETI training.

Allied Health Personnel↗

PubMed2002

Predictive non-linear modeling of complex data by artificial neural networks.

An artificial neural network (ANN) is an artificial intelligence tool that identifies arbitrary nonlinear multiparametric discriminant functions directly from experimental data. The use of ANNs has gained increasing popularity for applications where a mechanistic description of the dependency between dependent and independent variables is either unknown or very complex. This machine learning technique can be roughly described as a universal algebraic function that will distinguish signal from noise directly from experimental data. The application of ANNs to complex relationships makes them highly attractive for the study of biological systems. Recent applications include the analysis of expression profiles and genomic and proteomic sequences.

Biochemical Phenomena↗

PubMedDate not supplied

Hippocampal CA3 region predicts memory sequences: accounting for the phase precession of place cells.

Hippocampal recordings show that different place cells fire at different phases during the same theta oscillation, probably at the peak of different gamma cycles. As the rat moves through the place field of a given cell, the phase of firing during the theta cycle advances progressively. In this paper we have sought to determine whether a recently developed model of hippocampal and cortical memory function can explain this phase advance and other properties of place cells. According to this physiologically based model, the CA3 network stores information about the sequence of places traversed during learning. Here we show that the phase advance can be understood if it is assumed that the hippocampus is in a recall mode that operates when the animal is already familiar with a path. In this mode, sensory information about the current position triggers recall of the upcoming 5-6 places (memories) in the path at a rate of one memory per gamma cycle. The model predicts that the average phase advance will be one gamma cycle per theta cycle, a value in reasonable agreement with the data. The model also correctly accounts for (1) the fact that the firing of a place cell occurs during approximately 7 theta cycles (on average) as the animal crosses the place field; (2) the observation that the phase of place cell firing depends more systematically on position than on time; and (3) the fact that traversal of an already familiar path produces further modifications (shifts the firing of a cell to an earlier position in the path). This later finding suggests that recall of previously stored information strengthens the memory of that information. In the model, this occurs because of a novel role of N-methyl-D-aspartate channels in recall. The general success of the model provides support for the idea that the hippocampus stores sequence information and makes predictions of expected positions during gamma-frequency recall.

Computer Simulation↗

PubMed1994

International Commission for Protection Against Environmental Mutagens and Carcinogens. Two million rodent carcinogens? The role of SAR and QSAR in their detection.

The accurate prediction of chemical carcinogenicity can only be achieved by a balanced consideration of the following factors: the chemistry and metabolism of the test agent, the interaction between toxicity and genetic toxicity, the possibility of non-genotoxic events that trigger subsequent non-targeted mutagenesis, the difference between activities observed in vitro and in vivo, and the possible inadequacy and/or partiality of all datasets and observations. Extrapolation of activities within a series of congeners is usually possible, but predictions across different chemical classes/mechanisms of carcinogenicity are difficult. Artificial intelligence systems can be used to predict one or more of the above parameters given adequate learning sets, but the hope for a single, coherent and self-contained method of predicting all instances of carcinogenicity is unreal. The future of carcinogen/mutagen prediction lies with data-rich artificial intelligence systems based on known mechanistic principles used selectively within the context of chemical and biological human insight. The major current obstacle to progress is the assumption that mutagenicity and carcinogenicity are unitary phenomena that can be learned and predicted by artificial intelligence systems operating in isolation.

Alkylating Agents↗

PubMed1989

Sensory-perceptual qualities of images.

Four experiments demonstrated that such sensory-perceptual features of objects as weight, color, and numerosity affect imaginal performance involving images of those objects. For example, imaginary transport times of objects increased with both the hypothetical weight of the imagined object and the distance traversed. The transport functions were steeper when a map of the terrain was imagined than when it was perceived, suggesting that imaginal performance of heft did not parallel more perceptually guided performance. Corresponding to the view that images activate noncanonical information from long-term memory, mental transport times were longer for maps of familiar terrains than for those of presumably unelaborated unfamiliar terrains. Further, the effects of imaginary color discriminations depended on the familiarity of the object being imagined. Images of customarily colored familiar objects were generated faster when projected onto a surface of the same color than when projected onto a surface of another color, whereas images constructed from unfamiliar targets were recognized more accurately when the target's color differed substantially from that of the ground than when it differed by a smaller amount. The results were predicted by a model that assumed that images may incorporate ancillary characteristics in addition to canonical information.

Adult↗

PubMed2001

Counting models of temporal discrimination.

A three-category task was employed to test counting models for temporal discrimination. Unlike former approaches, the present one is not based on Weber functions. Specifically, the proposed test does not require the implicit but, nevertheless, debatable assumption that the pulse rate of the internal clock is constant for different durations of the standard interval. Furthermore, the present approach does not necessitate specific distributional assumptions about the interpulse times. An experiment was conducted to evaluate the predictions of this generalized counting model. The results are consistent with predictions of the generalized counting model. A further analysis suggests that the pulse rate decreases as the duration of the standard interval increases.

Adult↗

PubMed2005

Unitary IPSPs drive precise thalamic spiking in a circuit required for learning.

Song learning in birds requires a basal ganglia-thalamo-pallial loop that contains a calyceal GABAergic synapse in the thalamus. Information processing within this circuit is critical for proper song development; however, it is unclear whether activation of the inhibitory output of the basal ganglia structure Area X can drive sustained activity in its thalamic target, the medial portion of the dorsolateral thalamic nucleus (DLM). We show that high-frequency, random activation of this GABAergic synapse can drive precisely timed firing in DLM neurons in brain slices in the absence of excitatory input. Complex IPSP trains, including spike trains recorded in vivo, drive spiking in slices with high reproducibility, even between animals. Using a simple model, we can predict much of DLM's response to natural stimulus trains. These data elucidate basic rules by which thalamic relay neurons translate IPSPs into suprathreshold output and demonstrate extrathalamic GABAergic activation of thalamus.

Animals↗

PubMed2025

Deep learning-based annotation of plant abiotic stress resistance genes for crops.

The declining costs of DNA sequencing have expanded genomic data, crucial for understanding plant abiotic stress responses and crop improvement. However, accurate gene annotation remains challenging. To address this limitation, we propose the PASRGA, a deep learning approach that leverages transfer learning and contrastive learning to annotate genes related to drought, salt, cold, and UV resistance. PASRGA achieves high F1-scores, area under the receiver operating characteristic (AUROC), area under the precision-recall curve (AUPRC), and Matthews correlation coefficient (MCC) in annotating stress resistance genes, significantly outperforming the general protein annotation model CLEAN, the plant phosphatase gene annotation model PF-NET, the top-ranked model in the CAFA5 challenge NetGO 4.0, and four traditional machine learning methods. Its effectiveness was further validated with a salt stress treatment experiment in Eutrema salsugineum. To facilitate crop breeding practices, we utilized PASRGA to annotate the genomes of 17 major crops. To improve accessibility and utility, we incorporated both manually curated and PASRGA-predicted gene data, together with the PASRGA tool, into the PlantASRG database (https://bioinfor.nefu.edu.cn/PlantASRG/). This comprehensive resource aims to support crop breeding initiatives and ensure food security.

Crops, Agricultural↗

PubMed2006

A maximum likelihood framework for protein design.

BACKGROUND: The aim of protein design is to predict amino-acid sequences compatible with a given target structure. Traditionally envisioned as a purely thermodynamic question, this problem can also be understood in a wider context, where additional constraints are captured by learning the sequence patterns displayed by natural proteins of known conformation. In this latter perspective, however, we still need a theoretical formalization of the question, leading to general and efficient learning methods, and allowing for the selection of fast and accurate objective functions quantifying sequence/structure compatibility. RESULTS: We propose a formulation of the protein design problem in terms of model-based statistical inference. Our framework uses the maximum likelihood principle to optimize the unknown parameters of a statistical potential, which we call an inverse potential to contrast with classical potentials used for structure prediction. We propose an implementation based on Markov chain Monte Carlo, in which the likelihood is maximized by gradient descent and is numerically estimated by thermodynamic integration. The fit of the models is evaluated by cross-validation. We apply this to a simple pairwise contact potential, supplemented with a solvent-accessibility term, and show that the resulting models have a better predictive power than currently available pairwise potentials. Furthermore, the model comparison method presented here allows one to measure the relative contribution of each component of the potential, and to choose the optimal number of accessibility classes, which turns out to be much higher than classically considered. CONCLUSION: Altogether, this reformulation makes it possible to test a wide diversity of models, using different forms of potentials, or accounting for other factors than just the constraint of thermodynamic stability. Ultimately, such model-based statistical analyses may help to understand the forces shaping protein sequences, and driving their evolution.

Amino Acid Sequence↗

PubMed1993

Long-term training, transfer, and retention in learning to lipread.

A long-term training paradigm in lipreading was used to test the fuzzy logical model of perception (FLMP). This model has been used successfully to describe the joint contribution of audible and visible speech in bimodal speech perception. Tests of the model were extended in the present experiment to include the prediction of confusion matrices, as well as performance at several different levels of skill. The predictions of the FLMP were contrasted with the predictions of a prelabeling integration model (PRLM). Subjects were taught to lipread 22 initial consonants in three different vowel contexts. Training involved a variety of discrimination and identification lessons with the consonant-vowel syllables. Repeated testing was given on syllables, words, and sentences. The test items were presented visually, auditorily, and bimodally, at normal rate or three times normal rate. The subjects improved in their lipreading ability across all three types of test items. Replicating previous results, the present study illustrates that substantial gains in lipreading performance are possible. Relative to the PRLM, the FLMP gave a better description of the confusion matrices at both the beginning and the end of practice. One new finding from the present study is that the FLMP can account for the gains in bimodal speech perception as subjects improve their lipreading and listening abilities.

Adolescent↗

PubMed2026

CCNA2 orchestrates the PI3K/AKT signaling axis to propel prostate cancer metastasis.

BACKGROUND: Prostate cancer (PCa) remains one of the most common malignancies in men, posing a persistent global burden in terms of both public health and socioeconomic costs. Although early detection is essential for improving patient outcomes, existing clinical tools, including prostate-specific antigen (PSA) screening, digital rectal examination, and transrectal ultrasound-guided biopsy, are hampered by suboptimal specificity and positive predictive value, resulting in frequent overdiagnosis and overtreatment of indolent lesions while missing a subset of aggressive tumors at an early stage. In this context, the rapid advancement of high-throughput omics technologies, coupled with sophisticated machine learning (ML) algorithms, provides a powerful computational framework to dissect high-dimensional genomic data, uncover latent gene expression signatures, and identify candidate biomarkers with superior discriminative performance over conventional clinicopathological parameters. Therefore, in this study, we sought to screen for crucial ML-based biomarkers associated with PCa, with a particular focus on systematically assessing the diagnostic and prognostic value of CCNA2. Leveraging large-scale transcriptomic cohorts from public repositories, we employed an ensemble of ML approaches to prioritize candidate genes and subsequently evaluated the diagnostic performance of CCNA2 through receiver operating characteristic curve analysis, as well as its prognostic utility via Kaplan-Meier survival estimation and multivariate Cox proportional hazards modeling. Our findings are anticipated to elucidate the molecular landscape of PCa and offer a promising biomarker candidate for early detection and risk stratification. METHODS: This study integrated single-cell RNA sequencing, bulk transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) repositories, immunofluorescence, and multiple ML algorithms with in vitro functional assays to evaluate CCNA2 expression, clinical relevance, and biological behavior in PCa. RESULTS: CCNA2 was linked to metastasis and poor prognosis. High CCNA2 expression significantly correlated with adverse survival outcomes, and knockdown of CCNA2 suppressed proliferation, migration, and invasion in PCa cell lines. Mechanistically, CCNA2 modulated the PI3K/AKT signaling pathway. An ML-based diagnostic model incorporating CCNA2 demonstrated high predictive accuracy across multiple validation cohorts. CONCLUSIONS: CCNA2 serves as a promising prognostic biomarker and therapeutic target in prostate adenocarcinoma, driving tumor progression potentially via the PI3K/AKT axis.

CCNA2↗

PubMed2005

A simple neural network model of the hippocampus suggesting its pathfinding role in episodic memory retrieval.

The goal of this work is to extend the theoretical understanding of the relationship between hippocampal spatial and memory functions to the level of neurophysiological mechanisms underlying spatial navigation and episodic memory retrieval. The proposed unifying theory describes both phenomena within a unique framework, as based on one and the same pathfinding function of the hippocampus. We propose a mechanism of reconstruction of the context of experience involving a search for a nearly shortest path in the space of remembered contexts. To analyze this concept in detail, we define a simple connectionist model consistent with available rodent and human neurophysiological data. Numerical study of the model begins with the spatial domain as a simple analogy for more complex phenomena. It is demonstrated how a nearly shortest path is quickly found in a familiar environment. We prove numerically that associative learning during sharp waves can account for the necessary properties of hippocampal place cells. Computational study of the model is extended to other cognitive paradigms, with the main focus on episodic memory retrieval. We show that the ability to find a correct path may be vital for successful retrieval. The model robustly exhibits the pathfinding capacity within a wide range of several factors, including its memory load (up to 30,000 abstract contexts), the number of episodes that become associated with potential target contexts, and the level of dynamical noise. We offer several testable critical predictions in both spatial and memory domains to validate the theory. Our results suggest that (1) the pathfinding function of the hippocampus, in addition to its associative and memory indexing functions, may be vital for retrieval of certain episodic memories, and (2) the hippocampal spatial navigation function could be a precursor of its memory function.

Animals↗