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PubMed2003

Learning to control a brain-machine interface for reaching and grasping by primates.

Reaching and grasping in primates depend on the coordination of neural activity in large frontoparietal ensembles. Here we demonstrate that primates can learn to reach and grasp virtual objects by controlling a robot arm through a closed-loop brain-machine interface (BMIc) that uses multiple mathematical models to extract several motor parameters (i.e., hand position, velocity, gripping force, and the EMGs of multiple arm muscles) from the electrical activity of frontoparietal neuronal ensembles. As single neurons typically contribute to the encoding of several motor parameters, we observed that high BMIc accuracy required recording from large neuronal ensembles. Continuous BMIc operation by monkeys led to significant improvements in both model predictions and behavioral performance. Using visual feedback, monkeys succeeded in producing robot reach-and-grasp movements even when their arms did not move. Learning to operate the BMIc was paralleled by functional reorganization in multiple cortical areas, suggesting that the dynamic properties of the BMIc were incorporated into motor and sensory cortical representations.

Animals↗

PubMed2006

The use of wireless laptop computers for computer-assisted learning in pharmacokinetics.

OBJECTIVE: To implement computer-assisted learning workshops into pharmacokinetics courses in a doctor of pharmacy (PharmD) program. DESIGN: Workshops were designed for students to utilize computer software programs on laptop computers to build pharmacokinetic models to predict drug concentrations resulting from various dosage regimens. In addition, students were able to visualize through graphing programs how altering different parameters changed drug concentration-time curves. Surveys were conducted to measure students' attitudes toward computer technology before and after implementation. Finally, traditional examinations were used to evaluate student learning. ASSESSMENT: Doctor of pharmacy students responded favorably to the use of wireless laptop computers in problem-based pharmacokinetic workshops. Eighty-eight percent (n = 61/69) and 82% (n = 55/67) of PharmD students completed surveys before and after computer implementation, respectively. Prior to implementation, 95% of students agreed that computers would enhance learning in pharmacokinetics. After implementation, 98% of students strongly agreed (p < 0.05) that computers enhanced learning. Examination results were significantly higher after computer implementation (89% with computers vs. 84% without computers; p = 0.01). CONCLUSION: Implementation of wireless laptop computers in a pharmacokinetic course enabled students to construct their own pharmacokinetic models that could respond to changing parameters. Students had greater comprehension and were better able to interpret results and provide appropriate recommendations. Computer-assisted pharmacokinetic techniques can be powerful tools when making decisions about drug therapy.

Computer-Assisted Instruction↗

PubMed1999

Habituation and sensitization as phenomena modeling learning processes in two groups of children: normally developing and mentally deficient in biofeedback research.

Problems of habituation and sensitization as phenomena which help to control and to predict results of the learning speed in the 8-13 olds, depending on the level of their intellectual development, expressed by the intelligence quotient, were carried out with the use of the biofeedback method. The statistical analysis proved that there is relationship between the intelligence quotient and the level of the general organismic excitation has been observed in children with the intelligence quotient between 62 and 77, whereas the decreased one--at the intelligence quotient between 115 and 138. It seems that changes towards either the habituation or may a priori indicate the intelligence quotient value and vice versa. The correlation value has to be identified by further statistical-psychological analyses.

Adolescent↗

PubMed1995

Injury severity and probability of survival assessment in trauma patients using a predictive hierarchical network model derived from ICD-9 codes.

UNLABELLED: Accurate assessment of injury severity is critical for decision making related to the prevention, triage, and treatment of injured patients. Presently, the standard method of controlling for variations of injury severity between groups has been based upon the Injury Severity Score (ISS) and the Trauma Score and the Trauma and Injury Severity Score (TRISS) methodology. The purpose of this study was to attempt to build upon previous work using International Classification of Diseases, ninth revision (ICD-9) coded diagnosis, and procedure information available from standard hospital discharge abstracts (UB-82 Billing format) to create a hierarchical network to provide a tool for predicting injury severity and probability of survival. METHODS: Data were obtained for this analysis from the North Carolina Medical Database. Data were available on all trauma patients admitted to hospitals in North Carolina from January 1, 1988 until June 30, 1992. The dependent variable of interest was the patient's survival after injury, coded as live or die. The independent variables used in the study included the ISS derived using the technique described by MacKenzie Abbreviated Injury Score (AIS) and body system maximum AIS scores, mortality risk ratios derived from the ICD-9-DM primary, secondary, and tertiary diagnoses, primary and secondary procedures as described in previous work, age and gender. Network generation used a commercial software package, AIM (Abtech Corp., Charlottesville, Va.), which is a numeric modeling tool that automatically "learns" knowledge from a data base of examples. RESULTS: In the test data set an ISS and a prediction of survival based upon the derived network were calculated for each and every patient. The relative predictive power of these two scores were compared by calculating the overall accuracy, sensitivity, and specificity and the false positive and false negative rates. The receiver operator characteristic curves demonstrate that the network is a more effective tool in predicting the outcome of trauma patients. All the measures of predictive power show that the network was the better predictor of outcome than the ISS. CONCLUSIONS: Given the recognized limitations of the ISS, the widespread availability of the ICD-9 coded diagnoses and procedures, and the availability of many state and regional data bases that have no ISS or Trauma Score, the purpose of this study was to assess the ability of a network derived from limited but widely available hospital discharge data to predict the outcome of injured patients. The study confirms previous work showing that the ICD-9 codes were strongly associated with outcome. The study demonstrated that the network created from these data was a better predictor of outcome than the derived ISS. When the results of the network were compared with other published series, the network, created without access to physiologic information, was almost as accurate, sensitive, and specific as reported values for TRISS and A Severity Characterization of Trauma (ASCOT). Because the present study is the first of its type, further investigations are needed to validate these findings. If other studies corroborate this study, a network model based upon ICD-9 codes could become the principal method for grading injury severity. This would provide superior predictive power of injury severity with important cost savings and universal application.

Adult↗

PubMed2005

Learning statistical models for annotating proteins with function information using biomedical text.

BACKGROUND: The BioCreative text mining evaluation investigated the application of text mining methods to the task of automatically extracting information from text in biomedical research articles. We participated in Task 2 of the evaluation. For this task, we built a system to automatically annotate a given protein with codes from the Gene Ontology (GO) using the text of an article from the biomedical literature as evidence. METHODS: Our system relies on simple statistical analyses of the full text article provided. We learn n-gram models for each GO code using statistical methods and use these models to hypothesize annotations. We also learn a set of Naïve Bayes models that identify textual clues of possible connections between the given protein and a hypothesized annotation. These models are used to filter and rank the predictions of the n-gram models. RESULTS: We report experiments evaluating the utility of various components of our system on a set of data held out during development, and experiments evaluating the utility of external data sources that we used to learn our models. Finally, we report our evaluation results from the BioCreative organizers. CONCLUSION: We observe that, on the test data, our system performs quite well relative to the other systems submitted to the evaluation. From other experiments on the held-out data, we observe that (i) the Naïve Bayes models were effective in filtering and ranking the initially hypothesized annotations, and (ii) our learned models were significantly more accurate when external data sources were used during learning.

Bayes Theorem↗

PubMed2026

Multi-Omics Integration Identifies a Five-Gene Metabolic Signature With Experimental Validation in Clear Cell Renal Cell Carcinoma.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is hallmarked by profound metabolic reprogramming; however, its intricate crosstalk with the tumor immune microenvironment (TIME) and its clinical ramifications remain inadequately elucidated. This study aims to systematically decipher the metabolic-immune interplay in ccRCC through multi-omics integration, with the goal of identifying robust prognostic biomarkers and actionable therapeutic vulnerabilities. AIMS: This study aims to systematically decipher the metabolic-immune interplay in clear cell renal cell carcinoma (ccRCC) through multi‑omics integration, and to identify robust prognostic biomarkers and actionable therapeutic vulnerabilities that can inform precision risk stratification and individualized treatment strategies. METHODS: We integrated bulk transcriptomic, genomic, and clinical data from multiple ccRCC cohorts. Differential expression and functional enrichment analyses were performed to characterize metabolic pathway alterations. Mendelian randomization (MR) was employed to infer causal relationships between metabolic disorders and ccRCC risk. A machine learning-based prognostic framework, incorporating SHAP (SHapley Additive exPlanations) for feature interpretability, was constructed and rigorously validated. TIME heterogeneity was dissected using deconvolution algorithms, while drug sensitivity, tumor mutation burden (TMB), and TIDE scores were utilized to assess therapeutic responses and immune evasion. Candidate gene function was evaluated through in vitro gain- and loss-of-function assays, with expression validated via TCGA, HPA, western blot, and qRT-PCR. RESULTS: Enrichment analysis identified coordinated dysregulation in lipid metabolism, energy homeostasis, and hypoxia response pathways. MR analysis confirmed lipid metabolism disorders as a causal risk factor for ccRCC. Our machine-learning model, centered on five core SHAP-identified features (SUCLA2, ACAT1, PC, SUCLG1, and HMGCS2), demonstrated superior predictive accuracy over conventional clinical staging. Immune profiling unveiled dichotomous TIME states: the low-risk group retained active immune surveillance, whereas the high-risk group was enriched with immunosuppressive subsets. Drug sensitivity screening pinpointed LY2109761 and carmustine as high-risk-specific candidate agents. Furthermore, TMB and TIDE analyses stratified high-risk patients displaying genomic instability and immune evasion phenotypes. Functionally, SUCLA2 knockdown significantly enhanced ccRCC cell proliferation and invasion, while its overexpression suppressed these malignant phenotypes, corroborating its tumor-suppressive role. Expression patterns of the hub genes were consistently validated across multi-level datasets and experimental assays. CONCLUSION: This study establishes a precision oncology framework for ccRCC by functionally linking metabolic biomarkers, immunophenotypes, and stratified therapeutic strategies. Importantly, we identify SUCLA2 as a potential functional tumor suppressor and a promising target for further mechanistic and translational investigation.

Humans↗

PubMed2003

Patterning and predicting aquatic macroinvertebrate diversities using artificial neural network.

A counterpropagation neural network (CPN) was applied to predict species richness (SR) and Shannon diversity index (SH) of benthic macroinvertebrate communities using 34 environmental variables. The data were collected at 664 sites at 23 different water types such as springs, streams, rivers, canals, ditches, lakes, and pools in The Netherlands. By training the CPN, the sampling sites were classified into five groups and the classification was mainly related to pollution status and habitat type of the sampling sites. By visualizing environmental variables and diversity indices on the map of the trained model, the relationships between variables were evaluated. The trained CPN serves as a 'look-up table' for finding the corresponding values between environmental variables and community indices. The output of the model fitted SH and SR well showing a high accuracy of the prediction (r>0.90 and 0.67 for learning and testing process, respectively) for both SH and SR. Finally, the results of this study, which uses the capability of the CPN for patterning and predicting ecological data, suggest that the CPN can be effectively used as a tool for assessing ecological status and predicting water quality of target ecosystems.

Animals↗

PubMed1997

Optimization of walking in children.

Previous work demonstrated that adults naturally adopt a walking frequency to optimize physiological cost, symmetry, and stability. Furthermore, the optimal frequency is predictable using the force-driven harmonic oscillator (FDHO) model. However, no studies have established the developmental processes of optimization in children. Thus, the purposes of this study were to examine the predictability of the preferred stride frequency (PSF) and optimization features of 3- to 12-yr-old children using the FDHO model. Forty-five children and nine adults were measured for anthropometric data to calculate the predicted frequency. They later walked at three frequencies (PSF, PSF +25%, and PSF -25%) at a constant speed on a treadmill. The results indicated that the FDHO model was accurate in predicting the preferred frequency of children (prediction error < 0.07 s). We identified three stages of learning in the development of optimization: an early manifestation of sensitivity to resonant frequency, the subsequent development of ability to modulate walking frequency, and the final establishment of an adult optimization form at age seven. Our findings suggest that walking development may be determined by the dynamic cooperation of physiological, neural, and musculoskeletal systems with respect to the environmental context.

Adult↗

PubMed2026

Artificial Intelligence for Colorectal Surgeons-Part II: Research Applications, Challenges in Adoption, and Practical Resources.

BACKGROUND: This is part II of a 2-part series examining artificial intelligence in colorectal surgery. Part I established foundational concepts and clinical applications. Implementation, however, requires understanding research methodologies, available resources, and the specific challenges currently limiting widespread adoption. These topics are the focus of part II. OBJECTIVE: To examine artificial intelligence's transformation of surgical research, provide practical implementation resources, address adoption challenges, and explore future directions in colorectal surgery. METHODS: Comprehensive literature review focusing on artificial intelligence research methodology, implementation barriers, educational resources, and emerging technologies relevant to colorectal surgeons. RESULTS: Artificial intelligence streamlines clinical trial design through predictive modeling and natural language processing, reducing enrollment challenges that contribute to failed or inadequate trial accrual. Machine learning enables heterogeneity analysis within clinical trials, identifying treatment-responsive subgroups. Foundation models unlock analysis of unstructured electronic health record data at scale. Professional societies and universities offer specialized artificial intelligence education programs, with open-access data sets facilitating research participation. However, implementation faces multifaceted challenges: technical infrastructure demands, with real-time processing requiring dedicated graphics processing unit clusters; regulatory frameworks struggling with continuously evolving algorithms; undefined liability distribution for artificial intelligence-assisted decisions; algorithmic bias risking health care disparities; and the "black box" problem limiting clinical trust. Economic barriers include substantial initial costs without clear reimbursement pathways. Future directions include multimodal artificial intelligence integrating imaging, genomics, and histopathology; cognitive robotic systems with real-time decision support; digital twin technology for patient-specific surgical simulation; and global surgical artificial intelligence networks enabling distributed learning across institutions. CONCLUSIONS: Although artificial intelligence offers transformative potential for colorectal surgery research and practice, successful implementation requires addressing technical, regulatory, ethical, and economic challenges. The surgeon's evolving role demands both traditional expertise and computational fluency. Future advances in multimodal integration, autonomous systems, and global collaboration will fundamentally reshape surgical practice but will require thoughtful implementation prioritizing patient benefit and clinical value.

Humans↗

PubMed2006

Using a classic paper by Gottschalk and Mylle to teach the countercurrent model of urinary concentration.

Most undergraduates lack the scientific background to read and appreciate much of the primary literature in physiology. Even when the underlying concepts are elegantly simple, the inherent complexity of contemporary papers often makes the work inaccessible to them. However, with a little help, they can be guided to an understanding of the creative thought processes that underlie the research and to appreciate its significance. This is especially true of many classic papers in physiology that often rely on easily comprehensible techniques. Moreover, the American Physiological Society (APS) has invited prominent scientists to select important papers in their fields and to write essays that both put the work into historical context and explain why it is scientifically important. The APS Legacy Project makes these classic papers freely available online. One such paper by Gottschalk and Mylle presents data from a series of micropuncture studies that confirm all of the predictions of the countercurrent exchange model of concentrated urine production (2). The included handout of questions for discovery learning and teaching points suggest ways to use the paper as an instructional resource.

Animals↗

PubMedDate not supplied

The differential impact of academic self-regulatory methods on academic achievement among university students with and without learning disabilities.

Although research on academic self-regulation has proliferated in recent years, no studies have investigated the question of whether the perceived usefulness and the use of standard self-regulated learning strategies and compensation strategies provide a differential prediction of academic achievement for university students with and without learning disabilities (LD). We developed and tested a model explaining interrelationships among self-regulatory variables and grade point average (GPA) using structural equation modeling and multiple group analysis for students with LD (n = 53) and without LD (n = 421). Data were gathered using a new instrument, the Learning Strategies and Study Skills survey. The results of this study indicate that students with LD differed significantly from students without LD in the relationships between their motivation for and use of standard self-regulated learning strategies and compensation strategies, which in turn provided a differential explanation of academic achievement for students with and without LD. These paths of influence and idiosyncrasies of academic self-regulation among students with LD were interpreted in terms of social cognitive theory, metacognitive theory, and research conducted in the LD field.

Adaptation, Psychological↗

PubMed1995

Hierarchical neural networks for survival analysis.

Neural networks offer the potential of providing more accurate predictions of survival time than do traditional methods. Their use in medical applications has, however, been limited, especially when some data is censored or the frequency of events is low. To reduce the effect of these problems, we have developed a hierarchical architecture of neural networks that predicts survival in a stepwise manner. Predictions are made for the first time interval, then for the second, and so on. The system produces a survival estimate for patients at each interval, given relevant covariates, and is able to handle continuous and discrete variables, as well as censored data. We compared the hierarchical system of neural networks with a nonhierarchical system for a data set of 428 AIDS patients. The hierarchical model predicted survival more accurately than did the nonhierarchical (although both had low sensitivity). The hierarchical model could also learn the same patterns in less than half the time required by the nonhierarchical model. These results suggest that the use of hierarchical systems is advantageous when censored data is present, the number of events is small, and time-dependent variables are necessary.

Acquired Immunodeficiency Syndrome↗

PubMed2025

Machine learning on multiple epigenetic features reveals H3K27Ac as a driver of gene expression prediction across patients with glioblastoma.

Epigenetic mechanisms play a crucial role in driving transcript expression and shaping the phenotypic plasticity of glioblastoma stem cells (GSCs), contributing to tumor heterogeneity and therapeutic resistance. These mechanisms dynamically regulate the expression of key oncogenic and stemness-associated genes, enabling GSCs to adapt to environmental cues and evade targeted therapies. Importantly, epigenetic reprogramming allows GSCs to transition between cellular states, including therapy-resistant mesenchymal-like phenotypes, underscoring the need for epigenetic-targeting strategies to disrupt these adaptive processes. Understanding these epigenetic drivers of gene expression provides a foundation for novel therapeutic interventions aimed at eradicating GSCs and improving glioblastoma outcomes. Using machine learning (ML), we employ cross-patient prediction of transcript expression in GSCs by combining epigenetic features from various sources, including ATAC-seq, CTCF ChIP-seq, RNAPII ChIP-seq, H3K27Ac ChIP-seq, and RNA-seq. We investigate different ML and deep learning (DL) models for this task and ultimately build our final pipeline using XGBoost. The model trained on one patient generalizes to other 11 patients with high performance. Notably, H3K27Ac alone from a single patient is sufficient to predict gene expression in all 11 patients. Furthermore, the distribution of H3K27Ac peaks across the genomes of all patients is remarkably similar. These findings suggest that GSCs share a common distributional pattern of enhancer activity characterized by H3K27Ac, which can be utilized to predict gene expression in GSCs across patients. In summary, while GSCs are known for their transcriptomic and phenotypic heterogeneity, we propose that they share a common epigenetic pattern of enhancer activation that defines their underlying transcriptomic expression pattern. This pattern can predict gene expression across patient samples, providing valuable insights into the biology of GSCs.

Glioblastoma↗

PubMed2001

Inflammatory and hormonal measures predict neuropsychological functioning in systemic lupus erythematosus and rheumatoid arthritis patients.

Abnormalities of inflammatory and hormonal measures are common in SLE patients. Although cognitive dysfunction has been documented in SLE patients, the biological mechanism of these deficits has not been clarified. The goal of this study was to explore the relationship between inflammatory and hormonal activity and measures of learning, fluency, and attention in systemic lupus erythematosus patients without neuropsychiatric symptoms (non-CNS-SLE), patients with rheumatoid arthritis (RA), and healthy controls (HC). Fifteen non-CNS-SLE patients, 15 RA patients and 15 HC participants similar in age, education, and gender (female) were compared on tests of cognition, depression, and plasma levels of interleukin-6 (IL-6), dehydroepiandrosterone (DHEA), dehydroepiandrosterone sulfate (DHEA-S) and cortisol. Non-CNS-SLE patients demonstrated lower learning and poorer attention. Furthermore, non-CNS-SLE and RA patients had significantly lower levels of DHEA and DHEA-S than HC participants. Hierarchical regression analysis demonstrates that DHEA-S and IL-6 accounts for a unique portion of the variance in subject performance on measures of learning and attention after controlling for depression and corticosteroid treatment. This data highlights the value of hierarchical analyses with covariates, and provides evidence in humans of a relationship between peripheral cytokine levels and cognitive function.

Adult↗

PubMed2000

Muscarinic cholinergic neuromodulation reduces proactive interference between stored odor memories during associative learning in rats.

Previous electrophysiological studies and computational modeling suggest the hypothesis that cholinergic neuromodulation may reduce olfactory associative interference during learning (M. E. Hasselmo, B. P. Anderson, & J. M. Bower, 1992; M. E. Hasselmo & J. M. Bower, 1993). These results provide behavioral evidence supporting this hypothesis. A simultaneous discrimination task required learning a baseline odor pair (A+B-) and then, under the influence of scopolamine, a novel odor pair (A-C+) with an overlapping component (A) versus a novel odor pair (D+E-) with no overlapping component. As predicted by the model, rats that received scopolamine (0.50 and 0.25 mg/kg) were more impaired at acquiring overlapping than nonoverlapping odor pairs relative to their performance under normal saline or methylscopolamine. These results support the prediction that the physiological effects of acetylcholine can reduce interference between stored odor memories during associative learning.

Animals↗

PubMed2004

Cognitive navigation based on nonuniform Gabor space sampling, unsupervised growing networks, and reinforcement learning.

We study spatial learning and navigation for autonomous agents. A state space representation is constructed by unsupervised Hebbian learning during exploration. As a result of learning, a representation of the continuous two-dimensional (2-D) manifold in the high-dimensional input space is found. The representation consists of a population of localized overlapping place fields covering the 2-D space densely and uniformly. This space coding is comparable to the representation provided by hippocampal place cells in rats. Place fields are learned by extracting spatio-temporal properties of the environment from sensory inputs. The visual scene is modeled using the responses of modified Gabor filters placed at the nodes of a sparse Log-polar graph. Visual sensory aliasing is eliminated by taking into account self-motion signals via path integration. This solves the hidden state problem and provides a suitable representation for applying reinforcement learning in continuous space for action selection. A temporal-difference prediction scheme is used to learn sensorimotor mappings to perform goal-oriented navigation. Population vector coding is employed to interpret ensemble neural activity. The model is validated on a mobile Khepera miniature robot.

Cognition↗

PubMed2004

A theory of causal learning in children: causal maps and Bayes nets.

The authors outline a cognitive and computational account of causal learning in children. They propose that children use specialized cognitive systems that allow them to recover an accurate "causal map" of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or Bayes nets. Children's causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental results suggest that 2- to 4-year-old children construct new causal maps and that their learning is consistent with the Bayes net formalism.

Adult↗

PubMed1998

Quantization of human motions and learning of accurate movements.

This paper presents a mathematical model for the learning of accurate human arm movements. Its main features are that the movement is the superposition of smooth submovements, the intrinsic deviation of arm movements is considered, visual and kinesthetic feed-back are integrated in the motion control, and the movement duration and accuracy are optimized with practice. This model is consistent with the jerky arm movements of infants, and may explain how the adult motion behavior emerges from the infant behavior. Comparison with measurements of adult movements shows that the kinematics of accurate movements are well predicted by the model.

Adult↗