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PubMed1999

Computer models for masked hearing experiments with beluga whales (Delphinapterus leucas).

Environmental assessments of manmade noise and its effects on marine mammals need to address the question of how noise interferes with animal vocalizations. Seeking the answer with animal experiments is very time consuming, costly, and often infeasible. This article examines the possibility of estimating results with software models. A matched filter, spectrogram cross-correlation, critical band cross-correlation, and a back-propagation neural network detected a beluga vocalization in three types of ocean noise. Performance was compared to masked hearing experiments with a beluga whale [C. Erbe and D. M. Farmer, Deep-Sea Res. II 45, 1373-1388 (1998)]. The artificial neural network simulated the animal data most closely and raised confidence in its ability to predict the interference of a variety of noise source with a variety of vocalizations.

Animals↗
PubMed2026

Non-destructive prediction of lead content in oilseed rape leaves by fluorescence hyperspectral technology based on neural network.

Based on fluorescence hyperspectral imaging (FHSI), this study targeted rapid, non-destructive quantification of lead (Pb) content in oilseed rape leaves treated with varying silicon (Si) concentrations, acquiring fluorescence spectra over the 484.43-1001.61 nm wavelength range. To optimize spectral data quality, preprocessing methods (Savitzky-Golay smoothing, first derivative, detrending) were comprehensively compared. Characteristic wavelengths were then selected via interval variable iterative shrinkage, which effectively compressed data dimensionality and reduced computational load. A hybrid SE-CL1DA model, fusing a 1D convolutional neural network, a long short-term memory network and SE attention mechanism was constructed, with Bayesian optimization tuning hyperparameters to boost stability. The BO-SE-CL1DA outperformed both traditional machine learning and insufficiently optimized deep learning model (Rp2=0.9609, RMSE = 0.0377 mg/kg, RPD = 5.1736), thus enabling accurate Pb estimation, supporting Si-regulated heavy metal stress management and facilitating agricultural contamination monitoring.

Plant Leaves↗
PubMed2026

Deep learning-assisted, pathogenesis-informed lung histopathology scoring in preclinical mouse models of SARS-CoV-2 and influenza A infection.

INTRODUCTION: SARS-CoV-2 and influenza A virus (IAV) cause viral pneumonia, yet their lung lesions evolve with distinct spatial organization and resolution-phase architecture. In preclinical murine studies, H&E histopathology is a primary endpoint, but burden-focused semiquantitative scoring can miss pathogen- and phase-specific differences in lesion topology, compartmental involvement, inflammatory organization, and repair. We aimed to define virus- and phase-specific morphologic signatures and translate them into a practical, pathogenesis-informed scoring guide, supported by whole-slide convolutional neural network (CNN) analysis with class activation mapping (CAM). METHODS: Mice were infected under standardized conditions and evaluated during the early, peak-injury, and late phases of infection, corresponding to 2~3, 5~8, and 14 days post-infection (dpi), respectively. Lungs were assessed by H&E with semiquantitative scoring and by immunostaining to map viral antigen distribution and epithelial tropism. Whole-slide CNN models were trained for virus- and phase-specific classification, and CAM localized discriminative regions. RESULTS: Dose titration established reproducible lethal and sublethal infection conditions for both viruses. Viral antigen kinetics diverged, with SARS-CoV-2 peaking early and declining toward clearance by the resolution phase, whereas IAV peaked later and declined by the resolution phase, paralleling distinct injury-repair trajectories. CNN/CAM analysis distinguished virus- and phase-specific histologic patterns across the early, peak-injury, and resolution phases of infection and highlighted spatial signatures consistent with expert review. At the peak-injury phase, SARS-CoV-2 lungs showed broad alveolar/interstitial involvement, whereas IAV exhibited bronchocentric inflammatory organization. During the resolution phase, IAV showed prominent epithelial regeneration with remodeling-forward architecture, while SARS-CoV-2 more often retained localized residual inflammatory foci. Across both infections, tissue inflammatory composition shifted over time, with higher neutrophil representation during the peak-injury phase and a relative increase in lymphocytic representation during the resolution phase. Integrating lesion topology/distribution, edema, epithelial injury-regeneration, remodeling features, and lymphocyte predominance, we proposed a pathogen-resolved, phase-informed histopathology scoring guide with recommended evaluation windows for each model. CONCLUSION: Together, these findings define virus- and phase-specific morphologic programs that inform respiratory virus pathogenesis in mice and can be translated into practical scoring criteria for preclinical respiratory virus studies.

Animals↗
PubMed2005

Prediction of burn healing time using artificial neural networks and reflectance spectrometer.

BACKGROUND: Burn depth assessment is important as early excision and grafting is the treatment of choice for deep dermal burn. Inaccurate assessment causes prolonged hospital stay, increased medical expenses and morbidity. Based on reflected burn spectra, we have developed an artificial neural network to predict the burn healing time. PURPOSE: Our study is to develop a non-invasive objective method to predict burn-healing time. METHODS AND MATERIALS: Burns less than 20% TBSA was included. Burn spectra taken on the third postburn day using reflectance spectrometer were analyzed by an artificial neural network system. RESULTS: Forty-one spectra were collected. With the newly developed method, the predictive accuracy of burns healed in less than 14 days was 96%, and that in more than 14 days was 75%. CONCLUSIONS: Using reflectance spectrometer, we have developed an artificial neural network to determine the burn healing time with 86% overall predictive accuracy.

Adolescent↗
PubMed1996

Multiagent reinforcement learning in the Iterated Prisoner's Dilemma.

Reinforcement learning (RL) is based on the idea that the tendency to produce an action should be strengthened (reinforced) if it produces favorable results, and weakened if it produces unfavorable results. Q-learning is a recent RL algorithm that does not need a model of its environment and can be used on-line. Therefore, it is well suited for use in repeated games against an unknown opponent. Most RL research has been confined to single-agent settings or to multiagent settings where the agents have totally positively correlated payoffs (team problems) or totally negatively correlated payoffs (zero-sum games). This paper is an empirical study of reinforcement learning in the Iterated Prisoner's Dilemma (IPD), where the agents' payoffs are neither totally positively nor totally negatively correlated. RL is considerably more difficult in such a domain. This paper investigates the ability of a variety of Q-learning agents to play the IPD game against an unknown opponent. In some experiments, the opponent is the fixed strategy Tit-For-Tat, while in others it is another Q-learner. All the Q-learners learned to play optimally against Tit-For-Tat. Playing against another learner was more difficult because the adaptation of the other learner created a non-stationary environment, and because the other learner was not endowed with any a priori knowledge about the IPD game such as a policy designed to encourage cooperation. The learners that were studied varied along three dimensions: the length of history they received as context, the type of memory they employed (lookup tables based on restricted history windows or recurrent neural networks that can theoretically store features from arbitrarily deep in the past), and the exploration schedule they followed. Although all the learners faced difficulties when playing against other learners, agents with longer history windows, lookup table memories, and longer exploration schedules fared best in the IPD games.

Algorithms↗
PubMed2026

Artificial intelligence for anticancer drug discovery from natural products of macroalgae and sponges: A systematic review.

Marine natural products (MNPs) from macroalgae and marine sponges have inspired clinically important anticancer agents, including the cytarabine pharmacophore and the eribulin scaffold, while cyanobacterial dolastatin chemistry supplies the auristatin payloads of several marine-inspired antibody-drug conjugates (ADCs) such as brentuximab vedotin. Artificial intelligence (AI) methods, encompassing both classical machine learning (ML) with hand-engineered features and modern deep learning (DL) with many-layered neural networks, are increasingly supporting key decisions in natural-product anticancer drug discovery, including bioactivity prediction, target identification, absorption, distribution, metabolism, excretion and toxicity (ADMET) filtering, generative analogue design, and the selection of preclinical candidates. DL architectures relevant to this field include graph neural networks, transformer-based molecular generators, diffusion models for protein-ligand docking, and convolutional networks for mass spectrometry, while classical ML contributes interpretable fingerprint-based bioactivity models and molecular networking for dereplication. This review follows a systematic literature review methodology to organize the landscape of AI methods now applied to MNP anticancer discovery, distinguishing ML and DL approaches where relevant, situating them within the chemical context of macroalgal and sponge-derived oncology leads, and critically examining published case studies, including validation level (computational, in vitro, in vivo, clinical). The principal bottleneck for medical translation has shifted partly from algorithmic capability toward data infrastructure and experimental validation. Sparse, heterogeneous, and taxonomically biased bioactivity records limit what current models can learn and reduce the reliability of AI-prioritized candidates entering the preclinical pipeline. A roadmap is proposed that prioritizes open MNP-specific benchmarks, symbiont-aware modeling, and active learning loops with synthesizability and ADMET constraints. These AI workflows may accelerate the prioritization of marine-derived anticancer leads and support earlier, more evidence-based translational decisions in oncology drug development.

Biological Products↗
PubMed1999

Common model of evolution for living cell and central nervous system.

Both living cell and central nervous system can be treated as objects similar to artificial neural networks, actively studied now. One can see deep analogies between evolutionary processes in these systems, and correspondences can be established between some phenomena and objects. These are: genome vs. memory, gene vs. symbol, cell type vs. image, mitosis vs. sleep, organism vs. perception state, species vs. language, fertilization vs. attention, meiosis vs. paradoxal sleep. There is reason to study these correspondences on more technical level being based upon network nature of the living cell and nervous system.

Biological Evolution↗
PubMed2005

Common mechanisms of nerve and blood vessel wiring.

Blood vessels and nerve fibres course throughout the body in an orderly pattern, often alongside one another. Although superficially distinct, the mechanisms involved in wiring neural and vascular networks seem to share some deep similarities. The discovery of key axon guidance molecules over the past decade has shown that axons are guided to their targets by finely tuned codes of attractive and repulsive cues, and recent studies reveal that these cues also help blood vessels to navigate to their targets. Parallels have also emerged between the actions of growth factors that direct angiogenic sprouting and those that regulate axon terminal arborization.

Animals↗
PubMed2026

[Applications and Challenges of Deep Learning in Human Genome Research].

In recent years, the advent of high-throughput omics technologies has fueled an explosive growth in human genomic data. Uncovering the latent functions within this vast data has become a significant challenge in functional genomics research. While traditional statistical methods have proved successful for analyzing smaller-scale datasets in the past, they exhibit clear limitations in analytical efficiency and integrating multi-dimensional data, struggling to meet the escalating demands of contemporary genomic analysis. The introduction of deep learning (DL) technologies offers a novel paradigm for this field. This review systematically examines the advances in applying deep learning to human genomics research. Studies demonstrate that when ample labeled data is available, discriminative DL computational methods-such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs)-achieve high accuracy and efficiency in genomic variant discovery tasks. Furthermore, generative DL methods, particularly Large Language Models (LLMs) leveraging self-supervised pre-training strategies, effectively integrate complex genomic information and exhibit superior performance in functional genomic sequence annotation and gene regulation studies. This review also explores the application of LLMs in multi-omics data integration and prediction. Looking ahead, the continued accumulation of long-read sequencing and high-dimensional data is expected to enable DL technologies to integrate increasingly complex and heterogeneous genomic information, playing an increasingly crucial role in human genomics research.

Deep Learning↗
PubMed2025

DiCARN-DNase: enhancing cell-to-cell Hi-C resolution using dilated cascading ResNet with self-attention and DNase-seq chromatin accessibility data.

MOTIVATION: The spatial organization of chromatin is fundamental to gene regulation and essential for proper cellular function. The Hi-C technique remains the leading method for unraveling 3D genome structures, but the limited availability of high-resolution (HR) Hi-C data poses significant challenges for comprehensive analysis. Deep learning models have been developed to predict HR Hi-C data from low-resolution counterparts. Early Convolutional Neural Network (CNN)-based models improved resolution but struggled with issues like blurring and capturing fine details. In contrast, Generative Adversarial Network (GAN)-based methods encountered difficulties in maintaining diversity and generalization. Additionally, most existing algorithms perform poorly in cross-cell line generalization, where a model trained on one cell type is used to enhance HR data in another cell type. RESULTS: In this work, we propose Dilated Cascading Residual Network (DiCARN) to overcome these challenges and improve Hi-C data resolution. DiCARN leverages dilated convolutions and cascading residuals to capture a broader context while preserving fine-grained genomic interactions. Additionally, we incorporate DNase-seq data into our model, providing a robust framework that demonstrates superior generalizability across cell lines in HR Hi-C data reconstruction. AVAILABILITY AND IMPLEMENTATION: DiCARN is publicly available at https://github.com/OluwadareLab/DiCARN.

Chromatin↗
PubMed2003

CAD tool for burn diagnosis.

In this paper a new system for burn diagnosis is proposed. The aim of the system is to separate burn wounds from healthy skin, and the different types of burns (burn depths) from each other, identifying each one. The system is based on the colour and texture information, as these are the characteristics observed by physicians in order to give a diagnosis. We use a perceptually uniform colour space (L*u*v*), since Euclidean distances calculated in this space correspond to perceptually colour differences. After the burn is segmented, some colour and texture descriptors are calculated and they are the inputs to a Fuzzy-ARTMAP neural network. The neural network classifies them into three types of bums: superficial dermal, deep dermal and full thickness. Clinical effectiveness of the method was demonstrated on 62 clinical burn wound images obtained from digital colour photographs, yielding an average classification success rate of 82% compared to expert classified images.

Algorithms↗
PubMed2026

The Role of Artificial Intelligence Combined With Digital Cholangioscopy for Indeterminant and Malignant Biliary Strictures: A Systematic Review and Meta-analysis.

BACKGROUND: Current endoscopic retrograde cholangiopancreatography (ERCP) and cholangioscopic-based diagnostic sampling for indeterminant biliary strictures remain suboptimal. Artificial intelligence (AI)-based algorithms by means of computer vision in machine learning have been applied to cholangioscopy in an effort to improve diagnostic yield. The aim of this study was to perform a systematic review and meta-analysis to evaluate the diagnostic performance of AI-based diagnostic performance of AI-associated cholangioscopic diagnosis of indeterminant or malignant biliary strictures. METHODS: Individualized searches were developed in accordance with PRISMA and MOOSE guidelines, and meta-analysis according to Cochrane Diagnostic Test Accuracy working group methodology. A bivariate model was used to compute pooled sensitivity and specificity, likelihood ratio, diagnostic odds ratio, and summary receiver operating characteristics curve (SROC). RESULTS: Five studies (n=675 lesions; a total of 2,685,674 cholangioscopic images) were included. All but one study analyzed a deep learning AI-based system using a convoluted neural network (CNN) with an average image processing speed of 30 to 60 frames per second. The pooled sensitivity and specificity were 95% (95% CI: 85-98) and 88% (95% CI: 76-94), with a diagnostic accuracy (SROC) of 97% (95% CI: 95-98). Sensitivity analysis of CNN studies (4 studies, 538 patients) demonstrated a pooled sensitivity, specificity, and accuracy (SROC) of 95% (95% CI: 82-99), 88% (95% CI: 72-95), and 97% (95% CI: 95-98), respectively. CONCLUSIONS: Artificial intelligence-based machine learning of cholangioscopy images appears to be a promising modality for the diagnosis of indeterminant and malignant biliary strictures.

Humans↗
PubMed2006

Neural networks in frontotemporal dementia--a meta-analysis.

OBJECTIVE: Frontotemporal dementia is the most common form of frontotemporal lobar degeneration. It is characterized by deep alterations in behavior and personality. We conducted a systematic and quantitative meta-analysis to examine its neural correlates and place the disease in a framework of cognitive neuropsychiatry. METHODS: MedLine and Current Contents search engines were used to identify imaging studies investigating frontotemporal dementia between 1980 and 2005. Nine studies were identified reporting either atrophy or decreases in glucose utilization. Finally, the analysis involved 132 patients and 166 controls. A quantitative meta-analysis was performed. Maxima of the studies resulted in activation likelihood estimates. RESULTS: The meta-analysis revealed a particularly frontomedian network impaired in frontotemporal dementia. Additionally, right anterior insula, and medial thalamus were identified. CONCLUSIONS: Our study specifies frontotemporal dementia as the frontomedian variant of frontotemporal lobar degeneration. The disease affects neural networks enabling self-monitoring, theory of mind capabilities, processing/evaluation of internal mental states, perception of pain and emotions, and sustaining personality and self. Our study contributes to placing frontotemporal dementia in cognitive neuropsychiatry.

Aged↗
PubMed1995

Metabolic and functional mapping of the neural network subserving inferior collicular seizure generalization.

The sensory-motor portion of the inferior collicular cortex is capable of seizure genesis that is characterized initially by coincident wild running behaviors and localized electrographic afterdischarge. With repeated stimulations, this seizure activity spreads into the forebrain, producing generalized tonic-clonic or myoclonic seizure activity. In order to characterize the neural network subserving this caudal-rostral seizure generalization, three mapping techniques were used: 2-deoxyglucose (2-DG) utilization, c-fos expression and local anesthetic microinjection. Kindled seizure generalization from the inferior collicular cortex produced a global increase in 2-DG accumulation, while relative 2-DG increases were found in the inferior collicular cortex, dorsal lateral lemniscus, dorsal central gray, peripeduncular nucleus, medial geniculate nucleus, substantia nigra, entopeduncular nucleus, ventroposterior and centromedian thalamus and tenia tectum, as well as the perirhinal, somatosensory and frontal cortices. Kindled seizure generalization also increased c-fos-like immunoreactivity (FLI) in the inferior collicular cortex, cuneiform nucleus, dorsal lateral nucleus of the lateral lemniscus, peripeduncular nucleus, caudal central gray, dentate gyrus of the hippocampus, rhinal fissure area of the perirhinal cortex and the frontal cortex. Microinjections of procaine into the amygdala, perirhinal cortex, entopeduncular nucleus, substantia nigra, peripeduncular nucleus, dorsal central gray, and pontine reticular nucleus all prevented generalized seizure behaviors, but had no effect on the wild running seizures. Conversely, procaine microinjection into the area of the cuneiform nucleus/pedunculopontine tegmental nucleus prevented the wild running seizure but did not block the generalized seizure activity. Neither wild running, nor generalized seizures were altered following procaine microinjections into the anterior thalamus, sub-thalamus, lateral hypothalamus, hippocampus or deep superior colliculus. Thus, specific forebrain sites form a widespread neural network that mediates the generalization of seizure activity from the inferior collicular cortex into the forebrain.

Animals↗
PubMed1996

Effect of preoperative donation of autologous blood on deep-vein thrombosis following total joint arthroplasty of the hip or knee.

The effect of preoperative donation of autologous blood on postoperative deep-vein thrombosis was retrospectively studied in men who had been managed consecutively with elective total joint replacement of the hip or knee because of osteoarthrosis. The patients had, on the average, two of nine considered risk factors for deep-vein thrombosis. Two hundred and thirty-seven patients were evaluated postoperatively with ascending venography, and they form the basis of this study. Fifty-four patients had venographic evidence of deep-vein thrombosis of the lower extremity, with most having asymptomatic clots distal to the knee. The prevalence of deep-vein thrombosis was nineteen (16 per cent) of 116 after total hip arthroplasty, compared with thirty-five (29 per cent) of 121 after total knee arthroplasty (chi square=4.6, p=0.03). Deep-vein thrombosis developed in twenty-eight (17 per cent) of the 161 patients who had donated blood preoperatively, compared with twenty-six (34 per cent) of the seventy-six patients who had not donated blood preoperatively (chi square=7.7, p=0.006). Through logistic regression analysis, the donation of autologous blood was shown to reduce significantly the development of postoperative deep-vein thrombosis for patients managed with total knee arthroplasty (p<0.01) but not for patients managed with total hip arthroplasty. Additional neural network analysis showed the donation of autologous blood to be the most important prognostic factor in predicting the absence of postoperative deep-vein thrombosis. In addition to diminishing the need for transfusion of homologous blood after total joint arthroplasty, preoperative donation of autologous blood appears to protect against postoperative deep-vein thrombosis after total knee arthroplasty.

Adult↗
PubMed2026

PEARL: integrative multi-omics classification and omics feature discovery via deep graph learning.

MOTIVATION: Integrating multi-omics data provides valuable insights into biological processes by capturing information across multiple molecular layers, enabling a comprehensive understanding of complex diseases and driving advancements in precision medicine. However, existing computational methods for multi-omics integration face significant challenges, such as low reliability and poor generalizability, due to the high dimensionality and low sample size nature of omics data. RESULTS: To address these challenges, we present PEARL (Pearson-Enhanced spectrAl gRaph convoLutional networks), a novel deep graph learning method for biomedical classification and functional important omics features identification. PEARL leverages a simple yet effective learning architecture to achieve superior and robust performance in high-dimensional, low-sample-size multi-omics settings. Our results demonstrate that PEARL significantly outperforms existing state-of-the-art methods on both synthetic and real biomedical datasets. Furthermore, applied to Alzheimer's disease (AD) brain multi-omics data, features prioritized by PEARL lead to functionally important genes that demonstrate significant enrichment in AD-related pathways. These findings highlight PEARL's practical utility in biomedical research and its potential to enhance biological interpretability in multi-omics studies. AVAILABILITY AND IMPLEMENTATION: The source code of our computational framework is available at https://github.com/zqq121017/PEARL.

Multiomics↗
PubMedDate not supplied

Segmentation and classification of burn images by color and texture information.

In this paper, a burn color image segmentation and classification system is proposed. The aim of the system is to separate burn wounds from healthy skin, and to distinguish among the different types of burns (burn depths). Digital color photographs are used as inputs to the system. The system is based on color and texture information, since these are the characteristics observed by physicians in order to form a diagnosis. A perceptually uniform color space (L*u*v*) was used, since Euclidean distances calculated in this space correspond to perceptual color differences. After the burn is segmented, a set of color and texture features is calculated that serves as the input to a Fuzzy-ARTMAP neural network. The neural network classifies burns into three types of burn depths: superficial dermal, deep dermal, and full thickness. Clinical effectiveness of the method was demonstrated on 62 clinical burn wound images, yielding an average classification success rate of 82%.

Algorithms↗
PubMed2026

Systematic review of machine learning approaches for predicting sickle cell crisis and mortality risk at the climate-health nexus.

BACKGROUND: Sickle cell anemia (SCA) is a severe genetic blood disorder characterized by recurrent vaso-occlusive crises and increased mortality, with the greatest burden occurring in low- and middle-income countries. Climatic and environmental conditions, including temperature variability, humidity, rainfall, air pollution, and seasonal changes, have been associated with disease exacerbation. However, the extent to which these factors have been incorporated into predictive models remains unclear. This study systematically reviews the application of machine learning (ML) models for predicting SCA crises and mortality in relation to climate and environmental factors. METHODOLOGY: The PRISMA guidelines were used, and 34 peer-reviewed studies published between 2005 and 2026 were analyzed to identify the climate variables, ML approaches employed, and predictive performance. The reviewed studies applied a range of ML techniques, including artificial neural networks, random forests, support vector machines, decision trees, logistic regression, and deep learning models. Temperature, humidity, rainfall, wind speed, air quality indicators, and seasonal patterns were the most frequently examined environmental variables. RESULTS: The findings indicate that most existing models rely predominantly on clinical and demographic data, with limited integration of climate information and inadequate representation of high-burden regions, especially Sub-Saharan Africa. Studies incorporating environmental variables reported improved predictive performance and highlighted the potential of climate-informed early warning systems for SCA management. CONCLUSION: The review recommends development of interdisciplinary, climate-aware ML frameworks, expansion of longitudinal environmental datasets, and increased research in underrepresented regions to support climate-resilient and patient-centered SCA care.

Humans↗