搜索结果：cybernetics

Advances in synthetic biology and tissue engineering have enabled the design and assembly of neural constructs from first principles, renewing interest in cybernetics for understanding control, adaptation, and intelligence. To address shortcomings of artificial intelligence, researchers increasingly draw on the learning, robustness, and energy efficiency of living cognitive systems. Synthetic biological intelligences (SBIs) are beginning to leverage embodied biological computation, while cybernetics provides substrate-agnostic principles to guide minimal cognition research.

Built upon the core tenets of Cybernetic Big Five Theory, the present research explores how Autistic Traits operate dynamically within a unified network encompassing personality meta-traits (Plasticity and Stability) and multiple facets of prospective cognition. Based on three-wave longitudinal data of 570 non-clinical young adults, temporal network analysis was implemented to model cross-domain temporal interplay. Results revealed an interconnected system: Social Camouflaging negatively predicted subsequent Social Autistic Traits and formed mutually reinforcing bidirectional pathways with Future Imagination. Far-Target Orientation buffered Cognitive Rigidity but induced a notable "paradox of planning", as it concurrently suppressed Future Action Agency and Purpose Consciousness. Future Action Agency exerted positive prospective effects on later Purpose Consciousness, and Future Imagination maintained reciprocal positive temporal associations with Purpose Consciousness. Stability exhibited dual concurrent outcomes: it boosted Future Imagination while simultaneously increasing Rigidity. These findings provide the first within-person evidence that Autistic Traits develop dynamically through interactive processes across personality and future cognition. Practically, supportive strategies should strengthen Future Action Agency and Purpose Consciousness, redirect Future Imagination toward functional planning, and apply Far-Target Orientation to alleviate Rigidity while safeguarding present agency.

Ramkrishna, Kompala, and Tsao proposed the cybernetic model of microbial growth, in which cells allocate enzyme synthesis resources according to a matching rule that mimics rational decision-making. The matching rule was later shown to be optimal under general assumptions about the underlying return-on-investment structure, yet the specific objective the cell maximizes and the constraints bounding that choice were never written down as an explicit economic decision. Here we supply that missing decision, recasting cybernetic enzyme-synthesis control as a consumer choice problem from microeconomic theory: the cell allocates a limited proteome budget among competing catabolic enzymes as a linear program (LP), maximizing a linear growth utility subject to a linear proteome budget constraint. Because the utility is linear, the LP's solution is geometric: whenever the iso-utility line's slope differs from the budget constraint's, the optimum is a corner, and the entire proteome budget is allocated to the enzyme for the single most profitable substrate. Corner solutions correspond to diauxic growth, and sequential substrate consumption follows from the choice of corner rather than a distinct regulatory mechanism. Only when the two slopes coincide does the optimum spread across the entire budget line instead of concentrating at a single corner; this degenerate case is where simultaneous substrate use becomes admissible. Using kinetic parameters from single-substrate experiments together with a small set of model-level parameters set in this study, the LP-derived cybernetic variables reproduced the diauxic and triauxic batch growth of Klebsiella oxytoca on glucose-xylose and glucose-xylose-lactose mixtures, achieving a fit comparable to the classical matching law. Thus, sequential substrate use is the generic outcome of growth-maximizing specialization under perfect substitutability, and co-utilization is the degenerate case of equal profitability.

Habituation is a simple form of nonassociative learning that is characterized by a decrease in response to a repetitive stimulus. As escape responses can be energetically costly and disruptive to normal behavior, it is important that prospective prey learn whether a perceived stimulus is a genuine threat or an innocuous stimulus that they can ignore. In response to a visual looming stimulus, larval zebrafish perform a characteristic escape swim that reliably habituates, and because they are small and transparent, they have been an important model for characterizing brain-wide activity patterns during habituation. In this study, we explore the spatial properties of visual adaptation to gauge whether it is mediated by local, regional, or brain-wide circuits. We present repetitive visual loom stimuli either in a fixed position in visual space or in variable positions, while also performing brain-wide calcium imaging. Across the brain, we identify both neural responses that are specific to looms at particular positions within the visual field and responses that occur regardless of where the loom is presented. By quantifying the degree of adaptation across these responses, we show that brain-wide adaptation occurs more rapidly when the position of the loom remains unchanged and that alternate looms occurring in different parts of the visual field minimally contribute to adaptation for looms at the original position. We found that the tectum, homologous to the superior colliculus, has response profiles and spatial sensitivity indicative of important contributions to this position-specific visual adaptation.

To detect postoperative changes in the position of the mandibular condyles and their impact on the signs and symptoms of temporomandibular disorders (TMD) in patients with Angle class II malocclusion after mandibular advancement surgery. Twenty-three patients were selected for mandibular advancement by bilateral sagittal split ramus osteotomy (BSSO) and osteosynthesis with interfragmentary screws. Computed tomography (CT) scans of the skull and clinical evaluation were performed before surgery (T1), 14 days (T2), and 6 months (T3) after surgery. Temporomandibular joint (TMJ) pain and pathologic sounds were clinically registered, and the mandibular body and condylar positions were determined by 3D cephalometric analysis of the CT scans. Statistical results showed a mean mandibular advancement of 3.5 mm, which remained stable 6 months after surgery. Surgery resulted in significant changes in the condylar pitch and position on the X -axis ( P =0.002) and Y -axis ( P =0.002). There was a statistically significant probability of TMJ pain at T2 when the condyle was dislocated laterally along the X -axis ( P =0.007), when the condyle roll changed in the medial direction ( P =0.023), and when the condyle pitch decreased ( P =0.012). However, the overall visual analog scale (VAS) score for TMJ pain and pathologic sounds did not exhibit statistically significant changes over time. Mandibular advancement surgery using BSSO and osteosynthesis with interfragmentary screws showed stable results after 6 months and did not change the overall course of TMJ signs and symptoms, although there were small but statistically significant changes in the condylar position.

Chemical exchange saturation transfer (CEST) MRI provides insight into tissue metabolism by detecting low-concentration endogenous molecules. While studies at 7 Tesla (7T) have shown enhanced sensitivity and spectral separation, 3 Tesla (3T) remains the clinical standard, and the relative performance of these field strengths in a direct clinical head-to-head comparison remains unclear. This prospective cohort study provides a direct within-subject comparison of multi-pool CEST imaging at 3T and 7T, using age-related tissue changes and glioma molecular subtypes as representative applications of physiological and pathological CEST sensitivity. Forty-three patients (ages 18-76; 18 female) underwent 3T and 7T CEST MRI prior to surgery due to suspected brain tumour; following quality control, 36 datasets were included at 3T and 32 at 7T. CEST amplitudes from amide, amine, aliphatic relayed nuclear Overhauser effect (rNOE) and magnetization transfer pools were quantified in white matter, grey matter, deep grey matter and tumour tissue. A physics-informed conditional autoencoder (PICAE) was applied at 7T to correct B1 inhomogeneity. Age effects were tested using linear regression; tumour subtype differences were tested using the Wilcoxon rank-sum test. The significance level was set to α = 0.05, and the Holm-Bonferroni procedure was applied to correct for multiple testing. At 7T, amide and rNOE showed robust negative correlations with age in grey matter and deep grey matter, supporting the potential of CEST as an ageing biomarker. Age dependence at 3T was weaker, limited to rNOE (grey matter and deep grey matter) and magnetization transfer (white matter and deep grey matter). In contrast, tumour CEST metrics showed no significant age dependence at either field strength. Trends in relative amide contrast were consistent with prior findings but did not reach statistical significance. Sensitivity to tumour molecular subtype was similar across field strengths. Variability analyses showed that conventional 7T processing introduced higher technical variability than 3T, whereas PICAE substantially reduced variability and improved data quality at 7T. In conclusion, 7T CEST MRI demonstrates higher potential as a non-invasive marker of brain ageing, whereas our simplified pipeline did not yield additional information for tumour subtyping at either field strength. These findings underscore both the enhanced sensitivity and the higher technical demands of 7T, and highlight the importance of advanced correction strategies such as PICAE for robust use of single-transmit 7T CEST.

Two complementary editorials are presented by two IJT editors. The first editorial reflects upon aspects of the relationship between Artificial Intelligence (AI) and telerehabilitation and points out that current AI terminology, in which AI is employed as "an umbrella term" can be imprecise and confusing. The second editorial observes that telerehabilitation is now entering a new stage of development in which digital health, computer science, artificial intelligence, and rehabilitation medicine are becoming increasingly integrated. The future use of Human Digital Twins as dynamic, patient-centered digital representations will enable individualized assessment, rehabilitation planning, monitoring of patient trajectories, and decision support across distributed care environments.

Contamination with herbicides is a frequent environmental issue. The aim of the study was to evaluate haematological and blood biochemical parameters, and the microstructure of selected organs in control and herbicide-exposed fish. In this study, 108 common carp (Cyprinus carpio) were exposed to a commercial herbicide with nicosulfuron as its active substance. Three groups of fish were established: control, TAM1 (nicosulfuron at 1 mg/L), and TAM2 (nicosulfuron at 5 mg/L). Treatment lasted 1, 3 or 10 days. Functional (pathophysiological) and structural (histopathological) alterations in control and herbicide-exposed fish were evaluated. Various haematological changes and some biochemical alterations were detected. The pathophysiological changes suggested erythrocyte damage, compensatory response (as indicated by an increase in red blood cell count and erythroblast percentage), stress response (as evidenced by an increase in glucose concentration) and possible lipid metabolism disorders (as indicated by a decrease in cholesterol concentration). More differences in pathophysiological parameters were detected between TAM2 fish and control fish than between TAM1 fish and control fish. Histopathological analysis revealed renal tubule necrosis, of which the severity depended on herbicide concentration and exposure duration. The exposure of common carp to the tested nicosulfuron-based herbicide formulation led to pathophysiological and histopathological alterations, indicating the need for systematic detection and quantification of nicosulfuron in aquatic environments.

Respiratory diseases are a major global health challenge. However, identification of respiratory diseases is often limited by subjectivity, environmental noise and inter-clinician variability. This study presents an explainable multimodal deep learning framework for recording-level multiclass classification of respiratory audio signals. The proposed system integrates two complementary representations-a spectro-temporal encoder based on a CNN-BiLSTM-attention architecture and a handcrafted acoustic-feature encoder capturing acoustic descriptors commonly used in respiratory-audio analysis, including MFCCs, zero-crossing rate, spectral centroid, spectral bandwidth, chroma, RMS energy, and spectral rolloff features. These branches are combined through late-stage fusion to leverage both data-driven representation learning and domain-informed acoustic cues. The proposed model was trained and internally evaluated on the Asthma Detection Dataset Version 2, comprising five respiratory categories: bronchial disease, asthma, COPD, healthy, and pneumonia. Mono conversion, resampling to 16 kHz, 100-2000 Hz band-pass filtering, amplitude normalisation, fixed 4 s trimming or zero-padding, training-only augmentation, handcrafted-feature extraction, mel-spectrogram generation, quality control auditing, and stratified recording-level partitioning have been applied in the pre-processing steps. Across five repeated experiments with different random seeds, the proposed hybrid model achieved a mean held-out recording-level test accuracy of 0.9099±0.0163, balanced accuracy of 0.8936±0.0152, macro F1-score of 0.8937±0.0177, macro ROC-AUC of 0.9867±0.0010, and macro PR-AUC of 0.9489±0.0044. Conventional machine learning baseline comparisons showed that the proposed model achieved stronger internal accuracy, balanced accuracy, macro recall, macro F1-score, and macro ROC-AUC than classical machine learning algorithms trained on handcrafted acoustic features, although Random Forest remained competitive in macro PR-AUC. Ablation analysis shows that the deep spectro-temporal branch was the primary contributor to predictive performance, while the handcrafted branch provided complementary interpretable acoustic information rather than consistently improving all classification metrics. Explainability was incorporated using Grad-CAM and Integrated Gradients for spectrogram-based interpretation and SHAP for handcrafted-feature attribution. Domain-shift evaluation on the ICBHI Respiratory Sound Database and a COPD-focused cohort revealed substantial dataset shift effects, including poor healthy-case recognition on ICBHI and seed-dependent COPD recognition in the COPD-focused cohort. Identifier-aware sensitivity analyses showed lower performance than the main recording-level split, suggesting that subject-like or source-level overlap may inflate internal performance estimates. The findings should be interpreted as promising internal held-out recording-level algorithmic performance with limited external transfer, rather than evidence of readiness for clinical use.

Electricity markets depend on centralized clearing mechanisms that require participants to trust that submitted bids are preserved and accurately incorporated into the market-clearing process. Current blockchain-based energy market solutions either decentralize the auction mechanism or utilize the blockchain solely as a transaction log, lacking verifiable assurances that off-chain clearing employs the complete and unaltered set of submitted bids. This work introduces a hybrid blockchain-based governance architecture that enables verifiable bid integrity for centralized electricity market clearing while maintaining conventional off-chain clearing procedures. The architecture records cryptographic commitments of submitted orders on a permissioned Hyperledger Fabric blockchain, stores clear-text bids in restricted private collections, and anchors settlement outputs on-chain via an oracle interface. This design allows independent post-clearing verification that the orders used in clearing correspond precisely to those committed before auction closure, without disclosing confidential bid information. The system is evaluated using a real intraday electricity market dataset containing 46,643 orders from a full trading day in the Spanish market. Experimental results show that the architecture maintains one-to-one correspondence between submitted orders and on-chain commitments, enforces correct market lifecycle transitions, and detects inconsistencies between committed bids and the inputs used during clearing, providing tamper-evident guarantees of input integrity in adversarial scenarios. Performance benchmarking against a centralized database baseline shows that the blockchain implementation introduces additional latency and achieves 176.5 transactions per second under the evaluated configuration, reflecting a throughput-limited regime while remaining compatible with realistic intraday auction time windows. These findings demonstrate that blockchain technology can serve as a practical governance layer for electricity markets by shifting trust from unverifiable operator actions to cryptographically auditable input integrity, without requiring modifications to existing clearing algorithms. The approach does not verify the correctness of the clearing algorithm itself but ensures that its inputs are cryptographically auditable.

Exposure to the optical environment-often referred to as visual experience-profoundly influences human physiology and behavior across multiple time scales. In controlled laboratory settings, stimuli can be held constant or manipulated parametrically. However, such exposures rarely replicate real-world conditions, which are inherently complex and dynamic, generating high-dimensional datasets that demand rigorous and flexible analysis strategies. This tutorial presents an analysis pipeline for visual experience datasets, with a focus on reproducible workflows for human chronobiology and myopia research. Light exposure and its retinal encoding affect human physiology and behavior across multiple time scales. Here we provide step-by-step instructions for importing, visualizing, and processing viewing distance and light exposure data. This includes time-series analyses for working distance, biologically relevant light metrics, and spectral characteristics. The tasks are standardized through the open-source R package LightLogR. By leveraging a modular approach, the tutorial supports researchers in building flexible and robust pipelines that accommodate diverse experimental paradigms and measurement systems.

Transcranial magnetic stimulation (TMS) is a non-invasive neuromodulation technique with demonstrated efficacy in the treatment of multiple psychiatric and neurological disorders. ExoTMS represents an evolution in TMS system design, developed to optimize focused brain stimulation while enhancing patient comfort and operational efficiency. By leveraging established neuroplastic mechanisms in a more patient-centered format, ExoTMS may represent a scalable advancement when compared to conventional TMS systems. This review examines the technological characteristics, safety profile, and therapeutic implications of ExoTMS, with particular attention to patient-reported outcomes, treatment comfort, and safety. Eight studies were included, comprising 182 active-treated participants who received stimulation at effective intensity levels. Treatment protocols consisted of 4 to 6 sessions, with follow-up assessments conducted at 1 month and, in studies reporting extended follow-up, at 3 months post-treatment. Data collected included mapping time for coil positioning and setup, incidence of adverse events, and patient-reported outcomes. Subjective measures assessed treatment satisfaction and perceived comfort using questionnaires. Patients reported high satisfaction with ExoTMS treatment, with improvements observed immediately post-treatment, and further increases at follow-up. Improvements were noted across multiple domains, including overall well-being, mood, perceived stress, and mental energy. Treatment tolerability was high, with 93.3% of participants reporting the therapy as comfortable and associated with minimal to no pain. No serious adverse events were reported. ExoTMS appears to be a well-tolerated TMS-based intervention characterized by favorable treatment comfort, minimal pain, and the absence of serious adverse events. Patient-reported outcomes suggest improvements in well-being and self-regulatory domains that persist beyond the immediate treatment period. Compared with conventional TMS systems, ExoTMS may offer practical advantages, including reduced treatment burden and improved clinical workflow efficiency.

This article presents a sensorless method for cycle-level payload mass estimation in industrial robots using internal motor-current signals only. The approach is based on automated KUKA Trace acquisition of six-axis current traces from a KUKA KR3 controller, followed by statistical feature extraction and regression using a multilayer perceptron (MLP). Experiments were conducted on two nominally identical KUKA KR3 R540 manipulators under a repeatable handling motion with payloads ranging from approximately 0.4 kg to 2.6 kg, enabling the systematic engineering validation of controller-based current acquisition, feature-based payload regression, and cross-robot validation. This study investigated whether motor-current signals could serve as a reliable virtual sensing source without external force or weight sensors. Under repeated operating conditions, the most accurate MLP configuration achieved a testing mean absolute error (MAE) of 6.75 g and a mean squared error (MSE) of 68.28 g2. With mixed-source training, using data from both robots, the testing MAE decreased to 5.37 g, and the accuracy within a ±15 g tolerance reached 96.88%. In contrast, direct transfer to another nominally identical robot increased the MAE to 80.54 g, revealing a clear cross-robot generalization gap. Overall, this study demonstrates the feasibility of motor-current-based payload verification during repeated single-axis motion under controlled conditions and indicates its potential for gripping validation and missing-part detection in industrial handling applications.

Drug-resistant epilepsy affects tens of millions of people worldwide and is associated with considerable morbidity and mortality. Thalamic deep brain stimulation and cortical responsive neurostimulation are proven treatments for focal epilepsy. Both have been used to target a range of thalamic nuclei; yet, the roles of these thalamic nuclei in focal seizure generation remain incompletely understood. Thirteen patients with drug-resistant focal epilepsy undergoing intracranial EEG were consented to undergo investigation of thalamocortical networks. Sampled regions included cortical, mesial temporal, and thalamic brain regions. Visual and spectral analyses were performed to identify seizure onset patterns and correlate thalamic and cortical seizure activity. Thalamic ictal discharges were observed in 89% of seizures. Of these, 56% demonstrated synchronous thalamocortical activity with distinct patterns. These onset patterns included hypersynchronous spiking, low-voltage fast activity, ictal baseline shifts, and broadband suppression. Multiple thalamic nuclei were involved in ictal organization and propagation, with the specific nuclei depending on the cortical seizure network. The thalamus plays a crucial role in focal onset seizure generation and propagation, with distinct seizure onset patterns and nuclei involved. These findings support exploring a broader range of thalamic nuclei in epilepsy neurostimulation and have implications for seizure detection settings in intracranial sensing devices.

Current gene regulatory networks are limited by incomplete functional annotation and the difficulty of inferring causal relationships from expression data. Here we introduce Gene Deregulation Networks (GDNs), a new structure in which a directed link from gene C to gene E indicates that a deregulation of C makes a deregulation of E more probable. GDNs are inferred from expression data using a probabilistic theory of causation, without requiring prior biological knowledge. Using previously defined N- and T-genes with exclusive expression intervals for normal tissue and tumors, respectively, we construct separate GDNs for normal and for tumor tissues. Data are TCGA RNA-Seq bulk profiles from five cancer types. Links are identified via the Loevinger coefficient and pruned with Reichenbach-type and Mokken tests. We then project each sample onto its corresponding Gene Deregulation Network to visualize the deregulation cascades that have occurred. Finally, we define a simple dynamics: spontaneous evolution follows the direction of the GDN edges; interventions (e.g., gene knockdown) acting against spontaneous evolution induce cascades along the reversed network. The GDNs are represented by sparse, directed acyclic graphs. Genes with low deregulation frequency have high out-degrees, suggesting they act as upstream regulators. High-frequency genes have high in-degrees, indicating they are convergence points of cascades. Projecting samples onto the T-GDN reveals that early tumors rely mostly on spontaneous T-gene activations, whereas advanced tumors show wide, branching cascades. The N-GDNs show consistent size and structure across distinct tissues revealing similar protective machinery against tumor formation. In contrast, the T-GDNs quantitatively differ from tissue to tissue indicating different levels of transcriptional reprogramming. Simulated knockdown of EPHA10 and of an 8-gene panel illustrates how the network topology determines whether an intervention can be resisted by the tumor. A reported experiment on POM121 knockdown in two prostate cancer cell lines qualitatively confirms the predicted directionality of cascades. GDNs provide a robust, scalable, and annotation-free framework to understand cancer onset and progression. The separation into N- and T-GDNs, connected by NT-genes, offers a systematic basis for studying carcinogenesis and designing targeted therapies.

Oral drug therapy requires achieving a delicate balance between therapeutic efficacy and patient safety, yet current dosing strategies often rely on empirical trial-and-error methods that overlook the complex nonlinear dynamic nature of drug behavior in the human body. Conventional pharmacokinetic/pharmacodynamic (PK-PD) approaches provide valuable insights but lack a systematic method for designing dose sequences and formulations that achieve an optimal therapeutic response. This work introduces a structured optimization framework that combines PK-PD modeling, impulsive dosing concepts, and nonlinear optimization to determine optimal repeated oral dosing regimens. We model each orally administered dose as an impulsive input in a linear compartmental PK system and couple the resulting drug concentration profile with a nonlinear Hill-type PD model. To enable efficient optimization, we derive sensitivity functions describing how the therapeutic effect depends on dose size and adjustable drug-formulation parameters, allowing to construct the Jacobian required by the Gauss-Newton nonlinear least-squares algorithm. The proposed method jointly optimizes dose magnitude and formulation-dependent liberation (release) rate to match a clinically meaningful therapeutic effect trajectory. Using a four-compartment pharmacokinetic model, we demonstrate in silico that the method achieves rapid onset, stable long-term therapeutic effect, and reduced fluctuations of the therapeutic effect across repeated dosing cycles.

The role of vesicular systems in the field of drug delivery has led to increased research in the optimization of these novel delivery methods. Niosomes are bilayer structures that are capable of encapsulating lipophilic and hydrophilic compounds. In this study, niosomal systems formed from either Span 60 or Tween 60 non-ionic surfactants were investigated through experimental work and molecular dynamics simulations. Niosomes were prepared using thin-film hydration (TFH) and organic phase injection (OPI) methods and characterized in terms of particle size, polydispersity index, zeta potential, morphology, and encapsulation efficiency. A low-molecular-weight active pharmaceutical ingredient, tetracaine, and its water-soluble form, tetracaine HCl, were employed to understand the effect of pH on the encapsulation efficiency (EE) of the process. The molecular dynamics simulation of the niosomal system provided information on the distribution of Span 60 or Tween 60 surfactant molecules within the bilayer and the behavior of the tetracaine molecule.

Traumatic spinal cord injury (SCI) is a severe medical condition, often resulting in permanent disability, with significant impacts on patients' quality of life and burden on healthcare systems. Current therapeutic approaches for SCI are insufficient, advocating for the development of more effective treatments. As changes in transcriptome post-SCI can provide clues for novel treatment strategies and targets, substantial efforts have been made recently to characterize such transcriptional changes and their spatiotemporal features. This narrative review focuses on how transcriptomics, alone or in combination with other omics data, can contribute to understanding SCI pathobiology and the mechanisms of post-SCI regeneration and guide the development of novel SCI therapies. It covers an arsenal of tools for transcriptomics studies and provides a concise summary of findings from the latest relevant studies (predominantly from 2020 to 2025), representing the major directions in the field.

van der Waals (vdW) ferroelectrics are emerging nonlinear photonic materials that combine large second-order susceptibility χ(2) with heterostructure compatibility, offering an attractive route toward miniaturized spontaneous parametric down-conversion (SPDC) sources. Practical vdW SPDC, however, remains constrained by low brightness and poor stability in air under continuous irradiation, where ambient exposure and pump-induced heating cause material degradation. Here we demonstrate a bright, air-stable SPDC source based on ferroelectric NbOI2 enabled by graphene encapsulation. Graphene provides robust environmental protection and suppresses pump-induced degradation by enhancing heat dissipation. We report an absolute photon-pair generation rate of 258 Hz and a normalized brightness of 19 900 Hz mW-1 mm-1. Leveraging this stabilized platform, we further generate polarization-entangled photon pairs from graphene-encapsulated 90°-twisted bilayer NbOI2, with 94% fidelity to a maximally entangled Bell state. These results establish graphene-encapsulated NbOI2 as a practical vdW ferroelectric SPDC platform for integrated quantum photonic sources.