Search Results (1,228)

Sequence prediction is widely applied and has significant theoretical and practical application value in fields such as meteorology and medicine. Traditional models, such as LSTM(Long Short-Term Memory) and GRU(Gated Recurrent Unit), may perform better than this model when dealing with short-term dependencies, but their performance may decline on long sequences and complex data, especially in cases where sequence fluctuations are significant. However, the Transformer requires a large amount of computing resources (parallel computing) when dealing with long sequences. Aiming to solve the problems existing in sequence prediction models, such as insufficient modeling ability of long sequence dependencies, insufficient interpretability, and low efficiency of multi-element heterogeneous information fusion, this study embeds sequential data into the knowledge graph, enabling the model to associate context information when processing complex data and providing more reasonable decision support for the prediction results. Given the historical sequence and the predicted future sequence, three groups of sequence lengths were set in the experiment. And MAE (Mean Absolute Error)and MSE (Mean Square Error) are used as indicators for sequence prediction. In sequence prediction, dynamic evolution is conducive to enhancing the ability of the prediction model to capture the changing patterns of the current time series data and significantly improving the reliability of the prediction results. Experiments were conducted using five datasets from different application fields to verify the effectiveness of the prediction model. The experimental results show that based on the randomization of the prediction time step, the prediction model proposed in this study significantly improves the expression performance of stationary sequences. It has addressed the shortcomings of these traditional methods, such as maintaining good performance in the case of short sequences with large fluctuations. Full article

Background: Interstitial myocardial fibrosis is a crucial pathological feature of many cardiovascular disorders. Myocardial fibrosis resulting in extracellular volume (ECV) expansion can be quantified via cardiac MRI (CMR) with T₁ mapping before and after minimally invasive gadolinium (Gd) contrast agent administration. However, longitudinal repetitive ECV measurements are challenging in rodents due to the prolonged scan time with cardiac and respiratory gating that is required for conventional T₁ mapping and the invasive nature of the rodent intravenous lines. Methods: To address these challenges, the objective of this study is to establish a fast, free-breathing, and gating-free ECV procedure using a minimally invasive subcutaneous catheter for in-scanner Gd administration that can allow longitudinal repetitive ECV evaluations in rodent models. This is achieved by the (1) IntraGate sequence for free-breathing, gating-free cardiac imaging; (2) minimally invasive subcutaneous in-scanner Gd administration; and (3) fast T₁ mapping with a varied flip angle (VFA) in conjunction with (4) triple jugular vein blood T₁ normalization. Additionally, full cine CMR (multi-slice short-axis, long-axis 2-chamber, and long-axis 4-chamber) was acquired during the waiting period to assess comprehensive cardiac function and strain. Results: We successfully established a minimally invasive fast ECV quantification protocol to enable longitudinal repetitive ECV quantifications in rodents. Minimally invasive subcutaneous Gd bolus administration induced a reasonable dynamic contrast enhancement (DCE) time course, reaching a steady state in ~20 min for stable T₁ quantification. The free-breathing gating-free VFA T₁ quantification scheme allows for rapid cardiac (~2.5 min) and jugular vein (49 s) T₁ quantification with no motion artifacts. The triple jugular vein T₁ acquisitions (1 pre-contrast and 2 post-contrast) immediately flanking the heart T₁ acquisitions enable accurate myocardial ECV quantification. Our data demonstrated that left-ventricular myocardial ECV quantification was highly reproducible with repeated scans, and the ECV values (0.25) are comparable to reported ranges among humans and rodents. This protocol was successfully applied to the ischemia–reperfusion injury model to detect myocardial fibrosis, which was validated by histopathology. Conclusions: We established a simple, fast, minimally invasive, and robust CMR protocol in rodents that can enable longitudinal repetitive ECV quantification for cardiovascular disease progression. It can be used to monitor disease regression with interventions. Full article

As a critical component of the power system, transmission lines play a significant role in ensuring the safe and stable operation of the power grid. To address the challenge of accurately characterizing complex and diverse fault types, this paper proposes a fault detection and classification method for power lines that integrates Bayesian Reasoning (BR), Long Short-Term Memory (LSTM) networks, and the Attention mechanism. This approach effectively improves the accuracy of fault classification. Bayesian Reasoning is used to adjust the hyperparameters of the LSTM, while the LSTM network processes sequential data efficiently through its gating mechanism. The self-Attention mechanism adaptively assigns weights by focusing on the relationships between information at different positions in the sequence, capturing global dependencies. Test results demonstrate that the proposed Bayes–LSTM–Attention model achieves a fault classification accuracy of 94.5% for transmission lines, a significant improvement compared to the average accuracy of 80% achieved by traditional SVM multi-class classifiers. This indicates that the model has high precision in classifying transmission line faults. Additionally, the evaluation of classification results using the polygon area metric shows that the model exhibits balanced and robust performance in fault classification. Full article

Eye tracking scanpaths encode the temporal sequence and spatial distribution of eye movements, offering insights into visual attention and aesthetic perception. However, analysing scanpaths still requires substantial manual effort and specialised expertise, which limits scalability and constrains objectivity of eye tracking methods. This paper examines whether and how multimodal large language models (MLLMs) can provide objective, expert-level scanpath interpretations. We used GPT-4o as a case study to develop eye tracking scanpath analysis (ETSA) approach which integrates (1) structural information extraction to parse scanpath events, (2) knowledge base of visual-behaviour expertise, and (3) least-to-most and few-shot chain-of-thought prompt engineering to guide reasoning. We conducted two studies to evaluate the reliability and effectiveness of the approach, as well as an ablation analysis to quantify the contribution of the knowledge base and a cross-model evaluation to assess generalisability across different MLLMs. The results of repeated-measures experiment show high semantic similarity of 0.884, moderate feature-level agreement with expert scanpath interpretations (F1 = 0.476) and no significant differences from expert annotations based on the exact McNemar test (p = 0.545). Together with the ablation and cross-model findings, this study contributes a generalisable and reliable pipeline for MLLM-based scanpath interpretation, supporting efficient analysis of complex eye tracking data. Full article

Focusing on traditional dwellings in China’s Yangtze River Delta, this study integrates physical environment measurement and computer simulation to quantify the influence of spatial morphological parameters on the daylighting environment and analyze its temporal dynamic adaptation throughout the year. Moreover, by exploring the dynamic interaction between the daylighting environment and the dwellers’ behavior patterns, it elucidates how traditional spaces, through light, guide and support a specific lifestyle, and how this interaction, combined with regional aesthetic concepts and cultural traditions, fosters a unique culture of light. Based on the findings, the following conclusions can be drawn: (1) spatially, traditional dwellings adopt a synergetic daylighting mechanism, which is collectively determined by the morphological parameters of the south sky-well, the deployment of north crab eye sky-wells, and the coordination of orientation, depth, and window type; (2) temporally, traditional dwellings exhibit a dynamic daylighting regulation mechanism realized by the reasonable design and combination of spatial and interface components, and they precisely balance the light and thermal needs in different seasons; (3) the temporal daylighting regulation guides the dwellers to form a behavior sequence that is synchronized with natural rhythms, thereby shaping human behavior and local culture in the regions; (4) the daylighting environment in traditional dwellings satisfies the demand for practical functions, embodying the organic integration between technical rationality and humanistic spirit. In summary, from the perspectives of spatial layout, seasonal adaptation, behavior guidance, and cultural expression, this research clarifies the characteristics, formation mechanism, and implicit relationship with local culture of the daylighting environment in traditional dwellings in the Yangtze River Delta. It provides a new perspective for understanding the ecological adaptability of regional traditional architecture and offers valuable insights and references for the development of green architecture nowadays. Full article

Background: Secondary pathogenic/likely pathogenic germline variants (P/LPGVs) identified on solid tumor genomic profiling (TGP) are a commonly encountered clinical issue. A proportion of oncology patients that undergo TGP will have a secondary P/LPGV identified that may not have been otherwise discovered based on clinical and family history criteria for hereditary cancer syndrome screening. The confirmation of P/LPGVs on germline sequencing has potential treatment implications for patients. Methods: The study design was a retrospective review for secondary data analysis. The inclusion criteria for this study were adult patients with solid tumor malignancy who underwent TGP and germline sequencing. The objective of this study is to evaluate the concordance rate of secondary P/LPGVs on TGP of adult patients with solid tumor malignancy at Hoag Presbyterian Hospital and Tower Health-Reading Hospital. The second and third aims are to analyze if the confirmed P/LPGVs are concordant with the patient’s tumor type and to analyze the variant allele frequencies (VAFs) of the identified secondary P/LPGVs on the tumor genomic profiling. Results: The data included 75 patients who underwent both TGP and germline sequencing, with a median age of 62.5 years. The most represented genes with P/LPGVs in the combined data included BRCA1 and BRCA2, both with 14, and MSH2, with 9. The overall germline concordance rate for the combined population was 64.1%, with 59 out of 92 P/LPGVs identified on both germline and somatic tumor testing. Conclusions: The overall germline concordance rate of 64% for the combined population is in accordance with the reported literature. Possible reasons for the variability in rates could be related to reporting guidelines for secondary germline variants, which can vary by company, and differences between somatic and germline variant curation. The study of P/LPGVs in populations from community cancer centers has the potential to increase the data of underrepresented minority groups regarding this important clinical issue and help expand understanding of hereditary cancer syndrome phenotypes. Full article

Background and Clinical Significance: Familial short stature is a common reason for referral in clinical genetics. While often attributed to a single genetic cause, genetic heterogeneity can complicate diagnosis and management. This report describes a family in which three distinct pathogenic variants in SHOX, PDE4D and ACAN caused overlapping phenotypes of familial short stature. Case Presentation: Clinical, radiological and molecular data were collected retrospectively at the Reference Centre for Constitutional Bone Diseases at Montpellier University Hospital. Targeted gene panels, whole genome sequencing and Sanger sequencing were employed to identify pathogenic variants. Variant interpretation followed the guidelines of the American College of Medical Genetics. A pathogenic SHOX variant (c.452G>A; p.Ser151Asn) was identified in the proband and her mother, which is consistent with dyschondrosteosis. A de novo PDE4D variant (c.671C>T; p.Thr224Ile) was identified in a cousin presenting with syndromic acrodysostosis. An ACAN splice variant (c.6833-1G>A) was detected in several family members and is associated with short stature and skeletal anomalies. An individual carrying both the SHOX and ACAN variants exhibited a more severe phenotype, suggesting an additive effect. Conclusions: This case study highlights the importance of systematic molecular investigations in families with overlapping yet heterogeneous phenotypes. Comprehensive genetic familial analysis enables personalized care and accurate genetic counselling, particularly when multiple diagnoses coexist. A family history should not preclude molecular testing, since similar phenotypes can result from different genetic causes. Full article

This work represents the natural continuation of the development of the cognitive architecture developed and named Sophimatics, organically integrating the spatio-temporal processing mechanisms of the Super Time Cognitive Neural Network (STCNN) with the advanced principles of Sophimatics. Sophimatics’ goal is as challenging as it is fraught with obstacles, but its ultimate aim is to achieve a more humanized post-generative artificial intelligence, capable of understanding and analyzing context and evaluating the user’s purpose and intent, viewing time not only as a chronological sequence but also as an experiential continuum. The path to achieving this extremely ambitious goal has been made possible thanks to some previous work in which the philosophical thinking of interest in AI was first inherited as the inspiration for the aforementioned capabilities of the Sophimatic framework, then the issue of mapping concepts and philosophical thinking in Sophimatics’ AI infrastructure was addressed, and finally a cognitive-inspired network such as STCNN was created. This work, on the other hand, addresses the challenge of how to endow the infrastructure with both chronological and experiential time and its powerful implications, such as the innate ability to resolve paradoxes, which generative AI does not have among its prerogatives precisely because of structural limitations. To reach these results, the model operates in the two-dimensional complex time domain ℂ², extending cognitive processing capabilities through the implementation of dual temporal operators that simultaneously manage the real temporal dimension, where past, present, and future are managed and the imaginary one, that considers memory, creativity, and imagination. The resulting architecture demonstrates superior capabilities in resolving informational paradoxes and integrating apparently contradictory cognitive states, maintaining computational coherence through adaptive Sophimatic mechanisms. In conclusion, this work introduces Phase 4 of the Sophimatic framework, enabling management of two-dimensional time within a novel cognitively inspired neural architecture grounded in philosophical concepts. It connects with existing research on temporal cognition, hybrid symbolic–connectionist models, and ethical AI. The methodology translates philosophical insights into formal computational systems, culminating in a mathematical formalization that supports two-dimensional temporal reasoning and paradox resolution. Experimental results demonstrate efficiency, predictive accuracy, and computational feasibility, highlighting potential real-world applications, future research directions, and present limitations. Full article

Behavior-rule reasoning aims to infer the corresponding applicable rules from specific behaviors and is a type of inductive reasoning that goes from special cases to general ones. This paper proposes a behavior-rule inference model based on hyponymy–hypernymy knowledge trees, which maps behaviors to corresponding rules through deep learning by processing textual behavior sequences. The primary contributions of this work are threefold: we present a systematic framework that adapts K-BERT to the legal domain by integrating domain-specific hyponymy–hypernymy knowledge trees, addressing the unique challenges of legal text understanding; we conduct comprehensive optimization of key components, including context length, loss function, and base model selection, providing empirical guidelines for applying pre-trained models to legal reasoning tasks; and we propose a practical evaluation metric (tolerance) that mimics real-world legal decision-making processes, providing extensive analysis on the effectiveness of different knowledge types in legal inference. Full article

Time Series Classification (TSC) plays a crucial role in machine learning applications across domains such as healthcare, finance, and industrial systems. In these domains, TSC requires accurate predictions and reliable explanations, as misclassifications may lead to severe consequences. In addition, scalability issues, including training time and memory consumption, are critical for practice usage. To address these challenges, we propose ProtoPGTN, a prototype-based interpretable framework that unifies gated transformers with prototype reasoning for scalable time series classification. Unlike existing prototype-based interpretable TSC models which rely on recurrent structure for sequence processing and Euclidean distance for similarity computation, ProtoPGTN adapts Gated Transformer Networks (GTN), which uses an attention mechanism to capture both temporal and spatial long-range dependencies in time series data and integrates the prototype learning framework from ProtoPNet with cosine similarity to enhance metric consistency and interpretability. Extensive experiments are conducted on 165 publicly available datasets from the UCR and UEA repositories, covering both univariate and multivariate tasks. Results show that ProtoPGTN obtains at least the same performance as existing prototype-based interpretable models on both multivariate and univariate datasets. The average accuracy on multivariate and univariate datasets stands at 67.69% and 76.99%, respectively. ProtoPGTN achieves up to 20× faster training and up to 200× lower memory consumption than existing prototype-based interpretable models. Full article

Arteriovenous malformations (AVMs) are fast-flow vascular malformations formed by direct artery-to-vein shunts without an intervening capillary bed, which increases the risk of hemorrhage and organ-specific damage. A synthesis of recent advances shows that AVMs arise from interplay between germline susceptibility (ENG, ACVRL1, SMAD4, RASA1, EPHB4), somatic mosaicism (KRAS, MAP2K1, PIK3CA), perturbed signaling (TGF-β/BMP, Notch, VEGF, PI3K/AKT, RAS/MAPK), hemodynamic stress, and inflammation. Multimodal imaging—digital subtraction angiography (DSA), MRI/MRA with perfusion and susceptibility sequences, CTA, Doppler ultrasound, and 3D rotational angiography—underpins diagnosis and risk stratification, while arterial spin labeling and 4D flow techniques refine hemodynamic assessment. Management is individualized and multidisciplinary, combining endovascular embolization, microsurgical resection, and stereotactic radiosurgery (SRS); a non-surgical approach and monitoring remain reasonable for some asymptomatic AVMs. Device and technique innovations (detachable-tip microcatheters, pressure-cooker approaches, and newer liquid embolics such as PHIL and Squid) have broadened candidacy, and precision-medicine strategies, including pathway-targeted pharmacotherapy, are emerging for syndromic and somatic-mutation–driven AVMs. Animal models and computational/radiomics tools increasingly guide hypothesis generation and treatment selection. We outline practical updates and future priorities: integrated genomic-imaging risk scores, genotype-informed medical therapy, rational hybrid sequencing, and long-term outcome standards focused on hemorrhage prevention and quality of life. Full article

Conventional plant breeding relies on the occurrence of chromosomal crossover and spontaneous or non-targeted mutations in the genome induced by physical or chemical stressors. However, constraints exist concerning the number and variation of genotypes that can be achieved in this way, as the occurrence and combination of mutations are not equally distributed across the genome. The underlying mechanisms and causes of reproductive constraints can be considered the result of evolution to maintain the genomic stability of a species while at the same time allowing necessary adaptations. A continuous horizon scan was carried out to identify plants derived from new genomic techniques (NGTs), which show that CRISPR/Cas is able to circumvent at least some of these mechanisms and constraints. The reason for this is the specific mode of action: While physico-chemical mutagens such as radiation or chemicals merely cause a break in DNA, recombinant enzymatic mutagens (REMs), such as CRISPR/Cas, additionally interfere with cellular repair mechanisms. More recently developed REMs even expand the capabilities of NGTs to introduce new genetic variations within the target sequences. Thus, NGTs introduce genetic changes and combinations that are unknown in the current breeding pool and that are also unlikely to occur as a result of any previously used breeding methods. The resulting genotypes may need to be considered as ‘new to the environment’. The technical potential of NGTs should also be taken into account in regulatory provisions. Previously unknown genotypes and phenotypes may negatively impact plant health, ecosystems, biodiversity, and plant breeding. It must further be acknowledged that the different outcomes of NGTs and conventional breeding are not always evident at first sight. As a starting point, within a process-oriented approval process, molecular characterization can inform the following steps in risk assessment and guide requests for further data. Full article

In this study is introduced a novel generalization of repunit and one-zero numbers through the formulation of the generalized One-kZero. This sequence extends the classical families of repunit and one-zero numbers by establishing a unified framework in which the parameter $(k\ge 0)$ specifies the number of consecutive zeros separating two ones in the decimal representation. We introduce the new family of sequences, the generalized One-kZero numbers, and investigate some of their properties. The main purpose is to present a generalization for the recurrence relation of kind One-Zero numbers and determine some relations and properties. The reason that led us to this method is that the recurrence relation of One-Zero and Repunit numbers has a second-order difference equation as a specific case of the Horadam-type sequence. The Binet formula, generating function, sum formulas and many other relations will therefore be much easier to find. Also, some other identities that have not been found before in the particular case of One-Zero and Repunit sequences are also included in this study. Full article

Objectives: Alexander disease (AxD) is a rare neurodegenerative disorder that represents a group of leukodystrophies with severe disability and premature death, mostly with an infancy/childhood onset. In rare cases of late-onset phenotypes, symptoms are often milder and difficult to diagnose. We present a diagnostic journey of a teenage male patient with a progressive gait disorder starting at the age of 13 years, with a final diagnosis of Alexander disease. Early in the course of the disease, the boy exhibited distinctive cognitive involvement and neuropsychological deterioration characterized by selective impairment of visual and long-term auditory memory, along with a decline in IQ but preserved reasoning abilities. Methods: The patient underwent an extensive neurological diagnostic workup, which included magnetic resonance imaging (MRI) of the brain, spine, and abdomen, as well as electrophysiological, metabolic, and biochemical tests. Numerous specialist consultations were conducted, including genetic, cardiology, ophthalmology, pulmonology, oncohematology, psychological, and speech–language pathology consultations. In addition, a focused literature review was performed using PubMed, Scopus, Web of Science, and Google Scholar with the search terms “Alexander disease,” “GFAP gene,” “late-onset,” “spastic paraplegia” and “GFAP variant p/Gly18Val”. Results: Whole exome sequencing revealed an extremely rare missense GFAP heterozygous variant NM_002055.5: c.54G>T (p/Gly18Val), confirming the diagnosis of AxD. Conclusions: The presented case highlights the importance of whole-exome sequencing in the diagnosis of unexplained otherwise neurological symptoms, such as progressive spastic paraplegia. Full article

Background: Understanding how accident risk escalates during unfolding industrial events is essential for developing intelligent safety systems. This study proposes a large language model (LLM)-based framework that simulates human-like risk reasoning over sequential accident precursors. Methods: Using 100 investigation reports from the U.S. Chemical Safety Board (CSB), two Generative Pre-trained Transformer (GPT) agents were developed: (1) an Accident Precursor Extractor to identify and classify time-ordered events, and (2) a Subjective Probability Estimator to update perceived accident likelihood as precursors unfold. Results: The subjective accident probability increases near-linearly, with an average escalation of 8.0% ± 0.9% per precursor ($p<0.05$). A consistent tipping point occurs at the fourth precursor, marking a perceptual shift to high-risk awareness. Across 90 analyzed cases, Agent 1 achieved 0.88 precision and 0.84 recall, while Agent 2 reproduced human-like probabilistic reasoning within ±0.08 of expert baselines. The magnitude of escalation differed across precursor types. Organizational factors were perceived as the highest risk (median = 0.56), followed by human error (median = 0.47). Technical and environmental factors demonstrated comparatively smaller effects. Conclusions: These findings confirm that LLM agents can emulate Bayesian-like updating in dynamic risk perception, offering a scalable and explainable foundation for adaptive, sequence-aware safety monitoring in safety-critical systems. Full article