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AI-Driven EMG Monitoring and Decision Support Framework
Get PDFDigital Twin (DT) technology, a core pillar of Healthcare 4.0 (H4.0), enables intelligent, non-invasive, and personalized patient monitoring. This research presents a pilot AI-enabled Electromyography (EMG)-driven DT framework for muscular activity assessments. The EMG data enables monitoring of muscle engagements and provides a comprehensive representation of physiological states. The raw EMG data, consisting of 7 activities, i.e., Sitting, Standing, Walking, Relax, Stress Ball, Hand at Rest, and Fist, is subjected to denoising techniques of mean removal, smoothing, and digital filtering. Within the DT model, AI serves as the intelligence core that transforms these denoised signals into relevant digital states. Supervised and semi-supervised classifiers act as inference engines, continuously refining the DT as new data is incorporated, allowing it to evolve in synchrony with the patient’s condition. The decision support using ML and DL is employed for EMG classification, utilizing statistical features and autonomous feature extraction methodologies of AutoEncoder (AE) and Stacked AutoEncoder (SAE). The feature data is enriched and enlarged through Gaussian noise feature data augmentation for both feature extraction approaches. The Fine KNN algorithm provides classification accuracy of 94.6% and 91.6%. However, the autonomous feature extraction through the SAE (32-16-32) with Medium KNN provides an overall accuracy of 96.4% and 93.3%. The promising results validate the effectiveness of the proposed framework as a dynamic, AI-driven DT system for prospective holistic patient multi-physiological monitoring and decision support
A survey on learning an autonomous dynamic system for human–robot skills transfer from demonstration
Get PDFAutonomous dynamic systems (ADS) have become a key area of research in the field of robotics, aiming to enable robots to acquire human-like operational skills and perform complex tasks in dynamic environments without external intervention. Despite significant progress, current technologies have yet to enable robots to fully achieve autonomous skill transfer in real-world applications. The prevailing approach to bridge this gap is Learning from Demonstration (LfD), where robots learn by observing and imitating expert demonstrations. Dynamic systems-based methods, particularly those utilizing Lyapunov stability theory, have shown great potential in effectively encoding human motor skills, ensuring the stability, accuracy, and generalization of learned behaviors during the learning process. This survey provides an overview of the recent advancements in dynamic systems for skill transfer, focusing on methods that enable robots to replicate human actions, as demonstrated by experts. We present a classification of existing dynamic systems approaches, highlight landmark studies, and discuss their key features, advantages, and limitations. This paper also explores the applications of these methods and identifies major challenges that remain in both theoretical and practical aspects of robot skill learning
Construction of an Artistic Self: Sophie Gaudier-Brzeska’s Poetry Notebooks and Journal
Get PDFThis study focuses on journal passages and verses from Sophie Gaudier-Brzeska’s poetry notebooks that demonstrate her drive to construct an artistic self. Although her artistic ambitions were long central to her idealisation of aesthetic values, she faced patriarchal prejudices, poverty, and psychological challenges in her efforts to achieve artistic recognition. Both she and her much younger and much lauded partner, sculptor Henri Gaudier-Brzeska, adopted a transgressive posture towards what they regarded as the corrupt bourgeois conventions of late nineteenth- and early twentieth-century European and British society. Their joint development of an artistic project foregrounding primordial and transcendent forces is reflected in his letters with reference to their conversations and her letters (not extant). After Henri’s tragic death fighting in northern France (June 1915), Sophie continues to reflect on that project in her journal and in her earlier poems and creation of new works, both constantly revised. For Sophie, her artistic self is realised through her invocation of Henri’s continuing presence in her journal and poetry that reflect her experiences and anguish. While her life ended in obscurity, and madness, her remarkable artistic achievements can be reconstituted from the debris of her diaries and poetry notebooks
Analysis of cell-free DNA for cancer diagnostics using liquid biopsies
No full textChromatin organisation influences gene regulation and genome stability, yet its dysregulation in cancer remains incompletely understood. This has become particularly important for patient diagnostics using cell-free DNA (cfDNA) from body fluids, based on computational analyses of nucleosome occupancy landscapes reconstructed from cfDNA. This thesis establishes the first comprehensive atlas of nucleosome positioning signatures across repetitive elements in tumour tissues and cfDNA. Using original approaches to cfDNA analysis, I demonstrate that genomic repetitive elements, including satellite repeats, LINEs, SINEs, and LTRs, undergo reproducible family-, subfamily-, and tissue-specific nucleosome repositioning in cancer. Integration with DNA methylation profiling reveals coordinated changes in methylation and nucleosome positioning at specific genomic repeats, underscoring the epigenetic interplay that drives chromatin instability. Quantifying cfDNA profiles at DNA sequence repeats, including unmappable genomic regions, identified highly informative cfDNA signatures. Machine learning models trained on these cfDNA repeat features successfully discriminated healthy individuals from patients with colorectal, pancreatic, bile lung, and gastric cancer, achieving robust performance even when minimal sets of repeated elements were used as top features. My analyses showed that nucleosome signatures at genomic repeats vary substantially across cfDNA fragment-size fractions, offering both pan-cancer and tissue-specific diagnostic insights. Additional nucleosomics-based approaches were benchmarked in glioblastoma (GBM). Using differential nucleosome occupancy in paired GBM and normal brain tissues, repeat-based cfDNA profiling achieved high accuracy and clearly distinguished patients from healthy controls. Combined analyses revealed that nucleosome organisation of repetitive elements in cfDNA mirrors tumour-specific chromatin reprogramming, providing a non-invasive readout of disease state. Together, these findings highlight the repetitive genome as a structured, regulatory, and clinically tractable layer of cancer epigenetics, establishing a conceptual and methodological foundation for repeat-based cfDNA diagnostics for early cancer detection and patient stratification
Three Algorithms for Parallel Graph Summarization
No full textMost graph summarization algorithms are tailored to a specific graph summary model and were designed for one‐time computations only, that is, batch‐based computations. We developed a universal approach for parallel graph summarization and three algorithms to compute graph summaries—a batch‐based algorithm for static graphs, an incremental algorithm for evolving graphs, and a hash‐based algorithm that scales to large graphs and large schema structures, that is, using paths of length up to to define vertex equivalence. Experimenting with benchmark and real‐world datasets, we observe that the incremental algorithm almost always runs faster than batch computation, even when 50% of the graph changes, and even when using fewer cores; however, it only uses 8% more memory (). Furthermore, we show that the hash‐based algorithm can compute 10‐hop equivalent subgraphs on graphs with over 10 M edges within seconds, on graphs of 100 + M edges within a few minutes, and on graphs of 1 + B edges in less than an hour. We analyse the complexity of our algorithms in detail and prove that the incremental algorithm is correct. Overall, we show with these three algorithms that our parallel approach for graph summarisation is versatile and opens the path for various applications that require summaries of large‐scale graphs
Resting-State EEG Microstates Across a Dimensional Spectrum of Autistic Traits: From Typical Development to Diagnosed ASD
Get PDFAutism spectrum disorder (ASD) has been linked to atypical large-scale brain dynamics, but it is unclear how these alterations extend across the broader autism phenotype. We applied a seven-class resting-state EEG microstate model (A–G) to adults with clinical ASD and to typically developing adults with high (TD-High) and low (TD-Low) autistic traits, quantified with the Autism-Spectrum Quotient. We compared temporal parameters, spatial coverage, explained variance, and both observed and chance-corrected transition probabilities.
Across all microstates, the ASD group showed a globally more fragmented regime than both TD groups, with markedly shorter but more frequent microstate episodes and reduced duration variability. By contrast, TD-High and TD-Low were similar on these global indices. At the network level, Microstate C showed reduced explained variance and coverage in ASD relative to both TD groups. In Microstates E and G, explained variance and coverage increased from TD-Low to TD-High to ASD, with TD-High consistently occupying an intermediate position. Mean GFP and GFP variability for Microstate E were also elevated in ASD relative to both TD groups.
Transition analyses revealed reduced short-range transitions within an early A–C ensemble and increased transitions from these states into other microstates in ASD, with TD-High again showing an attenuated, intermediate pattern. Chance-corrected transitions confirmed that sensory/self-related routes occurred less often than expected, whereas routes from these states into other microstates were over-expressed.
These findings support a dimensional account in which EEG microstates index autism-related network organisation across clinical and subclinical ranges
Hydroxo-bridged active site of flavodiiron NO reductase revealed by NRVS and DFT
No full textThe use of oxygen and nitrate as terminal electron acceptors provides organisms with a huge amount of available energy but necessitates methods to detoxify reactive intermediates. The mechanisms of NO and O
2
detoxification in many organisms involve flavodiiron proteins (FDPs). Although the proteinaceous ligands that coordinate the diiron active site of these enzymes are well established, its exact coordination environment remains under debate due to conflicting interpretations of crystallographic and spectroscopic/theoretical studies. Using
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Fe nuclear resonance vibrational spectroscopy (NRVS), complemented by Mössbauer spectroscopy and density functional theory, we elucidated the redox-linked structural changes in the FDP from
Escherichia coli
. The as-isolated diferric state is best described as a dihydroxo-bridged Fe(III)–(μOH
−
)
2
–Fe(III) core, which upon reduction converts to a monohydroxo Fe(II)–(μOH
−
)–Fe(II) center through the loss of one bridging ligand. This ligand rearrangement defines the structural basis for redox-linked reactivity in FDPs. The study further demonstrates that photoreduction of a stable metalloprotein species can occur under NRVS conditions, indicating that synchrotron-based vibrational measurements may induce subtle redox changes even under low photon flux. These findings provide a mechanistic framework for interpreting redox-linked ligand dynamics in diiron enzymes and highlight the need to collect damage-free X-ray crystal structures avoiding potential beam-induced reduction. Furthermore, diiron active sites are found in numerous other enzyme classes (e.g., methane monooxygenase), and therefore, our findings have implications way beyond the FDPs
Stronger institutional performance correlates with ecological effectiveness
Get PDFThe literature distinguishes between the institutional and ecological effectiveness of international environmental organizations. The former refers to the compliance with stated objectives, the latter is about measurable improvements in environmental quality. The relationship of both concepts – and, thus, overall effectiveness – remains insufficiently understood, however. Here, we address that gap by examining the impact of institutional effectiveness on ecological performance. Leveraging data on international environmental organizations between 2008 and 2018, the findings reveal a clear link: stronger institutional performance correlates with ecological effectiveness as measured by reduced particulate pollution. By shedding light on the conditions under which international organizations drive meaningful environmental change, this research offers new insights for strengthening global environmental governance
Transcending boredom in daily life: The impact of self-transcendent emotions and meaning in life.
No full textBoredom is a pervasive emotion linked to various mental health and societal issues. Recent cross-sectional and experimental research suggests that self-transcendent emotions—a subcategory of positive emotions that decrease self-focus and increase feelings of connection— predict less boredom by increasing perceptions of meaning. We investigate for the first time (a) if the ability of self-transcendent emotions to ward off boredom materializes in day-to-day life, and (b) if these effects occur at the within-person level (day-to-day fluctuations within an individual). We conducted a preregistered 14-day diary study (N = 1,531 daily reports from 114 participants) to investigate this. On days that people experienced more awe, gratitude, compassion, and/or self-compassion than usual, they reported less daily boredom, even when controlling for other forms of affect, supporting our hypotheses. Further analyses showed that daily perceptions of meaning in life mediated the link between self-transcendent emotions and boredom. Our results have important theoretical and practical implications, suggesting that self- transcendent emotions promote a sense of meaning, thereby helping to counter boredom in everyday life
How Do Unexpected Networks Help Female Entrepreneurs in the Global South Survive in Adverse Contexts? A Case Study of Bangladesh
Get PDFThis study investigates the role of various unexpected networks in supporting the survival of female-owned SMEs in the Global South. The research focuses upon Bangladesh, which is a context marked by institutional adversity and postcolonial legacies. Grounded in Social Network Theory and informed by a decolonial perspective, the research examines personal, professional, and virtual networks to identify how these relational resources are able to empower women entrepreneurs, in an area where formal systems tend not to be inclusive. Using a sample of 156 female entrepreneurs, hierarchical regression analysis reveals that personal and virtual networks significantly enhance business survival, while professional networks do not show a significant effect. The interaction of personal and virtual networks with adverse contexts further strengthens their impact, highlighting their role as adaptive infrastructures in constrained environments. In contrast, professional networks remain limited in their influence. These findings challenge Western-centric assumptions about entrepreneurial networking and underscore the importance of inclusive context-sensitive strategies for supporting female entrepreneurship in the Global South