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The properties of cardio-respiratory coupling (CRC) are affected by various pathological conditions related to the cardiovascular and/or respiratory systems. In heart failure, one of the most common cardiac pathological conditions, the degree of CRC changes primarily depend on the type of heart-rhythm alterations. In this work, we investigated CRC in heart-failure patients, applying measures from information theory, i.e., Granger Causality (), Transfer Entropy () and Cross Entropy (), to quantify the directed coupling and causality between cardiac () and respiratory () time series. Patients were divided into three groups depending on their heart rhythm (sinus rhythm and presence of low/high number of ventricular extrasystoles) and were studied also after cardiac resynchronization therapy (CRT), distinguishing responders and non-responders to the therapy. The information-theoretic analysis of bidirectional cardio-respiratory interactions in HF patients revealed the strong effect of nonlinear components in the (high number of ventricular extrasystoles) and in the time series (respiratory sinus arrhythmia) as well as in their causal interactions. We showed that as a linear model measure is not sensitive to both nonlinear components and only model free measures as and may quantify them. CRT responders mainly exhibit unchanged asymmetry in the values, with statistically significant dominance of the information flow from to over the opposite flow from to , before and after CRT. In non-responders this asymmetry was statistically significant only after CRT. Our results indicate that the success of CRT is related to corresponding information transfer between the cardiac and respiratory signal quantified at baseline measurements, which could contribute to a better selection of patients for this type of therapy.
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http://dx.doi.org/10.3390/e25071072 | DOI Listing |
Ecology
September 2025
Department of Biology, University of North Carolina, Chapel Hill, North Carolina, USA.
Widespread declines in the abundance of insects portend ill-fated futures for their host ecosystems, all of which require their services to function. For many such reports, human activities have directly altered the land or water of these ecosystems, raising questions about how insects in less impacted environments are faring. I quantified the abundance of flying insects during 15 seasons spanning 2004-2024 on a relatively unscathed, subalpine meadow in Colorado, where weather data have been recorded for 38 years.
View Article and Find Full Text PDFEBioMedicine
September 2025
Department of Environmental Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Linus Biotechnology, Inc., North Brunswick Township, NJ, USA. Electronic address:
Background: Amyotrophic lateral sclerosis (ALS) is a rare motor neurodegenerative disorder and is predominantly diagnosed in older adults. Altered levels of essential and toxic elements have been implicated in ALS pathophysiology; however, little is known about the longitudinal biodynamic patterns of these elements in patients with ALS.
Methods: Using a single individual hair strand, we generated time series data of 400-800 time points approximately at 2 to 4 hourly resolution on 17 elemental intensities in ALS-positive cases and ALS-negative controls from a national collection and a regional centre in the US (on a total sample of 391, with 295 cases and 96 controls, with median age at hair collection over 60 years).
Neural Netw
August 2025
Institute of Software Chinese Academy of Sciences, Beijing, 100190, China; Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China. Electronic address:
Data inefficiency has long posed a significant challenge in the application of visual reinforcement learning methods to complex scenarios. To address this issue, recent studies have incorporated representation learning mechanisms to extract discriminative features by introducing auxiliary objectives that contrast pixel observations. However, our investigations suggest that these representations may not sufficiently capture the essential information for effective decision-making and could potentially impede policy learning.
View Article and Find Full Text PDFComput Biol Chem
August 2025
Laboratory of Regenerative Medicine, Faculty of Biology and Biotechnology, University of Science, Ho Chi Minh City, 700000, Viet Nam; Viet Nam National University, Ho Chi Minh City, 720325, Viet Nam. Electronic address:
Single-cell RNA-seq (scRNA-seq) analysis demands representations that are robust to sparsity and technical noise. We present scInfoMaxVAE, a mutual-information-maximizing variational autoencoder with a zero-inflated count likelihood tailored for scRNA-seq, designed for dimensionality reduction and cell-type classification. We evaluated the model on 12 public scRNA-seq datasets spanning multiple tissues and platforms using a unified pipeline with cell- and gene-level quality control (minimum detected genes), library-size normalization, log-transform, and reference-based cell-type annotation.
View Article and Find Full Text PDFEntropy (Basel)
July 2025
School of Computer Science and Artificial Intelligence, Nanjing University of Finance and Economics, Nanjing 210023, China.
Large language models (LLMs) pose significant challenges to content authentication, as their sophisticated generation capabilities make distinguishing AI-produced text from human writing increasingly difficult. Current detection methods suffer from limited information capture, poor rate-distortion trade-offs, and vulnerability to adversarial perturbations. We present CurveMark, a novel dual-channel detection framework that combines probability curvature analysis with dynamic semantic watermarking, grounded in information-theoretic principles to maximize mutual information between text sources and observable features.
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