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The fossil record provides the only direct evidence of temporal trends in biodiversity over evolutionary timescales. Studies of biodiversity using the fossil record are, however, largely limited to discussions of taxonomic and/or morphological diversity. Behavioural and physiological traits that are likely to be under strong selection are largely obscured from the body fossil record. Similar problems exist in modern ecosystems where animals are difficult to access. In this review, we illustrate some of the common conceptual and methodological ground shared between those studying behavioural ecology in deep time and in inaccessible modern ecosystems. We discuss emerging ecogeochemical methods used to explore population connectivity and genetic drift, life-history traits and field metabolic rate and discuss some of the additional problems associated with applying these methods in deep time.
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http://dx.doi.org/10.1098/rstb.2015.0223 | DOI Listing |
ACS Chem Neurosci
September 2025
Chemical and Biomolecular Engineering Dept, University of California, Los Angeles, Los Angeles, California 90095, United States.
Simulations in three dimensions and time provide guidance on implantable, electroenzymatic glutamate sensor design; relative placement in planar sensor arrays; feasibility of sensing synaptic release events; and interpretation of sensor data. Electroenzymatic sensors based on the immobilization of oxidases on microelectrodes have proven valuable for the monitoring of neurotransmitter signaling in deep brain structures; however, the complex extracellular milieu featuring slow diffusive mass transport makes rational sensor design and data interpretation challenging. Simulations show that miniaturization of the disk-shaped device size below a radius of ∼25 μm improves sensitivity, spatial resolution, and the accuracy of glutamate concentration measurements based on calibration factors determined .
View Article and Find Full Text PDFAsian Cardiovasc Thorac Ann
September 2025
Cardiothoracic Surgery Department, Faculty of Medicine, Cairo University, Cairo, Egypt.
BackgroundThe optimal cerebral protection strategy during complex aortic surgery remains controversial, and various brain monitoring modalities are used to provide different information to improve cerebral protection. This study aims to compare the effect of the change in cerebral oxygen saturation during hypothermic circulatory arrest on the early postoperative neurological outcome in antegrade cerebral perfusion (ACP) versus retrograde cerebral perfusion (RCP) during circulatory arrest in adult aortic surgery using cerebral oximetry.MethodsThis was a cross-sectional analytic study that enrolled a total of 84 patients undergoing total circulatory arrest during adult aortic surgery divided into two groups.
View Article and Find Full Text PDFComput Methods Biomech Biomed Engin
September 2025
School of Medicine, Tzu Chi University, Hualien, Taiwan.
This study explores deep feature representations from photoplethysmography (PPG) signals for coronary artery disease (CAD) identification in 80 participants (40 with CAD). Finger PPG signals were processed using multilayer perceptron (MLP) and convolutional neural network (CNN) autoencoders, with performance assessed via 5-fold cross-validation. The CNN autoencoder model achieved the best results (recall 96.
View Article and Find Full Text PDFAdv Healthc Mater
September 2025
Department of Biomedical Engineering, University of Houston, Houston, TX, 77204, USA.
Neurogenic bladder and lower urinary tract (LUT) dysfunctions encompass a wide variety of urinary disorders resulting from nervous system impairments. Unfortunately, conventional treatments are still limited and can have significant complication rates, especially when stent implantations or other surgical procedures are involved. Therefore, there is a critical need to develop novel therapeutic strategies and pharmacological approaches to address these challenging urological conditions.
View Article and Find Full Text PDFJ Integr Neurosci
August 2025
School of Computer Science, Guangdong Polytechnic Normal University, 510665 Guangzhou, Guangdong, China.
Background: Emotion recognition from electroencephalography (EEG) can play a pivotal role in the advancement of brain-computer interfaces (BCIs). Recent developments in deep learning, particularly convolutional neural networks (CNNs) and hybrid models, have significantly enhanced interest in this field. However, standard convolutional layers often conflate characteristics across various brain rhythms, complicating the identification of distinctive features vital for emotion recognition.
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