Projecting the fates of populations under climate change is one of global change biology's foremost challenges. Here, we seek to identify the contributions that temperature-mediated local adaptation and plasticity make to spatial variation in nesting phenology, a phenotypic trait showing strong responses to warming. We apply a mixed modeling framework to a Britain-wide spatiotemporal dataset comprising >100 000 records of first egg dates from four single-brooded passerine bird species. The average temperature during a specific time period (sliding window) strongly predicts spatiotemporal variation in lay date. All four species exhibit phenological plasticity, advancing lay date by 2-5 days °C(-1) . The initiation of this sliding window is delayed further north, which may be a response to a photoperiod threshold. Using clinal trends in phenology and temperature, we are able to estimate the temperature sensitivity of selection on lay date (B), but our estimates are highly sensitive to the temporal position of the sliding window. If the sliding window is of fixed duration with a start date determined by photoperiod, we find B is tracked by phenotypic plasticity. If, instead, we allow the start and duration of the sliding window to change with latitude, we find plasticity does not track B, although in this case, at odds with theoretical expectations, our estimates of B differ across latitude vs. longitude. We argue that a model combining photoperiod and mean temperature is most consistent with current understanding of phenological cues in passerines, the results from which suggest that each species could respond to projected increases in spring temperatures through plasticity alone. However, our estimates of B require further validation.
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Hum Brain Mapp
February 2025
Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, Harbin, China.
Pattern separation and pattern completion in the hippocampus play a critical role in episodic learning and memory. However, there is limited empirical evidence supporting the role of the hippocampal circuit in these processes during complex continuous experiences. In this study, we analyzed high-resolution fMRI data from the "Forrest Gump" open-access dataset (16 participants) using a sliding-window temporal autocorrelation approach to investigate whether the canonical hippocampal circuit (DG-CA3-CA1-SUB) shows evidence consistent with the occurrence of pattern separation or pattern completion during a naturalistic audio movie task.
View Article and Find Full Text PDFBMC Bioinformatics
January 2025
Centre for Big Data Research in Health, University of New South Wales, Sydney, Australia.
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View Article and Find Full Text PDFRev Cardiovasc Med
January 2025
Cardiology Department, Université de Mons, 7000 Mons, Belgium.
Background: Neuromodulation has been shown to increase the efficacy of atrial fibrillation (AF) ablation procedures. However, despite its ability to influence the autonomic nervous system (ANS), the exact mechanism of action remains unclear. The activity of the ANS via the intracardiac nervous system (ICNS) can be inferred from heart rate variability (HRV).
View Article and Find Full Text PDFInt J Mol Sci
January 2025
Department of Molecular Medicine and Surgery, Karolinska Institutet, 17176 Stockholm, Sweden.
A previous genome-wide association study (GWAS) in colorectal cancer (CRC) patients with gastric and/or prostate cancer in their families suggested genetic loci with a shared risk for these three cancers. A second haplotype GWAS was undertaken in the same colorectal cancer patients and different controls with the aim of confirming the result and finding novel loci. The haplotype GWAS analysis involved 685 patients with colorectal cancer cases and 1642 healthy controls from Sweden.
View Article and Find Full Text PDFPLoS One
January 2025
Faculty of Engineering, Free University of Bozen-Bolzano, Bolzano, South Tyrol, Italy.
Appraisal models, such as the Scherer's Component Process Model (CPM), represent an elegant framework for the interpretation of emotion processes, advocating for computational models that capture emotion dynamics. Today's emotion recognition research, however, typically classifies discrete qualities or categorised dimensions, neglecting the dynamic nature of emotional processes and thus limiting interpretability based on appraisal theory. In our research, we estimate emotion intensity from multiple physiological features associated to the CPM's neurophysiological component using dynamical models with the aim of bringing insights into the relationship between physiological dynamics and perceived emotion intensity.
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