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Mercury levels in fish in reservoirs and natural lakes have been monitored on a regular basis since 1978 at the La Grande hydroelectric complex located in the James Bay region of Quebec, Canada. The main analytical tools historically used were analysis of covariance (ANCOVA), linear regression of the mercury-to-length relationship and Student- Newman-Keuls (SNK) multiple comparisons of mean mercury levels. Inadequacy of linear regression (mercury-to-length relationships are often curvilinear) and difficulties in comparing mean mercury levels when regressions differ lead us to use polynomial regression with indicator variables. For comparisons between years, polynomial regression models relate mercury levels to length (L), length squared (L2), binary (dummy) indicator variables (Bn), each representing a sampled year, and the products of each of these explanatory variables (L x B1, L2 x B1, L x B2, etc.). Optimal transformations of the mercury levels (for normality and homogeneity) were found by the Box-Cox procedure. The models so obtained formed a partially nested series corresponding to four situations: (a) all years are well represented by a single polynomial model; (b) the year-models are of the same shape, but the means may differ; (c) the means are the same, but the year-models differ inshape; (d) both the means and shapes may differ among years. Since year-specific models came from the general one, rigorous statistical comparisons are possible between models. Polynomial regression with indicator variables allows rigorous statistical comparisons of mercury-to-length relationships among years, even when the shape of the relationships differ. It is simple to obtain accurate estimates of mercurylevels at standardized length, and multiple comparisons of these estimations are simple to perform. The method can also be applied to spatial analysis (comparison of sampling stations), or to the comparisonof [ABSTRACT FROM AUTHOR]

Fish mercury concentrations were monitored before and after the construction of the La Grande hydroelectric complex, located in the JamesBay region, Quebec, Canada. Monitoring results revealed that sympatric populations of dwarf and normal lake whitefish (Coregonus clupeaformis) occur in reservoirs and natural lakes of the area. Dwarf specimens bioaccumulate mercury more rapidly than normal individuals. In natural Serigny Lake, 6--7 year old dwarf specimens show mean total mercury levels 1.5 times higher than normal individuals of the same age (0.33 vs 0.21 mg kg-1 wet weight). Eleven years after the impoundment of the Caniapiscau reservoir, 6--7 year old dwarf specimens show total mercury concentrations 3 times higher than normal specimens of similar ages (0.74 vs 0.27 mg kg-1). Stomach content analysis showed that although large normal individuals feed on different prey than do dwarf individuals, similar methyl mercury levels were found in prey of both forms. We suggest that dwarf individuals bioaccumulate mercury more rapidly than normal specimens because they begin to mature at an earlier age (usually at age 2 to 3 compared to age 6 for normal specimens) and thus produce proportionally less flesh to dilute the mercury assimilated. Total mercury analysis indicating higher concentrations in flesh than in gonads support this hypothesis. Our results suggest that when monitoring mercury levels in fish in areas where populations of dwarf and normal lake whitefish coexist,dwarf and normal specimens must be distinguished because of their different bioaccumulation rates. [ABSTRACT FROM AUTHOR]

Sleep is essential for the optimal consolidation of newly acquired memories. This study examines the neurophysiological processes underlying memory consolidation during sleep, via reactivation. Here, we investigated the impact of slow wave - spindle (SW-SP) coupling on regionally-task-specific brain reactivations following motor sequence learning. Utilizing simultaneous EEG-fMRI during sleep, our findings revealed that memory reactivation occured time-locked to coupled SW-SP complexes, and specifically in areas critical for motor sequence learning. Notably, these reactivations were confined to the hemisphere actively involved in learning the task. This regional specificity highlights a precise and targeted neural mechanism, underscoring the crucial role of SW-SP coupling. In addition, we observed double-dissociation whereby primary sensory areas were recruited time-locked to uncoupled spindles; suggesting a role for uncoupled spindles in sleep maintenance. These findings advance our understanding the functional significance of SW-SP coupling for enhancing memory in a regionally-specific manner, that is functionally dissociable from uncoupled spindles., Competing Interests: Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Background and Purpose: There are distinct challenges in the preprocessing of spinal cord fMRI data, particularly concerning the mitigation of voluntary or involuntary movement artifacts during image acquisition. Despite the notable progress in data processing techniques for movement detection and correction, applying motion correction algorithms developed for the brain cortex to the brainstem and spinal cord remains a challenging endeavor., Methods: In this study, we employed a deep learning-based convolutional neural network (CNN) named DeepRetroMoCo, trained using an unsupervised learning algorithm. Our goal was to detect and rectify motion artifacts in axial T2*-weighted spinal cord data. The training dataset consisted of spinal cord fMRI data from 27 participants, comprising 135 runs for training and 81 runs for testing., Results: To evaluate the efficacy of DeepRetroMoCo, we compared its performance against the sct_fmri_moco method implemented in the spinal cord toolbox. We assessed the motion-corrected images using two metrics: the average temporal signal-to-noise ratio (tSNR) and Delta Variation Signal (DVARS) for both raw and motion-corrected data. Notably, the average tSNR in the cervical cord was significantly higher when DeepRetroMoCo was utilized for motion correction, compared to the sct_fmri_moco method. Additionally, the average DVARS values were lower in images corrected by DeepRetroMoCo, indicating a superior reduction in motion artifacts. Moreover, DeepRetroMoCo exhibited a significantly shorter processing time compared to sct_fmri_moco., Conclusion: Our findings strongly support the notion that DeepRetroMoCo represents a substantial improvement in motion correction procedures for fMRI data acquired from the cervical spinal cord. This novel deep learning-based approach showcases enhanced performance, offering a promising solution to address the challenges posed by motion artifacts in spinal cord fMRI data., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Mobarak-Abadi, Mahmoudi-Aznaveh, Dehghani, Zarei, Vahdat, Doyon and Khatibi.)

Background: Addressing complex and interconnected ecological, social, and health issues necessitates upstream, solutions-oriented, and whole-systems thinking. Specifically, exploring what it means to live in reciprocity with the planet and all living systems, now and for generations to come, can have a crucial role in advancing planetary health., Methods: In this presentation, we show findings from four gatherings that we co-designed and co-hosted to connect communities, lands, waters, climate, and health. We draw lessons from two events in the Cowichan watershed (co-hosted by Cowichan Tribes) and the Stellako and Nechako watersheds (co-hosted by Stellat'en First Nation) that were co-designed with the ECHO Network. These gatherings brought together youth, Indigenous leaders, researchers, and health, community, and environmental decision-makers across British Columbia to learn how to address connected health, environmental, and community concerns. We supplement these findings with insights from two co-hosted conversations, that connected five continents and more than 40 countries, each enabling 24 h of continuous dialogue on the theme of working together for a healthy, just, and sustainable planet., Findings: Gatherings took place between Oct 12, 2022, and Dec 1, 2023. These gatherings each provided lessons about how Indigenous-led, integrative approaches that connect the health of people to lands, waters, and ecosystems can elevate and enhance our work and scholarship to address ecological, social, and health issues. Specifically, initiatives co-designed in this way help overcome challenges that arise when addressing boundary-crossing, intersectional and intersectoral issues at the nexus of climate, public health, and planetary health. Our findings include insights into strengthening research and public health capacity for integrative approaches to complex issues that are relevant to place-based contexts and have implications and applications for planetary health., Interpretation: Our presentation summarises how these gatherings-each of which were fuelled by a sense of love for the planet, each other, and future generations-have progressed intersectoral, intergenerational, boundary-crossing, and transformative approaches to planetary health. These insights help guide future directions for planetary health research, practice, and policy., Funding: The Canadian Institute of Health Research Environment (IP4150712), Michael Smith Health Research BC (RA-2022-2872), and Vancouver Foundation (FOI19-1781)., Competing Interests: Declaration of interests We declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)