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No effect of snow on shrub xylem traits: Insights from a snow-manipulation experiment on Disko Island, Greenland.
No full textWidespread shrubification across the Arctic has been generally attributed to increasing air temperatures, but responses vary across species and sites. Wood structures related to the plant hydraulic architecture may respond to local environmental conditions and potentially impact shrub growth, but these relationships remain understudied. Using methods of dendroanatomy, we analysed shrub ring width (RW) and xylem anatomical traits of 80 individuals of Salix glauca L. and Betula nana L. at a snow manipulation experiment in Western Greenland. We assessed how their responses differed between treatments (increased versus ambient snow depth) and soil moisture regimes (wet and dry). Despite an increase in snow depth due to snow fences (28-39 %), neither RW nor anatomical traits in either species showed significant responses to this increase. In contrast, irrespective of the snow treatment, the xylem specific hydraulic conductivity (Ks) and early-wood vessel size (LA95) for the study period were larger in S. glauca (p < 0.1, p < 0.01) and B. nana (p < 0.01, p < 0.001) at the wet than the dry site, while both species had larger vessel groups at the dry than the wet site (p < 0.01). RW of B. nana was higher at the wet site (p < 0.01), but no differences were observed for S. glauca. Additionally, B. nana Ks and LA95 showed different trends over the study period, with decreases observed at the dry site (p < 0.001), while for other responses no difference was observed. Our results indicate that, taking into account ontogenetic and allometric trends, hydraulic related xylem traits of both species, along with B. nana growth, were influenced by soil moisture. These findings suggest that soil moisture regime, but not snow cover, may determine xylem responses to future climate change and thus add to the heterogeneity of Arctic shrub dynamics, though more long-term species- and site- specific studies are needed
Human Resting-State Complexity of BOLD fMRI in Ultra-High-Field MRI at 7T: a primer
No full textSynopsis
Keywords: fMRI Analysis, fMRI (resting state), complexity
Motivation: BOLD-fMRI intrinsic functional connectivity has limited capability to assess the temporal dynamics of complex brain networks. The insufficient signal-to-noise ratio of 3T MRI might prevent the detection of subtle alterations.
Goal(s): Detecting resting-state complexity alterations in healthy subsamples using 7T MRI.
Approach: Multiscale entropy was computed for ten scales from 0.1 to 1 Hz. A whole-brain ANCOVA was conducted to assess entropy differences of the scales between 30 healthy adults with spider phobia and 45 without.
Results: Spider phobia showed decreased entropy in several fear-related brain regions in all scales except 1 Hz.
Impact: 7T fMRI detected reduced high-frequency resting-state multiscale entropy related to spider phobia, indicating worse local processing of fear and memory-related brain regions.
Introduction
Intrinsic functional connectivity (iFC) derived from BOLD-fMRI data is still widely used to map the brain’s functional architecture at rest. Despite the substantial insight gained into diseased brain networks and corroborated markers for cognitive symptomatology, the method’s major shortcoming, one correlative metric over the entire scanning time, fails at characterizing the temporal dynamics of complex brain systems. Recently, multiscale entropy (MSE) analyses of resting-state BOLD fMRI signals have gained increased attention in basic and clinical neuroscience. MSE detects self-similarity of complex signals across multiple time scales in a random noise environment [1]. The MSE’s main advantage is that it can assess alterations and interactions of neuronal circuits on spatial and temporal scales. Hence, numerous studies have yielded novel insights into temporal dynamics of the brain’s functional reorganization [2-4]. Both iFC and MSE rely on a sufficient temporal and spatial signal-to-noise ratio (SNR) of the data. With advances in image processing algorithms, and especially with the availability of 7T MRI, images with increased SNR allow the detection of more subtle effects [5,6]. Hence, this study explored the MSE of a set of 7T BOLD-fMRI data that consisted of healthy participants, who were subdivided into a spider phobia (PH) and a control group (HC) sample. The rationale was to test whether MSE at 7T is sufficiently sensitive to detect MSE differences between these groups even though no spider images, the fear-triggering stimulus defining spider phobia, were shown during data acquisition. Nevertheless, assuming a hyperactive fear circuit in PH, we hypothesized decreased local processing complexity as measured with high-frequency MSE in brain regions involving the amygdala, hippocampus, parahippocampus, medial temporal lobe, fusiform gyrus, and anterior cingulate cortex [7].
Methods
Study participant demographics and statistics are depicted in Figure 1. Resting-state fMRI data was obtained with a 32-channel head/neck coil in a Siemens Magnetom Terra 7T machine at the University Hospital Bern. A multiband echo-planar protocol with 360 measurements, 60 slices, TR/TE = 1000/25 ms, and iso-voxel size = 2 mm^3 was applied. Image preprocessing included motion-realignment, slice-time correction, detrending, denoising, normalization, and 3-mm-smoothing. MSE was computed using the LOFT Complexity Toolbox [8] with pattern matching threshold r = 0.2, pattern length = 2, scales = 1 – 10 (1 – 0.1 Hz) [9-11]. All images were masked with a mean grey matter mask of all subjects. Voxel-wise statistics were computed in SPM12 and comprised an ANCOVA with factors scale (1 – 10) and diagnosis (HC, PH), and age as a covariate. The significance threshold was pFWE < 0.05 and a cluster-size threshold of 5 voxels. Significant clusters were overlaid with the aal atlas [12-14] and segmented accordingly for a post-hoc ROI analysis, a non-parametric ANOVA investigating mean ROI entropy for each scale between the diagnosis groups.
Results
The voxel-wise interaction of the 2 × 10 ANCOVA revealed several significant clusters (F(9,729) = 5.49, p(FWE) = 0.05, cluster-size threshold = 5 voxels), which were subdivided into seven major regions of interest (ROIs): amygdala, caudate nucleus, fusiform gyrus, hippocampus, parahippocampus, putamen, and thalamus (Figure 2). To disentangle the two-way interaction involving these ROIs, the post-hoc ANOVA revealed the main effects of diagnosis (F(1) = 5.26, p = 0.02), ROI (F(3.8) = 20.0, p < 0.0001), and scale (F(1.5) = 114.8, p < 0.0001, see Figure 3). Merely the scale × ROI two-way interaction was significant (F(14) = 5.0, p < 0.0001). Note that the three-way interaction diagnosis × ROI × scale was not significant.
Discussion
This proof-of-concept study revealed reduced entropy in anxiety, memory, and emotion-regulation brain regions in PH compared to HC. Most brain regions with decreased MSE, such as the amygdala, hippocampus, parahippocampus, and fusiform gyrus, are hyperactive in PH [7]. The thalamus has been linked with autonomous arousal in PH [15], while the striatum, including putamen and caudate nucleus, was shown active during threat monitoring [16]. These MSE reductions were found in 9 of 10 frequencies (0.1 – 0.5 Hz). In the 1 Hz frequency, no group differences could be observed (Figure 3). With the scanning protocol used in this study, lower frequencies could not be assessed (i.e., < 0.1 Hz), which is a limitation and might explain the uniformity of the effects between most scales.
Conclusions
MSE analysis is a promising method that takes advantage of the higher temporal SNR of 7T fMRI, as demonstrated in this study. Using MSE as an add-on to iFC measures, a more refined picture of the dynamics of complex neuronal systems can be achieved.
Acknowledgements
This study was supported by the University Hospital for Psychiatry and Psychotherapy Bern, Switzerland. We thank the following contributors: Andrea Federspiel and Piotr Radojewski of the Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland, for technical and clinical support at the MRI scanner site; Dilmini Wijesinghe of the Laboratory of FMRI Technology, USC Stevens Neuroimaging and Informatics Institute, University of Southern California, Keck School of Medicine, Los Angeles, California, USA, for providing valuable insight in fMRI complexity developments; Thomas Dierks of the Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Switzerland, for conceptual advice.
References
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2. Jann K, Boudreau J, Albrecht D, Cen SY, Cabeen RP, Ringman JM, Wang DJJ. FMRI Complexity Correlates with Tau-PET and Cognitive Decline in Late-Onset and Autosomal Dominant Alzheimer’s Disease. J Alz Dis. 2023;95(2):437-451
3. Grieder M, Wang DJJ, Dierks T, Wahlund LO, Jann K. Default Mode Network Complexity and Cognitive Decline in Mild Alzheimer’s Disease. Front Neurosci. 2018;12(770)
4. Xin X, Long S, Sun M, Gao X. The Application of Complexity Analysis in Brain Blood-Oxygen Signal. Brain Sci. 2021;11(11):1415
5. Dumoulin SO, Fracasso A, van der Zwaag W, Siero JCW, Petridou N. Ultra-high field MRI: Advancing systems neuroscience towards mesoscopic human brain function. NeuroImage. 2018;168:345-357
6. Pohmann R, Speck O, Scheffler K. Signal-to-noise ratio and MR tissue parameters in human brain imaging at 3, 7, and 9.4 tesla using current receive coil arrays. Magn Res Med. 2016;5(2):801-809
7. Soravia LM, Orosz A, Schwab S, Nakataki M, Wiest R, Federspiel A. CBT reduces CBF: cognitive-behavioral therapy reduces cerebral blood flow in fear-relevant brain regions in spider phobia. Brain Behav. 2016;6(9):e00510
8. Laboratory of Functional MRI Technology (LOFT), Department of Neurology, USC Developed by Jothi A, Sharma N, Adhikari S, Wang DJJ, Jann K
9. Li X, Zhu Z, Zhao W, Sun Y, Wen D, Xie Y, Liu X, Niu H, Han Y. Decreased resting-state brain signal complexity in patients with mild cognitive impairment and Alzheimer’s disease: a multi-scale entropy analysis. Biomed Opt Express. 2018;9(4):1916-1929
10. Smith RX, Yan L, Wang DJJ. Multiple time scale complexity analysis of resting state FMRI. Brain Imaging Behav. 2014;8:284–291
11. Sokunbi MO. Sample entropy reveals high discriminative power between young and elderly adults in short fMRI data sets. Front Neuroinform. 2014;8:69
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16. Mobbs D, Yu R, Rowe JB, Eich H, FeldmanHall O, Dalgleish T. Neural activity associated with monitoring the oscillating threat value of a tarantula. Proc Natl Acad Sci U S A. 2010;107(47):20582-2058
Reconstructing the environmental impact of mining on mountain lakes.
No full textMountain lakes are particularly fragile ecosystems undergoing important transformations associated with ongoing global change. However, the history of anthropogenic impacts on mountain lakes and their catchments is much longer, in many cases featuring millennia of summer pastoral farming. More recently, the growing demand for raw materials and energy linked to industrialization, particularly accelerated since the 19th century CE, meant a further increase in human impact on mountain areas. The Cantabrian Range (northern Spain) constitutes a paradigmatic case of southern European mountain range experiencing intense human impact during the past few millennia and particularly the past two centuries. Here, we have reconstructed the environmental dynamics of this area during the last millennium, with a particular focus on the impact of mining, based on the multidisciplinary analysis (sedimentology, biogeochemistry, magnetic susceptibility, diatoms, pollen, charcoal and dung fungal spores) of sediment cores from Lago de La Cueva (43°03'N, 6°06'W, 1550 m a.s.l.). Changing land use and climate have driven lake dynamics during the last centuries. A major fire-caused deforestation event dated to the late 15th century CE increased erosion and the frequency of intense runoff episodes, in the context of the wetter and colder Little Ice Age. The onset of iron mining activities in the catchment ca. 200 years ago had a strong impact on the lake. Sedimentation rates notably raised and mining waste containing hematite and potentially toxic elements (e.g. Fe, Co, As) was washed into the lake. Additionally, diatom assemblages showed that lake regulation since the early 20th century CE severely altered the natural hydrological regime introducing rapid seasonal lake-level oscillations and increased lakeshore erosion, water turbidity and nutrient loads. The recent environmental restoration, finished in 2006, has involved the re-deposition of large volumes of mine tailing. Although some mining wastewater still arrives into the lake because mine drainage is still active, restoration works have succeeded in reducing erosion rates and nutrient loads. Lower land-use intensity has also contributed to natural vegetation recovery, further diminishing erosion. This study illustrates the complex interactions between human activities (grazing, mining, hydropower) and climate change in defining mountain landscape shifts through time. Moreover, our results highlight the usefulness of paleolimnological research to quantitatively assess the effectiveness of lake restoration programs
Family environment and self-esteem development in adolescence: A replication and extension
No full textA study by Krauss et al. (2020) suggested that the family environment (e.g., parental warmth, economic conditions of family) plays an important role for self-esteem development in adolescence. The present research sought to closely replicate and extend the study, using 4-wave longitudinal data from the Iowa Youth and Families Project, including 451 families. To replicate the prior study, we conducted the same set of analyses with similar measures and multi-informant assessments of mothers, fathers, and children from the same families. To extend the previous study, we tested novel aspects (i.e., controlling for prior exposure and testing the effect of the quality of sibling relationships). Overall, the findings provide no evidence for prospective effects between family environment and self-esteem in adolescence
Drought alters aboveground biomass production efficiency: Insights from two European beech forests.
No full textThe fraction of photosynthetically assimilated carbon that trees allocate to long-lasting woody biomass pools (biomass production efficiency - BPE), is a key metric of the forest carbon balance. Its apparent simplicity belies the complex interplay between underlying processes of photosynthesis, respiration, litter and fruit production, and tree growth that respond differently to climate variability. Whereas the magnitude of BPE has been routinely quantified in ecological studies, its temporal dynamics and responses to extreme events such as drought remain less well understood. Here, we combine long-term records of aboveground carbon increment (ACI) obtained from tree rings with stand-level gross primary productivity (GPP) from eddy covariance (EC) records to empirically quantify aboveground BPE (= ACI/GPP) and its interannual variability in two European beech forests (Hainich, DE-Hai, Germany; Sorø, DK-Sor, Denmark). We found significant negative correlations between BPE and a daily-resolved drought index at both sites, indicating that woody growth is de-prioritized under water limitation. During identified extreme years, early-season drought reduced same-year BPE by 29 % (Hainich, 2011), 31 % (Sorø, 2006), and 14 % (Sorø, 2013). By contrast, the 2003 late-summer drought resulted in a 17 % reduction of post-drought year BPE at Hainich. Across the entire EC period, the daily-to-seasonal drought response of BPE resembled that of ACI, rather than that of GPP. This indicates that BPE follows sink dynamics more closely than source dynamics, which appear to be decoupled given the distinctive climate response patterns of GPP and ACI. Based on our observations, we caution against estimating the magnitude and variability of the carbon sink in European beech (and likely other temperate forests) based on carbon fluxes alone. We also encourage comparable studies at other long-term EC measurement sites from different ecosystems to further constrain the BPE response to rare climatic events
Controls of temperature and mineral growth rate on lithium and sodium incorporation in abiotic aragonite
No full textHomogenized finite element simulations can predict the primary stability of dental implants in human jawbone
No full textTOLLIP and MUC5B modulate the effect of ambient NO2 on respiratory symptoms in infancy.
No full textBACKGROUND
Current knowledge suggests that the gene region containing MUC5B and TOLLIP plays a role in airway defence and airway inflammation, and hence respiratory disease. It is also known that exposure to air pollution increases susceptibility to respiratory disease. We aimed to study whether the effect of air pollutants on the immune response and respiratory symptoms in infants may be modified by polymorphisms in MUC5B and TOLLIP genes.
METHODS
359 healthy term infants from the prospective Basel-Bern Infant Lung Development (BILD) birth cohort were included in the study. The main outcome was the score of weekly assessed respiratory symptoms in the first year of life. Using the candidate gene approach, we selected 10 single nucleotide polymorphisms (SNPs) from the MUC5B and TOLLIP regions. Nitrogen dioxide (NO2) and particulate matter ≤10μm in aerodynamic diameter (PM10) exposure was estimated on a weekly basis. We used generalised additive mixed models adjusted for known covariates. To validate our results in vitro, cells from a lung epithelial cell line were downregulated in TOLLIP expression and exposed to diesel particulate matter (DPM) and polyinosinic-polycytidylic acid.
RESULTS
Significant interaction was observed between modelled air pollution (weekly NO2 exposure) and 5 SNPs within MUC5B and TOLLIP genes regarding respiratory symptoms as outcome: E.g., infants carrying minor alleles of rs5744034, rs3793965 and rs3750920 (all TOLLIP) had an increased risk of respiratory symptoms with increasing NO2 exposure. In vitro experiments showed that cells downregulated for TOLLIP react differently to environmental pollutant exposure with DPM and viral stimulation.
CONCLUSION
Our findings suggest that the effect of air pollution on respiratory symptoms in infancy may be influenced by the genotype of specific SNPs from the MUC5B and TOLLIP regions. For validation of the findings, we provided in vitro evidence for the interaction of TOLLIP with air pollution
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