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Svensson, J.

Publications and source records attributed to Svensson, J..

7 recordsLinked to original sources

Allometric correlates of heat tolerance in birds: A test using quail breeds with extreme size variation

Birds and mammals are shrinking and shapeshifting as global temperatures rise. Ecogeographic rules predict that such changes should ease heat stress by increasing surface-area-to-volume ratios, and thus, the capacity for heat exchange. This has led to the hypothesis that body size reductions are driven by thermoregulatory selection or adaptive plasticity, although recent syntheses point to more complex, multifactorial causes. Crucially, recent theoretical models predict that thermoregulatory benefits of smaller body size only emerge at extreme deviations from average phenotypes. Here, we exploit agricultural selection in Japanese quail to directly test this hypothesis, using three breeds spanning extreme differences in body mass, surface area, and relative appendage lengths. Evaporative cooling capacity and the scope for evaporative water loss broadly followed allometric predictions when contrasting small and larger breeds. As expected, this allowed the smallest breed to tolerate higher air temperatures. However, differences in heat tolerance limits between breeds were consistently much smaller than predicted. Additionally, the breadth of thermoneutral zones overlapped in full, and upper critical temperatures were remarkably similar, between breeds. Together, these results show that heat tolerance is only weakly linked to surface-area-to-volume relationships and cannot be explained by size alone. Thus, although smaller bodies may modestly enhance heat dissipation when size variation in a population is substantial, our findings suggest that recent body size reductions and morphological shifts are unlikely to be driven primarily by thermoregulatory benefits.

evolutionary biology↗

The contribution of small protected areas to the big picture of forest conservation and restoration in Sweden

In forest regions worldwide, industrial forestry has left fragments of natural forests behind. This challenges biodiversity conservation and calls for ecological restoration for sustainable forest management and conservation. The functionality of protected areas need to be improved and forest ecosystems set in a state that better favors biodiversity, resilience and provisioning of ecosystem services. Sweden contributes a substantial share of the European forests, with dominance of non-industrial forest ownership and extensive forestry footprint, and hence with immediate need for advanced conservation and restoration. Protection through voluntary nature conservation agreements and regulated biotope protection areas exists since the 1990s, with schemes involving economic compensation to landowners to facilitate conservation and restoration. Across entire Sweden and all ecoregions, we assessed their accumulated capacity over a 30-year period, including forest types protected, type of restoration management, rotation intervals, and selection of tree species. These nearly 14,000 different areas covering over 70,000ha are small, ranging in size from 5ha but rarely larger than 20ha. Their contribution is important, particularly in south Sweden with low and fragmented forest cover among many different owners. Active restoration dominates over passive set asides, coniferous forest types are less represented than more rare forest types, many different tree species are favored, and different restoration types occur but with few types dominating. In recognizing their critical importance, we find that the practices are narrow and repetitive, and that a greater restoration diversification is needed. The decreasing trend in protection is alarming since these contribute key forest type representativeness and functionality.

ecology↗

Multimodal brain age prediction using machine learning: combining structural MRI and 5-HT2AR PET derived features

To better assess the pathology of neurodegenerative disorders and the efficacy of neuroprotective interventions, it is necessary to develop biomarkers that can accurately capture age-related biological changes in the human brain. Brain serotonin 2A receptors (5-HT2AR) show a particularly profound age-related decline and are also reduced in neurodegenerative disorders, such as Alzheimers disease. This study investigates whether the decline in 5-HT2AR binding, measured in vivo using positron emission tomography (PET), can be used as a biomarker for brain aging. Specifically, we aim to 1) predict brain age using 5-HT2AR binding outcomes, 2) compare 5-HT2AR-based predictions of brain age to predictions based on gray matter (GM) volume, as determined with structural magnetic resonance imaging (MRI), and 3) investigate whether combining 5-HT2AR and GM volume data improves prediction. We used PET and MR images from 209 healthy individuals aged between 18 and 85 years (mean=38, std=18), and estimated 5-HT2AR binding and GM volume for 14 cortical and subcortical regions. Different machine learning algorithms were applied to predict chronological age based on 5-HT2AR binding, GM volume, and the combined measures. The mean absolute error (MAE) and a cross-validation approach were used for evaluation and model comparison. We find that both the cerebral 5-HT2AR binding (mean MAE=6.63 years, std=0.74 years) and GM volume (mean MAE=6.95 years, std=0.83 years) predict chronological age accurately. Combining the two measures improves the prediction further (mean MAE=5.54 years, std=0.68). In conclusion, 5-HT2AR binding measured using PET might be useful for improving the quantification of a biomarker for brain aging.

neuroscience↗

Mapping forests with different levels of naturalness using machine learning and landscape data mining

To conserve biodiversity, it is imperative to maintain and restore sufficient amounts of functional habitat networks. Hence, locating remaining forests with natural structures and processes over landscapes and large regions is a key task. We integrated machine learning (Random Forest) and open landscape data to scan all forest landscapes in Sweden with a 1 ha spatial resolution with respect to the relative likelihood of hosting High Conservation Value Forests (HCVF). Using independent spatial stand-and plot-level validation data we confirmed that our predictions (ROC AUC in the range of 0.89 - 0.90) correctly represent forests with different levels of naturalness, from deteriorated to those with high and associated biodiversity conservation values. Given ambitious national and international conservation objectives, and increasingly intensive forestry, our model and the resulting wall-to-wall mapping fills an urgent gap for assessing fulfilment of evidence-based conservation targets, spatial planning, and designing forest landscape restoration.

ecology↗

Prediction of brain age using structural magnetic resonance imaging: A comparison of accuracy and test-retest reliability of publicly available software packages

BackgroundBrain age prediction algorithms using structural magnetic resonance imaging (MRI) aim to assess the biological age of the human brain. The difference between a persons chronological age and the estimated brain age is thought to reflect deviations from a normal aging trajectory, indicating a slower, or accelerated, biological aging process. Several pre-trained software packages for predicting brain age are publicly available. In this study, we perform a head-to-head comparison of such packages with respect to 1) predictive accuracy, 2) test-retest reliability, and 3) the ability to track age progression over time. MethodsWe evaluated the six brain age prediction packages: brainageR, DeepBrainNet, brainage, ENIGMA, pyment, and mccqrnn. The accuracy and test-retest reliability were assessed on MRI data from 372 healthy people aged between 18.4 and 86.2 years (mean 38.7 {+/-} 17.5 years). ResultsAll packages showed significant correlations between predicted brain age and chronological age (r = 0.66 to 0.97, p < 0.001), with pyment displaying the strongest correlation. The mean absolute error was between 3.56 (pyment) and 9.54 years (ENIGMA). brainageR, pyment, and mccqrnn were superior in terms of reliability (ICC values between 0.94 - 0.98), as well as predicting age progression over a longer time span. ConclusionOf the six packages, pyment and brainageR consistently showed the highest accuracy and test-retest reliability.

neuroscience↗

Validity and reliability of extrastriatal raclopride binding quantification in the living human brain

[11C]raclopride is a well established PET tracer for the quantification of dopamine 2/3 receptors (D2/3R) in the striatum. Outside of the striatum the receptor density is up to two orders of magnitude lower. In contrast to striatal binding, the characteristics of extrastriatal [11C]raclopride binding quantification has not been thoroughly described. Still, binding data for e.g., neocortex is frequently reported in the scientific literature. Here we evaluate the validity and reliability of extrastriatal [11C]raclopride binding quantification. Two sets of healthy control subjects were examined with HRRT and [11C]raclopride: i) To assess the validity of extrastriatal [11C]raclopride binding estimates, eleven subjects were examined at baseline and after dosing with quetiapine, a D2/3R antagonist. ii) To assess test-retest repeatability, nine subjects were examined twice. Non displaceable binding potential (BPND) was quantified using the simplified reference tissue model. Quetiapine dosing was associated with decrease in [11C]raclopride BPND in temporal cortex (18{+/-}17% occupancy) and thalamus (20{+/-}17%), but not in frontal cortex. Extrastriatal occupancy was lower than in putamen (51{+/-}4%). The mean absolute variation was 4-7% in the striatal regions, 17% in thalamus, and 13-59% in cortical regions. Our data indicate that [11C]raclopride PET is not a suitable tool for D2/3R binding quantification in extrastriatal regions.

neuroscience↗

The MS4A gene cluster is a key regulator of soluble TREM2 and Alzheimer disease risk

Soluble triggering receptor expressed on myeloid cells 2 (sTREM2) levels in the cerebrospinal fluid (CSF) have been associated with Alzheimer disease (AD) status. TREM2 plays a critical role in microglial activation, survival, and phagocytosis; however, the pathophysiological role of sTREM2 in AD is not well understood. Understanding the role of sTREM2 in AD may help reveal biological mechanisms underlying AD and identify novel therapeutic targets. We performed a genome-wide association study (GWAS) to identify genetic modifiers of CSF sTREM2 levels. Common variants in the membrane-spanning 4-domains subfamily A (MS4A) gene region were associated with higher CSF sTREM2 levels (rs1582763; P = 1.15x10-15) and replicated in independent datasets. The variants associated with increased levels of sTREM2 are also associated with reduced AD risk and delayed age-at-onset. Rs1582763 influences expression of MS4A4A and MS4A6A in multiple tissues, suggesting that one or both of these genes are important for regulating sTREM2. MS4A genes encode transmembrane proteins that may play a role in intracellular protein trafficking in microglia. We used human macrophages to begin to test the relationship between MS4A4A and TREM2 and found that they co-localize intracellularly and that antibody-mediated targeting of MS4A4A reduces sTREM2. Thus, genetic, molecular, and cellular findings suggest that MS4A4A regulates sTREM2. These findings also provide a mechanistic explanation of the original GWAS signal in the MS4A locus for AD risk and indicate that TREM2 is involved in sporadic AD risk in general, not only in TREM2 risk-variant carriers.

genomics↗