Natural quantitative genetic variance in plant growth differs in response to ecologically relevant temperature heterogeneity

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Biophysical suitability, economic pressure and land-cover change: a global probabilistic approach and insights for REDD+

There has been a concerted effort by the international scientific community to understand the multiple causes and patterns of land-cover change to support sustainable land management. Here, we examined biophysical suitability, and a novel integrated index of “Economic Pressure on Land” (EPL) to explain land cover in the year 2000, and estimated the likelihood of future land-cover change through 2050, including protected area effectiveness. Biophysical suitability and EPL explained almost half of the global pattern of land cover (R 2 = 0.45), increasing to almost two-thirds in areas where a long-term equilibrium is likely to have been reached (e.g. R 2 = 0.64 in Europe). We identify a high likelihood of future land-cover change in vast areas with relatively lower current and past deforestation (e.g. the Congo Basin). Further, we simulated emissions arising from a “business as usual” and two reducing emissions from deforestation and forest degradation (REDD) scenarios by incorporating data on biomass carbon. As our model incorporates all biome types, it highlights a crucial aspect of the ongoing REDD + debate: if restricted to forests, “cross-biome leakage” would severely reduce REDD + effectiveness for climate change mitigation. If forests were protected from deforestation yet without measures to tackle the drivers of land-cover change, REDD + would only reduce 30 % of total emissions from land-cover change. Fifty-five percent of emissions reductions from forests would be compensated by increased emissions in other biomes. These results suggest that, although REDD + remains a very promising mitigation tool, implementation of complementary measures to reduce land demand is necessary to prevent this leakage

Soil fertility limits carbon sequestration by forest ecosystems in a CO2-enriched atmosphere

Northern mid-latitude forests are a large terrestrial carbon sink(1-4). Ignoring nutrient limitations, large increases in carbon sequestration from carbon dioxide (CO2) fertilization are expected in these forests(5). Yet, forests are usually relegated to sites of moderate to poor fertility, where tree growth is often limited by nutrient supply, in particular nitrogen(6,7). Here we present evidence that estimates of increases in carbon sequestration of forests, which is expected to partially compensate for increasing CO2 in the atmosphere, are unduly optimistic(8). In two forest experiments on maturing pines exposed to elevated atmospheric CO2, the CO2-induced biomass carbon increment without added nutrients was undetectable at a nutritionally poor site, and the stimulation at a nutritionally moderate site was transient, stabilizing at a marginal gain after three years. However, a large synergistic gain from higher CO2 and nutrients was detected with nutrients added. This gain was even larger at the poor site (threefold higher than the expected additive effect) than at the moderate site (twofold higher). Thus, fertility can restrain the response of wood carbon sequestration to increased atmospheric CO2. Assessment of future carbon sequestration should consider the limitations imposed by soil fertility, as well as interactions with nitrogen deposition.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62517/1/411469a0.pd

Metal-saturated sulfide assemblages in NWA 2737: Evidence for impact-related sulfur devolatilization in Martian meteorites

International audienceNWA 2737, a Martian meteorite from the Chassignite subclass, contains minute amounts (0.010 ± 0.005 vol%) of metal-saturated Fe-Ni sulfides. These latter bear evidence of the strong shock effects documented by abundant Fe nanoparticles and planar defects in Northwest Africa (NWA) 2737 olivine. A Ni-poor troilite (Fe/S = 1.0 ± 0.01), sometimes Cr-bearing (up to 1 wt%), coexists with micrometer-sized taenite/tetrataenite-type native Ni-Fe alloys (Ni/Fe = 1) and Fe-Os-Ir-(Ru) alloys a few hundreds of nanometers across. The troilite has exsolved flame-like pentlandite (Fe/Fe + Ni = 0.5-0.6). Chalcopyrite is almost lacking, and no pyrite has been found. As a hot desert find, NWA 2737 shows astonishingly fresh sulfides. The composition of troilite coexisting with Ni-Fe alloys is completely at odds with Chassigny and Nahkla sulfides (pyrite + metal-deficient monoclinic-type pyrrhotite). It indicates strongly reducing crystallization conditions (close to IW), several log units below the fO2 conditions inferred from chromites compositions and accepted for Chassignites (FMQ-1 log unit). It is proposed that reduction in sulfides into base and precious metal alloys is operated via sulfur degassing, which is supported by the highly resorbed and denticulated shape of sulfide blebs and their spongy textures. Shock-related S degassing may be responsible for considerable damages in magmatic sulfide structures and sulfide assemblages, with concomitant loss of magnetic properties as documented in some other Martian meteorites

Higher CSF sTREM2 attenuates ApoE4-related risk for cognitive decline and neurodegeneration

BACKGROUND: The Apolipoprotein E ε4 allele (i.e. ApoE4) is the strongest genetic risk factor for sporadic Alzheimer's disease (AD). TREM2 (i.e. Triggering receptor expressed on myeloid cells 2) is a microglial transmembrane protein brain that plays a central role in microglia activation in response to AD brain pathologies. Whether higher TREM2-related microglia activity modulates the risk to develop clinical AD is an open question. Thus, the aim of the current study was to assess whether higher sTREM2 attenuates the effects of ApoE4-effects on future cognitive decline and neurodegeneration. METHODS: We included 708 subjects ranging from cognitively normal (CN, n = 221) to mild cognitive impairment (MCI, n = 414) and AD dementia (n = 73) from the Alzheimer's disease Neuroimaging Initiative. We used linear regression to test the interaction between ApoE4-carriage by CSF-assessed sTREM2 levels as a predictor of longitudinally assessed cognitive decline and MRI-assessed changes in hippocampal volume changes (mean follow-up of 4 years, range of 1.7-7 years). RESULTS: Across the entire sample, we found that higher CSF sTREM2 at baseline was associated with attenuated effects of ApoE4-carriage (i.e. sTREM2 x ApoE4 interaction) on longitudinal global cognitive (p = 0.001, Cohen's f2 = 0.137) and memory decline (p = 0.006, Cohen's f2 = 0.104) as well as longitudinally assessed hippocampal atrophy (p = 0.046, Cohen's f2 = 0.089), independent of CSF markers of primary AD pathology (i.e. Aβ1-42, p-tau181). While overall effects of sTREM2 were small, exploratory subanalyses stratified by diagnostic groups showed that beneficial effects of sTREM2 were pronounced in the MCI group. CONCLUSION: Our results suggest that a higher CSF sTREM2 levels are associated with attenuated ApoE4-related risk for future cognitive decline and AD-typical neurodegeneration. These findings provide further evidence that TREM2 may be protective against the development of AD

Robust automated detection of microstructural white matter degeneration in Alzheimer’s disease using machine learning classification of multicenter DTI data

Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample

Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows

Background: Predicting progression from a stage of Mild Cognitive Impairment to dementia is a major pursuit in current research. It is broadly accepted that cognition declines with a continuum between MCI and dementia. As such, cohorts of MCI patients are usually heterogeneous, containing patients at different stages of the neurodegenerative process. This hampers the prognostic task. Nevertheless, when learning prognostic models, most studies use the entire cohort of MCI patients regardless of their disease stages. In this paper, we propose a Time Windows approach to predict conversion to dementia, learning with patients stratified using time windows, thus fine-tuning the prognosis regarding the time to conversion. Methods: In the proposed Time Windows approach, we grouped patients based on the clinical information of whether they converted (converter MCI) or remained MCI (stable MCI) within a specific time window. We tested time windows of 2, 3, 4 and 5 years. We developed a prognostic model for each time window using clinical and neuropsychological data and compared this approach with the commonly used in the literature, where all patients are used to learn the models, named as First Last approach. This enables to move from the traditional question "Will a MCI patient convert to dementia somewhere in the future" to the question "Will a MCI patient convert to dementia in a specific time window". Results: The proposed Time Windows approach outperformed the First Last approach. The results showed that we can predict conversion to dementia as early as 5 years before the event with an AUC of 0.88 in the cross-validation set and 0.76 in an independent validation set. Conclusions: Prognostic models using time windows have higher performance when predicting progression from MCI to dementia, when compared to the prognostic approach commonly used in the literature. Furthermore, the proposed Time Windows approach is more relevant from a clinical point of view, predicting conversion within a temporal interval rather than sometime in the future and allowing clinicians to timely adjust treatments and clinical appointments.FCT under the Neuroclinomics2 project [PTDC/EEI-SII/1937/2014, SFRH/BD/95846/2013]; INESC-ID plurianual [UID/CEC/50021/2013]; LASIGE Research Unit [UID/CEC/00408/2013

The GimA Locus of Extraintestinal Pathogenic E. coli: Does Reductive Evolution Correlate with Habitat and Pathotype?

IbeA (invasion of brain endothelium), which is located on a genomic island termed GimA, is involved in the pathogenesis of several extraintestinal pathogenic E. coli (ExPEC) pathotypes, including newborn meningitic E. coli (NMEC) and avian pathogenic E. coli (APEC). To unravel the phylogeny of GimA and to investigate its island character, the putative insertion locus of GimA was determined via Long Range PCR and DNA-DNA hybridization in 410 E. coli isolates, including APEC, NMEC, uropathogenic (UPEC), septicemia-associated E. coli (SEPEC), and human and animal fecal isolates as well as in 72 strains of the E. coli reference (ECOR) collection. In addition to a complete GimA (∼20.3 kb) and a locus lacking GimA we found a third pattern containing a 342 bp remnant of GimA in this strain collection. The presence of GimA was almost exclusively detected in strains belonging to phylogenetic group B2. In addition, the complete GimA was significantly more frequent in APEC and NMEC strains while the GimA remnant showed a higher association with UPEC strains. A detailed analysis of the ibeA sequences revealed the phylogeny of this gene to be consistent with that obtained by Multi Locus Sequence Typing of the strains. Although common criteria for genomic islands are partially fulfilled, GimA rather seems to be an ancestral part of phylogenetic group B2, and it would therefore be more appropriate to term this genomic region GimA locus instead of genomic island. The existence of two other patterns reflects a genomic rearrangement in a reductive evolution-like manner