The Effects of Spectral Pretreatments on Chemometric Analyses of Soil Profiles Using Laboratory Imaging Spectroscopy

Laboratory imaging spectroscopy can be used to explore physical and chemical variations in soil profiles on a submillimetre scale. We used a hyperspectral scanner in the 400 to 1000 nm spectral range mounted in a laboratory frame to record images of two soil cores. Samples from these cores were chemically analyzed, and spectra of the sampled regions were used to train chemometric PLS regression models. With these models detailed maps of the elemental concentrations in the soil cores could be produced. Eight different spectral pretreatments were applied to the sample spectra and to the resulting images in order to explore the influence of these pre-treatments on the estimation of elemental concentrations. We found that spectral preprocessing has a minor influence on chemometry results when powerful regression algorithms like PLSR are used

Empirical carbon dioxide emissions of electric vehicles in a French-German commuter fleet test

According to many governments electric vehicles are an efficient mean to mitigate carbon dioxide emissions in the transport sector. However, the energy charged causes carbon dioxide emissions in the energy sector. This study demonstrates results from measuring time-dependent electricity consumption of electric vehicles during driving and charging. The electric vehicles were used in a French-German commuter scenario between March 2013 and August 2013. The electric vehicles travelled a total distance of 38,365 kilometers. 639 individual charging events were recorded. Vehicle specific data on electricity consumption are matched to disaggregated electricity generation data with time dependent national electricity generation mixes and corresponding carbon dioxide emissions with an hourly time resolution. Carbon dioxide emission reduction potentials of different charging strategies are identified. As carbon dioxide emission intensities change over time according to the electric power systems, specific smart charging services are a convincing strategy to reduce electric vehicle specific carbon dioxide emissions. Our results indicate that charging in France causes only about ten percent of the carbon dioxide emissions compared to Germany, where the carbon intensity is more diverse

Empirical carbon dioxide emissions of electric vehicles in a French-German commuter fleet test

According to many governments electric vehicles are seen as an efficient mean to mitigate carbon dioxide emissions in the transport sector. However, the energy charged causes carbon dioxide emissions in the energy sector. This study demonstrates results from measuring time-dependent electricity consumption of electric vehicles during driving and charging. The electric vehicles were used in a French-German commuter scenario between March and August 2013. The electric vehicles ran a total distance of 38,365 km. 639 individual charging events were recorded. Vehicle specific data on electricity consumption are matched to disaggregated electricity generation data with time-dependent national electricity generation mixes and corresponding carbon dioxide emissions with an hourly time resolution. Carbon dioxide emission reduction potentials of different charging strategies are identified. As carbon dioxide emission intensities change over time according to the electric power systems, specific smart charging services are a convincing strategy to reduce electric vehicle specific carbon dioxide emissions. Our results indicate that charging in France causes only about ten percent of the carbon dioxide emissions compared to Germany, where the carbon intensity is more diverse

ApoE attenuates unresolvable inflammation by complex formation with activated C1q

Apolipoprotein-E (ApoE) has been implicated in Alzheimer's disease, atherosclerosis, and other unresolvable inflammatory conditions but a common mechanism of action remains elusive. We found in ApoE-deficient mice that oxidized lipids activated the classical complement cascade (CCC), resulting in leukocyte infiltration of the choroid plexus (ChP). All human ApoE iso-forms attenuated CCC activity via high-affinity binding to the activated CCC-initiating C1q protein (K-D similar to 140-580 pM) in vitro, and C1q-ApoE complexes emerged as markers for ongoing complement activity of diseased ChPs, A beta plaques, and atherosclerosis in vivo. C1q-ApoE complexes in human ChPs, A beta plaques, and arteries correlated with cognitive decline and atherosclerosis, respectively. Treatment with small interfering RNA (siRNA) against C5, which is formed by all complement pathways, attenuated murine ChP inflammation, A beta-associated microglia accumulation, and atherosclerosis. Thus, ApoE is a direct checkpoint inhibitor of unresolvable inflammation, and reducing C5 attenuates disease burden

The genetics of the mood disorder spectrum:genome-wide association analyses of over 185,000 cases and 439,000 controls

Background Mood disorders (including major depressive disorder and bipolar disorder) affect 10-20% of the population. They range from brief, mild episodes to severe, incapacitating conditions that markedly impact lives. Despite their diagnostic distinction, multiple approaches have shown considerable sharing of risk factors across the mood disorders. Methods To clarify their shared molecular genetic basis, and to highlight disorder-specific associations, we meta-analysed data from the latest Psychiatric Genomics Consortium (PGC) genome-wide association studies of major depression (including data from 23andMe) and bipolar disorder, and an additional major depressive disorder cohort from UK Biobank (total: 185,285 cases, 439,741 controls; non-overlapping N = 609,424). Results Seventy-three loci reached genome-wide significance in the meta-analysis, including 15 that are novel for mood disorders. More genome-wide significant loci from the PGC analysis of major depression than bipolar disorder reached genome-wide significance. Genetic correlations revealed that type 2 bipolar disorder correlates strongly with recurrent and single episode major depressive disorder. Systems biology analyses highlight both similarities and differences between the mood disorders, particularly in the mouse brain cell-types implicated by the expression patterns of associated genes. The mood disorders also differ in their genetic correlation with educational attainment – positive in bipolar disorder but negative in major depressive disorder. Conclusions The mood disorders share several genetic associations, and can be combined effectively to increase variant discovery. However, we demonstrate several differences between these disorders. Analysing subtypes of major depressive disorder and bipolar disorder provides evidence for a genetic mood disorders spectrum

Bipolar multiplex families have an increased burden of common risk variants for psychiatric disorders.

Multiplex families with a high prevalence of a psychiatric disorder are often examined to identify rare genetic variants with large effect sizes. In the present study, we analysed whether the risk for bipolar disorder (BD) in BD multiplex families is influenced by common genetic variants. Furthermore, we investigated whether this risk is conferred mainly by BD-specific risk variants or by variants also associated with the susceptibility to schizophrenia or major depression. In total, 395 individuals from 33 Andalusian BD multiplex families (166 BD, 78 major depressive disorder, 151 unaffected) as well as 438 subjects from an independent, BD case/control cohort (161 unrelated BD, 277 unrelated controls) were analysed. Polygenic risk scores (PRS) for BD, schizophrenia (SCZ), and major depression were calculated and compared between the cohorts. Both the familial BD cases and unaffected family members had higher PRS for all three psychiatric disorders than the independent controls, with BD and SCZ being significant after correction for multiple testing, suggesting a high baseline risk for several psychiatric disorders in the families. Moreover, familial BD cases showed significantly higher BD PRS than unaffected family members and unrelated BD cases. A plausible hypothesis is that, in multiplex families with a general increase in risk for psychiatric disease, BD development is attributable to a high burden of common variants that confer a specific risk for BD. The present analyses demonstrated that common genetic risk variants for psychiatric disorders are likely to contribute to the high incidence of affective psychiatric disorders in the multiplex families. However, the PRS explained only part of the observed phenotypic variance, and rare variants might have also contributed to disease development