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In the present work, three different techniques are used to separate ice-nucleating particles (INP) and ice particle residuals (IPR) from non-ice-active particles: the Ice Selective Inlet (ISI) and the Ice Counterflow Virtual Impactor (Ice-CVI), which sample ice particles from mixed phase clouds and allow for the analysis of the residuals, as well as the combination of the Fast Ice Nucleus Chamber (FINCH) and the Ice Nuclei Pumped Virtual Impactor (IN-PCVI), which provides ice-activating conditions to aerosol particles and extracts the activated ones for analysis. The collected particles were analyzed by scanning electron microscopy and energy-dispersive X-ray microanalysis to determine their size, chemical composition and mixing state. Samples were taken during January/February 2013 at the High Alpine Research Station Jungfraujoch. All INP/IPR-separating techniques had considerable abundances (median 20–70%) of contamination artifacts (ISI: Si-O spheres, probably calibration aerosol; Ice-CVI: Al-O particles; FINCH + IN-PCVI: steel particles). Also, potential measurement artifacts (soluble material) occurred (median abundance < 20%). After removal of the contamination particles, silicates and Ca-rich particles, carbonaceous material and metal oxides were the major INP/IPR particle types separated by all three techniques. Minor types include soot and Pb-bearing particles. Sea-salt and sulfates were identified by all three methods as INP/IPR. Lead was identified in less than 10% of the INP/IPR. It was mainly present as an internal mixture with other particle types, but also external lead-rich particles were found. Most samples showed a maximum of the INP/IPR size distribution at 400 nm geometric diameter. In a few cases, a second super-micron maximum was identified. Soot/carbonaceous material and metal oxides were present mainly in the submicron range. ISI and FINCH yielded silicates and Ca-rich particles mainly with diameters above 1 μm, while the Ice-CVI also sampled many submicron particles. Probably owing to the different meteorological conditions, the INP/IPR composition was highly variable on a sample to sample basis. Thus, some part of the discrepancies between the different techniques may result from the (unavoidable) non-parallel sampling. The observed differences of the particles group abundances as well as the mixing state of INP/IPR point to the need of further studies to better understand the influence of the separating techniques on the INP/IPR chemical composition.
During January/February 2013, at the High Alpine Research Station Jungfraujoch a measurement campaign was carried out, which was centered on atmospheric ice-nucleating particles (INP) and ice particle residuals (IPR). Three different techniques for separation of INP and IPR from the non-ice-active particles are compared. The Ice Selective Inlet (ISI) and the Ice Counterflow Virtual Impactor (Ice-CVI) sample ice particles from mixed phase clouds and allow for the analysis of the residuals. The combination of the Fast Ice Nucleus Chamber (FINCH) and the Ice Nuclei Pumped Counterflow Virtual Impactor (IN-PCVI) provides ice-activating conditions to aerosol particles and extracts the activated INP for analysis.Collected particles were analyzed by scanning electron microscopy and energy-dispersive X-ray microanalysis to determine size, chemical composition and mixing state. All INP/IPR-separating techniques had considerable abundances (median 20 – 70 %) of instrumental contamination artifacts (ISI: Si-O spheres, probably calibration aerosol; Ice-CVI: Al-O particles; FINCH+IN-PCVI: steel particles). Also, potential sampling artifacts (e.g., pure soluble material) occurred with a median abundance of < 20 %. While these could be explained as IPR by ice break-up, for INP their IN-ability pathway is less clear. After removal of the contamination artifacts, silicates and Ca-rich particles, carbonaceous material and metal oxides were the major INP/IPR particle types separated by all three techniques. Soot was a minor contributor. Lead was detected in less than 10 % of the particles, of which the majority were internal mixtures with other particle types. Sea-salt and sulfates were identified by all three methods as INP/IPR. Most samples showed a maximum of the INP/IPR size distribution at 400 nm geometric diameter. In a few cases, a second super-micron maximum was identified. Soot/carbonaceous material and metal oxides were present mainly in the submicron range. ISI and FINCH yielded silicates and Ca-rich particles mainly with diameters above 1 μm, while the Ice-CVI also separated many submicron IPR. As strictly parallel sampling could not be performed, a part of the discrepancies between the different techniques may result from variations in meteorological conditions and subsequent INP/IPR composition. The observed differences in the particle group abundances as well as in the mixing state of INP/IPR express the need for further studies to better understand the influence of the separating techniques on the INP/IPR chemical
composition.
Risk stratification for bipolar disorder using polygenic risk scores among young high-risk adults
(2020)
Objective: Identifying high-risk groups with an increased genetic liability for bipolar disorder (BD) will provide insights into the etiology of BD and contribute to early detection of BD. We used the BD polygenic risk score (PRS) derived from BD genome-wide association studies (GWAS) to explore how such genetic risk manifests in young, high-risk adults. We postulated that BD-PRS would be associated with risk factors for BD.
Methods: A final sample of 185 young, high-risk German adults (aged 18–35 years) were grouped into three risk groups and compared to a healthy control group (n = 1,100). The risk groups comprised 117 cases with attention deficit hyperactivity disorder (ADHD), 45 with major depressive disorder (MDD), and 23 help-seeking adults with early recognition symptoms [ER: positive family history for BD, (sub)threshold affective symptomatology and/or mood swings, sleeping disorder]. BD-PRS was computed for each participant. Logistic regression models (controlling for sex, age, and the first five ancestry principal components) were used to assess associations of BD-PRS and the high-risk phenotypes.
Results: We observed an association between BD-PRS and combined risk group status (OR = 1.48, p < 0.001), ADHD diagnosis (OR = 1.32, p = 0.009), MDD diagnosis (OR = 1.96, p < 0.001), and ER group status (OR = 1.7, p = 0.025; not significant after correction for multiple testing) compared to healthy controls.
Conclusions: In the present study, increased genetic risk for BD was a significant predictor for MDD and ADHD status, but not for ER. These findings support an underlying shared risk for both MDD and BD as well as ADHD and BD. Improving our understanding of the underlying genetic architecture of these phenotypes may aid in early identification and risk stratification.
Sedimentary charcoal records are widely used to reconstruct regional changes in fire regimes through time in the geological past. Existing global compilations are not geographically comprehensive and do not provide consistent metadata for all sites. Furthermore, the age models provided for these records are not harmonised and many are based on older calibrations of the radiocarbon ages. These issues limit the use of existing compilations for research into past fire regimes. Here, we present an expanded database of charcoal records, accompanied by new age models based on recalibration of radiocarbon ages using IntCal20 and Bayesian age-modelling software. We document the structure and contents of the database, the construction of the age models, and the quality control measures applied. We also record the expansion of geographical coverage relative to previous charcoal compilations and the expansion of metadata that can be used to inform analyses. This first version of the Reading Palaeofire Database contains 1676 records (entities) from 1480 sites worldwide. The database (RPDv1b – Harrison et al., 2021) is available at https://doi.org/10.17864/1947.000345.
The last decade has seen a sharp increase in the number of scientific publications describing physiological and pathological functions of extracellular vesicles (EVs), a collective term covering various subtypes of cell-released, membranous structures, called exosomes, microvesicles, microparticles, ectosomes, oncosomes, apoptotic bodies, and many other names. However, specific issues arise when working with these entities, whose size and amount often make them difficult to obtain as relatively pure preparations, and to characterize properly. The International Society for Extracellular Vesicles (ISEV) proposed Minimal Information for Studies of Extracellular Vesicles (“MISEV”) guidelines for the field in 2014. We now update these “MISEV2014” guidelines based on evolution of the collective knowledge in the last four years. An important point to consider is that ascribing a specific function to EVs in general, or to subtypes of EVs, requires reporting of specific information beyond mere description of function in a crude, potentially contaminated, and heterogeneous preparation. For example, claims that exosomes are endowed with exquisite and specific activities remain difficult to support experimentally, given our still limited knowledge of their specific molecular machineries of biogenesis and release, as compared with other biophysically similar EVs. The MISEV2018 guidelines include tables and outlines of suggested protocols and steps to follow to document specific EV-associated functional activities. Finally, a checklist is provided with summaries of key points.
Purpose: The role of obesity in glioblastoma remains unclear, as previous analyses have reported contradicting results. Here, we evaluate the prognostic impact of obesity in two trial populations; CeTeG/NOA-09 (n = 129) for MGMT methylated glioblastoma patients comparing temozolomide (TMZ) to lomustine/TMZ, and GLARIUS (n = 170) for MGMT unmethylated glioblastoma patients comparing TMZ to bevacizumab/irinotecan, both in addition to surgery and radiotherapy.
Methods: The impact of obesity (BMI ≥ 30 kg/m2) on overall survival (OS) and progression-free survival (PFS) was investigated with Kaplan–Meier analysis and log-rank tests. A multivariable Cox regression analysis was performed including known prognostic factors as covariables.
Results: Overall, 22.6% of patients (67 of 297) were obese. Obesity was associated with shorter survival in patients with MGMT methylated glioblastoma (median OS 22.9 (95% CI 17.7–30.8) vs. 43.2 (32.5–54.4) months for obese and non-obese patients respectively, p = 0.001), but not in MGMT unmethylated glioblastoma (median OS 17.1 (15.8–18.9) vs 17.6 (14.7–20.8) months, p = 0.26). The prognostic impact of obesity in MGMT methylated glioblastoma was confirmed in a multivariable Cox regression (adjusted odds ratio: 2.57 (95% CI 1.53–4.31), p < 0.001) adjusted for age, sex, extent of resection, baseline steroids, Karnofsky performance score, and treatment arm.
Conclusion: Obesity was associated with shorter survival in MGMT methylated, but not in MGMT unmethylated glioblastoma patients.