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Modern condensation particle counters (CPCs) are indispensable instruments for studies of aerosols in all measurement environments. Relying on heterogeneous nucleation as the basic principle, the composition dependence of particle activation is a source of profound uncertainty for the accurate assessment of new particle formation (NPF) events. While development efforts successfully pushed down minimum detectable particle sizes in recent years, composition-dependent counting efficiencies have remained to be a persisting issue in aerosol research. Addressing this pressing problem, we present calibrations of a newly developed CPC, the Airmodus A30 (Airmodus Ltd., Helsinki, Finland), that uses non-hazardous propylene glycol as working fluid. Our results conclusively demonstrate that composition-dependent particle detection can be reduced to the brink of disappearance by choice of the working fluid and corresponding high supersaturation. Counting efficiencies were determined for a set of size-selected and chemically diverse seed particles, and the measured 50 % cutoff diameters were compared to previous studies. Using computational fluid dynamics simulations, we show that the composition dependence appears to decrease with increasing saturation ratios achieved inside the CPC. Hence, our study assists in the development of future CPCs and elucidates a potential mechanism to reduce measurement uncertainties arising from composition-dependent counting efficiencies.

Background: In genomics, highly sensitive point mutation detection is particularly relevant for cancer diagnosis and early relapse detection. Next-generation sequencing combined with unique molecular identifiers (UMIs) is known to improve the mutation detection sensitivity. Methods: We present an open-source bioinformatics framework named Interface for Point Mutation Identification (IMPI) with a graphical user interface (GUI) for processing especially small-scale NGS data to identify variants. IMPI ensures detailed UMI analysis and clustering, as well as initial raw read processing, and consensus sequence building. Furthermore, the effects of custom algorithm and parameter settings for NGS data pre-processing and UMI collapsing (e.g., UMI clustered versus unclustered (raw) reads) can be investigated. Additionally, IMPI implements optimization and quality control methods; an evolution strategy is used for parameter optimization. Results: IMPI was designed, implemented, and tested using BCR::ABL1 fusion gene kinase domain sequencing data. In summary, IMPI enables a detailed analysis of the impact of UMI clustering and parameter setting changes on the measured allele frequencies. Conclusions: Regarding the BCR::ABL1 data, IMPI’s results underlined the need for caution while designing specialized single amplicon NGS approaches due to methodical limitations (e.g., high PCR-mediated recombination rate). This cannot be corrected using UMIs.

Polydora hoplura is an invasive species and a harmful pest in commercial shellfish aquaculture, particularly for farmed abalone species. Different lines of treatment for Polydora affecting abalone have been explored, but with limited success. Using selective breeding to increase abalone resistance to and reduce shell damage from Polydora infestation could be a promising alternative. However, in abalone there is currently no information on the genetic variability for resistance to Polydora infestation. In the present study, heritability (h2) of traits associated with Pacific abalone resistance to P. hoplura were estimated after challenging abalone juveniles with trojan abalones heavily infested, using a nested experimental design, with 1 male crossed with 2–4 females. The heritability values were low but significant for some of those traits: survival h2=0.09; Infestation Intensity at Q50 h2 = 0.16 ± 0.11; and the proportion of the shell area occupied by blisters caused by the mud-worms h2 = 0.29 ± 0.14. These results suggest a significant potential for improving resistance or tolerance to P. hoplura infestation through selective breeding in Pacific abalone.

Abstract We have previously identified a network of higher-order brain regions particularly vulnerable to the ageing process, schizophrenia and Alzheimer’s disease. However, it remains unknown what the genetic influences on this fragile brain network are, and whether it can be altered by the most common modifiable risk factors for dementia. Here, in ~40,000 UK Biobank participants, we first show significant genome-wide associations between this brain network and seven genetic clusters implicated in cardiovascular deaths, schizophrenia, Alzheimer’s and Parkinson’s disease, and with the two antigens of the XG blood group located in the pseudoautosomal region of the sex chromosomes. We further reveal that the most deleterious modifiable risk factors for this vulnerable brain network are diabetes, nitrogen dioxide – a proxy for traffic-related air pollution – and alcohol intake frequency. The extent of these associations was uncovered by examining these modifiable risk factors in a single model to assess the unique contribution of each on the vulnerable brain network, above and beyond the dominating effects of age and sex. These results provide a comprehensive picture of the role played by genetic and modifiable risk factors on these fragile parts of the brain.

Abstract Histopathological examination of tissue samples is essential for identifying tumor malignancy and the diagnosis of different types of tumor. In the case of lymphoma classification, nuclear size of the neoplastic lymphocytes is one of the key features to differentiate the different subtypes. Based on the combination of artificial intelligence and advanced image processing, we provide a workflow for the classification of lymphoma with regards to their nuclear size (small, intermediate, and large). As the baseline for our workflow testing, we use a Unet++ model trained on histological images of canine lymphoma with individually labeled nuclei. As an alternative to the Unet++, we also used a publicly available pre-trained and unmodified instance segmentation model called Stardist to demonstrate that our modular classification workflow can be combined with different types of segmentation models if they can provide proper nuclei segmentation. Subsequent to nuclear segmentation, we optimize algorithmic parameters for accurate classification of nuclear size using a newly derived reference size and final image classification based on a pathologists-derived ground truth. Our image classification module achieves a classification accuracy of up to 92% on canine lymphoma data. Compared to the accuracy ranging from 66.67 to 84% achieved using measurements provided by three individual pathologists, our algorithm provides a higher accuracy level and reproducible results. Our workflow also demonstrates a high transferability to feline lymphoma, as shown by its accuracy of up to 84.21%, even though our workflow was not optimized for feline lymphoma images. By determining the nuclear size distribution in tumor areas, our workflow can assist pathologists in subtyping lymphoma based on the nuclei size and potentially improve reproducibility. Our proposed approach is modular and comprehensible, thus allowing adaptation for specific tasks and increasing the users’ trust in computer-assisted image classification.

The integration of deep learning-based tools into diagnostic workflows is increasingly prevalent due to their efficiency and reproducibility in various settings. We investigated the utility of automated nuclear morphometry for assessing nuclear pleomorphism (NP), a criterion of malignancy in the current grading system in canine pulmonary carcinoma (cPC), and its prognostic implications. We developed a deep learning-based algorithm for evaluating NP (variation in size, i.e., anisokaryosis and/or shape) using a segmentation model. Its performance was evaluated on 46 cPC cases with comprehensive follow-up data regarding its accuracy in nuclear segmentation and its prognostic ability. Its assessment of NP was compared to manual morphometry and established prognostic tests (pathologists’ NP estimates (n = 11), mitotic count, histological grading, and TNM-stage). The standard deviation (SD) of the nuclear area, indicative of anisokaryosis, exhibited good discriminatory ability for tumor-specific survival, with an area under the curve (AUC) of 0.80 and a hazard ratio (HR) of 3.38. The algorithm achieved values comparable to manual morphometry. In contrast, the pathologists’ estimates of anisokaryosis resulted in HR values ranging from 0.86 to 34.8, with slight inter-observer reproducibility (k = 0.204). Other conventional tests had no significant prognostic value in our study cohort. Fully automated morphometry promises a time-efficient and reproducible assessment of NP with a high prognostic value. Further refinement of the algorithm, particularly to address undersegmentation, and application to a larger study population are required.

State-of-the-art Earth system models typically employ grid spacings of O(100 km), which is too coarse to explicitly resolve main drivers of the flow of energy and matter across the Earth system. In this paper, we present the new ICON-Sapphire model configuration, which targets a representation of the components of the Earth system and their interactions with a grid spacing of 10 km and finer. Through the use of selected simulation examples, we demonstrate that ICON-Sapphire can (i) be run coupled globally on seasonal timescales with a grid spacing of 5 km, on monthly timescales with a grid spacing of 2.5 km, and on daily timescales with a grid spacing of 1.25 km; (ii) resolve large eddies in the atmosphere using hectometer grid spacings on limited-area domains in atmosphere-only simulations; (iii) resolve submesoscale ocean eddies by using a global uniform grid of 1.25 km or a telescoping grid with the finest grid spacing at 530 m, the latter coupled to a uniform atmosphere; and (iv) simulate biogeochemistry in an ocean-only simulation integrated for 4 years at 10 km. Comparison of basic features of the climate system to observations reveals no obvious pitfalls, even though some observed aspects remain difficult to capture. The throughput of the coupled 5 km global simulation is 126 simulated days per day employing 21 % of the latest machine of the German Climate Computing Center. Extrapolating from these results, multi-decadal global simulations including interactive carbon are now possible, and short global simulations resolving large eddies in the atmosphere and submesoscale eddies in the ocean are within reach.

The understanding of new particle formation and growth processes is critical for evaluating the role of aerosols in climate change. One of the knowledge gaps is the ion–particle interaction during the early growth process, especially in the sub-3 nm range, where direct observations are sparse. While molecular interactions would imply faster growth rates of ions compared to neutral particles, this phenomenon is not widely observed in the atmosphere. Here, we show field measurements in the boreal forest indicating a smaller apparent growth rate of the ion population compared to the total particles. We use aerosol dynamics simulations to demonstrate that this effect can be caused by the changing importance of ion-induced nucleation mechanisms during the day. We further compare these results with chamber experiments under similar conditions, where we demonstrate that this effect critically depends on the abundance of condensable vapors and the related strength of ion-induced nucleation. Our results imply that atmospheric ion growth rate measurements below 3 nm need to be evaluated very carefully as they do not represent condensational growth alone but are influenced by ion–particle population interactions.

Background and objective: Transcranial direct current stimulation (tDCS) has wide ranging applications in neuro-behavioural and physiological research, and in neurological rehabilitation. However, it is currently limited by substantial inter-subject variability in responses, which may be explained, at least in part, by anatomical differences that lead to variability in the electric field (E-field) induced in the cortex. Here, we tested whether the variability in the E-field in the stimulated cortex during anodal tDCS, estimated using computational simulations, explains the variability in tDCS induced changes in GABA, a neurophysiological marker of stimulation effect. Methods: Data from five previously conducted MRS studies were combined. The anode was placed over the left primary motor cortex (M1, 3 studies, N = 24) or right temporal cortex (2 studies, N = 32), with the cathode over the contralateral supraorbital ridge. Single voxel spectroscopy was performed in a 2x2x2cm voxel under the anode in all cases. MRS data were acquired before and either during or after 1 mA tDCS using either a sLASER sequence (7T) or a MEGA-PRESS sequence (3T). sLASER MRS data were analysed using LCModel, and MEGA-PRESS using FID-A and Gannet. E-fields were simulated in a finite element model of the head, based on individual structural MR images, using SimNIBS. Separate linear mixed effects models were run for each E-field variable (mean and 95th percentile; magnitude, and components normal and tangential to grey matter surface, within the MRS voxel). The model included effects of time (pre or post tDCS), E-field, grey matter volume in the MRS voxel, and a 3-way interaction between time, E-field and grey matter volume. Additionally, we ran a permutation analysis using PALM to determine whether E-field anywhere in the brain, not just in the MRS voxel, correlated with GABA change. Results: In M1, higher mean E-field magnitude was associated with greater anodal tDCS-induced decreases in GABA (t(24) = 3.24, p = 0.003). Further, the association between mean E-field magnitude and GABA change was moderated by the grey matter volume in the MRS voxel (t(24) = −3.55, p = 0.002). These relationships were consistent across all E-field variables except the mean of the normal component. No significant relationship was found between tDCS-induced GABA decrease and E-field in the temporal voxel. No significant clusters were found in the whole brain analysis. Conclusions: Our data suggest that the electric field induced by tDCS within the brain is variable, and is significantly related to anodal tDCS-induced decrease in GABA, a key neurophysiological marker of stimulation. These findings strongly support individualised dosing of tDCS, at least in M1. Further studies examining E-fields in relation to other outcome measures, including behaviour, will help determine the optimal E-fields required for any desired effects.

We present an electrical mobility classifier for mass–mobility measurements of atmospheric ions. Size segregation coupled with mass spectrometric detection of naturally occurring ions in the atmosphere is challenging due to the low ion concentration. Conventional electrical mobility classifying devices were not yet coupled with mass spectrometry to resolve natural ion composition. This is due to either the insufficient transmission efficiency or design concepts being incompatible with this application, e.g. using high electric fields close to the inlets to push ions from high to low electric potential. Here, we introduce an axial ion mobility classifier, termed AMC, with the aim to achieve higher transmission efficiencies to segregate natural ions at reasonable sizing resolution. Similar to the recently introduced principle of the high-pass electrical mobility filter (HP-EMF) presented by Bezantakos et al. (2015) and Surawski et al. (2017), ions are classified via an electric field that is opposed to the gas flow direction carrying the ions. Compared to the HP-EMF concept, we make use of sheath flows to improve the size resolution in the sub-3 nm range. With our new design we achieve a sizing resolution of 7 Z/ΔZ with a transmission efficiency of about 70 %.

Abstract Background Next-generation sequencing (NGS) is nowadays the most used high-throughput technology for DNA sequencing. Among others NGS enables the in-depth analysis of immune repertoires. Research in the field of T cell receptor (TCR) and immunoglobulin (IG) repertoires aids in understanding immunological diseases. A main objective is the analysis of the V(D)J recombination defining the structure and specificity of the immune repertoire. Accurate processing, evaluation and visualization of immune repertoire NGS data is important for better understanding immune responses and immunological behavior. Results ImmunoDataAnalyzer (IMDA) is a pipeline we have developed for automatizing the analysis of immunological NGS data. IMDA unites the functionality from carefully selected immune repertoire analysis software tools and covers the whole spectrum from initial quality control up to the comparison of multiple immune repertoires. It provides methods for automated pre-processing of barcoded and UMI tagged immune repertoire NGS data, facilitates the assembly of clonotypes and calculates key figures for describing the immune repertoire. These include commonly used clonality and diversity measures, as well as indicators for V(D)J gene segment usage and between sample similarity. IMDA reports all relevant information in a compact summary containing visualizations, calculations, and sample details, all of which serve for a more detailed overview. IMDA further generates an output file including key figures for all samples, designed to serve as input for machine learning frameworks to find models for differentiating between specific traits of samples. Conclusions IMDA constructs TCR and IG repertoire data from raw NGS reads and facilitates descriptive data analysis and comparison of immune repertoires. The IMDA workflow focus on quality control and ease of use for non-computer scientists. The provided output directly facilitates the interpretation of input data and includes information about clonality, diversity, clonotype overlap as well as similarity, and V(D)J gene segment usage. IMDA further supports the detection of sample swaps and cross-sample contamination that potentially occurred during sample preparation. In summary, IMDA reduces the effort usually required for immune repertoire data analysis by providing an automated workflow for processing raw NGS data into immune repertoires and subsequent analysis. The implementation is open-source and available on https://bioinformatics.fh-hagenberg.at/immunoanalyzer/ .

Biogenic organic precursors play an important role in atmospheric new particle formation (NPF). One of the major precursor species is α-pinene, which upon oxidation can form a suite of products covering a wide range of volatilities. Highly oxygenated organic molecules (HOMs) comprise a fraction of the oxidation products formed. While it is known that HOMs contribute to secondary organic aerosol (SOA) formation, including NPF, they have not been well studied in newly formed particles due to their very low mass concentrations. Here we present gas- and particle-phase chemical composition data from experimental studies of α-pinene oxidation, including in the presence of isoprene, at temperatures (−50 and −30 ∘C) and relative humidities (20 % and 60 %) relevant in the upper free troposphere. The measurements took place at the CERN Cosmics Leaving Outdoor Droplets (CLOUD) chamber. The particle chemical composition was analyzed by a thermal desorption differential mobility analyzer (TD-DMA) coupled to a nitrate chemical ionization–atmospheric pressure interface–time-of-flight (CI-APi-TOF) mass spectrometer. CI-APi-TOF was used for particle- and gas-phase measurements, applying the same ionization and detection scheme. Our measurements revealed the presence of C8−10 monomers and C18−20 dimers as the major compounds in the particles (diameter up to ∼ 100 nm). Particularly, for the system with isoprene added, C5 (C5H10O5−7) and C15 compounds (C15H24O5−10) were detected. This observation is consistent with the previously observed formation of such compounds in the gas phase. However, although the C5 and C15 compounds do not easily nucleate, our measurements indicate that they can still contribute to the particle growth at free tropospheric conditions. For the experiments reported here, most likely isoprene oxidation products enhance the growth of particles larger than 15 nm. Additionally, we report on the nucleation rates measured at 1.7 nm (J1.7 nm) and compared with previous studies, we found lower J1.7 nm values, very likely due to the higher α-pinene and ozone mixing ratios used in the present study.

Abstract The goal of this study was to compare brain structure between individuals with generalized anxiety disorder (GAD) and healthy controls. Previous studies have generated inconsistent findings, possibly due to small sample sizes, or clinical/analytic heterogeneity. To address these concerns, we combined data from 28 research sites worldwide through the ENIGMA-Anxiety Working Group, using a single, pre-registered mega-analysis. Structural magnetic resonance imaging data from children and adults (5–90 years) were processed using FreeSurfer. The main analysis included the regional and vertex-wise cortical thickness, cortical surface area, and subcortical volume as dependent variables, and GAD, age, age-squared, sex, and their interactions as independent variables. Nuisance variables included IQ, years of education, medication use, comorbidities, and global brain measures. The main analysis (1020 individuals with GAD and 2999 healthy controls) included random slopes per site and random intercepts per scanner. A secondary analysis (1112 individuals with GAD and 3282 healthy controls) included fixed slopes and random intercepts per scanner with the same variables. The main analysis showed no effect of GAD on brain structure, nor interactions involving GAD, age, or sex. The secondary analysis showed increased volume in the right ventral diencephalon in male individuals with GAD compared to male healthy controls, whereas female individuals with GAD did not differ from female healthy controls. This mega-analysis combining worldwide data showed that differences in brain structure related to GAD are small, possibly reflecting heterogeneity or those structural alterations are not a major component of its pathophysiology.

New particle formation (NPF) is a significant source of atmospheric particles, affecting climate and air quality. Understanding the mechanisms involved in urban aerosols is important to develop effective mitigation strategies. However, NPF rates reported in the polluted boundary layer span more than 4 orders of magnitude, and the reasons behind this variability are the subject of intense scientific debate. Multiple atmospheric vapours have been postulated to participate in NPF, including sulfuric acid, ammonia, amines and organics, but their relative roles remain unclear. We investigated NPF in the CLOUD chamber using mixtures of anthropogenic vapours that simulate polluted boundary layer conditions. We demonstrate that NPF in polluted environments is largely driven by the formation of sulfuric acid–base clusters, stabilized by the presence of amines, high ammonia concentrations and lower temperatures. Aromatic oxidation products, despite their extremely low volatility, play a minor role in NPF in the chosen urban environment but can be important for particle growth and hence for the survival of newly formed particles. Our measurements quantitatively account for NPF in highly diverse urban environments and explain its large observed variability. Such quantitative information obtained under controlled laboratory conditions will help the interpretation of future ambient observations of NPF rates in polluted atmospheres.

The European Committee for Standardization defines zonings and calculation criteria for different European regions to assign snow loads for structural design. In the Alpine region these defaults are quite coarse; countries therefore use their own products, and inconsistencies at national borders are a common problem. A new methodology to derive a snow load map for Austria is presented, which is reproducible and could be used across borders. It is based on (i) modeling snow loads with the specially developed Δsnow model at 897 sophistically quality controlled snow depth series in Austria and neighboring countries and (ii) a generalized additive model where covariates and their combinations are represented by penalized regression splines, fitted to series of yearly snow load maxima derived in the first step. This results in spatially modeled snow load extremes. The new approach outperforms a standard smooth model and is much more accurate than the currently used Austrian snow load map when compared to the RMSE of the 50-year snow load return values through a cross-validation procedure. No zoning is necessary, and the new map's RMSE of station-wise estimated 50-year generalized extreme value (GEV) return levels gradually rises to 2.2 kN m−2 at an elevation of 2000 m. The bias is 0.18 kN m−2 and positive across all elevations. When restricting the range of validity of the new map to 2000 m elevation, negative bias values that significantly underestimate 50-year snow loads at a very small number of stations are the only objective problem that has to be solved before the new map can be proposed as a successor of the current Austrian snow load map.

Reliable historical manual measurements of snow depths are available for many years, sometimes decades, across the globe, and increasingly snow depth data are also available from automatic stations and remote sensing platforms. In contrast, records of snow water equivalent (SWE) are sparse, which is significant as SWE is commonly the most important snowpack feature for hydrology, climatology, agriculture, natural hazards, and other fields. Existing methods of modeling SWE either rely on detailed meteorological forcing being available or are not intended to simulate individual SWE values, such as seasonal “peak SWE”. Here we present a new semiempirical multilayer model, Δsnow, for simulating SWE and bulk snow density solely from a regular time series of snow depths. The model, which is freely available as an R package, treats snow compaction following the rules of Newtonian viscosity, considers errors in measured snow depth, and treats overburden loads due to new snow as additional unsteady compaction; if snow is melted, the water mass is stepwise distributed from top to bottom in the snowpack. Seven model parameters are subject to calibration. Snow observations of 67 winters from 14 stations, well-distributed over different altitudes and climatic regions of the Alps, are used to find an optimal parameter setting. Data from another 71 independent winters from 15 stations are used for validation. Results are very promising: median bias and root mean square error for SWE are only −3.0 and 30.8 kg m−2, and +0.3 and 36.3 kg m−2 for peak SWE, respectively. This is a major advance compared to snow models relying on empirical regressions, and even sophisticated thermodynamic snow models do not necessarily perform better. As such, the new model offers a means to derive robust SWE estimates from historical snow depth data and, with some modification, to generate distributed SWE from remotely sensed estimates of spatial snow depth distribution.

The European Alps stretch over a range of climate zones which affect the spatial distribution of snow. Previous analyses of station observations of snow were confined to regional analyses. Here, we present an Alpine-wide analysis of snow depth from six Alpine countries – Austria, France, Germany, Italy, Slovenia, and Switzerland – including altogether more than 2000 stations of which more than 800 were used for the trend assessment. Using a principal component analysis and k-means clustering, we identified five main modes of variability and five regions which match the climatic forcing zones: north and high Alpine, north-east, north-west, south-east, and south and high Alpine. Linear trends of monthly mean snow depth between 1971 and 2019 showed decreases in snow depth for most stations from November to May. The average trend among all stations for seasonal (November to May) mean snow depth was −8.4 % per decade, for seasonal maximum snow depth −5.6 % per decade, and for seasonal snow cover duration −5.6 % per decade. Stronger and more significant trends were observed for periods and elevations where the transition from snow to snow-free occurs, which is consistent with an enhanced albedo feedback. Additionally, regional trends differed substantially at the same elevation, which challenges the notion of generalizing results from one region to another or to the whole Alps. This study presents an analysis of station snow depth series with the most comprehensive spatial coverage in the European Alps to date.

In this paper we present a low-cost approach to mapping vegetation cover by means of high-resolution close-range terrestrial photogrammetry. A total of 249 clusters of nine 1 m2 plots each, arranged in a 3 × 3 grid, were set up on 18 summits in Mediterranean mountain regions and in the Alps to capture images for photogrammetric processing and in-situ vegetation cover estimates. This was done with a hand-held pole-mounted digital single-lens reflex (DSLR) camera. Low-growing vegetation was automatically segmented using high-resolution point clouds. For classifying vegetation we used a two-step semi-supervised Random Forest approach. First, we applied an expert-based rule set using the Excess Green index (ExG) to predefine non-vegetation and vegetation points. Second, we applied a Random Forest classifier to further enhance the classification of vegetation points using selected topographic parameters (elevation, slope, aspect, roughness, potential solar irradiation) and additional vegetation indices (Excess Green Minus Excess Red (ExGR) and the vegetation index VEG). For ground cover estimation the photogrammetric point clouds were meshed using Screened Poisson Reconstruction. The relative influence of the topographic parameters on the vegetation cover was determined with linear mixed-effects models (LMMs). Analysis of the LMMs revealed a high impact of elevation, aspect, solar irradiation, and standard deviation of slope. The presented approach goes beyond vegetation cover values based on conventional orthoimages and in-situ vegetation cover estimates from field surveys in that it is able to differentiate complete 3D surface areas, including overhangs, and can distinguish between vegetation-covered and other surfaces in an automated manner. The results of the Random Forest classification confirmed it as suitable for vegetation classification, but the relative feature importance values indicate that the classifier did not leverage the potential of the included topographic parameters. In contrast, our application of LMMs utilized the topographic parameters and was able to reveal dependencies in the two biomes, such as elevation and aspect, which were able to explain between 87% and 92.5% of variance.

Due to their positive properties, AMC materials represent a current trend in industrial applications and thus a major research focus. But currently their difficult machinability limits a full establishment. Therefore, suitable joining processes are needed which allow a targeted and punctual use of the AMC-materials and thus minimize the machining effort.This paper presents a feasibility study of a direct material bond between an AlSi10MnMg-alloy reinforced to 30 Vol.-% with silicon carbide particles and the adequate monolithic material by using friction welding. For this purpose, in addition to the general feasibility, the achievable strengths are evaluated and the underlying material behavior as well as metallurgical processes are analyzed.As a result, the principle friction weldability of the used AMC material is demonstrated. Essentially, the joint results from a material bond between the aluminum alloy and the matrix material. The reinforcing phase contained in the latter remains unaffected by the joining process. The test results show neither transport nor mechanical comminution of the silicon carbide particles. It is proven that the quality of the produced material joint as well as the process stability depend on the setting of the variable process parameters. In particular, the ratio between frictional and forge pressure as well as the burn-off length are shown to have a major influence on the joint quality. The intensity of the individual influences is illustrated by the presentation of the parameter main and interaction effects as well as predominant significances. Because the used AMC material is softened by process- and material-related annealing effects, a large proportion of the weld specimens fail outside the joining zone. On the part of the unreinforced aluminum alloy, the absence of such temperature-induced softening is demonstrated. As a consequence, the investigated friction welding process can unusually not be aligned with the lower-strength material.

Influential theories implicate variations in the mechanisms supporting threat learning in the severity of anxiety symptoms. We use computational models of associative learning in conjunction with structural imaging to explicate links among the mechanisms underlying threat learning, their neuroanatomical substrates, and anxiety severity in humans. We recorded skin-conductance data during a threat-learning task from individuals with and without anxiety disorders (N=251; 8-50 years; 116 females). Reinforcement-learning model variants quantified processes hypothesized to relate to anxiety: threat conditioning, threat generalization, safety learning, and threat extinction. We identified the best-fitting models for these processes and tested associations among latent learning parameters, whole-brain anatomy, and anxiety severity. Results indicate that greater anxiety severity related specifically to slower safety learning and slower extinction of response to safe stimuli. Nucleus accumbens gray-matter volume moderated learning-anxiety associations. Using a modeling approach, we identify computational mechanisms linking threat learning and anxiety severity and their neuroanatomical substrates.

In this study the charging efficiency of a radioactive and a non-radioactive plasma bipolar diffusion charger (Gilbert Mark I plasma charger) for sub-12 nm particles has been investigated at various aerosol flow rates. The results were compared to classic theoretical approaches. In addition, the chemical composition and electrical mobilities of the charger ions have been examined using an atmospheric pressure interface time-of-flight mass spectrometer (APi-TOF MS). A comparison of the different neutralization methods revealed an increased charging efficiency for negatively charged particles using the non-radioactive plasma charger with nitrogen as the working gas compared to a radioactive americium bipolar diffusion charger. The mobility and mass spectrometric measurements show that the generated bipolar diffusion charger ions are of the same mobilities and composition independent of the examined bipolar diffusion charger. It was the first time that the Gilbert Mark I plasma charger was characterized in comparison to a commercial TSI X-Ray (TSI Inc, Model 3088) and a radioactive americium bipolar diffusion charger. We observed that the plasma charger with nitrogen as the working gas can enhance the charging probability for sub-10 nm particles compared to a radioactive americium bipolar diffusion charger. As a result, the widely used classical charging theory disagrees for the plasma charger and for the radioactive chargers with increased aerosol flow rates. Consequently, in-depth measurements of the charging distribution are necessary for accurate measurements with differential or scanning particle sizers for laboratory and field applications.

Nucleation of atmospheric vapours produces more than half of global cloud condensation nuclei and so has an important influence on climate. Recent studies show that monoterpene (C10H16) oxidation yields highly oxygenated products that can nucleate with or without sulfuric acid. Monoterpenes are emitted mainly by trees, frequently together with isoprene (C5H8), which has the highest global emission of all organic vapours. Previous studies have shown that isoprene suppresses new-particle formation from monoterpenes, but the cause of this suppression is under debate. Here, in experiments performed under atmospheric conditions in the CERN CLOUD chamber, we show that isoprene reduces the yield of highly oxygenated dimers with 19 or 20 carbon atoms – which drive particle nucleation and early growth – while increasing the production of dimers with 14 or 15 carbon atoms. The dimers (termed C20 and C15, respectively) are produced by termination reactions between pairs of peroxy radicals (RO2⚫) arising from monoterpenes or isoprene. Compared with pure monoterpene conditions, isoprene reduces nucleation rates at 1.7 nm (depending on the isoprene ∕ monoterpene ratio) and approximately halves particle growth rates between 1.3 and 3.2 nm. However, above 3.2 nm, C15 dimers contribute to secondary organic aerosol, and the growth rates are unaffected by isoprene. We further show that increased hydroxyl radical (OH⚫) reduces particle formation in our chemical system rather than enhances it as previously proposed, since it increases isoprene-derived RO2⚫ radicals that reduce C20 formation. RO2⚫ termination emerges as the critical step that determines the highly oxygenated organic molecule (HOM) distribution and the corresponding nucleation capability. Species that reduce the C20 yield, such as NO, HO2 and as we show isoprene, can thus effectively reduce biogenic nucleation and early growth. Therefore the formation rate of organic aerosol in a particular region of the atmosphere under study will vary according to the precise ambient conditions.

The ability to alter surface chemistry on a variety of materials makes polydopamine (PDA) and excellent surface coating material, but weak wear resistance and high surface roughness limits its application. Here, the authors demonstrate a laser annealing process to improve the mechanical properties of PDA coatings.

Highly oxygenated organic molecules (HOMs) contribute substantially to the formation and growth of atmospheric aerosol particles, which affect air quality, human health and Earth's climate. HOMs are formed by rapid, gas-phase autoxidation of volatile organic compounds (VOCs) such as α-pinene, the most abundant monoterpene in the atmosphere. Due to their abundance and low volatility, HOMs can play an important role in new-particle formation (NPF) and the early growth of atmospheric aerosols, even without any further assistance of other low-volatility compounds such as sulfuric acid. Both the autoxidation reaction forming HOMs and their NPF rates are expected to be strongly dependent on temperature. However, experimental data on both effects are limited. Dedicated experiments were performed at the CLOUD (Cosmics Leaving OUtdoor Droplets) chamber at CERN to address this question. In this study, we show that a decrease in temperature (from +25 to −50 ∘C) results in a reduced HOM yield and reduced oxidation state of the products, whereas the NPF rates (J1.7 nm) increase substantially. Measurements with two different chemical ionization mass spectrometers (using nitrate and protonated water as reagent ion, respectively) provide the molecular composition of the gaseous oxidation products, and a two-dimensional volatility basis set (2D VBS) model provides their volatility distribution. The HOM yield decreases with temperature from 6.2 % at 25 ∘C to 0.7 % at −50 ∘C. However, there is a strong reduction of the saturation vapor pressure of each oxidation state as the temperature is reduced. Overall, the reduction in volatility with temperature leads to an increase in the nucleation rates by up to 3 orders of magnitude at −50 ∘C compared with 25 ∘C. In addition, the enhancement of the nucleation rates by ions decreases with decreasing temperature, since the neutral molecular clusters have increased stability against evaporation. The resulting data quantify how the interplay between the temperature-dependent oxidation pathways and the associated vapor pressures affect biogenic NPF at the molecular level. Our measurements, therefore, improve our understanding of pure biogenic NPF for a wide range of tropospheric temperatures and precursor concentrations.

Condensation particle counters (CPCs) are crucial instruments for detecting sub-10 nm aerosol particles. Understanding the detection performance of a CPC requires thorough characterization under well-controlled laboratory conditions. Besides the size of the seed particles, chemical interactions between the working fluid and the seed particles also influence the activation efficiencies. However, common seed particle materials used for CPC characterizations are not chosen with respect to chemical interactions with vapor molecules of the working fluid by default. Here, we present experiments on the influence of the seed particle material on the detection efficiencies and the 50 % cutoff diameters of commonly used CPCs for the detection of sub-10 nm particles. A remarkable set consisting of six different and commercially available particle detectors, including the newly developed TSI V-WCPC 3789 and a tuned TSI 3776, was tested. The corresponding working fluids of the instruments are n-butanol, diethylene glycol and water. Among other materials we were able to measure detection efficiencies with nanometer-sized organic seed particles reproducibly generated by the oxidation of β-caryophyllene vapor in a flow tube. Theoretical simulations of supersaturation profiles in the condensers were successfully related to measured detection efficiencies. Our results demonstrate the importance of chemical similarities between seed particles and the working fluids used when CPCs are characterized. We anticipate our study to contribute to a deeper understanding of chemical interactions during heterogeneous nucleation processes.

Elevation gradients of meteorological variables in mountains are of interest to a number of scientific disciplines and often required as parameters in modeling frameworks. Measurements of such gradients on glaciers, however, are particularly scarce and strongly skewed towards the midlatitudes and valley glaciers. This article adds a tropical perspective and presents 4 years of overlapping measurements at 5603 and 5873 m on Kersten Glacier, Kilimanjaro (East Africa), between 2009 and 2013. Mean gradients in near-surface air temperature (T), water vapor pressure (VP), and snow accumulation (ACC) per 100 m elevation are −0.75 ∘C, −0.16 hPa, and -114±16 mm w.e. yr−1, respectively. An intriguing feature is a strong diurnal cycle of the T and VP gradients, which are (depending on season) 2–4 times larger between early and late morning than in the hours of weak gradients. The ACC decrease with elevation, furthermore, is mostly the result of a lower recorded frequency of ACC events at the upper measurement site and not due to contrasting amounts at the two altitudes during events. A novel facet of our study is the linking of measured on-glacier gradients to a high-resolution atmospheric modeling data set, which reveals the importance of the mesoscale atmospheric circulation. A thermally direct circulation is established over the mountain in response to diabatic surface heating or cooling with upslope flow during the day and downslope flow in the night. This persistent circulation communicates heat and moisture changes in the lowlands to the higher elevations during morning and early afternoon, which is evident in the advection patterns of potential temperature and VP, and shapes the time variability in gradients as recorded by our weather stations on the glacier. A few local processes seem to matter as well (glacier sublimation, turbulent heat fluxes), yet they show a secondary influence only during limited time windows. Atmospheric model data also demonstrate that declining moist entropy and water vapor fluxes in the summit zone favor formation of the negative ACC gradient. The results extend the empirical basis of elevation gradients in high mountains, in particular over glacier surfaces, by the unusual case of a slope glacier on an equatorial, free-standing massif. Our measurement–model link, moreover, demonstrates an approach for future studies to put observations of elevation gradients more systematically in a multiscale process context.

In the present-day atmosphere, sulfuric acid is the most important vapour for aerosol particle formation and initial growth. However, the growth rates of nanoparticles ( nm) from sulfuric acid remain poorly measured. Therefore, the effect of stabilizing bases, the contribution of ions and the impact of attractive forces on molecular collisions are under debate. Here, we present precise growth rate measurements of uncharged sulfuric acid particles from 1.8 to 10 nm, performed under atmospheric conditions in the CERN (European Organization for Nuclear Research) CLOUD chamber. Our results show that the evaporation of sulfuric acid particles above 2 nm is negligible, and growth proceeds kinetically even at low ammonia concentrations. The experimental growth rates exceed the hard-sphere kinetic limit for the condensation of sulfuric acid. We demonstrate that this results from van der Waals forces between the vapour molecules and particles and disentangle it from charge–dipole interactions. The magnitude of the enhancement depends on the assumed particle hydration and collision kinetics but is increasingly important at smaller sizes, resulting in a steep rise in the observed growth rates with decreasing size. Including the experimental results in a global model, we find that the enhanced growth rate of sulfuric acid particles increases the predicted particle number concentrations in the upper free troposphere by more than 50 %.