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Spatial proxies, such as coordinates and distance fields, are often added as predictors in random forest (RF) models without any modifications being made to the algorithm to account for residual autocorrelation and improve predictions. However, their suitability under different predictive conditions encountered in environmental applications has not yet been assessed. We investigate (1) the suitability of spatial proxies depending on the modelling objective (interpolation vs. extrapolation), the strength of the residual spatial autocorrelation, and the sampling pattern; (2) which validation methods can be used as a model selection tool to empirically assess the suitability of spatial proxies; and (3) the effect of using spatial proxies in real-world environmental applications. We designed a simulation study to assess the suitability of RF regression models using three different types of spatial proxies: coordinates, Euclidean distance fields (EDFs), and random forest spatial prediction (RFsp). We also tested the ability of probability sampling test points, random k-fold cross-validation (CV), and k-fold nearest neighbour distance matching (kNNDM) CV to reflect the true prediction performance and correctly rank models. As real-world case studies, we modelled annual average air temperature and fine particulate air pollution for continental Spain. In the simulation study, we found that RFs with spatial proxies were poorly suited for spatial extrapolation to new areas due to significant feature extrapolation. For spatial interpolation, proxies were beneficial when both strong residual autocorrelation and regularly or randomly distributed training samples were present. In all other cases, proxies were neutral or counterproductive. Random k-fold cross-validation generally favoured models with spatial proxies even when it was not appropriate, whereas probability test samples and kNNDM CV correctly ranked models. In the case studies, air temperature stations were well spread within the prediction area, and measurements exhibited strong spatial autocorrelation, leading to an effective use of spatial proxies. Air pollution stations were clustered and autocorrelation was weaker and thus spatial proxies were not beneficial. As the benefits of spatial proxies are not universal, we recommend using spatial exploratory and validation analyses to determine their suitability, as well as considering alternative inherently spatial modelling approaches.

Random and spatial cross-validation (CV) methods are commonly used to evaluate machine-learning-based spatial prediction models, and the performance values obtained are often interpreted as map accuracy estimates. However, the appropriateness of such approaches is currently the subject of controversy. For the common case where no probability sample for validation purposes is available, in Milà et al. (2022) we proposed the nearest-neighbour distance matching (NNDM) leave-one-out (LOO) CV method. This method produces a distribution of geographical nearest-neighbour distances (NNDs) between test and training locations during CV that matches the distribution of NNDs between prediction and training locations. Hence, it creates predictive conditions during CV that are comparable to what is required when predicting a defined area. Although NNDM LOO CV produced largely reliable map accuracy estimates in our analysis, as a LOO-based method, it cannot be applied to the large datasets found in many studies. Here, we propose a novel k-fold CV strategy for map accuracy estimation inspired by the concepts of NNDM LOO CV: the k-fold NNDM (kNNDM) CV. The kNNDM algorithm tries to find a k-fold configuration such that the empirical cumulative distribution function (ECDF) of NNDs between test and training locations during CV is matched to the ECDF of NNDs between prediction and training locations. We tested kNNDM CV in a simulation study with different sampling distributions and compared it to other CV methods including NNDM LOO CV. We found that kNNDM CV performed similarly to NNDM LOO CV and produced reasonably reliable map accuracy estimates across sampling patterns. However, compared to NNDM LOO CV, kNNDM resulted in significantly reduced computation times. In an experiment using 4000 strongly clustered training points, kNNDM CV reduced the time spent on fold assignment and model training from 4.8 d to 1.2 min. Furthermore, we found a positive association between the quality of the match of the two ECDFs in kNNDM and the reliability of the map accuracy estimates. kNNDM provided the advantages of our original NNDM LOO CV strategy while bypassing its sample size limitations.

The mapping of environmental information based on remote sensing requires a workflow that involves image processing, model training usually based on machine learning, as well as model application and validation. Remote sensing data processing capabilities are nowadays simplified by cloud computing platforms. State of the art machine learning methods for spatial data which involve a reduction of spatial overfitting, handling of extrapolation situations and a spatially explicit error assessment, however, are currently mostly implemented in local computation frameworks. Here we present a workflow that combines the improved processing capabilities of the cloud computation platform openEO with state-of-the-art machine learning model development in R. OpenEO is used for standardized imagery acquisition and preprocessing to provide predictors for model training. To reduce overfitting, predictors which are meaningful for the mapping are identified via spatial variable selection as implemented in R packages. The mapping accuracy is assessed via spatial cross-validation and predictions are limited to the ’Area of Applicability’ of the model. The workflow is designed to enhance and assess the spatial transferability of machine learning models which is demonstrated by a case study of a landcover classification based on Sentinel-2 imagery.

Abstract LOPES, the LOFAR prototype station, was an antenna array for cosmic-ray air showers operating from 2003 to 2013 within the KASCADE-Grande experiment. Meanwhile, the analysis is finished and the data of air-shower events measured by LOPES are available with open access in the KASCADE Cosmic Ray Data Center (KCDC). This article intends to provide a summary of the achievements, results, and lessons learned from LOPES. By digital, interferometric beamforming the detection of air showers became possible in the radio-loud environment of the Karlsruhe Institute of Technology (KIT). As a prototype experiment, LOPES tested several antenna types, array configurations and calibration techniques, and pioneered analysis methods for the reconstruction of the most important shower parameters, i.e., the arrival direction, the energy, and mass-dependent observables such as the position of the shower maximum. In addition to a review and update of previously published results, we also present new results based on end-to-end simulations including all known instrumental properties. For this, we applied the detector response to radio signals simulated with the CoREAS extension of CORSIKA, and analyzed them in the same way as measured data. Thus, we were able to study the detector performance more accurately than before, including some previously inaccessible features such as the impact of noise on the interferometric cross-correlation beam. These results led to several improvements, which are documented in this paper and can provide useful input for the design of future cosmic-ray experiments based on the digital radio-detection technique.

The Copernicus Polar Ice and Snow Topography Altimeter (CRISTAL) mission is one of six high-priority candidate missions (HPCMs) under consideration by the European Commission to enlarge the Copernicus Space Component. Together, the high-priority candidate missions fill gaps in the measurement capability of the existing Copernicus Space Component to address emerging and urgent user requirements in relation to monitoring anthropogenic CO2 emissions, polar environments, and land surfaces. The ambition is to enlarge the Copernicus Space Component with the high-priority candidate missions in the mid-2020s to provide enhanced continuity of services in synergy with the next generation of the existing Copernicus Sentinel missions. CRISTAL will carry a dual-frequency synthetic-aperture radar altimeter as its primary payload for measuring surface height and a passive microwave radiometer to support atmospheric corrections and surface-type classification. The altimeter will have interferometric capabilities at Ku-band for improved ground resolution and a second (non-interferometric) Ka-band frequency to provide information on snow layer properties. This paper outlines the user consultations that have supported expansion of the Copernicus Space Component to include the high-priority candidate missions, describes the primary and secondary objectives of the CRISTAL mission, identifies the key contributions the CRISTAL mission will make, and presents a concept – as far as it is already defined – for the mission payload.

The radio technique is a promising method for detection of cosmic-ray air showers of energies around 100PeV and higher with an array of radio antennas. Since the amplitude of the radio signal can be measured absolutely and increases with the shower energy, radio measurements can be used to determine the air-shower energy on an absolute scale. We show that calibrated measurements of radio detectors operated in coincidence with host experiments measuring air showers based on other techniques can be used for comparing the energy scales of these host experiments. Using two approaches, first via direct amplitude measurements, and second via comparison of measurements with air shower simulations, we compare the energy scales of the air-shower experiments Tunka-133 and KASCADE-Grande, using their radio extensions, Tunka-Rex and LOPES, respectively. Due to the consistent amplitude calibration for Tunka-Rex and LOPES achieved by using the same reference source, this comparison reaches an accuracy of approximately 10% – limited by some shortcomings of LOPES, which was a prototype experiment for the digital radio technique for air showers. In particular we show that the energy scales of cosmic-ray measurements by the independently calibrated experiments KASCADE-Grande and Tunka-133 are consistent with each other on this level. Keywords: Cosmic rays, Radio detection, LOPES, Tunka-Rex, Tunka-133, KASCADE-Grande

We employ a pair of single-cycle near-infrared pulses to control coherent transfer of single electrons between the contacts of a plasmonic nanocircuit. As a result, attosecond and highly nonlinear phenomena occur at pJ pulse energies.

Consecrated in 1297 as the monastery church of the four years earlier founded St. Catherine’s monastery, the Gothic Church of St. Catherine was largely destroyed in a devastating bombing raid on January 2nd 1945. To counteract the process of disintegration, the departments of geo-information and lower monument protection authority of the City of Nuremburg decided to getting done a three dimensional building model of the Church of St. Catherine’s. A heterogeneous set of data was used for preparation of a parametric architectural model. In effect the modeling of historic buildings can profit from the so called BIM method (Building Information Modeling), as the necessary structuring of the basic data renders it into very sustainable information. The resulting model is perfectly suited to deliver a vivid impression of the interior and exterior of this former mendicant orders’ church to present observers.

Os radicais livres estão envolvidos em um grande número de enfermidades do ser humano. O cérebro tem níveis baixos de enzimas antioxidantes e um conteúdo lípidico elevado, tornando-se muito susceptível ao ataque de espécies reativas de oxigênio. Neste trabalho avaliou- se a lipoperoxidação em hipocampo e a atividade da enzima catalase em estriado e hipocampo de ratos tratados com o fungicida maneb (30 mg/kg) e o herbicida paraquat (10 mg/kg). Não houve alteração na lipoperoxidação nem na atividade enzimática no hipocampo dos animais tratados com ambos os praguicidas, porém foi observada uma inibição da catalase no estriado dos ratos tratados com maneb e com paraquat. Com estes resultados pode-se sugerir, de forma preliminar, uma ação tóxica maior sobre centros dopaminérgicos. Estudos sobre a toxicidade destes compostos são essenciais na compreensão do papel destes praguicidas e dos radicais livres na etiologia das doenças. Palavras-chave: catalase; paraquat; maneb; estriado; hipocampo; radicais livres.

Abstract Plant biomass is a fundamental ecosystem attribute that is sensitive to rapid climatic changes occurring in the Arctic. Nevertheless, measuring plant biomass in the Arctic is logistically challenging and resource intensive. Lack of accessible field data hinders efforts to understand the amount, composition, distribution, and changes in plant biomass in these northern ecosystems. Here, we present The Arctic plant aboveground biomass synthesis dataset, which includes field measurements of lichen, bryophyte, herb, shrub, and/or tree aboveground biomass (g m−2) on 2,327 sample plots from 636 field sites in seven countries. We created the synthesis dataset by assembling and harmonizing 32 individual datasets. Aboveground biomass was primarily quantified by harvesting sample plots during mid- to late-summer, though tree and often tall shrub biomass were quantified using surveys and allometric models. Each biomass measurement is associated with metadata including sample date, location, method, data source, and other information. This unique dataset can be leveraged to monitor, map, and model plant biomass across the rapidly warming Arctic.

Landscapes are often assumed to be homogeneous when interpreting eddy covariance fluxes, which can lead to biases when gap-filling and scaling up observations to determine regional carbon budgets. Tundra ecosystems are heterogeneous at multiple scales. Plant functional types, soil moisture, thaw depth, and microtopography, for example, vary across the landscape and influence net ecosystem exchange (NEE) of carbon dioxide (CO2) and methane (CH4) fluxes. With warming temperatures, Arctic ecosystems are changing from a net sink to a net source of carbon to the atmosphere in some locations, but the Arctic's carbon balance remains highly uncertain. In this study we report results from growing season NEE and CH4 fluxes from an eddy covariance tower in the Yukon–Kuskokwim Delta in Alaska. We used footprint models and Bayesian Markov chain Monte Carlo (MCMC) methods to unmix eddy covariance observations into constituent land-cover fluxes based on high-resolution land-cover maps of the region. We compared three types of footprint models and used two land-cover maps with varying complexity to determine the effects of these choices on derived ecosystem fluxes. We used artificially created gaps of withheld observations to compare gap-filling performance using our derived land-cover-specific fluxes and traditional gap-filling methods that assume homogeneous landscapes. We also compared resulting regional carbon budgets when scaling up observations using heterogeneous and homogeneous approaches. Traditional gap-filling methods performed worse at predicting artificially withheld gaps in NEE than those that accounted for heterogeneous landscapes, while there were only slight differences between footprint models and land-cover maps. We identified and quantified hot spots of carbon fluxes in the landscape (e.g., late growing season emissions from wetlands and small ponds). We resolved distinct seasonality in tundra growing season NEE fluxes. Scaling while assuming a homogeneous landscape overestimated the growing season CO2 sink by a factor of 2 and underestimated CH4 emissions by a factor of 2 when compared to scaling with any method that accounts for landscape heterogeneity. We show how Bayesian MCMC, analytical footprint models, and high-resolution land-cover maps can be leveraged to derive detailed land-cover carbon fluxes from eddy covariance time series. These results demonstrate the importance of landscape heterogeneity when scaling carbon emissions across the Arctic.

Abstract Behavioural and neuroimaging research has shown that older adults are less sensitive to financial losses compared to younger adults. Yet relatively less is known about age-related differences in social decisions and social reward processing. As part of a pilot study, we collected behavioural and functional magnetic resonance imaging (fMRI) data from 50 participants (Younger: N = 26, ages 18–34 years; Older: N = 24, ages 63–80 years) who completed three tasks in the scanner: an economic trust game as the investor with three partners (computer, stranger, friend) as the investee; a card-guessing task with monetary gains and losses shared with three partners (computer, stranger, friend); and an ultimatum game as responder to three anonymous proposers (computer, age-similar adults, age-dissimilar adults). We also collected B0 field maps and high-resolution structural images (T1-weighted and T2-weighted images). These data could be reused to answer questions about moment-to-moment variability in fMRI signal, representational similarity between tasks, and brain structure.

KASCADE-Grande is an extensive air shower experiment devoted to the study of cosmic rays in the 1016 ÷ 1018 eV energy range. The array is composed by different detectors allowing independent measurements of the number of muons (Nμ) and charged particles (Nch) of extensive air showers (EAS). These two observables are then used to study the primary energy spectrum, separating the events in two samples, on the basis of the shower size ratio, corrected for attenuation in the atmosphere, lnNμ/ lnNch. The two samples represent the light and heavy mass groups of the primaries. In the studied energy range only the spectrum of heavy primaries shows a significant change of slope, the energy (estimated using the QGSJET II hadronic interaction model) of this feature is in agreeement with the expectations of a rigidity dependent knee feature.

The observation of ultrahigh energy neutrinos (UHEνs) has become a priority in experimental astroparticle physics. UHEνs can be detected with a variety of techniques. In particular, neutrinos can interact in the atmosphere (downward-going ν) or in the Earth crust (Earth-skimming ν), producing air showers that can be observed with arrays of detectors at the ground. With the surface detector array of the Pierre Auger Observatory we can detect these types of cascades. The distinguishing signature for neutrino events is the presence of very inclined showers produced close to the ground (i.e., after having traversed a large amount of atmosphere). In this work we review the procedure and criteria established to search for UHEνs in the data collected with the ground array of the Pierre Auger Observatory. This includes Earth-skimming as well as downward-going neutrinos. No neutrino candidates have been found, which allows us to place competitive limits to the diffuse flux of UHEνs in the EeV range and above.

The carotenoid pigments of different cultivars of Cucurbita maxima L. (pumpkin) were investigated in pulp, peel and genuine juice by means of HPLC. In dependence of the variety different concentrations and distributions of α-carotene, β-carotene, violaxanthin, neoxanthin, all-trans-lutein, zeaxanthin, lutheoxanthin and the isomers 9-cis-β-carotene and 13-cis-β-carotene were obtained. The total carotenoid content (expressed as dry weight) of the pumpkin peel depends on the variety with 12 mg/kg for „Butternut“ up to 1751 mg/kg for „Rouge“, whereas total carotenoid contents in the pulp differed from 17 mg/kg for „Baby Bear“ to 683 mg/kg for „Rouge“. The variety „Hokkaido“ showed high total carotenoid contents in pulp (218 mg/kg) and peel (1048 mg/kg), whereas for „Butternut“ minor concentrations in pulp (44 mg/kg) and peel (12 mg/kg) were detected. The red and orange coloured Cucurbita maxima L. varieties are valuable sources of carotenoids among vegetables or vegetable juices.

Os radicais livres estão envolvidos em um grande nú- mero de enfermidades do ser humano. O cérebro tem níveis baixos de enzimas antioxidantes e um conteúdo lípidico elevado, tornando-se muito susceptível ao ataque de espécies reativas de oxigênio. Neste trabalho avaliou-se a lipoperoxidação em hipocampo e a atividade da enzima catalase em estriado e hipocampo de ratos tratados com o fungicida maneb (30 mg/kg) e o herbicida paraquat (10 mg/kg). Não houve alteração na lipoperoxidação nem na atividade enzimática no hipocampo dos animais tratados com ambos os praguicidas, porém foi observada uma inibição da catalase no estriado dos ratos tratados com maneb e com paraquat. Com estes resultados pode-se sugerir, de forma preliminar, uma ação tóxica maior sobre centros dopaminérgicos. Estudos sobre a toxicidade destes compostos são essenciais na compreensão do papel destes praguicidas e dos radicais livres na etiologia das doenças.

Abstract Since the beginning of the pandemic in 2020, people have been worried about COVID-19. As one of the risk groups, persons aged 65 and older are especially vulnerable. Additionally, minorities and migrants are hit harder by the pandemic than natives. Using data from the TransAge survey, a study including over 3000 older persons (65+) living in Switzerland and Italy, we show that the levels of worry about the pandemic are significantly higher among Italian international migrants living in Switzerland than among Swiss natives. We are not able to fully explain the difference using sociodemographic variables, the COVID-19 situation at the time of the interview, and international migrants’ transnationalism behavior. Nevertheless, transnationalism explains a large part of the difference in worry between the two groups and our study sheds light on the importance of two specific transnational aspects, having Swiss nationality and voting behavior, for the prevention of elevated levels of worry of international migrants.

Abstract In cirrhotic patients with hepatocellular carcinoma (HCC), right-sided radioembolization (RE) with Yttrium-90-loaded microspheres is an established palliative therapy and can be considered a “curative intention” treatment when aiming for sequential tumor resection. To become surgical candidate, hypertrophy of the left liver lobe to > 40% (future liver remnant, FLR) is mandatory, which can develop after RE. The amount of radiation-induced shrinkage of the right lobe and compensatory hypertrophy of the left lobe is difficult for clinicians to predict. This study aimed to utilize machine learning to predict left lobe liver hypertrophy in patients with HCC and cirrhosis scheduled for right lobe RE, with external validation. The results revealed that machine learning can accurately predict relative and absolute volume changes of the left liver lobe after right lobe RE. This prediction algorithm could help to estimate the chances of conversion from palliative RE to curative major hepatectomy following significant FLR hypertrophy.

Introduction: Metakaolin (MK) is used as supplementary cementitious material to reduce the CO2 footprint of Portland cements. However, the early hydration of Portland cement (OPC) is often retarded due to its use. The present work investigates the mechanisms of this retardation. Focus is laid on the interaction of MK with the main clinker phase C3S (Ca3SiO5, pure form of alite) that is known to govern the kinetics of early hydration of OPC.Methods: Hydration reactions of MK and C3S were analysed by optical emission spectroscopy, electron microscopy, thermal analysis, X-ray diffraction and reaction calorimetry.Results: Results on MK showed that compared to sodium ions the presence of calcium ions reduced the maximum amounts of silicate and aluminate ions released into solution by MK. For MK + C3S mixtures, C-A-S-H was formed at the surfaces of both C3S and MK within minutes with a composition of (CaO)1.3(SiO2)0.8(Al2O3)0.2(H2O)2.7. The solubility constant of (CaO)1.3(SiO2)0.8(Al2O3)0.2(H2O)2.7 was determined.Discussion: C-A-S-H appeared to be an unsuitable substrate for C-S-H nucleation. Therefore, its formation during early hydration is expected to play an important role in the retardation of C3S hydration. Indeed, when C-A-S-H seeds are formed, less C-S-H seeds are formed leading to lengthen the duration of the induction period. The presence of sulfate ions reduces the amount of C-A-S-H seeds as most aluminate ions are consumed to form ettringite. Consequently, sulfate ions induce an increase of the hydration kinetics such as observed in MK + C3S mixtures.

Recommended treatment options for advanced-stage hepatocellular carcinoma (HCC) include systemic therapy (ST) and trans-arterial radioembolization (TARE) with Yttrium-90 (Y90). Before the approval of immune-checkpoint inhibitors, a similar safety profile was reported for TARE and ST with tyrosine kinase inhibitors (TKI). However, whole-liver treatment and underlying cirrhosis were identified as risk factors for potentially lethal radioembolization-induced liver disease (REILD). Therefore, the safety and efficacy of TARE and ST with atezolizumab/bevacizumab were compared in patients with advanced HCC involving at least both liver lobes in a retrospective real-world cohort. In total, 74 patients with new or recurrent advanced-stage HCC (BCLC stage B/C) were included if treated with either bilobar TARE (n = 33) or systemic combination therapy with atezolizumab plus bevacizumab (n = 41). Most patients had compensated liver function (90.5% were classified as Child-Pugh Score A, 73% as ALBI Grade 1) at baseline. Although not significant, patients treated with ST showed a more prolonged overall survival than those treated with Y90 TARE (7.1 months vs. 13.0 months, p = 0.07). While a similar disease control rate could be achieved with bilobar TARE and atezolizumab/bevacizumab, in the TARE group, overall survival was curtailed by the occurrence of REILD. In patients with underlying liver cirrhosis, the liver function at baseline was a predictor for REILD.

Aim. The Multicentre Olmesartan atherosclerosis Regression Evaluation (MORE) study was a double-blind trial in patients with hypertension at increased cardiovascular risk with carotid wall thickening and a defined atherosclerotic plaque that used non-invasive 2- and 3-dimensional (D) ultrasound (US), to compare the effects of a 2-year treatment based on either olmesartan medoxomil or atenolol on common carotid (CC) intima-media thickness (IMT) and plaque volume (PV). Methods. A total of 165 patients (with systolic/diastolic blood pressure 140–180/90–105 mm Hg) were randomized to receive either olmesartan (20–40 mg/day) or atenolol (50–100 mg/day). US was performed at baseline and 28, 52 and 104 weeks. The primary efficacy outcome was the change from baseline (Δ) in CC-IMT assessed by 2D US. Secondary outcomes included ΔPV assessed by 3D US and blood pressure (BP). Results. Olmesartan and atenolol produced comparable significant reductions in CC-IMT; mean ΔIMT (SEM) was −0,090 (0,015) mm for olmesartan and −0,082 (0,014) mm for atenolol. Mean ΔPV was −4,4 (2,3) μl and 0,1 (1,5) μl in the olmesartan and atenolol treated subjects, respectively, without significant between-treatment differences. In the subgroup of patients with baseline PV ≥ median (33,7 μl), significant between-treatment differences existed in ΔPV (p=0,023), because PV regressed significantly with olmesartan (ΔPV: −11,5 (4,4) μl) but not with atenolol (ΔPV: 0,6 (2,5) μl). In these patients BP reductions were comparable. Conclusion. Carotid IMT and BP decreased similarly with olmesartan and atenolol, but only olmesartan reduced the volume of larger atherosclerotic plaques.

In the Arctic waterbodies are abundant and rapid thaw of permafrost is destabilizing the carbon cycle and changing hydrology. It is particularly important to quantify and accurately scale aquatic carbon emissions in arctic ecosystems. Recently available high-resolution remote sensing datasets capture the physical characteristics of arctic landscapes at unprecedented spatial resolution. We demonstrate how machine learning models can capitalize on these spatial datasets to greatly improve accuracy when scaling waterbody CO _2 and CH _4 fluxes across the YK Delta of south-west AK. We found that waterbody size and contour were strong predictors for aquatic CO _2 emissions, attributing greater than two-thirds of the influence to the scaling model. Small ponds (