julilili42/papers huggingface dataset:
Quantifying Microstructural Evolution in Moving Magma
2020-09-21

Many of the grand challenges in volcanic and magmatic research are focused on understanding the dynamics of highly heterogeneous systems and the critical conditions that enable magmas to move or eruptions to initiate. From the formation and development of magma reservoirs, through propagation and arrest of magma, to the conditions in the conduit, gas escape, eruption dynamics, and beyond into the environmental impacts of that eruption, we are trying to define how processes occur, their rates and timings, and their causes and consequences. However, we are usually unable to observe the processes directly. Here we give a short synopsis of the new capabilities and highlight the potential insights that in situ observation can provide. We present the XRheo and Pele furnace experimental apparatus and analytical toolkit for the in situ X-ray tomography-based quantification of magmatic microstructural evolution during rheological testing. We present the first 3D data showing the evolving textural heterogeneity within a shearing magma, highlighting the dynamic changes to microstructure that occur from the initiation of shear, and the variability of the microstructural response to that shear as deformation progresses. The particular shear experiments highlighted here focus on the effect of shear on bubble coalescence with a view to shedding light on both magma transport and fragmentation processes. The XRheo system is intended to help us understand the microstructural controls on the complex and non-Newtonian evolution of magma rheology, and is therefore used to elucidate the many mobilization, transport, and eruption phenomena controlled by the rheological evolution of a multi-phase magmatic flows. The detailed, in situ characterization of sample textures presented here therefore represents the opening of a new field for the accurate parameterization of dynamic microstructural control on rheological behavior.
Prominent health problems, socioeconomic deprivation, and higher brain age in lonely and isolated individuals: A population-based study
2020-09-21

Loneliness is linked to increased risk for Alzheimer’s disease, but little is known about factors potentially contributing to adverse brain health in lonely individuals. In this study, we used data from 24,867 UK Biobank participants to investigate risk factors related to loneliness and estimated brain age based on neuroimaging data. The results showed that on average, individuals who self-reported loneliness on a single yes/no item scored higher on neuroticism, depression, social isolation, and socioeconomic deprivation, performed less physical activity, and had higher BMI compared to individuals who did not report loneliness. In line with studies pointing to a genetic overlap of loneliness with neuroticism and depression, permutation feature importance ranked these factors as the most important for classifying lonely versus not lonely individuals (ROC AUC = 0.83). While strongly linked to loneliness, neuroticism and depression were not associated with brain age estimates. Conversely, objective social isolation showed a main effect on brain age, and individuals reporting both loneliness and social isolation showed higher brain age relative to controls - as part of a prominent risk profile with elevated scores on socioeconomic deprivation and unhealthy lifestyle behaviours, in addition to neuroticism and depression. While longitudinal studies are required to determine causality, this finding may indicate that the combination of social isolation and a genetic predisposition for loneliness involves a risk for adverse brain health. Importantly, the results underline the complexity in associations between loneliness and adverse health outcomes, where observed risks likely depend on a combination of interlinked variables including genetic as well as social, behavioural, physical, and socioeconomic factors.
Estimation of perceptual scales using ordinal embedding
2020-09-21

In this paper, we address the problem of measuring and analysing sensation, the subjective magnitude of one's experience. We do this in the context of the method of triads: the sensation of the stimulus is evaluated via relative judgments of the form: "Is stimulus S_i more similar to stimulus S_j or to stimulus S_k?". We propose to use ordinal embedding methods from machine learning to estimate the scaling function from the relative judgments. We review two relevant and well-known methods in psychophysics which are partially applicable in our setting: non-metric multi-dimensional scaling (NMDS) and the method of maximum likelihood difference scaling (MLDS). We perform an extensive set of simulations, considering various scaling functions, to demonstrate the performance of the ordinal embedding methods. We show that in contrast to existing approaches our ordinal embedding approach allows, first, to obtain reasonable scaling function from comparatively few relative judgments, second, the estimation of non-monotonous scaling functions, and, third, multi-dimensional perceptual scales. In addition to the simulations, we analyse data from two real psychophysics experiments using ordinal embedding methods. Our results show that in the one-dimensional, monotonically increasing perceptual scale our ordinal embedding approach works as well as MLDS, while in higher dimensions, only our ordinal embedding methods can produce a desirable scaling function. To make our methods widely accessible, we provide an R-implementation and general rules of thumb on how to use ordinal embedding in the context of psychophysics.
Empirical Calibration Coefficient Estimation in the Context of Multi-Functional RF Systems
2020-09-21

This paper presents an empirical approach to estimate the transfer function required for the calibration in general of a two-channel multi-functional broadband receiver developed at Fraunhofer FHR. Modern broadband receivers may enable increased functionality, provided they maintain adequate performance. For example, some modern active electronically scanned arrays (AESAs) and, increasingly, future systems may incorporate simultaneous operating modes, particularly multiple different radar modes together with communications and spectrum sensing modes. It is clear, therefore, that broadband hardware calibration is an essential feature for the realization of Multi-Functional RF Systems (MFRFS). In this paper the characteristics of the MFRF receiver are described via its transfer function. Under laboratory conditions, the receiver transfer function may be determined classically using a vector network analyzer. However, for in-theatre applications, on-site calibration will be necessary. A defined input signal is fed into the broadband receiver via an external signal source and the output signal is recorded. The receiver transfer function is then determined by comparing the input and output signals. A coherent signal processing approach using empirical transfer function estimation and a corresponding measurement concept will be presented resulting in reduced measurement and computational effort compared to state-of-the-art. The performance of the proposed approach is evaluated using real measurement data from a laboratory setup.
Long-Range Multibody Interactions and Three-Body Antiblockade in a Trapped Rydberg Ion Chain
2020-09-22

Trapped Rydberg ions represent a flexible platform for quantum simulation and information processing that combines a high degree of control over electronic and vibrational degrees of freedom. The possibility to individually excite ions to high-lying Rydberg levels provides a system where strong interactions between pairs of excited ions can be engineered and tuned via external laser fields. We show that the coupling between Rydberg pair interactions and collective motional modes gives rise to effective long-range and multibody interactions consisting of two, three, and four-body terms. Their shape, strength, and range can be controlled via the ion trap parameters and strongly depends on both the equilibrium configuration and vibrational modes of the ion crystal. By focusing on an experimentally feasible quasi one-dimensional setup of ^{88}Sr^{+} Rydberg ions, we demonstrate that multibody interactions are enhanced by the emergence of soft modes associated with, e.g., a structural phase transition. This has a striking impact on many-body electronic states and results-for example-in a three-body antiblockade effect that can be employed as a sensitive probe to detect structural phase transitions in Rydberg ion chains. Our study unveils the possibilities offered by trapped Rydberg ions for studying exotic phases of matter and quantum dynamics driven by enhanced multibody interactions.
Evaluation of a simple analytical model for offshore wind farm wake recovery by in situ data and Weather Research and Forecasting simulations
2020-09-22

Abstract The recovery of offshore wind farm wakes in the German Bight was analyzed by a unique in situ data set, measured on‐board the research aircraft Dornier Do‐128 during the WIPAFF project in 2016 and 2017. These observations were used to validate a simple analytical wake recovery model in five case studies. The observed recovery rates were compared with the results of the mesoscale Weather Research and Forecasting (WRF) model. The airborne data show that the wake recovery can be described by an exponential function as expected by the analytical model and strengthens the hypothesis that the vertical downward moment flux has an important influence on the wake recovery. However, the predicted wake recovery rates (by the analytical model) do not always fully agree with the observations. Although, as a first‐order approximation, the model seems to perform well, further optimization has to be implemented to account for wind park layout, turbine induced turbulence, and horizontal momentum flux. WRF simulations reveal an exponential recovery, although the mesoscale model does not reproduce the correct atmospheric conditions for most of the cases. Therefore, the wake recovery rates estimated by WRF disagree with the measured data in most of the studied cases.
Estonian Case Inflection Made Simple
2020-09-22

&lt;p&gt;According to word and paradigm morphology (Matthews 1974, Blevins 2016), the word is the basic cognitive unit over which paradigmatic analogy operates to predict form and meaning of novel forms. Baayen et al. (2019b, 2018) introduced a computational formalization of word and paradigm morphology which makes it possible to model the production and comprehension of complex words without requiring exponents, morphemes, inflectional classes, and separate treatment of regular and irregular morphology. This computational model, Linear Discriminative Learning (LDL), makes use of simple matrix algebra to move from words&rsquo; forms to meanings (comprehension) and from words&rsquo; meanings to their forms (production). In Baayen et al. (2018), we showed that LDL makes accurate predictions for Latin verb conjugations. The present study reports results for noun declension in Estonian. Consistent with previous findings, the model&rsquo;s predictions for comprehension and production are highly accurate. Importantly, the model achieves this high accuracy without being informed about stems, exponents, and inflectional classes. The speech errors produced by the model look like errors that native speakers might make. When the model is trained on incomplete paradigms, comprehension accuracy for unseen forms is hardly affected, but production accuracy decreases, reflecting the well-known asymmetry between comprehension and production.&lt;/p&gt;
Mixture Risk Drivers in Freshwater Sediments and Their Bioavailability Determined Using Passive Equilibrium Sampling
2020-09-22

The identification of mixture risk drivers is a great challenge for sediment assessment, especially when taking bioavailability into consideration. The bioavailable portion, which comprises the organic contaminants in pore water and the ones bound to organic carbon, was accessed by equilibrium partitioning to polydimethylsiloxane (PDMS). The exhaustive solvent and PDMS extracts were toxicologically characterized with a battery of in vitro reporter gene assays and chemically analyzed with liquid and gas chromatography coupled to high-resolution mass spectrometry. The bioavailable fractions of mixture effects and individual chemicals were mostly lower than 0.1, indicating that more than 90% of the substances are strongly bound and would not pose an immediate risk but could potentially be remobilized in the long term. Despite 655 organic chemicals analyzed, only 0.1-28% of the observed biological effects was explained by the detected compounds in whole sediments, while 0.009-3.3% was explained by bioavailable chemicals. The mixture effects were not only dominated by legacy pollutants (e.g., polycyclic aromatic hydrocarbons (PAHs) in the bioassay for activation of the aryl-hydrocarbon receptor (AhR) and oxidative stress response (AREc32)) but also by present-use chemicals (e.g., plastic additives for binding to the peroxisome proliferator-activated receptor γ (PPARγ)), with different fingerprints between whole sediments and bioavailable extracts. Our results highlight the necessity to involve different bioassays with diverse effect profiles and broader selection of contaminants along with bioavailability for the risk assessment of chemical mixtures in sediments.
Lack of cerebellar tDCS effects on learning of a complex whole body dynamic balance task in middle-aged (50–65 years) adults
2020-09-22

Abstract Background Cerebellar transcranial direct current stimulation (tDCS) is widely considered as a promising non-invasive tool to foster motor performance and learning in health and disease. The results of previous studies, however, are inconsistent. Our group failed to provide evidence for an effect of cerebellar tDCS on learning of a complex whole body dynamic balance task in young and healthy participants. Ceiling effects in the young study population are one possible explanation for the negative findings. Methods In the present study, we therefore tested 40 middle-aged healthy participants between the ages of 50 to 65 years. Participants received either anodal or sham cerebellar tDCS using a double-blinded study design while performing a balance task on a Lafayette Instrument 16,030 stability platform®. Mean platform angle and mean balance time were assessed as outcome measures. Results Significant learning effects were found in all participants. Balancing performance and learning rate was significantly less in the group of middle-aged adults compared to our previous group of young adults. No significant effects of cerebellar tDCS were observed. Conclusions Our findings are in line with other studies that have failed to prove robust effects of cerebellar tDCS on motor learning. The present findings, however, do not exclude cerebellar tDCS effects. tDCS effects may be more prominent after repeated stimulation, using other stimulus parameters, in patient populations, or in other motor learning tasks. Trial registration Not applicable.
NeutrobodyPlex - Nanobodies to monitor a SARS-CoV-2 neutralizing immune response
2020-09-22

Abstract As the COVID-19 pandemic escalates, the need for effective vaccination programs, therapeutic intervention, and diagnostic tools increases. Here, we identified 11 unique nanobodies (Nbs) specific for the SARS-CoV-2 spike receptor-binding domain (RBD) of which 8 Nbs potently inhibit the interaction of RBD with angiotensin-converting enzyme 2 (ACE2) as the major viral docking site. Following a detailed epitope determination and characterization of the binding mode by structural analysis, we constructed a hetero-bivalent Nb targeting two different epitopes within the RBD:ACE2 interface. This resulted in a high-affinity binder with a viral neutralization efficacy in the picomolar range. Using the bivalent Nb as a surrogate, we established a competitive multiplex binding assay (“NeutrobodyPlex”) for detailed analysis of the presence and performance of neutralizing RBD-binding antibodies in the serum of convalescent or vaccinated patients. As demonstrated, the NeutrobodyPlex enables high-throughput screening and detailed analysis of neutralizing immune responses in infected or vaccinated individuals, helping to monitor immune status or guide vaccine design. This approach is easily transferrable to diagnostic laboratories worldwide.
Paleomagnetic constraints on the duration of the Australia-Laurentia connection in the core of the Nuna supercontinent
2020-09-23

Abstract The Australia-Laurentia connection in the Paleoproterozoic to Mesoproterozoic supercontinent Nuna is thought to have initiated by ca. 1.6 Ga when both continents were locked in a proto-SWEAT (southwestern U.S.–East Antarctic) configuration. However, the longevity of that configuration is poorly constrained. Here, we present a new high-quality paleomagnetic pole from the ca. 1.3 Ga Derim Derim sills of northern Australia that suggests Australia and Laurentia were in the same configuration at that time. This new paleopole also supports a connection between Australia and North China and, in conjunction with previously reported data from all continents, indicates that the breakup of Nuna largely occurred between ca. 1.3 and 1.2 Ga.
The Innovative Strategies for Observations in the Arctic Atmospheric Boundary Layer Project (ISOBAR): Unique Finescale Observations under Stable and Very Stable Conditions
2020-09-23

Abstract The Innovative Strategies for Observations in the Arctic Atmospheric Boundary Layer Program (ISOBAR) is a research project investigating stable atmospheric boundary layer (SBL) processes, whose representation still poses significant challenges in state-of-the-art numerical weather prediction (NWP) models. In ISOBAR ground-based flux and profile observations are combined with boundary layer remote sensing methods and the extensive usage of different unmanned aircraft systems (UAS). During February 2017 and 2018 we carried out two major field campaigns over the sea ice of the northern Baltic Sea, close to the Finnish island of Hailuoto at 65°N. In total 14 intensive observational periods (IOPs) resulted in extensive SBL datasets with unprecedented spatiotemporal resolution, which will form the basis for various numerical modeling experiments. First results from the campaigns indicate numerous very stable boundary layer (VSBL) cases, characterized by strong stratification, weak winds, and clear skies, and give detailed insight in the temporal evolution and vertical structure of the entire SBL. The SBL is subject to rapid changes in its vertical structure, responding to a variety of different processes. In particular, we study cases involving a shear instability associated with a low-level jet, a rapid strong cooling event observed a few meters above ground, and a strong wave-breaking event that triggers intensive near-surface turbulence. Furthermore, we use observations from one IOP to validate three different atmospheric models. The unique finescale observations resulting from the ISOBAR observational approach will aid future research activities, focusing on a better understanding of the SBL and its implementation in numerical models.
Contrasting biomass allocation responses across ontogeny and stress gradients reveal plant adaptations to drought and cold
2020-09-23

Abstract How plants allocate their biomass to different organs is essential to understanding plant adaptations and distributions. Overall, biomass allocation may follow fixed rules across taxa. They are also likely to exhibit substantial departure from these rules during ontogeny and in response to particular limiting factors to optimize their growth and maximize their survival. However, how plants adjust their allocation priorities depending on size and age across stress gradients remain largely unknown in wild populations. We examined ontogenetic variation in biomass allocation in Himalayan forb Potentilla pamirica across its 5,250–5,900‐m elevation range, between populations from dry steppe, wet alpine and cold subnival zone. We evaluated whether biomass allocation followed optimal partitioning or fixed allometric rules using organ mass in 1,019 individuals spanning 1–73 years. We found shifting biomass fractions with plant size and age, supporting the optimal partitioning theory. Young plants (&lt;10 years) allocated similar proportions of biomass to leaves, stems and roots, intermediate‐aged plants (10–30 years) allocated more biomass to roots, while the oldest plants had 90% biomass in below‐ground stems. Major developmental processes including secondary thickening, branching and flowering begin 10–15 years earlier under more thermally favourable steppe conditions. Young steppe plants are larger than alpine and subnival plants, but these differences disappear in plants aged ~30, and the oldest alpine and subnival plants are larger than steppe plants. Plant age exerted significant control over biomass allocation after controlling for plant size. While in steppe plants the preference for stem biomass allocation increases with both size and age, for large alpine and subnival plants the stem prioritization decreases with age in favour of root and leaf mass fractions. We interpret root and leaf prioritization in older plants as a way to reduce carbon imbalances and the risk of frost damage to secure long life. Our analyses rejected ontogenetically fixed allometry and instead found high variation in biomass allocation depending on age, size and environment, supporting optimal partitioning theory. The uneven allocation of resources to different structures and functions during ontogenesis reflects plant adaptations to different levels of low‐temperature and water stress across species elevation range. A free Plain Language Summary can be found within the Supporting Information of this article.
Real-space cluster dynamical mean-field theory: Center-focused extrapolation on the one- and two particle-levels
2020-09-23

We revisit the cellular dynamical mean-field theory (CDMFT) for the single\\nband Hubbard model on the square lattice at half filling, reaching real-space\\ncluster sizes of up to 9 x 9 sites. Using benchmarks against direct lattice\\ndiagrammatic Monte Carlo at high temperature, we show that the self-energy\\nobtained from a cluster center focused extrapolation converges faster with the\\ncluster size than the periodization schemes previously introduced in the\\nliterature. The same benchmark also shows that the cluster spin susceptibility\\ncan be extrapolated to the exact result at large cluster size, even though its\\nspatial extension is larger than the cluster size.\\n
Consensus paper: current state of first- and second-line therapy in advanced clear-cell renal cell carcinoma
2020-09-23

The therapy of advanced (clear-cell) renal cell carcinoma (RCC) has recently experienced tremendous changes. Several new treatments have been developed, with PD-1 immune-checkpoint inhibition being the backbone of therapy. Diverse immunotherapy combinations change current first-line standards. These changes also require new approaches in subsequent lines of therapy. In an expert panel, we discussed the new treatment options and how they change clinical practice. While first-line immunotherapies introduce a new level of response rates, data on second-line therapies remains poor. This scenario poses a challenge for clinicians as guideline recommendations are based on historical patient cohorts and agents may lack the appropriate label for their in guidelines recommended use. Here, we summarize relevant clinical data and consider appropriate treatment strategies.