Weather extremes over Europe under 1.5 °C and 2.0 °C global warming from HAPPI regional climate ensemble simulations

This paper presents a novel data set of regional climate model simulations over Europe that significantly improves our ability to detect changes in weather extremes under low and moderate levels of global warming. The data set provides a unique and physically consistent data set, as it is derived from a large ensemble of regional climate model simulations. These simulations were driven by two global climate models from the international HAPPI consortium. The set consists of 100 × 10-year simulations and 25 × 10-year simulations, respectively. These large ensembles allow for regional climate change and weather extremes to be investigated with an improved signal-to-noise ratio compared to previous climate simulations. The changes in four climate indices for temperature targets of 1.5 °C and 2.0 °C global warming are quantified: number of days per year with daily mean near-surface apparent temperature of > 28 °C (ATG28); the yearly maximum 5-day sum of precipitation (RX5day); the daily precipitation intensity of the 50-yr return period (RI50yr); and the annual Consecutive Dry Days (CDD). This work shows that even for a small signal in projected global mean temperature, changes of extreme temperature and precipitation indices can be robustly estimated. For temperature related indices changes in percentiles can also be estimated with high confidence. Such data can form the basis for tailor-made climate information that can aid adaptive measures at a policy-relevant scales, indicating potential impacts at low levels of global warming at steps of 0.5 °C

Synaptic Signaling by All-Trans Retinoic Acid in Homeostatic Synaptic Plasticity

SummaryNormal brain function requires that the overall synaptic activity in neural circuits be kept constant. Long-term alterations of neural activity lead to homeostatic regulation of synaptic strength by a process known as synaptic scaling. The molecular mechanisms underlying synaptic scaling are largely unknown. Here, we report that all-trans retinoic acid (RA), a well-known developmental morphogen, unexpectedly mediates synaptic scaling in response to activity blockade. We show that activity blockade increases RA synthesis in neurons and that acute RA treatment enhances synaptic transmission. The RA-induced increase in synaptic strength is occluded by activity blockade-induced synaptic scaling. Suppression of RA synthesis prevents synaptic scaling. This form of RA signaling operates via a translation-dependent but transcription-independent mechanism, causes an upregulation of postsynaptic glutamate receptor levels, and requires RARα receptors. Together, our data suggest that RA functions in homeostatic plasticity as a signaling molecule that increases synaptic strength by a protein synthesis-dependent mechanism

Quantum teleportation from a telecom-wavelength photon to a solid-state quantum memory

In quantum teleportation, the state of a single quantum system is disembodied into classical information and purely quantum correlations, to be later reconstructed onto a second system that has never directly interacted with the first one. This counterintuitive phenomenon is a cornerstone of quantum information science due to its essential role in several important tasks such as the long-distance transmission of quantum information using quantum repeaters. In this context, a challenge of paramount importance is the distribution of entanglement between remote nodes, and to use this entanglement as a resource for long-distance light-to-matter quantum teleportation. Here we demonstrate quantum teleportation of the polarization state of a telecom-wavelength photon onto the state of a solid-state quantum memory. Entanglement is established between a rare-earth-ion doped crystal storing a single photon that is polarization-entangled with a flying telecom-wavelength photon. The latter is jointly measured with another flying qubit carrying the polarization state to be teleported, which heralds the teleportation. The fidelity of the polarization state of the photon retrieved from the memory is shown to be greater than the maximum fidelity achievable without entanglement, even when the combined distances travelled by the two flying qubits is 25 km of standard optical fibre. This light-to-matter teleportation channel paves the way towards long-distance implementations of quantum networks with solid-state quantum memories.Comment: 5 pages (main text) + appendix (10 pages

Geographically versus dynamically defined boundary layer cloud regimes and their use to evaluate general circulation model cloud parameterizations: Geographically versus dynamically defined boundary layer cloudregimes and their use to evaluate general circulation model cloud parameterizations

Regimes of tropical low-level clouds are commonly identified according to large-scale subsidence and lower tropospheric stability (LTS). This definition alone is insufficient for the distinction between regimes and limits the comparison of low-level clouds from CloudSat radar observations and the ECHAM5 GCM run with the COSP radar simulator. Comparisons of CloudSat radar cloud altitude-reflectivity histograms for stratocumulus and shallow cumulus regimes, as defined above, show nearly identical reflectivity profiles, because the distinction between the two regimes is dependent upon atmospheric stability below 700 hPa and observations above 1.5 km. Regional subsets, near California and Hawaii, for example, have large differences in reflectivity profiles than the dynamically defined domain; indicating different reflectivity profiles exist under a given large-scale environment. Regional subsets are better for the evaluation of low-level clouds in CloudSat and ECHAM5 as there is less contamination between 2.5 km and 7.5 km from precipitating hydrometeors which obscured cloud reflectivities

Transport of LAPTM5 to lysosomes requires association with the ubiquitin ligase Nedd4, but not LAPTM5 ubiquitination

LAPTM5 is a lysosomal transmembrane protein expressed in immune cells. We show that LAPTM5 binds the ubiquitin-ligase Nedd4 and GGA3 to promote LAPTM5 sorting from the Golgi to the lysosome, an event that is independent of LAPTM5 ubiquitination. LAPTM5 contains three PY motifs (L/PPxY), which bind Nedd4-WW domains, and a ubiquitin-interacting motif (UIM) motif. The Nedd4–LAPTM5 complex recruits ubiquitinated GGA3, which binds the LAPTM5-UIM; this interaction does not require the GGA3-GAT domain. LAPTM5 mutated in its Nedd4-binding sites (PY motifs) or its UIM is retained in the Golgi, as is LAPTM5 expressed in cells in which Nedd4 or GGA3 is knocked-down with RNAi. However, ubiquitination-impaired LAPTM5 can still traffic to the lysosome, suggesting that Nedd4 binding to LAPTM5, not LAPTM5 ubiquitination, is required for targeting. Interestingly, Nedd4 is also able to ubiquitinate GGA3. These results demonstrate a novel mechanism by which the ubiquitin-ligase Nedd4, via interactions with GGA3 and cargo (LAPTM5), regulates cargo trafficking to the lysosome without requiring cargo ubiquitination

Machine learning models to predict myocardial infarctions from past climatic and environmental conditions

Myocardial infarctions (MIs) are a major cause of death worldwide, and both high and low temperatures (i.e. heat and cold) may increase the risk of MI. The relationship between health impacts and climate is complex and influenced by a multitude of climatic, environmental, sociodemographic and behavioural factors. Here, we present a machine learning (ML) approach for predicting MI events based on multiple environmental and demographic variables. We derived data on MI events from the KORA MI registry dataset for Augsburg, Germany, between 1998 and 2015.Multivariable predictors include weather and climate, air pollution (PM10, NO, NO2, SO2 and O3), surrounding vegetation and demographic data. We tested the following ML regression algorithms: decision tree, random forest, multi-layer perceptron, gradient boosting and ridge regression. The models are able to predict the total annual number of MIs reasonably well (adjusted R2 = 0.62–0.71). Inter-annual variations and long-term trends are captured. Across models the most important predictors are air pollution and daily temperatures. Variables not related to environmental conditions, such as demographics need to be considered as well. This ML approachprovides a promising basis to model future MI under changing environmental conditions, as projected by scenarios for climate and other environmental changes

Curvature-informed multi-task learning for graph networks

Properties of interest for crystals and molecules, such as band gap, elasticity, and solubility, are generally related to each other: they are governed by the same underlying laws of physics. However, when state-of-the-art graph neural networks attempt to predict multiple properties simultaneously (the multi-task learning (MTL) setting), they frequently underperform a suite of single property predictors. This suggests graph networks may not be fully leveraging these underlying similarities. Here we investigate a potential explanation for this phenomenon: the curvature of each property's loss surface significantly varies, leading to inefficient learning. This difference in curvature can be assessed by looking at spectral properties of the Hessians of each property's loss function, which is done in a matrix-free manner via randomized numerical linear algebra. We evaluate our hypothesis on two benchmark datasets (Materials Project (MP) and QM8) and consider how these findings can inform the training of novel multi-task learning models.Comment: Published at the ICML 2022 AI for Science workshop: https://openreview.net/forum?id=m5RYtApKFO