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This study contributes to the existing literature on happiness studies by analyzing the effects of COVID-19 pandemic on subjective well-being (SWB) in a developing country, focusing specifically on satisfaction with socio-economic status. Drawing on survey data for urban Pakistan from before and after the outbreak of COVID-19, we find that during the early days of the pandemic and the related social distancing and potential lockdowns, SWB declined, particularly among unemployed, married couples, males and older people. Unexpectedly, we also observed that households having a higher income suffered more from the pandemic in terms of satisfaction with their socio-economic status compared to their poorer counterparts. We explain this finding by increased fear for falling into poverty due to lockdowns and inflation.

Neuromodulatory signalingviaG protein-coupled receptor (GPCRs) plays a pivotal role in regulating neural network function and animal behavior. Recent efforts have led to the development of optogenetic tools to induce G protein-mediated signaling, with the promise of acute and cell type-specific manipulation of neuromodulatory signals. However, designing and deploying optogenetically functionalized GPCRs (optoXRs) with accurate specificity and activity to mimic endogenous signalingin vivoremains challenging. Here we optimized the design of optoXRs by considering evolutionary conserved GPCR-G protein interactions and demonstrate the feasibility of this approach using twoDrosophilaDopamine receptors (optoDopRs). We validated these optoDopRs showing that they exhibit high signaling specificity and light sensitivityin vitro.In vivowe detected receptor and cell type-specific effects of dopaminergic signaling in various behaviors including the ability of optoDopRs to rescue loss of the endogenous receptors. This work demonstrates that OptoXRs can enable optical control of neuromodulatory receptor specific signaling in functional and behavioral studies.

Animals must maintain physiological processes within an optimal temperature range despite changes in their environment. While the preoptic area of the hypothalamus (PoA) acts as a thermostat in mammals through autonomic and behavioral adaptations, its role in temperature regulation of animals lacking internal homeostatic mechanisms is not known. Through novel behavioral assays, wholebrain functional imaging and neural ablations, we show that larval zebrafish achieve thermoregulation through movement and a neural network connecting the PoA to brain areas enabling spatial navigation. PoA drives reorientation when thermal conditions are worsening and conveys this information for instructing future motor actions to the navigation-controlling habenula (Hb) - interpeduncular nucleus (IPN) circuit. These results suggest a conserved function of the PoA in thermoregulation acting through species- specific neural networks. We propose that homeostatic navigation arose from an ancient chemotaxis navigation circuit that was subsequently extended to serve in other sensory modalities.

GSK878 is a newly described HIV-1 inhibitor that binds to the mature capsid (CA) hexamer in a pocket originally identified as the binding site of the well-studied CA inhibitor PF-74. Here, we show that GSK878 is highly potent, inhibiting an HIV-1 reporter virus in MT-2 cells with a mean 50% effective concentration (EC 50 ) of 39 pM and inhibiting a panel of 48 chimeric viruses containing diverse CA sequences with a mean EC 50 of 94 pM.

Some disciplines, e.g. Astrophysics or Earth system sciences, work with large to very large amounts of data. Storing this data, but also processing it, is a challenge for researchers because novel concepts for processing data and workflows have not developed as quickly. This problem will only become more pronounced with the ever increasing performance of High Performance Computing (HPC) – systems.At the German Climate Computing Center, we analysed the users, their goals and working methods. DKRZ provides the climate science community with resources such as high-performance computing (HPC), data storage and specialised services and hosts the World Data Center for Climate (WDCC). In analysing users, we distinguish between two main groups: those who need the HPC system to run resource-intensive simulations and then analyse them, and those who reuse, build on and analyse existing data. Each group subdivides into subgroups. We have analysed the workflows for each identified user and found identical parts in an abstracted form and derived Canonical Workflow Modules. In the process, we critically examined the possible use of so-called FAIR Digital Objects (FDOs) and checked to what extent the derived workflows and workflow modules are actually future-proof.We will show the analysis of the different users, the Canonical workflow and the vision of the FDOs. Furthermore, we will present the framework Freva and further developments and implementations at DKRZ with respect to the reproducibility of simulation-based research in the ESS.

Accurately interpreting past climate variability can be a challenging task, particularly when it comes to distinguishing between forced and unforced changes. In the case of large volcanic eruptions, ice core records are a very valuable tool but still often not sufficient to link reconstructed anomaly patterns to a volcanic eruption at all or to its geographical location. In this study, we developed a convolutional neural network (CNN) that is able to classify whether a volcanic eruption occurred and its location (northern hemisphere extratropical, southern hemisphere extratropical, or tropics) with an accuracy of 92%.To train the CNN, we used 100 member ensembles of the MPI-ESM-LR global climate model, generated using the easy volcanic aerosol (EVA) model, which provides the radiative forcing of idealized volcanic eruptions of different strengths and locations. The model considered global sea surface temperature and precipitation patterns 12 months after the eruption over a time period of 3 months.In addition to demonstrating the high accuracy of the CNN, we also applied layer-wise relevance propagation (LRP) to the model to understand its decision-making process and identify the input data that influenced its predictions. Our study demonstrates the potential of using CNNs and interpretability techniques for identifying and locating past volcanic eruptions as well as improving the accuracy and understanding of volcanic climate signals.

Slowly varying ocean heat content is one of the most important variables when describing cli-mate variability on interannual to decadal time scales. Since observation-based estimates ofocean heat content require extensive observational coverage, incomplete observations are oftencombined with numerical models via data assimilation to simulate the evolution of oceanic heat.However, incomplete observations, particularly in the subsurface ocean, lead to large uncertain-ties in the resulting model-based estimate. As an alternative approach, Kadow et al (2020) haveproven that artificial intelligence can successfully be utilized to reconstruct missing climate in-formation for surface temperatures. In the following, we investigate the possibility to train theirthree-dimensional convolutional neural network to reconstruct missing subsurface temperaturesto obtain ocean heat content estimates with a focus on the North Atlantic ocean.The network is trained and tested to reconstruct a 16 member Ensemble Kalman Filter assimi-lation ensemble constructed with the Max-Planck Institute Earth System Model for the periodfrom 1958 to 2020. Specifically, we examine whether the partial convolutional U-net representsa valid alternative to the Ensemble Kalman Filter assimilation to estimate North Atlantic sub-polar gyre ocean heat content.The neural network is capable of reproducing the assimilation reduced to datapoints with ob-servational coverages within its ensemble spread with a correlation coefficient of 0.93 over theentire time period and of 0.99 over 2004 – 2020 (the Argo-Era). Additionally, the network isable to reconstruct the observed ocean heat content directly from observations for 12 additionalmonths with a correlation of 0.97, essentially replacing the assimilation experiment by an extrap-olation. When reconstructing the pre-Argo-Era, the network is only trained with assimilationsfrom the Argo-Era. The lower correlation in the resulting reconstruction indicates higher un-certainties in the assimilation outside of its ensemble spread at times with low observationaldensity. These uncertainties are highlighted by inconsistencies in the assimilation’s represen-tations of the North Atlantic Current at times and grid points without observations detectedby the neural network. Our results demonstrate that a neural network is not only capable ofreproducing the observed ocean heat content over the training period, but also before and aftermaking the neural network a suitable candidate to step-wise extend or replace data assimilation.

The number of damaging events caused by natural disasters is increasing because of climate change. Projects of public interest such as ClimXtreme (Climate Change and Extreme Events [1, 2]), aim to improve our knowledge of extreme events, the influence of environmental changes and their societal impacts.ClimXtreme focuses on an integral evaluation through a three-pronged approach, namely: the physical processes behind the extremes, the statistical assessment of them, and their impact. The success of such a project depends on a coordinate effort from many interdisciplinary groups down to the management of computational and data storage resources. The ever-growing amount of available data at the researcher’s disposal is a two-sided blade that craves for greater resources to host, access, and evaluate them efficiently through High Performance Computing (HPC) infrastructures. Additionally, these last years the community is demanding an easier reproducibility of evaluation workflows and data FAIRness [3]. Frameworks like Freva (Free Evaluation System Framework [4, 5]) offer an efficient solution to handle customizable evaluation systems of large research projects, institutes or universities in the Earth system community [6-8] over the HPC environment and in a centralized manner. Mainly written on python, Freva offers:A centralized access. Freva can be accessed via command line interface, via web, and via python module (e.g. for jupyter notebooks) offering similar features. A standardized data search. Freva allows for a quick and intuitive incorporation and search of several datasets stored centrally. Flexible analysis. Freva provides a common interface for user defined data evaluation routines to plug them in to the system irrespective of the programming language. These plugins are able to search from and integrate own results back to Freva. This environment enables an ecosystem of plugins that fosters the interchange of results and ideas between researchers, and facilitates the portability to any other research project that uses a Freva instance. Transparent and reproducible results. Every analysis run through Freva (including parameter configuration and plugin version information) is stored in a central database and can be consulted, shared, modified and re-run by anyone within the project. Freva optimizes the usage of computational and storage resources and paves the way of traceability in line with FAIR data principles. Hosted at the DKRZ, ClimXtreme’s Freva instance (XCES [7]) offers quick access to more than 9 million datafiles of models (e.g. CMIP, CORDEX), observations (stations, gridded) and evaluation outputs. The ClimXtreme community has been actively contributing with plugins to XCES, its biggest asset, with close to 20 plugins of different disciplines at the disposal of everyone within the project, and more than 20,000 analysis run through the system. At present, any researcher can focus on a past, present or future period and a geographical region and run a series of evaluations ranging from coocurrence probabilities of extreme events, their impact on crops to wind tracking algorithms among many others. Freva facilitates comprehensive and exhaustive analysis of extreme events in an easy way. References:[1] https://www.fona.de/de/massnahmen/foerdermassnahmen/climxtreme.php[2] https://www.climxtreme.net/index.php/en/[3] https://www.go-fair.org/fair-principles/[4] http://doi.org/10.5334/jors.253[5] https://github.com/FREVA-CLINT/freva-deployment[6] freva.met.fu-berlin.de[7] https://www.xces.dkrz.de/[8] www-regiklim.dkrz.de

Evaluating the trends of extreme indices (EI) is crucial to detect and attribute extreme events (EE) and establish adaptation and mitigation strategies to the current and future climate conditions. However, the observational climate data used for the calculation of these indices often contains many missing values and leads to incomplete and inaccurate EI. This problem is even greater as we go back in time due to the scarcity of the older measurements.To tackle this problem, interpolation techniques such as the kriging method are often used to fill in the gaps. However, it has been shown that such techniques are inadequate to reconstruct specific climatic patterns [1]. Deep-learning based technologies give the possibility to surpass standard statistical methods by learning complex patterns and features in climate data.In this work, we are using an inpainting technique based on a U-Net neural network made of partial convolutional layers and a loss function designed to produce semantically meaningful predictions [1]. Models are trained using vast amounts of climate model data and can be used to reconstruct large and irregular regions of missing data with few computational resources.The efficiency of the method is well demonstrated through its application to the HadEX3 dataset [2]. This dataset contains gridded land surface EI, among which the TX90p index that measures the monthly (or annual) frequency of warm days (defined as a percentage of days where daily maximum temperature is above the 90th percentile). As for other EI, there is a lack of TX90p values in many regions of the world, even in recent years. It is particularly true when looking at an intermediate product of HadEX3 where the station-based indices have been combined without interpolation. This is illustrated by the left map of the figure where the gray pixels correspond to missing values. By training our model using data from the CMIP6 archive, we have been able to reconstruct the missing TX90p values for all the time steps of HadEX3 (see right map in the figure) and detect EE that were not included in the original dataset. The reconstructed dataset is being prepared for the community in the framework of the H2020 CLINT project [3] for further detection and attribution studies.[1] Kadow C. et al., Nat. Geosci., 13, 408-413 (2020)[2] Dunn R.J.H. et al., J. Geophys. Res. Atmos., 125, 1 (2020)[3] https://climateintelligence.eu/

Weather radars are a significant component of modern precipitation recordings,as they provide information with high spatial and temporal resolution. However, radars as a tool for weather applications emerged only after the 1950s. AI/ML methods have proven to be successful when it comes to determining patterns and connections between related fields in space and time. Moreover, AI/ML methods have exhibited remarkable skill in infilling missing climate information (see Kadow et al. 2020). Desired outcomes of the project include using these AI/ML techniques to build a spatial precipitation field by combining station and radar data. We will use data from two well-known datasets: RADOLAN and COSMO-REA2. The validity of this digital twin will be investigated by comparing its output with other reanalysis data (e.g. ERA5). Further evaluation can be carried out by testing the radar field’s accuracy in detecting extreme precipitation events in the past (e.g. heavy rain events in the summer of 2021 in Western Germany). We aim for the creation of a radar field that will be successfully projected in the past. Moreover, it will uncover new information on regional climatology, especially in areas where station data is sparse.

We present the CMIP6 version of the Max Planck Institute-Grand Ensemble (MPI-GE CMIP6) with 30 realisations for the historical period and five emission scenarios. The power of MPI-GE CMIP6 goes beyond its predecessor ensemble MPI-GE by providing high-frequency model output, the full range of emission scenarios including the highly policy relevant scenarios SSP1-1.9 and SSP1-2.6, and the opportunity to compare the ensemble to high resolution simulations of the same model version. We demonstrate with six novel application examples how to use the power of MPI-GE CMIP6 to better quantify and understand present and future extreme events in the Earth system, to inform about uncertainty in approaching Paris Agreement global warming limits, and to combine large ensembles and artificial intelligence. For instance, MPI-GE CMIP6 allows us to show that the recently observed Siberian and Pacific North American heat waves are projected to occur every year in 2071-2100 in high-emission scenarios, that the storm activity in most tropical to mid-latitude oceans is projected to decrease, and that the ensemble is sufficiently large to be used for infilling surface temperature observations with artificial intelligence.

Freva (the Free Evaluation System Framework [1; 2]) is a platform developed by the earth science community for the earth science community. Designed to work over HPC environments, it efficiently handles the data search and analysis of large projects, institutes or universities. Written on python, the framework has undergone a major update of the core. Freva offers:A centralized access. Freva comes in three different flavours with similar functionalities: a command line interface, a web user interface, and a python module that allows the usage of Freva in python environments, like jupyter notebooks. A standardized data search. Freva allows for a quick and intuitive search of several datasets stored centrally. The datasets are internally indexed in a SOLR server with an implemented metadata system that satisfies the international standards provided by the Earth System Grid Federation. Flexible analysis. Freva provides a common interface for user defined data analysis tools to plug them in to the system irrespective of the used language. Each plugin can be encapsulated in a personalized conda environment, facilitating the reproducibility and portability to any other Freva instance. These plugins are able to search from and integrate own results back to the database, enabling an ecosystem of different tools. This environment fosters the interchange of results and ideas between researchers, and the collaboration between users and plugin developers alike. Transparent and reproducible results. The analysis history and parameter configuration (including tool and system Git versioning) of every plugin run is stored in a MariaDB database. Any analysis configuration and result can be consulted and shared among the scientists, offering traceability in line with FAIR data principles, and optimizing the usage of computational and storage resources. Freva has also experienced an upgrade on the sysadmin side:Painless deployment via Ansible, with a highly customizable configuration of the services via Docker. Secure system configuration via Vault integration. Straightforward migration from old Freva database servers or between Freva instances. Improvements in the dataset incorporation. Automatic backup of database and SOLR services. [1] https://www.freva.dkrz.de/[2] https://github.com/FREVA-CLINT/freva-deployment

Heatwave episodes have severe consequences in the forms of excess mortality in many regions around the world, shortage of agricultural products, drastic changes in ecosystem function and health risks. Due to the global mean temperature rising, the acceleration of extreme temperature disturbing highly at the local scale level, particularly in urban areas. From an economic growth point of view, Major cities are contributing in terms of GDP more. Heatwaves have impacted European GDP significantly in recent years. Our work is to find the number of frequent heat wave days affecting cities which are contributing to the growth of the economy in terms of GDP and density of population wise in Europe over the near future, mid future and long future using the Apriori algorithm. The features of the heat wave and their attributes have been defined according to the criteria explained in ETCCDI. The dataset that contains heat wave days in Europe derived from EURO-CORDEX climate projections is used in this work.ReferencesCopernicus Climate Change Service (C3S): Heat waves and cold spells in Europe derived from climate projections, Climate Change Service Climate Data Store (CDS), DOI:10.24381/cds.9e7ca677 David García-León.et.al, Current and projected regional economic impacts of heatwaves in Europe, Nature Communications, https://doi.org/10.1038/s41467-021-26050-z Christophe Lavaysse.et.al, Towards a monitoring system of temperature extremes in Europe, Nat. Hazards Earth Syst. Sci,doi:10.5194/nhess-2017-181, 2017 Chloé Prodhomme. et.al, Seasonal prediction of European summer heatwaves,https://doi.org/10.1007/s00382-021-05828-3 S. E. Perkins and L.V.Alexander, On the Measurement of Heat Waves, DOI: https://doi.org/10.1175/JCLI-D-12-00383.1 S. E. Perkins-Kirkpatrick.et.al, Changes in regional heatwave characteristics as a function increasing global temperature, DOI:10.1038/s41598-017-12520-2 Agrawal, R. and Srikant, Fast Algorithms for Mining Association Rules in Large Databases. Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, Santiago de Chile, 12-15 September 1994, 487-499.

High resolution in weather and climate was always a common and ongoing goal of the community. In this regards, machine learning techniques accompanied numerical and statistical methods in recent years. Here we demonstrate that artificial intelligence can skilfully downscale low resolution climate model data when combined with numerical climate model data. We show that recently developed image inpainting technique perform accurate super-resolution via transfer learning using the HighResMIP of CMIP6 (Coupled Model Intercomparison Project Phase 6) experiments. Its huge data base offers a unique training opportunity for machine learning approaches. The transfer learning purpose allows also to downscale other CMIP6 experiments and models, as well as observational data like HadCRUT5. Combined with the technology of Kadow et al. 2020 of infilling missing climate data, we gain a neural network which reconstructs and downscales the important observational data set (IPCC AR6) at the same time. We further investigate the application of our method to downscale quantities predicted from a numerical ocean model (ICON-O) to improve computation times. In this process we focus on the ability of the model to predict eddies from low-resolution data.An extension to:Kadow, C., Hall, D.M. & Ulbrich, U. Artificial intelligence reconstructs missing climate information. Nature Geoscience 13, 408–413 (2020). https://doi.org/10.1038/s41561-020-0582-5

Intergovernmental Panel on Climate Change (IPCC) assessments are the trusted source of scientific evidence for climate negotiations taking place under the United Nations Framework Convention on Climate Change (UNFCCC), including the first global stocktake under the Paris Agreement that will conclude at COP28 in December 2023. Evidence-based decision making needs to be informed by up-to-date and timely information on key indicators of the state of the climate system and of the human influence on the global climate system. However, successive IPCC reports are published at intervals of 5–10 years, creating potential for an information gap between report cycles. We base this update on the assessment methods used in the IPCC Sixth Assessment Report (AR6) Working Group One (WGI) report, updating the monitoring datasets and to produce updated estimates for key climate indicators including emissions, greenhouse gas concentrations, radiative forcing, surface temperature changes, the Earth’s energy imbalance, warming attributed to human activities, the remaining carbon budget and estimates of global temperature extremes. The purpose of this effort, grounded in an open data, open science approach, is to make annually updated reliable global climate indicators available in the public domain (https://doi.org/10.5281/zenodo.7883758, Smith et al., 2023). As they are traceable and consistent with IPCC report methods, they can be trusted by all parties involved in UNFCCC negotiations and help convey wider understanding of the latest knowledge of the climate system and its direction of travel. The indicators show that human induced warming reached 1.14 [0.9 to 1.4] °C over the 2013–2022 period and 1.26 [1.0 to 1.6] °C in 2022. Human induced warming is increasing at an unprecedented rate of over 0.2 °C per decade. This high rate of warming is caused by a combination of greenhouse gas emissions being at an all-time high of 57 ± 5.6 GtCO2e over the last decade, as well as reductions in the strength of aerosol cooling. Despite this, there are signs that emission levels are starting to stabilise, and we can hope that a continued series of these annual updates might track a real-world change of direction for the climate over this critical decade.

Single-model initial-condition large ensembles are powerful tools to quantify the forced response, internal climate variability, and their evolution under global warming. Here, we present the CMIP6 version of the Max Planck Institute Grand Ensemble (MPI-GE CMIP6) with 30 realisations for the historical period and five emission scenarios. The power of MPI-GE CMIP6 goes beyond its predecessor ensemble MPI-GE by providing high-frequency output, the full range of emission scenarios including the highly policy-relevant low emission scenarios SSP1-1.9 and SSP1-2.6, and the opportunity to compare the ensemble to complementary high-resolution simulations. First, we describe MPI-GE CMIP6, evaluate it with observations and reanalyses and compare it to MPI-GE. Then, we demonstrate with six novel application examples how to use the power of the ensemble to better quantify and understand present and future climate extremes, to inform about uncertainty in approaching Paris Agreement global warming limits, and to combine large ensembles and artificial intelligence. For instance, MPI-GE CMIP6 allows us to show that the recently observed Siberian and Pacific North American heatwaves would only avoid reaching 1-2 year return periods in 2071-2100 with low emission scenarios, that recently observed European precipitation extremes are captured only by complementary high-resolution simulations, and that 3-hourly output projects a decreasing activity of storms in mid-latitude oceans. Further, the ensemble is ideal for estimates of probabilities of crossing global warming limits and the irreducible uncertainty introduced by internal variability, and is sufficiently large to be used for infilling surface temperature observations with artificial intelligence.

Movement-correlated brain activity has been found across species and brain regions. Here, we used fast whole brain lightfield imaging in adult Drosophila to investigate the relationship between walk and brain-wide neuronal activity. We observed a global change in activity that tightly correlated with spontaneous bouts of walk. While imaging specific sets of excitatory, inhibitory, and neuromodulatory neurons highlighted their joint contribution, spatial heterogeneity in walk- and turning-induced activity allowed parsing unique responses from subregions and sometimes individual candidate neurons. For example, previously uncharacterized serotonergic neurons were inhibited during walk. While activity onset in some areas preceded walk onset exclusively in spontaneously walking animals, spontaneous and forced walk elicited similar activity in most brain regions. These data suggest a major contribution of walk and walk-related sensory or proprioceptive information to global activity of all major neuronal classes.

Nervous systems continuously receive environmental signals with distinct behavioral meanings. To process ambiguous sensory inputs, neural circuits rely on hubs with compartmentalized synaptic structures. A new study has revealed how, in Drosophila larvae, this architecture with the local release of neuropeptides enables the control of flexible and context-dependent behavioral outcomes.

Tensor networks provide a useful tool to describe low-dimensional complex many-body systems. Finding efficient algorithms to use these methods for finite-temperature simulations in two dimensions is a continuing challenge. Here, we use the class of recently introduced isometric tensor network states, which can also be directly realized with unitary gates on a quantum computer. We utilize a purification ansatz to efficiently represent thermal states of the transverse field Ising model. By performing an imaginary-time evolution starting from infinite temperature, we find that this approach offers a different way with low computational complexity to represent states at finite temperatures., Comment: 10 pages, 7 figures

The cardiac conduction system is a network of heterogeneous cell population that initiates and propagates electric excitations in the myocardium. Purkinje fibers, a network of specialized myocardial cells, comprise the distal end of the conduction system in the ventricles. The developmental origins of Purkinje fibers and their roles during cardiac physiology and arrhythmia have been reported. However, it is not clear if they play a role during ischemic injury and heart regeneration. Here we introduce a novel tamoxifen-inducible Cre allele that specifically labels a broad range of components in the cardiac conduction system while excludes other cardiac cell types and vital organs. Using this new allele, we investigated the cellular and molecular response of Purkinje fibers to myocardial injury. In a neonatal mouse myocardial infarction model, we observed significant increase in Purkinje cell number in regenerating myocardium. RNA-Seq analysis using laser-captured Purkinje fibers showed a unique transcriptomic response to myocardial infarction. Our finds suggest a novel role of cardiac Purkinje fibers in heart injury.

All animals constantly need to weigh their options based on new experiences: something initially considered bad can become better in the light of something worse. A new study now shows how flies re-evaluate between better and worse.

Motherhood induces a drastic, sometimes long-lasting, change in internal state and behavior in many female animals. How a change in reproductive state or the discrete event of mating modulates specific female behaviors is still incompletely understood. Using calcium imaging of the whole brain of Drosophila females, we find that mating does not induce a global change in brain activity. Instead, mating modulates the pheromone response of dopaminergic neurons innervating the fly’s learning and memory center, the mushroom body (MB). Using the mating-induced increased attraction to the odor of important nutrients, polyamines, we show that disruption of the female fly’s ability to smell, for instance the pheromone cVA, during mating leads to a reduction in polyamine preference for days later indicating that the odor environment at mating lastingly influences female perception and choice behavior. Moreover, dopaminergic neurons including innervation of the β’1 compartment are sufficient to induce the lasting behavioral increase in polyamine preference. We further show that MB output neurons (MBON) of the β’1 compartment are activated by pheromone odor and their activity during mating bidirectionally modulates preference behavior in mated and virgin females. Their activity is not required, however, for the expression of polyamine attraction. Instead, inhibition of another type of MBON innervating the β’2 compartment enables expression of high odor attraction. In addition, the response of a lateral horn (LH) neuron, AD1b2, which output is required for the expression of polyamine attraction, shows a modulated polyamine response after mating. Taken together, our data in the fly suggests that mating-related sensory experience regulates female odor perception and expression of choice behavior through a dopamine-gated learning circuit.

Quantum spin liquids are fascinating phases of matter, hosting fractionalized spin excitations and unconventional long-range quantum entanglement. These exotic properties, however, also render their experimental characterization challenging, and finding ways to diagnose quantum spin liquids is therefore a pertinent challenge. Here, we numerically compute the spectral function of a single hole doped into the half-filled Hubbard model on the triangular lattice using techniques based on matrix product states. At half-filling the system has been proposed to realize a chiral spin liquid at intermediate interaction strength, surrounded by a magnetically ordered phase at strong interactions and a superconducting/metallic phase at weak interactions. We find that the spectra of these phases exhibit distinct signatures. By developing appropriate parton mean-field descriptions, we gain insight into the relevant low-energy features. While the magnetic phase is characterized by a dressed hole moving through the ordered spin background, we find indications of spinon dynamics in the chiral spin liquid. Our results suggest that the hole spectral function, as measured by angle-resolved photoemission spectroscopy, provides a useful tool to characterize quantum spin liquids., 8 pages, 6 figures (published version)

This study investigated whether sphingosine is effective as prophylaxis against

The fifth annual summer research summit organized by the Center of Excellence (COE) in Health Equity, Training and Research, Baylor College of Medicine (BCM), was held on May 17, 2022. The theme of this year’s summit was ‘Academic-Community Partnerships: Change Agents for Advancing Health Equity.’ Given the ongoing pandemic, the summit was conducted virtually through digital platforms. This program was intended for both BCM and external audiences interested in advancing health equity, diversity, and inclusion in healthcare among healthcare providers and trainees, biomedical scientists, social workers, nurses, and individuals involved in talent acquisition and development, such as hiring managers (HR professionals), supervisors, college and hospital affiliate leadership and administrators, as well as diversity and inclusion excellence practitioners. We had attendees from all regions of the United States as well as from Saudi Arabia. The content in this Book of Abstracts encapsulates a summary of the research efforts by the BCM COE scholars (which includes post-baccalaureate students, medical students, clinical fellows, and junior faculty from BCM) as well as the external summit participants. The range of topics in this year’s summit was quite diverse, encompassing disparities in relation to maternal and child health (MCH), immigrant health, cancers, vaccination uptakes, and COVID-19 infections. Various solutions were ardently presented to address these disparities, including community engagement and partnerships, improvement in health literacy, and the development of novel technologies and therapeutics. With this summit, BCM continues to build on its long history of educational outreach initiatives to promote diversity in medicine by focusing on programs aimed at increasing the number of diverse and highly qualified medical professionals ready to introduce effective and innovative approaches to reduce or eliminate health disparities. These programs will improve information resources, clinical education, curricula, research, and cultural competence as they relate to minority health issues and social determinants of health. The year’s summit was a great success! Copyright © 2022 Dongarwar et al. Published by Global Health and Education Projects, Inc. This is an open-access article distributed under the terms of the Creative Commons Attribution License CC BY 4.0.

Objective To decrease referrals to cardiology of patients ages 7 to 21 years with low-probability cardiac pathology who presented to primary care with chest pain by 50% within 24 months. Study Design A multidisciplinary team designed and implemented an initiative consisting of 1) a decision support tool (DST), 2) educational sessions, 3) routine feedback to improve use of referral criteria, and 4) patient family education. Four pediatric practices, comprising 34 pediatricians and 7 nurse practitioners, were included in this study. We tracked progress via statistical process control charts. Results A total of 421 patients ages 7 to 21 years presented with chest pain to their pediatrician. The utilization of the DST increased from baseline of 16% to 68%. Concurrently, the percentage of low-probability cardiology referrals in pediatric patients ages 7 to 21 years who presented with chest pain decreased from 17% to 5% after our interventions. At a median follow-up time of 0.9 years (interquartile range, 0.3–1.6 years), no patient had a life-threatening cardiac event. Conclusion Our health care improvement initiative to reduce low-probability cardiology referrals for children presenting to primary care practices with chest pain was feasible, effective, and safe.

Structure-property relationships associated with a series of (carbonyl)oxyalkyl amino acid ester prodrugs of the marketed HIV-1 protease inhibitor atazanavir (

Atrial fibrillation (AF), the most common sustained cardiac arrhythmia and a major risk factor for stroke, often arises through ectopic electrical impulses derived from the pulmonary veins (PVs). Sequence variants in enhancers controlling expression of the transcription factor PITX2, which is expressed in the cardiomyocytes (CMs) of the PV and left atrium (LA), have been implicated in AF predisposition. Single nuclei multiomic profiling of RNA and analysis of chromatin accessibility combined with spectral clustering uncovered distinct PV- and LA-enriched CM cell states. Pitx2-mutant PV and LA CMs exhibited gene expression changes consistent with cardiac dysfunction through cell type-distinct, PITX2-directed, cis-regulatory grammars controlling target gene expression. The perturbed network targets in each CM were enriched in distinct human AF predisposition genes, suggesting combinatorial risk for AF genesis. Our data further reveal that PV and LA Pitx2-mutant CMs signal to endothelial and endocardial cells through BMP10 signaling with pathogenic potential. This work provides a multiomic framework for interrogating the basis of AF predisposition in the PVs of humans.

Summary A recent study identifies the neuronal and molecular underpinnings of a key ecological difference between two Drosophila species using a remarkable genetic toolbox for a non-model species.

Historical temperature measurements are the basis of global climate datasets like HadCRUT4. This dataset contains many missing values, particularly for periods before the mid-twentieth century, although recent years are also incomplete. Here we demonstrate that artificial intelligence can skilfully fill these observational gaps when combined with numerical climate model data. We show that recently developed image inpainting techniques perform accurate monthly reconstructions via transfer learning using either 20CR (Twentieth-Century Reanalysis) or the CMIP5 (Coupled Model Intercomparison Project Phase 5) experiments. The resulting global annual mean temperature time series exhibit high Pearson correlation coefficients (≥0.9941) and low root mean squared errors (≤0.0547 °C) as compared with the original data. These techniques also provide advantages relative to state-of-the-art kriging interpolation and principal component analysis-based infilling. When applied to HadCRUT4, our method restores a missing spatial pattern of the documented El Niño from July 1877. With respect to the global mean temperature time series, a HadCRUT4 reconstruction by our method points to a cooler nineteenth century, a less apparent hiatus in the twenty-first century, an even warmer 2016 being the warmest year on record and a stronger global trend between 1850 and 2018 relative to previous estimates. We propose image inpainting as an approach to reconstruct missing climate information and thereby reduce uncertainties and biases in climate records.From:Kadow, C., Hall, D.M. & Ulbrich, U. Artificial intelligence reconstructs missing climate information. Nature Geoscience 13, 408–413 (2020). https://doi.org/10.1038/s41561-020-0582-5The presentation will tell from the journey of changing an image AI to a climate research application.

Gp120 is a critical viral proteins required for HIV-1 entry and infection. It facilitates HIV-1 binding to target cells, human-to-human transmission, relocation of virus from mucosa to lymph nodes, cell-cell infection and syncytium formation, and the bystander effect that kills uninfected CD4+ T-cells and other human cells. Molecules that bind to gp120 can inhibit its function by stabilizing conformations of the protein, leading to the inability to infect cells, and resulting in non-permissive. Small molecule-mediated stabilization of certain conformations of gp120 may also enhance recognition of HIV-1 infected cells by neutralizing antibodies and make the virus more susceptible to effector functions such as ADCC, which could potentially be part of future cure regimens. Additionally, HIV attachment inhibitors can complex with free gp120 and potentially repress both cytopathic effects from membrane-bound or soluble gp120. Fostemsavir (Rukobia

The complexity of the climate system calls for a combined approach of different knowledge areas. For that, increasingly larger projects need a coordinate effort that fosters an active collaboration between members. On the other hand, although the continuous improvement of numerical models and larger observational data availability provides researchers with a growing amount of data to analyze, the need for greater resources to host, access, and evaluate them efficiently through High Performance Computing (HPC) infrastructures is growing more than ever. Finally, the thriving emphasis on FAIR data principles [1] and the easy reproducibility of evaluation workflows also requires a framework that facilitates these tasks. Freva (Free Evaluation System Framework [2, 3]) is an efficient solution to handle customizable evaluation systems of large research projects, institutes or universities in the Earth system community [4-6] over the HPC environment and in a centralized manner. Freva is a scientific software infrastructure for standardized data and analysis tools (plugins) that provides all its available features both in a shell and web environment. Written in python, is equipped with a standardized model database, an application-programming interface (API) and a history of evaluations, among others:An implemented metadata system in SOLR with its own search tool allows scientists and their plugins to retrieve the required information from a centralized database. The databrowser interface satisfies the international standards provided by the Earth System Grid Federation (ESGF, e.g. [7]). An API allows scientific developers to connect their plugins with the evaluation system independently of the programming language. The connected plugins are able to access from and integrate their results back to the database, allowing for a concatenation of plugins as well. This ecosystem increases the number of scientists involved in the studies, boosting the interchange of results and ideas. It also fosters an active collaboration between plugin developers. The history and configuration sub-system stores every analysis performed with Freva in a MySQL database. Analysis configurations and results can be searched and shared among the scientists, offering transparency and reproducibility, and saving CPU hours, I/O, disk space and time. Freva efficiently frames the interaction between different technologies thus improving the Earth system modeling science. This framework has undergone major refactoring and restructuring of the core that will also be discussed. Among others:Major core Python update (2.7 to 3.9). Easier deployment and containerization of the framework via Docker. More secure system configuration via Vault integration. Direct Freva function calls via python client (e.g. for jupyter notebooks). Improvements in the dataset incorporation. References:[1] https://www.go-fair.org/fair-principles/[2] Kadow, C. et al. , 2021. Introduction to Freva – A Free Evaluation System Framework for Earth System Modeling. JORS. http://doi.org/10.5334/jors.253[3] gitlab.dkrz.de/freva[4] freva.met.fu-berlin.de[5] https://www.xces.dkrz.de/[6] www-regiklim.dkrz.de[7] https://esgf-data.dkrz.de/projects/esgf-dkrz/

Missing climate data is a widespread problem in climate science and leads to uncertainty of prediction models that rely on these data resources. So far, existing approaches for infilling missing precipitation data are mostly numerical or statistical techniques that require considerable computational resources and are not suitable for large regions with missing data. Most recently, there have been several approaches to infill missing climate data with machine learning methods such as convolutional neural networks or generative adversarial networks. They have proven to perform well on infilling missing temperature or satellite data. However, these techniques consider only spatial variability in the data whereas precipitation data is much more variable in both space and time. Rainfall extremes with high amplitudes play an important role. We propose a convolutional inpainting network that additionally considers a memory module. One approach investigates the temporal variability in the missing data regions using a long-short term memory. An attention-based module has also been added to the technology to consider further atmospheric variables provided by reanalysis data. The model was trained and evaluated on the RADOLAN data set which is based on radar precipitation recordings and weather station measurements. With the method we are able to complete gaps in this high quality, highly resolved spatial precipitation data set over Germany. In conclusion, we compare our approach to statistical techniques for infilling precipitation data as well as other state-of-the-art machine learning techniques. This well-combined technology of computer and atmospheric research components will be presented as a dedicated climate service component and data set.

The German Climate Computing Center (DKRZ) is an established topical IT service provider serving the needs of the German climate science community and their associated partners. At DKRZ, climate researchers have the means available to cover every aspect of the research life cycle, ranging from planning, model development and testing, model execution on the in-house HPC cluster (16 PFlops mainly CPU-based, 130 PB disk storage), data analysis (batch jobs, Jupyter, Freva), data publication and dissemination via the Earth System Grid Federation (ESGF) as well as long-term data preservation either at the project-level (little curation) or in the CoreTrustSeal certified World Data Center for Climate (WDCC) (extensive curation along the FAIR data principles). A plethora of user support services offered by domain-expert staff complement DKRZ’s portfolio. With the new HPC system coming online in early 2022 and a number of funded and to-be funded projects exploiting the available computational resources for conducting e.g. global storm-resolving (grid spacing O(1-3km)) simulations on climatic timescales, the current interplay DKRZ’s services needs to be revisited to devise a unified workflow that will be able to handle the upcoming challenges. This is why the above mentioned projects will supply a significant amount of funds to conceive a framework to efficiently orchestrate the entire model development, model execution and data handling workflow at DKRZ in close collaboration with the climate science community. In this contribution, we will detail our vision of a revamped and versatile ESM orchestration framework at DKRZ. Currently, this vision is based on having the orchestration performed by the Freva System (http://doi.org/10.5334/jors.253), in which users will be able to kick-off model compilation, compute and analysis jobs. Furthermore, Freva enables seamless provenance tracking of the entire workflow. Together with the implementation of data publication, long-term archiving and data dissemination workflows, the envisioned system provides a complete package of FAIR Digital Objects (FDOs) to researchers and allows for reproducibility, transparency and reduction of data redundancy.

Today's climate science is being driven by IT more than ever. Earth system models on high-performance computers (HPC) are common tools for researching the past and projecting it into the future. In addition to that, statistical modelling is reborn thanks to modern computer architectures equipped with artificial intelligence (from ensemble to deep learning). Future advances in machine learning will also shape climate research through analysis tools, prediction techniques, signal and event classification, post-processing, Model Output Statistics (MOS), evaluation and verification, etc. This presentation will look at nowadays research about the future (part one) and the past (part two) of our climate system using AI/ML ideas and technologies in combination with numerical climate models - from two publications accordingly. A special focus will be on the importance of climate science, where the needs are, and how to choose the AI/ML hammer wisely:(1) FUTURE: Derived from machine (ensemble) learning and bagging, a new hybrid climate prediction technique called 'Ensemble Dispersion Filter' is developed. It exploits two important climate prediction paradigms: the ocean's heat capacity and the advantage of the ensemble mean. The Ensemble Dispersion Filter averages the ocean temperatures of the ensemble members every three months, uses this ensemble mean as a restart condition for each member, and further executes the prediction. The evaluation shows that the Ensemble Dispersion Filter results in a significant improvement in the predictive skill compared to the unfiltered reference system. Even in comparison with prediction systems of a larger ensemble size and higher resolution, the Ensemble Dispersion Filter system performs better. In particular, the prediction of the global average temperature of the forecast years 2 to 5 shows a significant skill improvement. Kadow, C., Illing, S., Kröner, I., Ulbrich, U., and Cubasch, U. (2017), Decadal climate predictions improved by ocean ensemble dispersion filtering, J. Adv. Model. Earth Syst., 9, 1138– 1149, doi:10.1002/2016MS000787. (2) PAST: Nowadays climate change research relies on climate information of the past. Historic climate records of temperature observations form global gridded datasets like HadCRUT4, which is investigated e.g. in the IPCC reports. However, record combining data-sets are sparse in the past. Even today they contain missing values. Here we show that artificial intelligence (AI) technology can be applied to reconstruct these missing climate values. We found that recently successful image inpainting technologies, using partial convolutions in a CUDA accelerated deep neural network, can be trained by 20CR reanalysis and CMIP5 experiments. The derived AI networks are capable to independently reconstruct artificially trimmed versions of 20CR and CMIP5 in grid space for every given month using the HadCRUT4 missing value mask. The evaluation reaches high temporal correlations and low errors for the global mean temperature. Kadow, C., Hall, D.M. & Ulbrich, U. Artificial intelligence reconstructs missing climate information. Nat. Geosci. 13, 408–413 (2020). https://doi.org/10.1038/s41561-020-0582-5

Some disciplines, especially those that look at the Earth system, work with large to very large amounts of data. Storing this data, but also processing it, places completely new demands on scientific work itself.Let's take the example of climate research and specifically climate modelling. In addition to long-term meteorological measurements in the recent past, results from climate models form the main basis for research and statements on past and possible future global, regional and local climate. Climate models are very complex numerical models that require high-performance computing. However, with the current and future increasing spatial and temporal resolution of the models, the demand for computing resources and storage space is also increasing. Previous working methods and processes no longer hold up and need to be rethought.Taking the German Climate Computing Centre (DKRZ) as an example, we analysed the users, their goals and working methods. DKRZ provides the climate science community with resources such as high-performance computing (HPC), data storage and specialised services and hosts the World Data Center for Climate (WDCC). In analysing users, we distinguish between two groups: those who need the HPC system to run resource-intensive simulations and then analyse them, and those who reuse, build on and analyse existing data. Each group subdivides into subgroups. We have analysed the workflows for each identified user and found identical parts in an abstracted form and derived Canonical Workflow Modules.In the process, we critically examined the possible use of so-called FAIR Digital Objects (FDOs) and checked to what extent the derived workflows and workflow modules are actually future-proof.The vision is that the global integrated data space is formed by standardised, independent and persistent entities that contain all information about diverse data objects (data, documents, metadata, software, etc.) so that human and, above all, machine agents can find, access, interpret and reuse (FAIR) them in an efficient and cost-saving way. At the same time, these units become independent of technologies and heterogeneous organisation of data, and will contain a built-in mechanism that supports data sovereignty. This will make the handling of data sustainable and secure.So, each step in a research workflow can be a FDO. In this case, the research is fully reproducible, but parts can also be exchanged and, e.g. experiments can be varied transparently. FDOs can easily be linked to others. The redundancy of data is minimised and thus also the susceptibility to errors is reduced. FDOs open up the possibility of combining data, software or whole parts of workflows in a new and simple but at all times comprehensible way. FDOs will make an important contribution to the reproducibility of research results, but they are also crucial for saving storage space. There are already data that are FDOs, but also self-contained frameworks that store data via tracking workflows. Similar to the TCP/IP standard, DO interface protocols are already developed. However, there are still some open points that are currently being worked on and defined with regard to FDOs in order to make them a globally functioning system.

Producing and providing useful information for climate services requires vast volumes of data to come together which requires technical standards. Especially in the case of extreme climate events, where scientific methods for appropriate assessments, detection or even attribution are facing high complexity for the data processing workflows, therefore the production of climate information services requires optimal technical systems to underpinn climate services with science. These climate resilience information systems like the Climate Data Store (CDS) of the Copernicus Climate Change Service (C3S) can be enhanced when scientific workflows for extreme event detection are optimized as information production service, accordingly deployed to be usable by extreme event experts to facilitate their work through a frontend. Deployment into federated data processing systems like CDS requires that scientific methods and their algorithms be wrapped up as technical services following standards of application programming interfaces (API) and, as good practice, even FAIR principles. FAIR principles means to be Findable within federated data distribution architectures, including public catalogues of well documented scientific analytical processes. Remote storage and computation resources should be operationally Accessible to all, including low bandwidth regions and closing digital gaps to ‘Leave No One Behind’. including Data inputs, outputs, and processing API standards are the necessary conditions to ensure the system is Interoperable. And they should be built from Reusable building blocks that can be realized by modular architectures with swappable components, data provenance systems, and rich metadata.Here we present challenges and preliminary prototypes for service which are based on OGC API standards for processing (https://ogcapi.ogc.org/processes/) open geospatial consortium (OGC). We are presenting blueprints on how AI-based scientific workflows can be ingested into climate resilience information systems to enhance climate services related to extreme weather and impact events. The importance of API standards will be pointed out to ensure reliable data processing in federated spatial data infrastructures. Examples will be taken from the EU H2020 Climate Intelligence (CLINT; https://climateintelligence.eu/) project, where extreme events components will be developed for C3S. Within this project, appropriate technical services will be developed as building blocks ready to deploy into digital data infrastructures like C3S but also European Science Cloud, or the DIAS. This deployment flexibility results out of the standard compliance and FAIR principles. In particular, a service employing state-of-the-art deep learning based inpainting technology to reconstruct missing climate information of global temperature patterns will be developed. This OGC-standard based web processing service (WPS) will be used as a prototype and extended in the future to other climate variables. Developments focus on heatwaves and warm nights, extreme droughts, tropical cyclones and compound and concurrent events, including their impacts, whilst the concepts are targeting generalised opportunities to transfer any kind of scientific workflow to a technical service underpinning scientific climate service. The blueprints are taking into account how to chain the data processing from data search and fetch, event index definition and detection as well as identifying the drivers responsible for the intensity of the extreme event to construct storylines guiding to the event.

Allosteric HIV-1 integrase inhibitors (ALLINIs) have garnered special interest because of their novel mechanism of action: they inhibit HIV-1 replication by promoting aberrant integrase multimerization, leading to the production of replication-deficient viral particles. The binding site of ALLINIs is in a well-defined pocket formed at the interface of two integrase monomers that is characterized by conserved residues along with two polymorphic amino acids at residues 124 and 125. The design, synthesis, and optimization of pyridine-based allosteric integrase inhibitors are reported here. Optimization was conducted with a specific emphasis on the inhibition of the 124/125 polymorphs such that the designed compounds showed excellent potency

Despite several new developments in the treatment of multiple myeloma, all available therapies are only palliative without curative potential and all patients ultimately relapse. Thus, novel therapeutic options are urgently required to prolong survival of or to even cure myeloma. Here, we show that multiple myeloma cells express the potassium channel Kv1.3 in their mitochondria. The mitochondrial Kv1.3 inhibitors PAPTP and PCARBTP are efficient against two tested human multiple myeloma cell lines (L-363 and RPMI-8226) and against ex vivo cultured, patient-derived myeloma cells, while healthy bone marrow cells are spared from toxicity. Cell death after treatment with PAPTP and PCARBTP occurs via the mitochondrial apoptotic pathway. In addition, we identify up-regulation of the multidrug resistance pump MDR-1 as the main potential resistance mechanism. Combination with ABT-199 (venetoclax), an inhibitor of Bcl2, has a synergistic effect, suggesting that mitochondrial Kv1.3 inhibitors could potentially be used as combination partner to venetoclax, even in the treatment of t(11;14) negative multiple myeloma, which represent the major part of cases and are rather resistant to venetoclax alone. We thus identify mitochondrial Kv1.3 channels as druggable targets against multiple myeloma. OA Förderung 2022