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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). 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 follow methods as close as possible to those used in the IPCC Sixth Assessment Report (AR6) Working Group One (WGI) report. We compile monitoring datasets to produce estimates for key climate indicators related to forcing of the climate system: emissions of greenhouse gases and short-lived climate forcers, greenhouse gas concentrations, radiative forcing, the Earth's energy imbalance, surface temperature changes, 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.11388387, Smith et al., 2024a). As they are traceable to 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, for the 2014–2023 decade average, observed warming was 1.19 [1.06 to 1.30] °C, of which 1.19 [1.0 to 1.4] °C was human-induced. For the single-year average, human-induced warming reached 1.31 [1.1 to 1.7] °C in 2023 relative to 1850–1900. The best estimate is below the 2023-observed warming record of 1.43 [1.32 to 1.53] °C, indicating a substantial contribution of internal variability in the 2023 record. Human-induced warming has been increasing at a rate that is unprecedented in the instrumental record, reaching 0.26 [0.2–0.4] °C per decade over 2014–2023. This high rate of warming is caused by a combination of net greenhouse gas emissions being at a persistent high of 53±5.4 Gt CO2e yr−1 over the last decade, as well as reductions in the strength of aerosol cooling. Despite this, there is evidence that the rate of increase in CO2 emissions over the last decade has slowed compared to the 2000s, and depending on societal choices, a continued series of these annual updates over the critical 2020s decade could track a change of direction for some of the indicators presented here.

To manage Earth in the Anthropocene, new tools, new institutions, and new forms of international cooperation will be required. Earth Virtualization Engines is proposed as an international federation of centers of excellence to empower all people to respond to the immense and urgent challenges posed by climate change.

Abstract Neuromodulatory signaling via G protein-coupled receptors (GPCRs) plays a pivotal role in regulating neural network function and animal behavior. The recent development of optogenetic tools to induce G protein-mediated signaling provides 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 signaling in vivo remains challenging. Here we optimize the design of optoXRs by considering evolutionary conserved GPCR-G protein interactions and demonstrate the feasibility of this approach using two Drosophila Dopamine receptors (optoDopRs). These optoDopRs exhibit high signaling specificity and light sensitivity in vitro. In vivo, we show receptor and cell type-specific effects of dopaminergic signaling in various behaviors, including the ability of optoDopRs to rescue the loss of the endogenous receptors. This work demonstrates that optoXRs can enable optical control of neuromodulatory receptor-specific signaling in functional and behavioral studies.

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 follow methods as close as possible to those used in the IPCC Sixth Assessment Report (AR6) Working Group One (WGI) report. We compile monitoring datasets to produce estimates for key climate indicators related to forcing of the climate system: emissions of greenhouse gases and short-lived climate forcers, 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.8000192, Smith et al., 2023a). As they are traceable to 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 averaged over the 2013–2022 decade and 1.26 [1.0 to 1.6] ∘C in 2022. Over the 2013–2022 period, human-induced warming has been 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 54 ± 5.3 GtCO2e over the last decade, as well as reductions in the strength of aerosol cooling. Despite this, there is evidence that increases in greenhouse gas emissions have slowed, and depending on societal choices, a continued series of these annual updates over the critical 2020s decade could track a change of direction for human influence on climate.

Abstract 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 currently 30 realizations 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 reanalyzes and compare it to MPI‐GE. Then, we demonstrate with six 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 years 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.

The pandemic caused by SARS-CoV-2 is still a major health problem. Newly emerging variants and long-COVID-19 represent a challenge for the global health system. In particular, individuals in developing countries with insufficient health care need easily accessible, affordable and effective treatments of COVID-19. Previous studies have demonstrated the efficacy of functional inhibitors of acid sphingomyelinase against infections with various viruses, including early variants of SARS-CoV-2. This work investigated whether the acid sphingomyelinase inhibitors fluoxetine and sertraline, usually used as antidepressant molecules in clinical practice, can inhibit the replication of the former and recently emerged SARS-CoV-2 variants in vitro. Fluoxetine and sertraline potently inhibited the infection with pseudotyped virus-like particles and SARS-CoV-2 variants D614G, alpha, delta, omicron BA.1 and omicron BA.5. These results highlight fluoxetine and sertraline as priority candidates for large-scale phase 3 clinical trials at different stages of SARS-CoV-2 infections, either alone or in combination with other medications.

Background: The transition from fossil-derived jet fuels to sustainable aviation fuels represents one of the most viable strategies to decarbonize air transport and mitigate CO2 emissions generated by fossil fuel combustion. In the present investigation, a catalytic hydroprocessing upgrading approach was used to transform Yellow Dodolla oil—one of the most prominent inedible Brassica carinata vegetable oils (indigenous to Ethiopia)—into bio-jet fuel. Methods: The feedstock was upgraded to jet fuel through catalytic hydroprocessing under elevated hydrogen pressure (21 bar), varying temperatures (300 and 500 °C), and employing supported carbon-coated mesoporous and crystalline nanocatalysts (β-Mo2C/AlPO4-18, Ni3C/AlPO4-18, and WC/AlPO4-18) in a laboratory-scale continuous three-phase fixed-bed reactor system. Other variables, such as the volumetric flow rate of oil feedstock, volumetric flow rate of hydrogen gas, hydrogen gas-to-oil ratio, catalyst-to-oil ratio, liquid hourly space velocity, weight hourly space velocity, and residence time, were maintained constant throughout the experimental procedure. Subsequent to an in-depth evaluation of catalytic performance parameters (conversion, selectivity, yield, and deoxygenation rate), a detailed characterization of the liquid phase products was undertaken to explore their most significant properties. Results: The analysis results demonstrated that the catalytic hydroconversion of the feedstock resulted in a conversion range of 71.57–79.76 wt.%, with the highest conversion of 79.76 wt.% achieved by Ni3C/AlPO4–18 at the maximum temperature. Moreover, the rate of deoxygenation varied from 8.08 to 11.67 wt.% at 300 °C, with nickel catalyst reaching the maximum rate, while it sharply rose to vary from 57.31 to 96.67 wt.% using molybdenum as the temperature increased to 500 °C. It was also discovered that in comparison to bio-gasoline (2.63–8.72 wt.%) and biodiesel (1.18–4.58 wt.%), bio-jet fuel (C8–C16) had noticeably higher yields (23.34–27.31 wt.%), selectivity (37–45 wt.%), and a superb hydrocarbon product distribution (C9–C16) at the maximum temperature, with WC/AlPO4-18 producing the highest yields and selectivity of jet fuel. The characterization results revealed that the hydrocracked liquid products possessed virtually identical physicochemical properties, chemical compositions, hydrogen-to-carbon atomic ratios (1.90–1.92), oxygen-to-carbon atomic ratios (0.002–0.030), and gravimetric energy densities (41.35–42.89 MJ kg−1) to those of conventional jet fuels. Conclusions: The conclusions of the study demonstrated that the non-food Yellow Dodolla oil was successfully hydrocracked into sustainable aviation fuel using AlPO4-18 supported metal carbide catalyst nanoparticles under the right reaction conditions and reactor system, potentially supporting the significant efforts of the aviation industry to lower its carbon footprint. [ABSTRACT FROM AUTHOR]

Developing high-efficiency, high-stability, and low-cost deoxygenation and hydrocracking catalysts could be considered one of the most significant breakthroughs in catalytic hydroprocessing. The present study utilized aluminophosphate (AlPO4-18), a zeolite-like molecular sieve, as catalyst support for producing carbon-coated β-Mo2C, Ni3C, and WC nanoparticles. The synthesis used an incipient wetness impregnation followed by a temperature-programmed reduction-carburization approach which involved cracking a hydrocarbon gas, propane, in a hydrogen environment. The synthesis parameters were a 1:7 propane/hydrogen reductive-carburizing gas stream, 15 wt.% metal loading, an 800 °C carburization temperature ramped-up at a heating rate of 10 °C min−1, a 2-h holding time, and a 1-h holding time in hydrogen. The synthesized catalysts were characterized using thermogravimetry mass spectroscopy/temperature-programmed oxidation (TPO TG-MS), nitrogen physisorption at 77 K, X-ray diffraction (XRD), and transmission electron microscopy/energy-dispersive X-ray spectroscopy (TEM EDS). TPO TG-MS, nitrogen physisorption, TEM, and XRD characterization results proved that atomic carbon was successfully incorporated into the lattice interstitials, resulting in thermally stable, well-dispersed, crystalline and mesoporous β-Mo2C/AlPO4-18, Ni3C/AlPO4-18, and WC/AlPO4-18 nanoparticles. XRD analysis showed structural evolution during reduction-carburization, with average crystallite sizes of metal-containing particles of 8.2–9.22, 6.64–8.50, and 6.03–7.56 nm for β-Mo2C/AlPO4-18, Ni3C/AlPO4-18, and WC/AlPO4-18, respectively. These values did not significantly deviate from high-resolution TEM analysis. The surface areas of the nanoparticles were categorized in decreasing order as WC/AlPO4-18 > Ni3C/AlPO4-18 > β-Mo2C/AlPO4-18, with values of 193.79, 169.05, and 66.57 m2 g−1, respectively. In conclusion, these carbon-coated metal carbide nanoparticles with excellent thermal, structural, microscopic, and textural properties can be viable alternatives to noble metal catalysts for producing bio-jet fuel using the hydroprocessing pathway. [ABSTRACT FROM AUTHOR]

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.