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Score-based diffusion models are a class of generative models whose dynamics is described by stochastic differential equations that map noise into data. While recent works have started to lay down a theoretical foundation for these models, a detailed understanding of the role of the diffusion time T is still lacking. Current best practice advocates for a large T to ensure that the forward dynamics brings the diffusion sufficiently close to a known and simple noise distribution; however, a smaller value of T should be preferred for a better approximation of the score-matching objective and higher computational efficiency. Starting from a variational interpretation of diffusion models, in this work we quantify this trade-off and suggest a new method to improve quality and efficiency of both training and sampling, by adopting smaller diffusion times. Indeed, we show how an auxiliary model can be used to bridge the gap between the ideal and the simulated forward dynamics, followed by a standard reverse diffusion process. Empirical results support our analysis; for image data, our method is competitive with regard to the state of the art, according to standard sample quality metrics and log-likelihood.

Greenhouse gas (GHG) emissions from agriculture, including crop and livestock production, forestry and associated land use changes, are responsible for a significant fraction of anthropogenic emissions, up to 30% according to the Intergovernmental Panel on Climate Change (IPCC). Yet while emissions from fossil fuels are updated yearly and by multiple sources-including national-level statistics from the International Energy Agency (IEA)-no comparable efforts for reporting global statistics for agriculture, forestry and other land use (AFOLU) emissions exist: the latest complete assessment was the 2007 IPCC report, based on 2005 emission data. This gap is critical for several reasons. First, potentially large climate funding could be linked in coming decades to more precise estimates of emissions and mitigation potentials. For many developing countries, and especially the least developed ones, this requires improved assessments of AFOLU emissions. Second, growth in global emissions from fossil fuels has outpaced that from AFOLU during every decade of the period 1961-2010, so the relative contribution of the latter to total climate forcing has diminished over time, with a need for regular updates. We present results from a new GHG database developed at FAO, providing a complete and coherent time series of emission statistics over a reference period 1961-2010, at country level, based on FAOSTAT activity data and IPCC Tier 1 methodology. We discuss results at global and regional level, focusing on trends in the agriculture sector and net deforestation. Our results complement those available from the IPCC, extending trend analysis to a longer historical period and, critically, beyond 2005 to more recent years. In particular, from 2000 to 2010, we find that agricultural emissions increased by 1.1% annually, reaching 4.6 Gt CO2 yr−1 in 2010 (up to 5.4-5.8 Gt CO2 yr−1 with emissions from biomass burning and organic soils included). Over the same decade 2000-2010, the ratio of agriculture to fossil fuel emissions has decreased, from 17.2% to 13.7%, and the decrease is even greater for the ratio of net deforestation to fossil fuel emissions: from 19.1% to 10.1%. In fact, in the year 2000, emissions from agriculture have been consistently larger-about 1.2 Gt CO2 yr−1 in 2010-than those from net deforestation.

Neuromodulation refers to invasive, minimally invasive or non-invasive techniques to stimulate discrete cortical or subcortical brain regions with therapeutic purposes in otherwise intractable patients: for example, thousands of advanced Parkinsonian patients, as well as patients with tremor or dystonia, benefited by deep brain stimulation (DBS) procedures (neural targets: basal ganglia nuclei). A new era for DBS is currently opening for patients with drug-resistant depression, obsessive-compulsive disorders, severe epilepsy, migraine and chronic pain (neural targets: basal ganglia and other subcortical nuclei or associative fibres). Vagal nerve stimulation (VNS) has shown clinical benefits in patients with pharmacoresistant epilepsy and depression. Non-invasive brain stimulation neuromodulatory techniques such as repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (tDCS) are also being increasingly investigated for their therapeutic potential in several neurological and psychiatric disorders. In this review, we first address the most common neural targets of each of the mentioned brain stimulation techniques, and the known mechanisms of their neuromodulatory action on stimulated brain networks. Then, we discuss how DBS, VNS, rTMS and tDCS could impact on the function of brainstem centres controlling vital functions, critically reviewing their acute and long-term effects on brain sympathetic outflow controlling heart function and blood pressure. Finally, as there is clear experimental evidence in animals that brain stimulation can affect autonomic and heart functions, we will try to give a critical perspective on how it may enhance our understanding of the cortical/subcortical mechanisms of autonomic cardiovascular regulation, and also if it might find a place among therapeutic opportunities in patients with otherwise intractable autonomic dysfunctions.

Transcranial alternating current stimulation (tACS) is a non-invasive brain stimulation technique that allows interaction with endogenous cortical oscillatory rhythms by means of external sinusoidal potentials. The physiological mechanisms underlying tACS effects are still under debate. Whereas online (e.g., ongoing) tACS over the motor cortex induces robust state-, phase- and frequency-dependent effects on cortical excitability, the offline effects (i.e. after-effects) of tACS are less clear. Here, we explored online and offline effects of tACS in two single-blind, sham-controlled experiments. In both experiments we used neuronavigated transcranial magnetic stimulation (TMS) of the primary motor cortex (M1) as a probe to index changes of cortical excitability and delivered M1 tACS at 10 Hz (alpha), 20 Hz (beta) and sham (30 s of low-frequency transcranial random noise stimulation; tRNS). Corticospinal excitability was measured by single pulse TMS-induced motor evoked potentials (MEPs). tACS was delivered online in Experiment 1 and offline in Experiment 2. In Experiment 1, the increase of MEPs size was maximal with the 20 Hz stimulation, however in Experiment 2 neither the 10 Hz nor the 20 Hz stimulation induced tACS offline effects. These findings support the idea that tACS affects cortical excitability only during online application, at least when delivered on the scalp overlying M1, thereby contributing to the development of effective protocols that can be applied to clinical populations.

Mitigation pathways by Integrated Assessment Models (IAMs) describe future emissions that keep global warming below specific temperature limits and are compared with countries’ collective greenhouse gas (GHG) emission reduction pledges. This is needed to assess mitigation progress and inform emission targets under the Paris Agreement. Currently, however, a mismatch of ~5.5 GtCO2 yr−1 exists between the global land-use fluxes estimated with IAMs and from countries’ GHG inventories. Here we present a ‘Rosetta stone’ adjustment to translate IAMs’ land-use mitigation pathways to estimates more comparable with GHG inventories. This does not change the original decarbonization pathways, but reallocates part of the land sink to be consistent with GHG inventories. Adjusted cumulative emissions over the period until net zero for 1.5 or 2 °C limits are reduced by 120–192 GtCO2 relative to the original IAM pathways. These differences should be taken into account to ensure an accurate assessment of progress towards the Paris Agreement.There is a mismatch between emission estimates from global land use calculated from IAMs and countries’ greenhouse gas inventories. This study presents a method for reconciling these estimates by reallocating part of the land-use sink, facilitating progress assessment towards climate goals.

National, regional and global CO2 emissions and removals from forests were estimated for the period 1990–2020 using as input the country reports of the Global Forest Resources Assessment 2020. The new Food and Agriculture Organization of the United Nations (FAO) estimates, based on a simple carbon stock change approach, update published information on net emissions and removals from forests in relation to (a) net forest conversion and (b) forest land. Results show a significant reduction in global emissions from net forest conversion over the study period, from a mean of 4.3 in 1991–2000 to 2.9 Gt CO2 yr−1 in 2016–2020. At the same time, forest land was a significant carbon sink globally but decreased in strength over the study period, from −3.5 to −2.6 Gt CO2 yr−1. Combining net forest conversion with forest land, our estimates indicated that globally forests were a small net source of CO2 to the atmosphere on average during 1990–2020, with mean net emissions of 0.4 Gt CO2 yr−1. The exception was the brief period 2011–2015, when forest land removals counterbalanced emissions from net forest conversion, resulting in a global net sink of −0.7 Gt CO2 yr−1. Importantly, the new estimates allow for the first time in the literature the characterization of forest emissions and removals for the decade just concluded, 2011–2020, showing that in this period the net contribution of forests to the atmosphere was very small, i.e., a sink of less than −0.2 Gt CO2 yr−1 – an estimate not yet reported in the literature. This near-zero balance was nonetheless the result of large global fluxes of opposite sign, namely net forest conversion emissions of 3.1 Gt CO2 yr−1 counterbalanced by net removals on forest land of −3.3 Gt CO2 yr−1. Finally, we compared our estimates with data independently reported by countries to the United Nations Framework on Climate Change, indicating close agreement between FAO and country emissions and removals estimates. Data from this study are openly available via the Zenodo portal (Tubiello, 2020), with DOI https://doi.org/10.5281/zenodo.3941973, as well as in the FAOSTAT (Food and Agriculture Organization Corporate Statistical Database) emissions database (FAO, 2021a).

Congenital heart defects occur in approximately 1% of newborns in the US annually. Currently, less than a third of congenital heart defects can be traced to a known genetic or environmental cause, suggesting that a large proportion of disease-causing mechanisms have yet to be fully characterized. Hemodynamic forces such as wall shear stress are critical for heart development and are known to induce changes in embryonic cardiac patterning leading to malformations. However, measuring these hemodynamic factors in vivo is infeasible due to physical limitations, such as the small size and constant motion of the embryonic heart. This serves as a significant barrier towards developing a mechanics-based understanding of the origins of congenital heart defects. An alternative approach is to recapitulate the hemodynamic environment by simulating blood flow and calculating the resulting hemodynamic forces through computational fluid dynamics modeling. Thus, we have developed a robust computational fluid dynamics modeling pipeline to quantify hemodynamics within cell-accurate anatomies of embryonic chick hearts. Here we describe the implementation of single plane illumination light sheet fluorescent microscopy to generate full three-dimensional reconstructions of the embryonic heart in silico , quantitative geometric morphometric methods for identifying anatomic variability across samples, and computational fluid dynamic approaches for calculating flow, pressure, and wall shear stress within complex tissue architectures. Together, these methods produce a fast, robust, and accessible system of analysis for generating high-resolution, quantitative descriptions of anatomical variability and hemodynamic forces in the embryonic heart.

Efficient and precise track reconstruction is critical for the results of the Compact Muon Solenoid (CMS) experiment. The current CMS track reconstruction algorithm is a multi-step procedure that consists in a Cellular Automaton technique to create track seeds, followed by Kalman filter based methods to build the trajectory pattern and final fit. Multiple parameters regulate the reconstruction steps, populating a large phase space of possible solutions. The fine-tuning of these parameters is necessary to ensure an optimal reconstruction. This report presents an original tool based on the established Particle Swarm heuristic optimization algorithm (PSO) to perform parameter tuning of the pixel track reconstruction software. The software enables Multi-Objective Optimization against tracking efficiency and fake rate, resulting in the individuation of a Pareto front of valid parameters’ sets for reconstruction. The algorithm has been tested at the end of the data-taking period of 2023 with excellent results. The parameters obtained with the optimization resulted in comparable reconstruction’s efficiency with a 50% reduction in misidentified tracks, especially for low transverse momentum of the particles.

Achieving the long-term temperature goal of the Paris Agreement requires forest-based mitigation. Collective progress towards this goal will be assessed by the Paris Agreement’s Global stocktake. At present, there is a discrepancy of about 4 GtCO2 yr−1 in global anthropogenic net land-use emissions between global models (reflected in IPCC assessment reports) and aggregated national GHG inventories (under the UNFCCC). We show that a substantial part of this discrepancy (about 3.2 GtCO2 yr−1) can be explained by conceptual differences in anthropogenic forest sink estimation, related to the representation of environmental change impacts and the areas considered as managed. For a more credible tracking of collective progress under the Global stocktake, these conceptual differences between models and inventories need to be reconciled. We implement a new method of disaggregation of global land model results that allows greater comparability with GHG inventories. This provides a deeper understanding of model–inventory differences, allowing more transparent analysis of forest-based mitigation and facilitating a more accurate Global stocktake.

Despite an increasing attention on the role of land in meeting countries' climate pledges under the Paris Agreement, the range of estimates of carbon fluxes from land use, land-use change, and forestry (LULUCF) in available databases is very large. A good understanding of the LULUCF data reported by countries under the United Nations Framework Convention on Climate Change (UNFCCC) – and of the differences with other datasets based on country-reported data – is crucial to increase confidence in land-based climate change mitigation efforts. Here we present a new data compilation of LULUCF fluxes of carbon dioxide (CO2) on managed land, aiming at providing a consolidated view on the subject. Our database builds on a detailed analysis of data from national greenhouse gas inventories (NGHGIs) communicated via a range of country reports to the UNFCCC, which report anthropogenic emissions and removals based on the IPCC (Intergovernmental Panel on Climate Change) methodology. Specifically, for Annex I countries, data are sourced from annual GHG inventories. For non-Annex I countries, we compiled the most recent and complete information from different sources, including national communications, biennial update reports, submissions to the REDD+ (reducing emissions from deforestation and forest degradation) framework, and nationally determined contributions. The data are disaggregated into fluxes from forest land, deforestation, organic soils, and other sources (including non-forest land uses). The CO2 flux database is complemented by information on managed and unmanaged forest area as available in NGHGIs. To ensure completeness of time series, we filled the gaps without altering the levels and trends of the country reported data. Expert judgement was applied in a few cases when data inconsistencies existed. Results indicate a mean net global sink of −1.6 Gt CO2 yr−1 over the period 2000–2020, largely determined by a sink on forest land (−6.4 Gt CO2 yr−1), followed by source from deforestation (+4.4 Gt CO2 yr−1), with smaller fluxes from organic soils (+0.9 Gt CO2 yr−1) and other land uses (−0.6 Gt CO2 yr−1). Furthermore, we compare our NGHGI database with two other sets of country-based data: those included in the UNFCCC GHG data interface, and those based on forest resources data reported by countries to the Food and Agriculture Organization of the United Nations (FAO) and used as inputs into estimates of GHG emissions in FAOSTAT. The first dataset, once gap filled as in our study, results in a net global LULUCF sink of −5.4 Gt CO2 yr−1. The difference with the NGHGI database is in this case mostly explained by more updated and comprehensive data in our compilation for non-Annex I countries. The FAOSTAT GHG dataset instead estimates a net global LULUCF source of +1.1 Gt CO2 yr−1. In this case, most of the difference to our results is due to a much greater forest sink for non-Annex I countries in the NGHGI database than in FAOSTAT. The difference between these datasets can be mostly explained by a more complete coverage in the NGHGI database, including for non-biomass carbon pools and non-forest land uses, and by different underlying data on forest land. The latter reflects the different scopes of the country reporting to FAO, which focuses on area and biomass, and to UNFCCC, which explicitly focuses on carbon fluxes. Bearing in mind the respective strengths and weaknesses, both our NGHGI database and FAO offer a fundamental, yet incomplete, source of information on carbon-related variables for the scientific and policy communities, including under the Global stocktake. Overall, while the quality and quantity of the LULUCF data submitted by countries to the UNFCCC significantly improved in recent years, important gaps still remain. Most developing countries still do not explicitly separate managed vs. unmanaged forest land, a few report implausibly high forest sinks, and several report incomplete estimates. With these limits in mind, the NGHGI database presented here represents the most up-to-date and complete compilation of LULUCF data based on country submissions to UNFCCC. Data from this study are openly available via the Zenodo portal (Grassi et al., 2022), at https://doi.org/10.5281/zenodo.7190601.