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This study aimed to investigate the gap between adaptive functioning and cognitive functioning, especially verbal and nonverbal intelligence quotient (IQ) in Chinese children with ASD. We systematically explored cognitive functioning, ASD severity, early signs of developmental abnormalities, and socioeconomic factors as mediating factors of adaptive functioning. We enrolled 151 children (age: 2.5?6 years) with ASD and categorized them into one group with IQ [greater than or equal to] 70 and another with IQ < 70. The two groups were calibrated for age, age at diagnosis, and IQ, and the relationship of adaptive skills with vocabulary acquisition index (VAI) and nonverbal index (NVI) were separately analyzed. Results show that the gap between IQ and adaptive functioning was significant in children with ASD having IQ [greater than or equal to] 70, with both VAI and NVI showing statistically significant differences (all P < 0.001). VAI correlated positively with scores for overall adaptive skills and specific domains, whereas NVI had no significant correlations with adaptive skill scores. Age of first walking unaided had an independent positive correlation (all P < 0.05) with scores of adaptive skills and specific domains. IQ-adaptive functioning gap is significant in children with ASD having IQ [greater than or equal to] 70, suggesting that defining "high-functioning autism" merely on the basis of IQ is not appropriate. Verbal IQ and early signs of motor development are specific and possible predictors of adaptive functioning in children with ASD, respectively.

Abstract Background Traumatic brain injury (TBI) has been a worldwide problem for neurosurgeons. Patients with severe TBI may undergo craniotomy. These patients often require sedation after craniotomy. Dexmedetomidine (DEX) has been used in patients receiving anesthesia and in intensive care units. Not much is known about the postoperative effect of DEX in patients with severe TBIs undergoing craniotomy. The purpose of this study was to explore the effects of postoperative DEX administration on severe TBI patients who underwent craniotomy. Methods Patients who underwent craniectomy for severe TBI at our hospital between January 2019 and February 2022 were included in this study. The patients were admitted to the intensive care unit (ICU) after surgery to receive sedative medication. The patients were then divided into DEX and control groups. We analyzed the sedation, hemodynamics, and other conditions of the patients (hypoxemia, duration of ventilation during endotracheal intubation, whether tracheotomy was performed, and the duration in the ICU) during their ICU stay. Other conditions, such as delirium after the patients were transferred to the general ward, were also analyzed. Results A total of 122 patients were included in this study. Among them, 53 patients received DEX, and the remaining 69 did not. The incidence of delirium in the general ward in the DEX group was significantly lower than that in the control group (P 0.05). Conclusion The use of DEX in the ICU can effectively reduce the incidence of delirium in patients who return to the general ward after craniotomy. DEX had no adverse effect on the prognosis of patients other than causing bradycardia.

Abstract Emissions from fossil fuel exploitation are a leading contributor to global anthropogenic methane emissions, but are highly uncertain. The lack of reliable estimates hinders monitoring of the progress on pledges towards methane reductions. Here we analyze methane emissions from exploitation of coal, oil and gas for major producing nations across a suite of bottom-up inventories and global inversions. Larger disagreement in emissions exists for the oil/gas sector across the inventories compared to coal, arising mostly from disparate data sources for emission factors. Moreover, emissions reported to the United Nations Framework Convention on Climate Change are lower than other bottom-up and inversion estimates, with many countries lacking reporting in the past decades. Finally, comparison with previous global inversions, revealed a strong influence of the prior inventory on the inferred sub-sectoral emissions magnitude. This study highlights the need to improve consensus on the methodological inputs among the bottom-up inventories in order to obtain more consistent inverse modelling results at the sub-sectoral level.

Abstract Quantifying atmospheric CO2 over long periods from space is crucial in understanding the carbon cycle's response to climate change. However, a single satellite offers limited spatiotemporal coverage, making comprehensive monitoring challenging. Moreover, biases among various satellite retrievals hinder their direct integration. This study proposed a machine learning framework for fusing the column‐averaged dry‐air mole fraction of CO2 (XCO2) retrievals from Greenhouse Gases Observing Satellite (GOSAT) and OCO‐2 satellites. The best model (R2 = 0.85) presented improved consistency of GOSAT retrievals by reducing 71.5% of the average monthly bias while using OCO‐2 retrievals as a benchmark, indicating the fusion data set's potential to enhance observation coverage. Incorporating the adjusted GOSAT XCO2 retrievals into the OCO‐2 data set added an average of 84.7 thousand observations annually, enhancing the yearly temporal coverage by 53.6% (from 14 to 21.5 days per grid). This method can be adapted to other satellites, maximizing satellite resources for a more robust carbon flux inversion.

Abstract With the urgent need to implement the EU countries pledges and to monitor the effectiveness of Green Deal plan, Monitoring Reporting and Verification tools are needed to track how emissions are changing for all the sectors. Current official inventories only provide annual estimates of national CO2 emissions with a lag of 1+ year which do not capture the variations of emissions due to recent shocks including COVID lockdowns and economic rebounds, war in Ukraine. Here we present a near-real-time country-level dataset of daily fossil fuel and cement emissions from January 2019 through December 2021 for 27 EU countries and UK, which called Carbon Monitor Europe. The data are calculated separately for six sectors: power, industry, ground transportation, domestic aviation, international aviation and residential. Daily CO2 emissions are estimated from a large set of activity data compiled from different sources. The goal of this dataset is to improve the timeliness and temporal resolution of emissions for European countries, to inform the public and decision makers about current emissions changes in Europe.

Abstract We constructed a frequently updated, near-real-time global power generation dataset: CarbonMonitor-Power since January, 2016 at national levels with near-global coverage and hourly-to-daily time resolution. The data presented here are collected from 37 countries across all continents for eight source groups, including three types of fossil sources (coal, gas, and oil), nuclear energy and four groups of renewable energy sources (solar energy, wind energy, hydro energy and other renewables including biomass, geothermal, etc.). The global near-real-time power dataset shows the dynamics of the global power system, including its hourly, daily, weekly and seasonal patterns as influenced by daily periodical activities, weekends, seasonal cycles, regular and irregular events (i.e., holidays) and extreme events (i.e., the COVID-19 pandemic). The CarbonMonitor-Power dataset reveals that the COVID-19 pandemic caused strong disruptions in some countries (i.e., China and India), leading to a temporary or long-lasting shift to low carbon intensity, while it had only little impact in some other countries (i.e., Australia). This dataset offers a large range of opportunities for power-related scientific research and policy-making.

Abstract We present a near-real-time global gridded daily CO2 emissions dataset (GRACED) throughout 2021. GRACED provides gridded CO2 emissions at a 0.1° × 0.1° spatial resolution and 1-day temporal resolution from cement production and fossil fuel combustion over seven sectors, including industry, power, residential consumption, ground transportation, international aviation, domestic aviation, and international shipping. GRACED is prepared from the near-real-time daily national CO2 emissions estimates (Carbon Monitor), multi-source spatial activity data emissions and satellite NO2 data for time variations of those spatial activity data. GRACED provides the most timely overview of emissions distribution changes, which enables more accurate and timely identification of when and where fossil CO2 emissions have rebounded and decreased. Uncertainty analysis of GRACED gives a grid-level two-sigma uncertainty of value of ±19.9% in 2021, indicating the reliability of GRACED was not sacrificed for the sake of higher spatiotemporal resolution that GRACED provides. Continuing to update GRACED in a timely manner could help policymakers monitor energy and climate policies’ effectiveness and make adjustments quickly.

Measurement(s) carbon dioxide emissions Technology Type(s) fossil fuel consumption

Abstract The Global Stocktake (GST), implemented by the Paris Agreement, requires rapid developments in the capabilities to quantify annual greenhouse gas (GHG) emissions and removals consistently from the global to the national scale and improvements to national GHG inventories. In particular, new capabilities are needed for accurate attribution of sources and sinks and their trends to natural and anthropogenic processes. On the one hand, this is still a major challenge as national GHG inventories follow globally harmonized methodologies based on the guidelines established by the Intergovernmental Panel on Climate Change, but these can be implemented differently for individual countries. Moreover, in many countries the capability to systematically produce detailed and annually updated GHG inventories is still lacking. On the other hand, spatially-explicit datasets quantifying sources and sinks of carbon dioxide, methane and nitrous oxide emissions from Earth Observations (EO) are still limited by many sources of uncertainty. While national GHG inventories follow diverse methodologies depending on the availability of activity data in the different countries, the proposed comparison with EO-based estimates can help improve our understanding of the comparability of the estimates published by the different countries. Indeed, EO networks and satellite platforms have seen a massive expansion in the past decade, now covering a wide range of essential climate variables and offering high potential to improve the quantification of global and regional GHG budgets and advance process understanding. Yet, there is no EO data that quantifies greenhouse gas fluxes directly, rather there are observations of variables or proxies that can be transformed into fluxes using models. Here, we report results and lessons from the ESA-CCI RECCAP2 project, whose goal was to engage with National Inventory Agencies to improve understanding about the methods used by each community to estimate sources and sinks of GHGs and to evaluate the potential for satellite and in-situ EO to improve national GHG estimates. Based on this dialogue and recent studies, we discuss the potential of EO approaches to provide estimates of GHG budgets that can be compared with those of national GHG inventories. We outline a roadmap for implementation of an EO carbon-monitoring program that can contribute to the Paris Agreement.

Measurement(s) carbon dioxide emissions Technology Type(s) carbon monitor Sample Characteristic - Environment city Sample Characteristic - Location worldwide

Climate change is the greatest environmental threat to humans and the planet in the 21st century. Global anthropogenic greenhouse gas emissions are one of the main causes of the increasing number of extreme climate events. Cumulative carbon dioxide (CO2) emissions showed a linear relationship with cumulative temperature rise since the pre-industrial stage, and this accounts for approximately 80% of the total anthropogenic greenhouse gases. Therefore, accurate and reliable carbon emission data are the foundation and scientific basis for most emission reduction policymaking and target setting. Currently, China has made clear the ambitious goal of achieving the peak of carbon emissions by 2030 and achieving carbon neutrality by 2060. The development of a finer-grained spatiotemporal carbon emission database is urgently needed to achieve more accurate carbon emission monitoring for continuous implementation and the iterative improvement of emission reduction policies. Near-real-time carbon emission monitoring is not only a major national demand but also a scientific question at the frontier of this discipline. This article reviews existing annual-based carbon accounting methods, with a focus on the newly developed real-time carbon emission technology and its current application trends. We also present a framework for the latest near-real-time carbon emission accounting technology that can be widely used. The development of relevant data and methods will provide strong database support to the policymaking for China's “carbon neutrality” strategy. Finally, this article provides an outlook on the future of real-time carbon emission monitoring technology.

Image-based 3D reconstruction is widely used in practice due to less hardware constraints,lower cost and higher flexibility.Especially for the problems of sparseness and uneven density of the three-dimensional point cloud data generated by the image due to the occlusion between various parts of the object,it has always been a difficulty and hot issue to deal with.In this paper,a mask-based sparse point cloud filtering algorithm is proposed.Firstly,the bounding box of the point cloud is calculated and the grid is adaptively divided according to the sparseness of the point cloud.Secondly,Depth-first search is used to recursively find all customized connected domains composed of grids generated at the first step.Then adaptively calculating the threshold based on the quantized importance index,selecting the connected domains that should be retained based on the adaptive threshold,and defining the set of all retained connected domains as a mask,which is used to describe the global spatial topology information of the sparse point cloud.Finally,points covered by the mask are retained while points of the uncovered area are removed,so as to filter the outliers.This method can handle the point cloud data generated by occlusion and with great differences in spatial density.It can effectively remove outliers in the original three-dimensional point cloud data,while maintaining the detailed information of the point cloud.

BackgroundAcute exacerbation of chronic obstructive pulmonary disease (AECOPD) is associated with high mortality rates. Viral and bacterial coinfection is the primary cause of AECOPD. How coinfection with these microbes influences host inflammatory response and the gut microbiota composition is not entirely understood.MethodsWe developed a mouse model of AECOPD by cigarette smoke exposure and sequential infection with influenza H1N1 virus and non-typeable Haemophilus influenzae (NTHi). Viral and bacterial titer was determined using MDCK cells and chocolate agar plates, respectively. The levels of cytokines, adhesion molecules, and inflammatory cells in the lungs were measured using Bio-Plex and flow cytometry assays. Gut microbiota was analyzed using 16S rRNA gene sequencing. Correlations between cytokines and gut microbiota were determined using Spearman’s rank correlation coefficient test.ResultsCoinfection with H1N1 and NTHi resulted in more severe lung injury, higher mortality, declined lung function in COPD mice. H1N1 enhanced NTHi growth in the lungs, but NTHi had no effect on H1N1. In addition, coinfection increased the levels of cytokines and adhesion molecules, as well as immune cells including total and M1 macrophages, neutrophils, monocytes, NK cells, and CD4 + T cells. In contrast, alveolar macrophages were depleted. Furthermore, coinfection caused a decline in the diversity of gut bacteria. Muribaculaceae, Lactobacillus, Akkermansia, Lachnospiraceae, and Rikenella were further found to be negatively correlated with cytokine levels, whereas Bacteroides was positively correlated.ConclusionCoinfection with H1N1 and NTHi causes a deterioration in COPD mice due to increased lung inflammation, which is correlated with dysbiosis of the gut microbiota.

Summary: In this perspective, we proposed an innovative strategy that coupled near-real-time emission data with satellite observations to make a reliable and precise global carbon-monitoring system.

Seismic data plays a vital role in oil and gas exploration and geological exploration.Accurate and detailed seismic data can help to provide accurate guidance for oil and gas exploration,reduce the risk of exploration,and generate huge social and economic benefits.In terms of improving the resolution of seismic data,the existing methods are difficult to recover detailed geolo-gical information when facing large amounts of data,and have poor results in high-resolution recovery,denoising performance and efficiency,and it is difficult to meet the actual needs.Seismic data can reflect the composition of geological structures and strata,and have the characteristics of high local correlation and low global correlation.At the same time,the high frequency part of seismic data usually contain important geological exploration information such as layering,chasm,etc.In view of the characteristics of seismic data,this paper innovatively transforms the problem of seismic data reconstruction into the problem of image super-resolution,and proposes a method for super-resolution of seismic data based on the generative adversarial networks.In view of the characteristics of high local correlation and low global correlation of seismic data distribution,a residual attention module is designed to mine the inherent correlation of seismic data,so as to recover more refined seismic data.By training a generative adversarial network model with a relative generative adversarial loss function,the generative network is used to perform super-resolution recovery of the seismic data to recover more refined seismic data.In this paper,experimental verification is carried out on real seismic data,and the experimental results show that the proposed method has a good effect on super-resolution of seismic data and has strong practicability.

The COVID-19 pandemic has stopped many human activities, which has had significant impact on emissions of greenhouse gases. Here, the authors present daily estimates of country-level CO2 emissions for different economic sectors and show that there has been a 8.8% decrease in global CO2 emissions in the first half of 2020.

Precise and high-resolution carbon dioxide (CO2) emission data is of great importance in achieving carbon neutrality around the world. Here we present for the first time the near-real-time Global Gridded Daily CO2 Emissions Dataset (GRACED) from fossil fuel and cement production with a global spatial resolution of 0.1° by 0.1° and a temporal resolution of 1 day. Gridded fossil emissions are computed for different sectors based on the daily national CO2 emissions from near-real-time dataset (Carbon Monitor), the spatial patterns of point source emission dataset Global Energy Infrastructure Emissions Database (GID), Emission Database for Global Atmospheric Research (EDGAR), and spatiotemporal patters of satellite nitrogen dioxide (NO2) retrievals. Our study on the global CO2 emissions responds to the growing and urgent need for high-quality, fine-grained, near-real-time CO2 emissions estimates to support global emissions monitoring across various spatial scales. We show the spatial patterns of emission changes for power, industry, residential consumption, ground transportation, domestic and international aviation, and international shipping sectors from January 1, 2019, to December 31, 2020. This gives thorough insights into the relative contributions from each sector. Furthermore, it provides the most up-to-date and fine-grained overview of where and when fossil CO2 emissions have decreased and rebounded in response to emergencies (e.g., coronavirus disease 2019 [COVID-19]) and other disturbances of human activities of any previously published dataset. As the world recovers from the pandemic and decarbonizes its energy systems, regular updates of this dataset will enable policymakers to more closely monitor the effectiveness of climate and energy policies and quickly adapt. Public summary: • We present the first near-real-time Global Gridded Daily CO2 Emissions Dataset (GRACED) • GRACED can be updated in near real time with a spatial resolution of 0.1° and a temporal resolution of 1 day • GRACED shows gridded emissions of seven sectors: power, industry, residential consumption, ground transport, domestic aviation, international aviation, and international shipping • Regular updates of GRACED will enable policymakers to more closely monitor the effectiveness of climate and energy policies and quickly adapt on various spatial scales

Abstract As the COVID‐19 virus spread over the world, governments restricted mobility to slow transmission. Public health measures had different intensities across European countries but all had significant impact on people's daily lives and economic activities, causing a drop of CO2 emissions of about 10% for the whole year 2020. Here, we analyze changes in natural gas use in the industry and gas distribution to the built environment during the first half of year 2020 with daily gas flows data from pipeline and storage facilities in Europe. We find that reductions of industrial gas use reflect decreases in industrial production across most countries. Surprisingly, natural gas use in the built environment also decreased despite most people being confined at home and cold spells in March 2020. Those reductions that we attribute to the impacts of COVID‐19 remain of comparable magnitude to previous variations induced by cold or warm climate anomalies in the cold season. We conclude that climate variations played a larger role than COVID‐19 induced stay‐home orders in natural gas consumption across Europe.

Nitrogen dioxide (NO2) is one of the most important atmospheric pollutants and the precursors of acid rain, tropospheric ozone, and atmospheric aerosols. However, due to the poor quality of source data and the computing power of the models, current ground-level NO2 concentration data lack either high-resolution coverage or full nation-wide coverage. This study estimates the ground-level NO2 concentration in China with national coverage at relatively high spatiotemporal resolution (0.25°; daily intervals) over the newest past 6 years (2013–2018). We developed an advanced model, named Random Forest model integrated K-means (RF-K), for the estimates with multi-source parameters. Besides meteorological parameters, satellite retrievals parameters, and anthropogenic emission inventories parameters, we also innovatively introduce socioeconomic parameters to assess the impact of human activities. Our results show that: (1) the RF-K model developed by us shows better prediction performance than others. (2) the annual average NO2 concentration of China showed a weak declining trend (-0.013±0.217 μgm−3yr−1) from 2013 to 2018, indicating that pollutant controlling targets had been achieved in China overall. By mapping daily nationwide ground-level NO2 concentrations, this study provides high-quality timely, and detailed data for air quality management and epidemiological analyses for China. The RF-K model can be used easily for other pollutants (e.g. SO2 and O3) considering that their ground-level concentrations can be estimated depending on the similar emitting sources and influence factors, and our model's input data sources also cover information on other pollutants.

Fossil CO _2 emissions in 2021 grew an estimated 4.2% (3.5%–4.8%) to 36.2 billion metric tons compared with 2020, pushing global emissions back close to 2019 levels (36.7 Gt CO _2 ).

A Correction to this paper has been published: https://doi.org/10.1038/s41467-020-20254-5