Increasing impacts of China's oil and gas demands on global CH4 emissions.

The energy sector stands out as a main contributor to increasing global methane (CH4) emissions. Given China's heavy dependence on energy imports, a closer examination of its oil and gas-related CH4 emissions becomes imperative. This study conducts an in-depth analysis of China's contribution to global CH4 emissions stemming from its consumption of crude oil and natural gas since 2000. The results indicate that CH4 emissions from crude oil and natural gas imports rose from 614 Gg in 2000 to 7692 Gg in 2019. When considering domestic production, the demand-induced CH4 emissions in 2019 increased to approximately 10754 Gg (equivalent to 320 Mt CO2-eq and 887 Mt CO2-eq based on global warming potential (GWP) values at the 100-year and 20-year time period), of which 72 % were related to crude oil and natural gas imports. The primary contributor to this increase in CH4 emissions was the expansion of the trade scale. The growth trend of crude oil imports-induced CH4 emissions was also positively influenced by emission intensity and trade structure, but these two drivers had a negative impact on the growth of natural gas imports-induced CH4 emissions. The virtual transfer of CH4 emissions via international oil and gas trade requires urgent policy attention. In collaboration with its trading partners, China should take aggressive actions to achieve meaningful mitigation in CH4 emissions associated with the oil and gas trade.

Global trade networks bring targeted opportunity for energy-related CH4 emission mitigation.

Recent literature highlights the contributions the global energy sector has made to anthropogenic CH4 emissions, calling for immediate action. However, extant studies have failed to reveal the energy-related CH4 emissions induced by global trades of intermediate and final commodities or services. This paper traces fugitive CH4 emissions via global trade networks using the multi-regional input-output and complex network models. Results show that approximately four-fifths of global fugitive CH4 emissions in 2014 were associated with international trade, of which 83.07% and 16.93% were embodied in the intermediate and final trades, respectively. Japan, India, the USA, South Korea, and Germany were the world's five largest net importers of embodied fugitive CH4 emissions, while Indonesia, Russia, Nigeria, Qatar, and Iran were the five largest net exporters. Gas-related embodied emission transfers were the largest in both the intermediate and the final trade networks. The fugitive CH4 emissions embodied within the intermediate and final trade networks were all characterized by five trading communities. The virtual fugitive CH4 emission transfers via intermediate trade were largely determined by global energy trade patterns, especially the trade in regionally integrated crude oil and natural gas. Significant heterogeneity was revealed by the coexistence of numerous loosely linked economies and several hub economies (e.g., China, Germany, the USA, and South Africa). Interventions on the demand side of interregional and intraregional trade partners in different communities and hub economies will bring targeted opportunities for global energy-related CH4 emission reduction.

Tracing global N2O emission mitigation strategies through trade networks.

Nitrous oxide (N2O) is the third most potent greenhouse gas (GHG) and the most important ozone depleting substance. But how global N2O emissions are connected through the interwoven trade network remains unclear. This paper attempts to specifically trace anthropogenic N2O emissions via global trade networks using a multi-regional input-output model and a complex network model. Nearly one quarter of global N2O emissions can be linked to products traded internationally in 2014. The top 20 economies contribute to about 70% of the total embodied N2O emission flows. In terms of the trade embodied emissions classified by sources, cropland-, livestock-, chemistry-, and other industries-related embodied N2O emissions account for 41.9%, 31.2%, 19.9%, and 7.0%, respectively. Clustering structure of the global N2O flow network is revealed by the regional integration of 5 trading communities. Hub economies such as mainland China and the USA are collectors and distributors, and some emerging countries, such as Mexico, Brazil, India, and Russia, also exhibit dominance in different kinds of networks. This study selects the cattle sector to further verify that low production-side emission intensities and trade cooperation can lead to N2O emission reduction. In view of the impact of trade networks on global N2O emissions, achieving N2O emission reduction calls for vigorous international cooperation.

China's Trade of Agricultural Products Drives Substantial Greenhouse Gas Emissions.

China's trade of agricultural products has expanded rapidly over the past two decades, resulting in considerable shifts in greenhouse gas (GHG) emissions worldwide. This study aims to explore the evolution of GHG emissions embodied in China's trade of agricultural products from 1995 to 2015. The GHG emissions embodied in China's exports of agricultural products experienced three stages of fluctuation, showing a significant upward trend (1995-2003), a fluctuating trend (2004-2007), and a fall back to the previous level (2008-2015). The embodied GHG emissions in China's imports were witnessed at times of sustained growth, rising from 10.5 Mt CO2-eq in 1995 to 107.7 Mt CO2-eq in 2015. The net import of embodied GHG emissions has grown at an average annual rate of 25.1% since 2008. In terms of regional contribution, the distribution of China's trading partners tended to be diversified. The increasing net imports of oil crops to China resulted in a significant GHG emissions shift from China to the US and Brazil. Asian countries contributed to 76.9% of the total GHG emissions embodied in China's agricultural exports. The prominent impacts of China's trade of agricultural products on global GHG emissions provide important implications for climate-related policy choices.

Tea consumption during the periconceptional period does not significantly increase the prevalence of neural tube defects: A systematic review and dose-response meta-analysis.

Tea consumption may potentially promote the prevalence of neural tube defects (NTDs) because catechins, the main components of tea polyphenols, can lead to the depletion of bioavailable folate. Many epidemiologic studies have explored the association between tea consumption and NTD risk; however, the findings are inconsistent. This systematic review and meta-analysis investigated the association between tea consumption and NTD. We hypothesized that tea consumption during the periconceptional period would significantly promote NTD prevalence. Three electronic databases (PubMed, Web of Science, and Embase) were searched from their inception through July 14, 2021. Pooled odds ratios (ORs) with corresponding 95% confidence intervals (CIs) were obtained using random-effects models. Subgroup analyses were performed to explore potential confounders. In addition, a dose-response analysis was conducted to examine further the association between tea consumption and NTD. Seven articles with nine studies yielded 2834 cases and 19,924 participants. The results showed that tea consumption during the periconceptional period did not significantly increase NTD prevalence (OR, 1.37; 95% CI, 0.96-1.95; P = .08). This finding was consistent with the evaluation of 3 subtypes of NTDs: anencephaly (OR, 1.36; 95% CI, 0.84-2.20; P = .22), spina bifida (OR, 1.51; 95% CI, 0.84-2.72; P = .17), and encephalocele (OR, 0.99; 95% CI, 0.46-2.15; P = .98). Furthermore, a significant dose-response association between tea consumption and the risk of NTDs was not evident (P > .05). Our meta-analysis suggests that maternal tea consumption during the periconceptional period did not significantly increase the prevalence of NTDs. Further studies are needed to ascertain the association between tea consumption and NTD prevalence.

Disturbance Observer-Based Feedback Linearization Control for a Quadruple-Tank Liquid Level System.

A novel input/output feedback linearization control method by utilizing nonlinear disturbance observer (NDOB) is proposed for a quadruple-tank liquid level (QTLL) system in this paper. Firstly, the mathematical model of QTLL system is established by using Bernoulli's law and mass conservation. Secondly, in view of the nonlinear and coupling characteristics of the QTLL system, a input/output feedback linearization controller is designed. Then, a NDOB is proposed to estimate disturbances and applied to compensation control. Finally, simulation and experimental results show that the proposed strategy has better control performances than PID control and the disturbance observer-based sliding mode control (DOBSMC).

Learning the structure-activity relationship (SAR) of the Wittig reaction from genetically-encoded substrates.

The Wittig reaction can be used for late stage functionalization of proteins and peptides to ligate glycans, pharmacophores, and many other functionalities. In this manuscript, we modified 160 000 N-terminal glyoxaldehyde peptides displayed on phage with the Wittig reaction by using a biotin labeled ylide under conditions that functionalize only 1% of the library population. Deep-sequencing of the biotinylated and input populations estimated the rate of conversion for each sequence. This "deep conversion" (DC) from deep sequencing correlates with rate constants measured by HPLC. Peptide sequences with fast and slow reactivity highlighted the critical role of primary backbone amides (N-H) in accelerating the rate of the aqueous Wittig reaction. Experimental measurement of reaction rates and density functional theory (DFT) computation of the transition state geometries corroborated this relationship. We also collected deep-sequencing data to build structure-activity relationship (SAR) models that can predict the DC value of the Wittig reaction. By using these data, we trained two classifier models based on gradient boosted trees. These classifiers achieved area under the ROC (receiver operating characteristic) curve (ROC AUC) of 81.2 ± 0.4 and 73.7 ± 0.8 (90-92% accuracy) in determining whether a sequence belonged to the top 5% or the bottom 5% in terms of its reactivity. This model can suggest new peptides never observed experimentally with 'HIGH' or 'LOW' reactivity. Experimental measurement of reaction rates for 11 new sequences corroborated the predictions for 8 of them. We anticipate that phage-displayed peptides and related mRNA or DNA-displayed substrates can be employed in a similar fashion to study the substrate scope and mechanisms of many other chemical reactions.

The predicted trend of COVID-19 in the United States of America under the policy of "Opening Up America Again".

COVID-19 virus has been spreading worldwide for more than a year. At present, the situation of the new crown pneumonia epidemic remains full of tension and uncertainty. It is of concern is that the worst outbreak in the world is in the United States. The total number of confirmed new cases of COVID-19 and the total number of new deaths in the United States have entered their second and third cyclical peaks since the White House announced the "Open America Again" guidelines on April 16, 2020, and the start of the US presidential election season in August 2020. This paper combines the generalized exponential model (EXPM) with Chebyshev polynomials to develop a special generalized growth model (GGM) to predict the total number of daily new confirmed cases and the total number of new deaths in the United States for three periods under a 14-day sensitivity regression model. In this paper, the US epidemic is divided into three periods from early January 2020 to early January 2021, and three forecasts are made for the three periods. The first two prediction periods have already occurred and the predictions match well with known results, and the third period predicts that the total number of new confirmed cases of COVID-19 and the total number of new deaths in the United States will fall to a minimum level by next July, when the supply of COVID-19 vaccine has already begun. The results suggest that the "Open America Again" policy and the events of the 2020 US presidential election season have contributed to the worsening of the COVID-19 in the United States.

Rapid Access to Dispirocyclic Scaffolds Enabled by Diastereoselective Intramolecular Double Functionalization of Benzene Rings.

Here we describe the diastereoselective synthesis of (5r,8r)-1,9-diazadispiro[4.2.48 .25 ]tetradecatrienes via domino double spirocyclization of N-arylamide derivatives. This reaction can serve as a fast way to synthesize diazadispirocycles, which are found in the core structures of bioactive natural products. Product diversification via Suzuki-Miyaura cross coupling and application to the synthesis of 1-oxa-9-azadispiro[4.2.48 .25 ]tetradecatrienes were also conducted.