Determination of Capillary Blood TSH and Free Thyroxine Levels Using Digital Immunoassay.

Background: The remote performance of thyroid function blood tests is complicated because it requires blood collection. Objective: To compare TSH and free thyroxine (FT4) levels between capillary and venous blood and assess the adequacy of measuring each value in capillary blood. Methods: This prospective intervention study was conducted at Ito Hospital and was based on the clinical research method. The participants were 5 healthy female volunteers and 50 patients (41 females and 9 males) between the ages of 23 and 81 years. To measure TSH and FT4 levels in capillary and venous blood, a digital immunoassay (d-IA) method capable of measuring trace samples was used. Chemiluminescence measurements were used as controls. Values obtained for each assay system were compared using Spearman's correlation analysis. Capillary blood was collected using an autologous device (TAP II; not approved in Japan). Results: Capillary plasma volume obtained using TAP II was 125 µL or more in 26 cases, 25 µL to 124 µL in 24 cases, and less than 25 µL in 5 cases. Strong correlations were noted in the TSH and FT4 levels between capillary and venous blood, with correlation coefficients of rs = 0.99 and rs = 0.97, respectively. Conclusion: Capillary TSH and FT4 levels strongly correlate with venous blood values. Trace samples can be used in high-precision d-IA methods. These results may promote telemedicine in assessing thyroid function.

How climate change may shift power demand in Japan: Insights from data-driven analysis.

The impact of climate change on power demand in Japan and its related CO2 emissions is a matter of concern for the Japanese authorities and power companies as it may have consequences on the power grid, but is also of global importance as Japan is a significant contributor to global greenhouse gas emissions. In this study, we trained random forest models against daily power data in ten Japanese regions and for different types of power generation to project changes in future power production and its carbon intensity. We used climate variables, heat stress indices, and one variable for the level of human activities. We then used the models trained from the present-day period to estimate the future power demand, carbon intensity, and pertaining CO2 emissions over the period 2020-2100 under three Shared Socioeconomic Pathways (SSPs) scenarios (SSP126, SSP370, and SSP585). The impact of climate change on CO2 emissions via power generation shows seasonal and regional disparities. In cold regions, a decrease in power demand during winter under future warming leads to an overall decrease in power demand over the year. In contrast, the decrease in winter power demand in hot regions can be overcompensated by an increase in summer power demand due to more frequent hot days, resulting in an overall annual increase. From our regional models, power demand is projected to increase the most in most Japanese regions in May, June, September, and October rather than in the middle of summer, as found in previous studies. This increase could result in regular power outages during those months as the power grid could become particularly tense. Overall, we observed that power demand in regions with extreme climates is more sensitive to global warming than in temperate regions. The impact of climate change on power demand induces a net annual decrease in CO2 emissions in all regions except for Okinawa, in which power demand strongly increases during the summer, resulting in a net annual increase in CO2 emissions. However, climate change's impact on carbon intensity may reverse the trend in some regions (Shikoku, Tohoku). Additionally, we assessed the relative impacts of socioeconomic factors such as population, GDP, and environmental policies on CO2 emissions. When combined with these factors, we found that the climate change effect is more important than when considered individually and significantly impacts total CO2 emissions under SSP585. The contrasting results observed in the warm and cold regions of Japan can offer valuable insight into the potential future variations in energy demand and resulting CO2 emissions on a global scale.

Accelerating the energy transition towards photovoltaic and wind in China.

China's goal to achieve carbon (C) neutrality by 2060 requires scaling up photovoltaic (PV) and wind power from 1 to 10-15 PWh year-1 (refs. 1-5). Following the historical rates of renewable installation1, a recent high-resolution energy-system model6 and forecasts based on China's 14th Five-year Energy Development (CFED)7, however, only indicate that the capacity will reach 5-9.5 PWh year-1 by 2060. Here we show that, by individually optimizing the deployment of 3,844 new utility-scale PV and wind power plants coordinated with ultra-high-voltage (UHV) transmission and energy storage and accounting for power-load flexibility and learning dynamics, the capacity of PV and wind power can be increased from 9 PWh year-1 (corresponding to the CFED path) to 15 PWh year-1, accompanied by a reduction in the average abatement cost from US$97 to US$6 per tonne of carbon dioxide (tCO2). To achieve this, annualized investment in PV and wind power should ramp up from US$77 billion in 2020 (current level) to US$127 billion in the 2020s and further to US$426 billion year-1 in the 2050s. The large-scale deployment of PV and wind power increases income for residents in the poorest regions as co-benefits. Our results highlight the importance of upgrading power systems by building energy storage, expanding transmission capacity and adjusting power load at the demand side to reduce the economic cost of deploying PV and wind power to achieve carbon neutrality in China.

Paris Agreement requires substantial, broad, and sustained policy efforts beyond COVID-19 public stimulus packages.

It has been claimed that COVID-19 public stimulus packages could be sufficient to meet the short-term energy investment needs to leverage a shift toward a pathway consistent with the 1.5 °C target of the Paris Agreement. Here, we provide complementary perspectives to reiterate that substantial, broad, and sustained policy efforts beyond stimulus packages will be needed for achieving the Paris Agreement long-term targets. Low-carbon investments will need to scale up and persist over the next several decades following short-term stimulus packages. The required total energy investments in the real world can be larger than the currently available estimates from integrated assessment models (IAMs). Existing databases from IAMs are not sufficient for analyzing the effect of public spending on emission reduction. To inform what role COVID-19 stimulus packages and public investments may play for reaching the Paris Agreement targets, explicit modelling of such policies is required.

Analysis of relationship between loading condition and cranial cracking pattern using a three-dimensional finite element model.

BACKGROUND: A hairline crack on the cranium can occur even under a small external load or impact and are thus often observed in patients who have experienced an accidental fall or collision. Typical finite element analysis is useful to analyze the stress concentration or the propagation of stress waves. However, a stress propagation model does not accurately reproduce the features of hairline cracks on the cranium. The objective in this study was to reproduce cranial hairline cracks. METHODS: A three-dimensional finite element model of the cranial bone was developed from a patient CT images. The model consists of the frontal, parietal, occipital and temporal bones, and the bones are connected with the sutures. Additionally, the model comprised three layers; the external and internal tables and the diploe. The model was analyzed using the extended finite element method (X-FEM), and a forming limit diagram (FLD) was embedded in the model. In this study, the model was symmetrized bilaterally using the model developed from the left side of the skull. The FLD in this study was assumed to be a relationship between the maximum and minimum strains when a fracture occurs. A total of 13 typical loadings were applied to the model: loading points on the top, left, and back of the cranium were considered, and at each loading point, loads were applied with four or five different directions, namely perpendicular to the cranium and inclined in the anterior, posterior, superior, or inferior at an angle of 45∘. RESULTS: Under all loading conditions, many small cracks formed radially at the loading points. Moreover, some large cracks formed under the certain loading conditions. The crack shapes on the top and left side could be associated with the specific loading directions, whereas cracks on the back did not show distinguishing characteristics depending on the loading directions. The present model was reproduced anatomically and morphologically, and the results were similar to those obtained in previous cadaver experiments. CONCLUSIONS: Through X-FEM analysis of the FE model embedded with an FLD, hairline cracks in the cranium were reproduced, and a few crack shapes were identified as potential markers for estimating the loading conditions.

Ir-Catalyzed cyclization of α,ω-dienes with an N-methyl group via two C-H activation steps.

Iridium-catalyzed sp3 C-H alkylation of an N-methyl group with 1,5- and 1,6-dienes proceeded to give five- and six-membered carbocyclic compounds, respectively, in high yields. The reaction involves intermolecular alkylation of the N-methyl group with a vinyl moiety and subsequent intramolecular cyclization at the ß-position of the initially formed alkylated intermediate. The reaction using a chiral bidentate phosphine ligand enabled the asymmetric synthesis of the cyclic compounds.

Ensuring that offsets and other internationally transferred mitigation outcomes contribute effectively to limiting global warming.

Ensuring the environmental integrity of internationally transferred mitigation outcomes, whether through offset arrangements, a market mechanism or non-market approaches, is a priority for the implementation of Article 6 of the Paris Agreement. Any conventional transferred mitigation outcome, such as an offset agreement, that involves exchanging greenhouse gases with different lifetimes can increase global warming on some timescales. We show that a simple 'do no harm' principle regarding the choice of metrics to use in such transactions can be used to guard against this, noting that it may also be applicable in other contexts such as voluntary and compliance carbon markets. We also show that both approximate and exact 'warming equivalent' exchanges are possible, but present challenges of implementation in any conventional market. Warming-equivalent emissions may, however, be useful in formulating warming budgets in a two-basket approach to mitigation and in reporting contributions to warming in the context of the global stocktake.

Cost-effective implementation of the Paris Agreement using flexible greenhouse gas metrics.

Greenhouse gas (GHG) metrics, that is, conversion factors to evaluate the emissions of non-CO2 GHGs on a common scale with CO2, serve crucial functions in the implementation of the Paris Agreement. While different metrics have been proposed, their economic cost-effectiveness has not been investigated under a range of pathways, including those substantially overshooting the temperature targets. Here, we show that cost-effective metrics for methane that minimize the overall mitigation costs are time-dependent, primarily determined by the pathway, and strongly influenced by temperature overshoot. Parties to the Paris Agreement have already adopted the conventional GWP100 (100-year global warming potential), which is shown to be a good approximation of cost-effective metrics for the coming decades. In the longer term, however, we suggest that parties consider adapting the choice of common metrics to the future pathway as it unfolds, as part of the recurring global stocktake, if global cost-effectiveness is a key consideration.

Author Correction: Near-real-time monitoring of global CO2 emissions reveals the effects of the COVID-19 pandemic.

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

Carbon Monitor, a near-real-time daily dataset of global CO2 emission from fossil fuel and cement production.

We constructed a near-real-time daily CO2 emission dataset, the Carbon Monitor, to monitor the variations in CO2 emissions from fossil fuel combustion and cement production since January 1, 2019, at the national level, with near-global coverage on a daily basis and the potential to be frequently updated. Daily CO2 emissions are estimated from a diverse range of activity data, including the hourly to daily electrical power generation data of 31 countries, monthly production data and production indices of industry processes of 62 countries/regions, and daily mobility data and mobility indices for the ground transportation of 416 cities worldwide. Individual flight location data and monthly data were utilized for aviation and maritime transportation sector estimates. In addition, monthly fuel consumption data corrected for the daily air temperature of 206 countries were used to estimate the emissions from commercial and residential buildings. This Carbon Monitor dataset manifests the dynamic nature of CO2 emissions through daily, weekly and seasonal variations as influenced by workdays and holidays, as well as by the unfolding impacts of the COVID-19 pandemic. The Carbon Monitor near-real-time CO2 emission dataset shows a 8.8% decline in CO2 emissions globally from January 1st to June 30th in 2020 when compared with the same period in 2019 and detects a regrowth of CO2 emissions by late April, which is mainly attributed to the recovery of economic activities in China and a partial easing of lockdowns in other countries. This daily updated CO2 emission dataset could offer a range of opportunities for related scientific research and policy making.

Future projection of greenhouse gas emissions due to permafrost degradation using a simple numerical scheme with a global land surface model.

The Yedoma layer, a permafrost layer containing a massive amount of underground ice in the Arctic regions, is reported to be rapidly thawing. In this study, we develop the Permafrost Degradation and Greenhouse gasses Emission Model (PDGEM), which describes the thawing of the Arctic permafrost including the Yedoma layer due to climate change and the greenhouse gas (GHG) emissions. The PDGEM includes the processes by which high-concentration GHGs (CO2 and CH4) contained in the pores of the Yedoma layer are released directly by dynamic degradation, as well as the processes by which GHGs are released by the decomposition of organic matter in the Yedoma layer and other permafrost. Our model simulations show that the total GHG emissions from permafrost degradation in the RCP8.5 scenario was estimated to be 31-63 PgC for CO2 and 1261-2821 TgCH4 for CH4 (68th percentile of the perturbed model simulations, corresponding to a global average surface air temperature change of 0.05-0.11 °C), and 14-28 PgC for CO2 and 618-1341 TgCH4 for CH4 (0.03-0.07 °C) in the RCP2.6 scenario. GHG emissions resulting from the dynamic degradation of the Yedoma layer were estimated to be less than 1% of the total emissions from the permafrost in both scenarios, possibly because of the small area ratio of the Yedoma layer. An advantage of PDGEM is that geographical distributions of GHG emissions can be estimated by combining a state-of-the-art land surface model featuring detailed physical processes with a GHG release model using a simple scheme, enabling us to consider a broad range of uncertainty regarding model parameters. In regions with large GHG emissions due to permafrost thawing, it may be possible to help reduce GHG emissions by taking measures such as restraining land development.

Near-real-time monitoring of global CO2 emissions reveals the effects of the COVID-19 pandemic.

The COVID-19 pandemic is impacting human activities, and in turn energy use and carbon dioxide (CO2) emissions. Here we present daily estimates of country-level CO2 emissions for different sectors based on near-real-time activity data. The key result is an abrupt 8.8% decrease in global CO2 emissions (-1551 Mt CO2) in the first half of 2020 compared to the same period in 2019. The magnitude of this decrease is larger than during previous economic downturns or World War II. The timing of emissions decreases corresponds to lockdown measures in each country. By July 1st, the pandemic's effects on global emissions diminished as lockdown restrictions relaxed and some economic activities restarted, especially in China and several European countries, but substantial differences persist between countries, with continuing emission declines in the U.S. where coronavirus cases are still increasing substantially.

How do climate-related uncertainties influence 2 and 1.5 °C pathways?

We investigate how uncertainties in key parameters in the carbon cycle and climate system propagate to the costs of climate change mitigation and adaptation needed to achieve the 2 and 1.5 °C targets by 2100 using a stochastic version of the simple climate model for optimization (SCM4OPT), an integrated assessment model. For the 2 °C target, we find a difference in 2100 CO2 emission levels of 20.5 GtCO2 (- 1.2 GtCO2 to 19.4 GtCO2), whereas this difference is 12.0 GtCO2 (- 6.9 GtCO2 to 5.1 GtCO2) for the 1.5 °C target (17-83% range). Total radiative forcing in 2100 is estimated to be 3.3 (2.7-3.9) Wm-2 for the 2 °C case and 2.5 (2.0-3.0) Wm-2 for the 1.5 °C case. Carbon prices in 2100 are 482 (181-732) USD(2005)/tCO2 and 713 (498-1014) USD(2005)/tCO2 for the 2 and 1.5 °C targets, respectively. We estimate GDP losses in 2100 that correspond to 1.9 (1.2-2.5)% of total gross output for the 2 °C target and 2.0 (1.5-2.7)% for the 1.5 °C target.

Climate effects of emission standards: the case for gasoline and diesel cars.

Passenger transport affects climate through various mechanisms involving both long-lived and short-lived climate forcers. Because diesel cars generally emit less CO(2) than gasoline cars, CO(2) emission taxes for vehicle registrations and fuels enhance the consumer preference for diesel cars over gasoline cars. However, with the non-CO(2) components, which have been changed and will be changed under the previous and upcoming vehicle emission standards, what does the shift from gasoline to diesel cars mean for the climate mitigation? By using a simple climate model, we demonstrate that, under the earlier emissions standards (EURO 3 and 4), a diesel car causes a larger warming up to a decade after the emissions than a similar gasoline car due to the higher emissions of black carbon and NO(X) (enhancing the O(3) production). Beyond a decade, the warming caused by a diesel car becomes, however, weaker because of the lower CO(2) emissions. As the latter emissions standards (EURO 5 and 6) are phased in, the short-term warming due to a diesel car becomes smaller primarily due to the lower black carbon emissions. Thus, although results are subject to restrictive assumptions and uncertainties, the switch from gasoline to diesel cars encouraged by CO(2) taxes does not contradict with the climate mitigation focusing on long-term consequences.