Publisher Correction: Large Chinese land carbon sink estimated from atmospheric carbon dioxide data.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Large Chinese land carbon sink estimated from atmospheric carbon dioxide data.

Limiting the rise in global mean temperatures relies on reducing carbon dioxide (CO2) emissions and on the removal of CO2 by land carbon sinks. China is currently the single largest emitter of CO2, responsible for approximately 27 per cent (2.67 petagrams of carbon per year) of global fossil fuel emissions in 20171. Understanding of Chinese land biosphere fluxes has been hampered by sparse data coverage2-4, which has resulted in a wide range of a posteriori estimates of flux. Here we present recently available data on the atmospheric mole fraction of CO2, measured from six sites across China during 2009 to 2016. Using these data, we estimate a mean Chinese land biosphere sink of -1.11 ± 0.38 petagrams of carbon per year during 2010 to 2016, equivalent to about 45 per cent of our estimate of annual Chinese anthropogenic emissions over that period. Our estimate reflects a previously underestimated land carbon sink over southwest China (Yunnan, Guizhou and Guangxi provinces) throughout the year, and over northeast China (especially Heilongjiang and Jilin provinces) during summer months. These provinces have established a pattern of rapid afforestation of progressively larger regions5,6, with provincial forest areas increasing by between 0.04 million and 0.44 million hectares per year over the past 10 to 15 years. These large-scale changes reflect the expansion of fast-growing plantation forests that contribute to timber exports and the domestic production of paper7. Space-borne observations of vegetation greenness show a large increase with time over this study period, supporting the timing and increase in the land carbon sink over these afforestation regions.

Evidence for climate change in the satellite cloud record.

Clouds substantially affect Earth's energy budget by reflecting solar radiation back to space and by restricting emission of thermal radiation to space. They are perhaps the largest uncertainty in our understanding of climate change, owing to disagreement among climate models and observational datasets over what cloud changes have occurred during recent decades and will occur in response to global warming. This is because observational systems originally designed for monitoring weather have lacked sufficient stability to detect cloud changes reliably over decades unless they have been corrected to remove artefacts. Here we show that several independent, empirically corrected satellite records exhibit large-scale patterns of cloud change between the 1980s and the 2000s that are similar to those produced by model simulations of climate with recent historical external radiative forcing. Observed and simulated cloud change patterns are consistent with poleward retreat of mid-latitude storm tracks, expansion of subtropical dry zones, and increasing height of the highest cloud tops at all latitudes. The primary drivers of these cloud changes appear to be increasing greenhouse gas concentrations and a recovery from volcanic radiative cooling. These results indicate that the cloud changes most consistently predicted by global climate models are currently occurring in nature.

Local Anomalies in the Column-Averaged Dry Air Mole Fractions of Carbon Dioxide Across the Globe During the First Months of the Coronavirus Recession.

We use a global transport model and satellite retrievals of the carbon dioxide (CO2) column average to explore the impact of CO2 emissions reductions that occurred during the economic downturn at the start of the Covid-19 pandemic. The changes in the column averages are substantial in a few places of the model global grid, but the induced gradients are most often less than the random errors of the retrievals. The current necessity to restrict the quality-assured column retrievals to almost cloud-free areas appears to be a major obstacle in identifying changes in CO2 emissions. Indeed, large changes have occurred in the presence of clouds, and in places that were cloud free in 2020, the comparison with previous years is hampered by different cloud conditions during these years. We therefore recommend to favor all-weather CO2 monitoring systems, at least in situ, to support international efforts to reduce emissions.

Validation of electronic systems to collect patient-reported outcome (PRO) data-recommendations for clinical trial teams: report of the ISPOR ePRO systems validation good research practices task force.

Outcomes research literature has many examples of high-quality, reliable patient-reported outcome (PRO) data entered directly by electronic means, ePRO, compared to data entered from original results on paper. Clinical trial managers are increasingly using ePRO data collection for PRO-based end points. Regulatory review dictates the rules to follow with ePRO data collection for medical label claims. A critical component for regulatory compliance is evidence of the validation of these electronic data collection systems. Validation of electronic systems is a process versus a focused activity that finishes at a single point in time. Eight steps need to be described and undertaken to qualify the validation of the data collection software in its target environment: requirements definition, design, coding, testing, tracing, user acceptance testing, installation and configuration, and decommissioning. These elements are consistent with recent regulatory guidance for systems validation. This report was written to explain how the validation process works for sponsors, trial teams, and other users of electronic data collection devices responsible for verifying the quality of the data entered into relational databases from such devices. It is a guide on the requirements and documentation needed from a data collection systems provider to demonstrate systems validation. It is a practical source of information for study teams to ensure that ePRO providers are using system validation and implementation processes that will ensure the systems and services: operate reliably when in practical use; produce accurate and complete data and data files; support management control and comply with any existing regulations. Furthermore, this short report will increase user understanding of the requirements for a technology review leading to more informed and balanced recommendations or decisions on electronic data collection methods.

Three-Dimensionally Printed Simulated Tracheas to Improve Cricothyrotomy Skills Among Anesthesia Providers.

Cricothyrotomy proficiency is imperative for anesthesia providers; however, opportunities to perform this skill are infrequent making skill maintenance essential. Increased accessibility of three-dimensional (3D) printing allows for production of low-cost simulation models. The models used for simulation-based teaching and deliberate practice facilitate skill development and refinement. A cost-effective, 3D printed airway model was designed and allowed 47 anesthesia providers to complete hands-on practice during a cricothyrotomy simulation-based training program. Assessment and comparison of pre- to post- intervention was completed for self-efficacy, knowledge, time to skill completion, compliance of required steps checklist, Global Rating Scale score, and successful ventilation. Statistically significant improvement from pre- to posttest was demonstrated in: 1) self-efficacy survey scores (P < .001); 2) knowledge test scores (P < .001); and 3) decreased time (minutes:seconds) to task completion (P < .001). Time from initial posttest to 3-month posttest (P = .046) significantly increased, however, the time at 3 months posttest remained significantly lower than pretest (P < .001). Providers' ability to ventilate, compliance with procedural steps, and technical skills significantly improved. 3D printing can produce anatomically similar simulation airway models that allow providers to practice and improve cricothyrotomy knowledge and skills. Developing an affordable and accessible simulation model provides a sustainable tool that allows providers multiple cricothyrotomy practice attempts.

Memory-Assisted Global Impression of Change (MAGIC).

BACKGROUND: The United States Food and Drug Administration is developing a series of patient-focused drug development guidance documents regarding the collection of patient experience data, including methods for understanding treatment benefit from the patient perspective. The goal of this research was to investigate the concern that the global impression of change scale is subject to recall error and thus not optimal for use as an anchor for estimating meaningful within-person change thresholds. We explored whether memory assistance for recalling baseline status would make a difference in how study participants diagnosed with Parkinson's disease (PD) responded to a patient global impression of change (PGIC) and patient global impression static (PGIS) item. METHODS: The research was completed as a secondary objective of a non-interventional 28-day (± 4 days) study among persons with Parkinson's disease and associated motor fluctuations. At baseline, participants completed the PGIS and then recorded a voice message to their future self in which they spoke about how their PD had affected their "day-to-day" activities over the preceding few days. At the final visit, the PGIC and PGIS were completed, after which participants listened to their memory assistance voice recording, and then completed both items for a second time to calculate a memory-assisted global impression static and change scores (MAGIS and MAGIC, respectively). Spearman correlations (ρ) were examined for the pre- and post- memory assistance evaluations. The degree of agreement pre- and post-memory assistance was quantified using the Shrout & Fleiss intraclass correlation coefficient (ICC [2,1]). An ICC(2,1) ≥ 0.7 served as the pre-specified criterion of acceptability for both the ρ and ICC(2,1) values. RESULTS: Participants in the analytic sample were mean age 68.7 and mostly white (91.7%) and male (69.4%). The average length of time since PD diagnosis was 6.5 years. Correlations between the PGIS and MAGIS were ρ = 0.88; correlations between PGIC and MAGIC were ρ = 0.86. The estimated ICC(2,1) for both the PGIS/MAGIS and PGIC/MAGIC exceeded target success criterion of ICC(2,1) ≥ 0.70. CONCLUSION: Our results show that the MAGIS/MAGIC methodology is feasible and that memory assistance did not substantially alter the PGIS/PGIC scores at the final visit.

Does scrolling affect measurement equivalence of electronic patient-reported outcome measures (ePROM)? Results of a quantitative equivalence study.

BACKGROUND: Scrolling is a perceived barrier in the use of bring your own device (BYOD) to capture electronic patient reported outcomes (ePROs). This study explored the impact of scrolling on the measurement equivalence of electronic patient-reported outcome measures (ePROMs) in the presence and absence of scrolling. METHODS: Adult participants with a chronic condition involving daily pain completed ePROMs on four devices with different scrolling properties: a large provisioned device not requiring scrolling; two provisioned devices requiring scrolling - one with a "smart-scrolling" feature that disabled the "next" button until all information was viewed, and a second without this feature; and BYOD with smart-scrolling. The ePROMs included were the SF-12, EQ-5D-5L, and three pain measures: a visual analogue scale, a numeric response scale and a Likert scale. Participants completed English or Spanish versions according to their first language. Associations between ePROM scores were assessed using intraclass correlation coefficients (ICCs), with lower bound of 95% confidence interval (CI) > 0.7 indicating comparability. RESULTS: One hundred fifteen English- or Spanish-speaking participants (21-75y) completed all four administrations. High associations between scrolling and non-scrolling were observed (ICCs: 0.71-0.96). The equivalence threshold was met for all but one SF-12 domain score (bodily pain; lower 95% CI: 0.65) and two EQ-5D-5L item scores (pain/discomfort, usual activities; lower 95% CI: 0.64/0.67). Age, language, and device size produced insignificant differences in scores. CONCLUSIONS: The measurement properties of PROMs are preserved even in the presence of scrolling on a handheld device. Further studies that assess scrolling impact over long-term, repeated use are recommended.

Regional impacts of COVID-19 on carbon dioxide detected worldwide from space.

Activity reductions in early 2020 due to the coronavirus disease 2019 pandemic led to unprecedented decreases in carbon dioxide (CO2) emissions. Despite their record size, the resulting atmospheric signals are smaller than and obscured by climate variability in atmospheric transport and biospheric fluxes, notably that related to the 2019­2020 Indian Ocean Dipole. Monitoring CO2 anomalies and distinguishing human and climatic causes thus remain a new frontier in Earth system science. We show that the impact of short-term regional changes in fossil fuel emissions on CO2 concentrations was observable from space. Starting in February and continuing through May, column CO2 over many of the world's largest emitting regions was 0.14 to 0.62 parts per million less than expected in a pandemic-free scenario, consistent with reductions of 3 to 13% in annual global emissions. Current spaceborne technologies are therefore approaching levels of accuracy and precision needed to support climate mitigation strategies with future missions expected to meet those needs.

Advancing the Use of Mobile Technologies in Clinical Trials: Recommendations from the Clinical Trials Transformation Initiative.

Mobile technologies offer the potential to reduce the costs of conducting clinical trials by collecting high-quality information on health outcomes in real-world settings that are relevant to patients and clinicians. However, widespread use of mobile technologies in clinical trials has been impeded by their perceived challenges. To advance solutions to these challenges, the Clinical Trials Transformation Initiative (CTTI) has issued best practices and realistic approaches that clinical trial sponsors can now use. These include CTTI recommendations on technology selection; data collection, analysis, and interpretation; data management; protocol design and execution; and US Food and Drug Administration submission and inspection. The scientific principles underpinning the clinical trials enterprise continue to apply to studies using mobile technologies. These recommendations provide a framework for including mobile technologies in clinical trials that can lead to more efficient assessment of new therapies for patients.

Response to Comment on "Contrasting carbon cycle responses of the tropical continents to the 2015-2016 El Niño".

Chevallier showed a column CO2 ([Formula: see text]) anomaly of ±0.5 parts per million forced by a uniform net biosphere exchange (NBE) anomaly of 2.5 gigatonnes of carbon over the tropical continents within a year, so he claimed that the inferred NBE uncertainties should be larger than presented in Liu et al We show that a much concentrated NBE anomaly led to much larger [Formula: see text] perturbations.

A laid-back trip through the Hennigian Forests.

BACKGROUND: This paper is a comment on the idea of matrix-free Cladistics. Demonstration of this idea's efficiency is a major goal of the study. Within the proposed framework, the ordinary (phenetic) matrix is necessary only as "source" of Hennigian trees, not as a primary subject of the analysis. Switching from the matrix-based thinking to the matrix-free Cladistic approach clearly reveals that optimizations of the character-state changes are related not to the real processes, but to the form of the data representation. METHODS: We focused our study on the binary data. We wrote the simple ruby-based script FORESTER version 1.0 that helps represent a binary matrix as an array of the rooted trees (as a "Hennigian forest"). The binary representations of the genomic (DNA) data have been made by script 1001. The Average Consensus method as well as the standard Maximum Parsimony (MP) approach has been used to analyze the data. PRINCIPLE FINDINGS: The binary matrix may be easily re-written as a set of rooted trees (maximal relationships). The latter might be analyzed by the Average Consensus method. Paradoxically, this method, if applied to the Hennigian forests, in principle can help to identify clades despite the absence of the direct evidence from the primary data. Our approach may handle the clock- or non clock-like matrices, as well as the hypothetical, molecular or morphological data. DISCUSSION: Our proposal clearly differs from the numerous phenetic alignment-free techniques of the construction of the phylogenetic trees. Dealing with the relations, not with the actual "data" also distinguishes our approach from all optimization-based methods, if the optimization is defined as a way to reconstruct the sequences of the character-state changes on a tree, either the standard alignment-based techniques or the "direct" alignment-free procedure. We are not viewing our recent framework as an alternative to the three-taxon statement analysis (3TA), but there are two major differences between our recent proposal and the 3TA, as originally designed and implemented: (1) the 3TA deals with the three-taxon statements or minimal relationships. According to the logic of 3TA, the set of the minimal trees must be established as a binary matrix and used as an input for the parsimony program. In this paper, we operate directly with maximal relationships written just as trees, not as binary matrices, while also using the Average Consensus method instead of the MP analysis. The solely 'reversal'-based groups can always be found by our method without the separate scoring of the putative reversals before analyses.

Evaluation of Cloud Liquid Water Path Trends Using a Multi-Decadal Record of Passive Microwave Observations.

In this study, observed cloud liquid water path (LWP) trends from the Multisensor Advanced Climatology of Liquid Water Path (MAC-LWP) dataset (1988 - 2014) are compared to trends computed from the temporally-coincident records of 16 global climate models (GCMs) participating in the Coupled Model Intercomparison Project 5 (CMIP5). For many regions, observed trend magnitudes are several times larger than the corresponding model mean trend magnitudes. Muted model mean trends are thought to be the result of cancellation effects arising from differing interannual variability characteristics and differences in model physics/dynamics. In most regions, the majority of modeled trends were statisically consistent with the observed trends. This was thought to be because of large estimated errors in both the observations and the models due to interannual variability. Over the southern oceans (south of 40S latitude), general agreement between the observed trend and virtually all GCM trends is also found (~ 1-2 g m-2 decade-1). Observed trends are also compared to those from the atmospheric model intercomparison project (AMIP). Like the CMIP5 models, the majority of modeled AMIP trends were statistically consistent with the observed trends. It was also found that, in regions where the AMIP model mean time series better captures observed interannual variability, it tends to better capture the magnitude of the observed trends.

The Multi-Sensor Advanced Climatology of Liquid Water Path (MAC-LWP).

The Multi-Sensor Advanced Climatology of Liquid Water Path (MAC-LWP), an updated and enhanced version of the University of Wisconsin (UWisc) cloud liquid water path (CLWP) climatology, currently provides 29 years (1988 - 2016) of monthly gridded (1°) oceanic CLWP information constructed using Remote Sensing Systems (RSS) inter-calibrated 0.25°-resolution retrievals. Satellite sources include SSM/I, TMI, AMSR-E, WindSat, SSMIS, AMSR-2 and GMI. To mitigate spurious CLWP trends, the climatology is corrected for drifting satellite overpass times by simultaneously solving for the monthly average CLWP and monthly-mean diurnal cycle. In addition to a longer record and six additional satellite products, major enhancements relative to the UWisc climatology include updating the input to version 7 RSS retrievals, a correction for a CLWP bias (based on matchups to clear-sky MODIS scenes), and the construction of a total (cloud+rain) liquid water path (TLWP) record for use in analyses of columnar liquid water in raining clouds. Because the microwave emission signal from cloud water is similar to that of precipitation-sized hydrometeors, greater uncertainty in the CLWP record is expected in regions of substantial precipitation. Therefore, the TLWP field can also be used as a quality-control screen, where uncertainty increases as the ratio of CLWP to TLWP decreases. For regions where confidence in CLWP is highest (i.e. CLWP:TLWP > 0.8), systematic differences in MAC CLWP relative to UWisc CLWP range from -15% (e.g. global oceanic stratocumulus decks) to +5-10% (e.g. portions of the higher-latitudes, storm tracks, and shallower convection regions straddling the ITCZ). The dataset is currently hosted at the Goddard Earth Science Data and Information Services Center (http://disc.sci.gsfc.nasa.gov).

Contrasting carbon cycle responses of the tropical continents to the 2015-2016 El Niño.

The 2015-2016 El Niño led to historically high temperatures and low precipitation over the tropics, while the growth rate of atmospheric carbon dioxide (CO2) was the largest on record. Here we quantify the response of tropical net biosphere exchange, gross primary production, biomass burning, and respiration to these climate anomalies by assimilating column CO2, solar-induced chlorophyll fluorescence, and carbon monoxide observations from multiple satellites. Relative to the 2011 La Niña, the pantropical biosphere released 2.5 ± 0.34 gigatons more carbon into the atmosphere in 2015, consisting of approximately even contributions from three tropical continents but dominated by diverse carbon exchange processes. The heterogeneity of the carbon-exchange processes indicated here challenges previous studies that suggested that a single dominant process determines carbon cycle interannual variability.

Spaceborne detection of localized carbon dioxide sources.

Spaceborne measurements by NASA's Orbiting Carbon Observatory-2 (OCO-2) at the kilometer scale reveal distinct structures of atmospheric carbon dioxide (CO2) caused by known anthropogenic and natural point sources. OCO-2 transects across the Los Angeles megacity (USA) show that anthropogenic CO2 enhancements peak over the urban core and decrease through suburban areas to rural background values more than ~100 kilometers away, varying seasonally from ~4.4 to 6.1 parts per million. A transect passing directly downwind of the persistent isolated natural CO2 plume from Yasur volcano (Vanuatu) shows a narrow filament of enhanced CO2 values (~3.4 parts per million), consistent with a CO2 point source emitting 41.6 kilotons per day. These examples highlight the potential of the OCO-2 sensor, with its unprecedented resolution and sensitivity, to detect localized natural and anthropogenic CO2 sources.

TOLKIN--Tree of Life Knowledge and Information Network: filling a gap for collaborative research in biological systematics.

The development of biological informatics infrastructure capable of supporting growing data management and analysis environments is an increasing need within the systematics biology community. Although significant progress has been made in recent years on developing new algorithms and tools for analyzing and visualizing large phylogenetic data and trees, implementation of these resources is often carried out by bioinformatics experts, using one-off scripts. Therefore, a gap exists in providing data management support for a large set of non-technical users. The TOLKIN project (Tree of Life Knowledge and Information Network) addresses this need by supporting capabilities to manage, integrate, and provide public access to molecular, morphological, and biocollections data and research outcomes through a collaborative, web application. This data management framework allows aggregation and import of sequences, underlying documentation about their source, including vouchers, tissues, and DNA extraction. It combines features of LIMS and workflow environments by supporting management at the level of individual observations, sequences, and specimens, as well as assembly and versioning of data sets used in phylogenetic inference. As a web application, the system provides multi-user support that obviates current practices of sharing data sets as files or spreadsheets via email.