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Nat. Biotechnol. 32, 724–726 (2014); published online 7 August 2014; corrected after print 10 October 2014; 10.1038/nbt.2973 In the version of this article initially published, two author names were misspelled: the correct names are Cavagnaro, not Cavanagro; Feigal not Feigel. In addition, the name for the Health Insurance Portability and Accountability Act (HIPAA) was given as Health Insurance Portability and Privacy Act (HIPPA).

In his Jan. 8 letter E. B., of Redondo Beach, gave a perfect example of how prejudice effects a person's thinking process.

Changes in atmospheric methane abundance have implications for both chemistry and climate as methane is both a strong greenhouse gas and an important precursor for tropospheric ozone. A better understanding of the drivers of trends and variability in methane abundance over the recent past is therefore critical for building confidence in projections of future methane levels. In this work, the representation of methane in the atmospheric chemistry model AM4.1 is improved by optimizing total methane emissions (to an annual mean of 580±34 Tg yr−1) to match surface observations over 1980–2017. The simulations with optimized global emissions are in general able to capture the observed trend, variability, seasonal cycle, and latitudinal gradient of methane. Simulations with different emission adjustments suggest that increases in methane emissions (mainly from agriculture, energy, and waste sectors) balanced by increases in methane sinks (mainly due to increases in OH levels) lead to methane stabilization (with an imbalance of 5 Tg yr−1) during 1999–2006 and that increases in methane emissions (mainly from agriculture, energy, and waste sectors) combined with little change in sinks (despite small decreases in OH levels) during 2007–2012 lead to renewed growth in methane (with an imbalance of 14 Tg yr−1 for 2007–2017). Compared to 1999–2006, both methane emissions and sinks are greater (by 31 and 22 Tg yr−1, respectively) during 2007–2017. Our tagged tracer analysis indicates that anthropogenic sources (such as agriculture, energy, and waste sectors) are more likely major contributors to the renewed growth in methane after 2006. A sharp increase in wetland emissions (a likely scenario) with a concomitant sharp decrease in anthropogenic emissions (a less likely scenario), would be required starting in 2006 to drive the methane growth by wetland tracer. Simulations with varying OH levels indicate that a 1 % change in OH levels could lead to an annual mean difference of ∼4 Tg yr−1 in the optimized emissions and a 0.08-year difference in the estimated tropospheric methane lifetime. Continued increases in methane emissions along with decreases in tropospheric OH concentrations during 2008–2015 prolong methane's lifetime and therefore amplify the response of methane concentrations to emission changes. Uncertainties still exist in the partitioning of emissions among individual sources and regions.

The future of e–commerce in emerging markets is bright, but also clouded by a number of legal and technological uncertainties. To some extent, the uncertainties can be resolved by examining the course of e-commerce in the developed countries, where it was launched and has been so successful. But in other respects, countries and societies in the developing world will have to find their own ways of using e-commerce to afford consumers and businesses the benefits of lower cost and added convenience that the Internet is now bringing to the developed world. In this closing chapter, four knowledgeable experts on

To replicate Stone et al's (2022) finding that the distribution of response in clinical antidepressant trials is trimodal with large, medium-effect, and small subgroups. To apply finite mixture modeling to pre-post Hamilton Depression Rating Scale (HDRS) differences (n = 2184) of STAR∗D study's level 1, a single-arm, open-label study. For a successful replication, the best fitting model had to be trimodal, with comparable components as in Stone et al. Secondary/sensitivity analyses repeated the analysis for different baseline levels of depression severity, imputed values, and patient-reported depression symptoms. The best fitting models were either bimodal or trimodal but the trimodal solution did not meet criteria for replication. The bimodal model had 1 component with HDRS mean change of M = −13.0, SD = 6.7 and included 65.3% of patients, and another component with M = −1.8, SD = 5.1, 34.7%, respectively. For the trimodal model, the component with the largest change (M = −14.3, SD = 6.4) applied to 52% of patients, which differed substantially from the large effect component in Stone et al (M = −18.8, SD = 5.1), which applied to 7.2%. Secondary/sensitivity analyses arrived at similar conclusions, and for patient-reported depression symptoms the best fitting models were unimodal or bimodal. This analysis failed to identify the trimodal distribution of response reported in Stone et al. In addition to being difficult to operationalize for regulatory purposes, results from mixture modeling are not sufficiently reliable to replace the more robust approach of comparing mean differences in depression rating scale scores between treatment arms.

IntroductionTargeted oxygenation protocols in mechanically ventilated patients are critical in avoiding the deleterious effects of hypoxaemia and hyperoxaemia. Peripheral oxygen saturation (SpO2) is a practical metric that commonly drives oxygen titration protocols and guidelines but has inaccuracies attributable to patient variability that can lead to occult hypoxaemia. Conversely, arterial oxygen saturation (SaO2) offers accuracy but is costly and invasive. We aim to develop a novel approach to targeted oxygenation that collectively uses the accuracy of SaO2 and the feasibility of SpO2 to mitigate occult hypoxaemia and prevent hyperoxaemia.Methods and analysisThe Optimization of Inspired Oxygen during Mechanical Ventilation trial is a pragmatic stepped wedge, open label, cluster-randomised controlled trial of an algorithm-based SpO2-SaO2 electronic alert-based oxygen titration protocol. The intervention arm includes targeted oxygenation via an electronic SpO2-SaO2 driven alert protocol. The control group will be subjected to oxygen titration according to standard practice. Within the intervention arm, patients will be assigned to groups with different SpO2 targets based on the degree of SpO2-SaO2 difference. In the ‘Conserve O2’ group, where SpO2SaO2 by 1–2%, electronic alerts will be used to titrate FiO2 to a target SpO2 of 90–94%. In the ‘Boosted O2’ group, where SpO2>SaO2 by 3–5%, electronic alerts will be used to titrate FiO2 to a target SpO2 of 93–97%. Patients with an SpO2-SaO2 difference >5% in either direction will be monitored but not assigned to either group. The sample size to determine efficacy is 1620 subjects, randomised over 60 weeks. The primary outcome is the proportion of time during mechanical ventilation spent within the target range, SpO2 of 90–94% (Conserve O2) or SpO2 of 93–97% (Boosted O2) at any FiO2. Secondary outcomes include the proportion of time with SpO2 >94% or SpO2 >97% with FiO2 ≤0.4 within each respective algorithm, the proportion of time with SpO2 <90% or SpO2 <93% within each respective algorithm, length of intensive care unit and hospital stay, hospital mortality, ventilator and vasopressor free days, new onset of arrhythmia when SpO2 <90%, and change to comfort care status (DNRCC) and time to DNRCC after enrolment.Ethics and disseminationThe protocol was approved by The Ohio State University Institutional Review Board (Protocol # 2023H0016) and is registered at ClinicalTrials.gov (NCT 05923853). Progress and safety of the trial are monitored by an independent Data and Safety Monitoring Board. Study results will be published in peer-reviewed medical journals. This study is being carried out with a waiver of consent as participation in the study presents no more than minimal incremental risk compared with routine clinical care for mechanically ventilated critically ill adults outside of the study.Trial registration numberNCT05923853.