Neurophysiological explorations across the spectrum of psychosis, autism, and depression, during wakefulness and sleep: protocol of a prospective case-control transdiagnostic multimodal study (DEMETER).

BACKGROUND: Quantitative electroencephalography (EEG) analysis offers the opportunity to study high-level cognitive processes across psychiatric disorders. In particular, EEG microstates translate the temporal dynamics of neuronal networks throughout the brain. Their alteration may reflect transdiagnostic anomalies in neurophysiological functions that are impaired in mood, psychosis, and autism spectrum disorders, such as sensorimotor integration, speech, sleep, and sense of self. The main questions this study aims to answer are as follows: 1) Are EEG microstate anomalies associated with clinical and functional prognosis, both in resting conditions and during sleep, across psychiatric disorders? 2) Are EEG microstate anomalies associated with differences in sensorimotor integration, speech, sense of self, and sleep? 3) Can the dynamic of EEG microstates be modulated by a non-drug intervention such as light hypnosis? METHODS: This prospective cohort will include a population of adolescents and young adults, aged 15 to 30 years old, with ultra-high-risk of psychosis (UHR), first-episode psychosis (FEP), schizophrenia (SCZ), autism spectrum disorder (ASD), and major depressive disorder (MDD), as well as healthy controls (CTRL) (N = 21 × 6), who will be assessed at baseline and after one year of follow-up. Participants will undergo deep phenotyping based on psychopathology, neuropsychological assessments, 64-channel EEG recordings, and biological sampling at the two timepoints. At baseline, the EEG recording will also be coupled to a sensorimotor task and a recording of the characteristics of their speech (prosody and turn-taking), a one-night polysomnography, a self-reference effect task in virtual reality (only in UHR, FEP, and CTRL). An interventional ancillary study will involve only healthy controls, in order to assess whether light hypnosis can modify the EEG microstate architecture in a direction opposite to what is seen in disease. DISCUSSION: This transdiagnostic longitudinal case-control study will provide a multimodal neurophysiological assessment of clinical dimensions (sensorimotor integration, speech, sleep, and sense of self) that are disrupted across mood, psychosis, and autism spectrum disorders. It will further test the relevance of EEG microstates as dimensional functional biomarkers. TRIAL REGISTRATION: ClinicalTrials.gov Identifier NCT06045897.

Fossil extraction bans and carbon taxes: Assessing their interplay through multiple models.

Given concerns about the ambition and effectiveness of current climate policies, a case has been made for the combination of demand side policies such as carbon pricing with supply side bans on fossil fuel extraction. However, little is known about their interplay in the context of climate stabilization strategies. Here, we present a multi-model assessment quantifying the effectiveness of supply side policies and their interactions with demand-side ones. We explore a variety of fossil fuel bans with four integrated assessment models and find that international supply side policies reduce carbon emissions but not at sufficient levels to stabilize temperature increase to well below 2°C. When combined with demand side policies, supply side policies reduce the required carbon price, dampen reliance on CO2 removal technologies, and increase investment in renewable energy. The results indicate the opportunity to integrate fossil fuel bans alongside price-based policies when exploring pathways to reach ambitious mitigation targets.

Internalising health-economic impacts of air pollution into climate policy: a global modelling study.

BACKGROUND: Climate change and air pollution are two major societal problems. Their complex interplay calls for an advanced evaluation framework that can support decision making. Previous assessments have looked at the co-benefits of climate policies for air pollution, but few have optimised air pollution benefits. In our study, we lay out a modelling framework that internalises air pollution's economic impacts on human mortality, while considering climate constraints and aerosol feedback. METHODS: We developed a modelling framework based on an integrated assessment model (World Induced Technical Change Hybrid [WITCH]) designed to assess optimal climate change mitigation policies. We included structural and end-of-pipe measures in a detailed process integrated assessment model, that is hard-linked to air pollution and climate models. We analysed a large set of baseline scenarios, including five shared socioeconomic pathways (SSPs). SSP scenarios were also tested with three different levels of value per statistical life, and were combined with the Paris Agreement temperature targets (TTs), focusing on the 2°C and 1·5°C TTs by the end of the century. FINDINGS: We found that, in the baseline scenarios, where no policies are applied, the number of annual premature deaths grew before declining slightly to 4·45 (range 3·86-6·11) million annual premature deaths by 2050. Reaching the Paris Agreement TT decreases mortality by approximately 0·47 million premature deaths by 2050 (up to 1·28 million premature deaths in SSP3 -1·5°C) with respect to the baseline. We showed that welfare-maximising policies accounting for air pollution benefits reduces premature mortality by 1·62 million deaths annually. This is three times greater than the co-benefits of climate policies. China is the region where most of the avoided mortality is possible, whereas the reforming economies (ie, non-EU eastern European countries, including Russia) region has the greatest welfare benefits. We find that global and regional welfare increases when air pollution impacts are internalised, with no negative repercussions on global inequality. INTERPRETATION: Air pollution control strategies are found to be an important complement to structural emission reductions. Accounting for air pollution impacts reduces climate mitigation costs and inequality and increases global and regional welfare. Results are robust to a broad set of scenarios and assumptions, including debated normative choices on how to value improved health. FUNDING: EU Commission projects: INNOPATHS, NAVIGATE, ENGAGE, and COMMIT.

Global roll-out of comprehensive policy measures may aid in bridging emissions gap.

Closing the emissions gap between Nationally Determined Contributions (NDCs) and the global emissions levels needed to achieve the Paris Agreement's climate goals will require a comprehensive package of policy measures. National and sectoral policies can help fill the gap, but success stories in one country cannot be automatically replicated in other countries. They need to be adapted to the local context. Here, we develop a new Bridge scenario based on nationally relevant, short-term measures informed by interactions with country experts. These good practice policies are rolled out globally between now and 2030 and combined with carbon pricing thereafter. We implement this scenario with an ensemble of global integrated assessment models. We show that the Bridge scenario closes two-thirds of the emissions gap between NDC and 2 °C scenarios by 2030 and enables a pathway in line with the 2 °C goal when combined with the necessary long-term changes, i.e. more comprehensive pricing measures after 2030. The Bridge scenario leads to a scale-up of renewable energy (reaching 52%-88% of global electricity supply by 2050), electrification of end-uses, efficiency improvements in energy demand sectors, and enhanced afforestation and reforestation. Our analysis suggests that early action via good-practice policies is less costly than a delay in global climate cooperation.

A Satisficing Framework for Environmental Policy Under Model Uncertainty.

We propose a novel framework for the economic assessment of environmental policy. Our main point of departure from existing work is the adoption of a satisficing, as opposed to optimizing, modeling approach. Along these lines, we place primary emphasis on the extent to which different policies meet a set of goals at a specific future date instead of their performance vis-a-vis some intertemporal objective function. Consistent to the nature of environmental policymaking, our model takes explicit account of model uncertainty. To this end, the decision criterion we propose is an analog of the well-known success-probability criterion adapted to settings characterized by model uncertainty. We apply our criterion to the climate-change context and the probability distributions constructed by Drouet et al. (2015) linking carbon budgets to future consumption. Insights from computational geometry facilitate computations considerably and allow for the efficient application of the model in high-dimensional settings.

Taking stock of national climate policies to evaluate implementation of the Paris Agreement.

Many countries have implemented national climate policies to accomplish pledged Nationally Determined Contributions and to contribute to the temperature objectives of the Paris Agreement on climate change. In 2023, the global stocktake will assess the combined effort of countries. Here, based on a public policy database and a multi-model scenario analysis, we show that implementation of current policies leaves a median emission gap of 22.4 to 28.2 GtCO2eq by 2030 with the optimal pathways to implement the well below 2 °C and 1.5 °C Paris goals. If Nationally Determined Contributions would be fully implemented, this gap would be reduced by a third. Interestingly, the countries evaluated were found to not achieve their pledged contributions with implemented policies (implementation gap), or to have an ambition gap with optimal pathways towards well below 2 °C. This shows that all countries would need to accelerate the implementation of policies for renewable technologies, while efficiency improvements are especially important in emerging countries and fossil-fuel-dependent countries.

An inter-model assessment of the role of direct air capture in deep mitigation pathways.

The feasibility of large-scale biological CO2 removal to achieve stringent climate targets remains unclear. Direct Air Carbon Capture and Storage (DACCS) offers an alternative negative emissions technology (NET) option. Here we conduct the first inter-model comparison on the role of DACCS in 1.5 and 2 °C scenarios, under a variety of techno-economic assumptions. Deploying DACCS significantly reduces mitigation costs, and it complements rather than substitutes other NETs. The key factor limiting DACCS deployment is the rate at which it can be scaled up. Our scenarios' average DACCS scale-up rates of 1.5 GtCO2/yr would require considerable sorbent production and up to 300 EJ/yr of energy input by 2100. The risk of assuming that DACCS can be deployed at scale, and finding it to be subsequently unavailable, leads to a global temperature overshoot of up to 0.8 °C. DACCS should therefore be developed and deployed alongside, rather than instead of, other mitigation options.

Proficiency monitoring of monoclonal antibody cocktail-based enzyme-linked immunosorbent assay for detection of allergen-specific immunoglobulin E in dogs.

The purpose of our study was to document the continued comparative proficiency of different laboratories that perform a monoclonal antibody-based enzyme-linked immunosorbent assay (macELISA) for detection of allergen-specific immunoglobulin (Ig)E in dogs. Replicate samples of 18 different sera pools were independently evaluated in a single blinded fashion by each of 16 different operators functioning in 10 different laboratories. The average intra-assay variance among reactive assay calibrators in all laboratories was 6.0% (range: 2.7-16.1%), while the average intralaboratory interassay variance was 7.5% (range: 3.9-10.9%). The overall interassay interlaboratory variance was consistent among laboratories and averaged 11.4% (range: 8.5-12.5%). All laboratories yielded similar profiles and magnitudes of responses for replicate unknown samples; dose response profiles observed in each of the laboratories were indistinguishable. Considering the positive or negative results, interassay interlaboratory concordance of results exceeded 90%. Correlation of optical density values between and among all laboratories was strong (r > 0.9, P < 0.001). Collectively, the results demonstrated that the macELISA for measuring allergen-specific canine IgE is reproducible, and documents that consistency of results can be achieved not only in an individual laboratory by differing operators but also among laboratories using the same monoclonal-based ELISA.

Intra and inter-laboratory reproducibility of a monoclonal antibody cocktail based ELISA for detection of allergen specific IgE in dogs: proficiency monitoring of macELISA in six laboratories.

The purpose of this study was to evaluate the reproducibility of results yielded using a monoclonal antibody based ELISA for detection of allergen specific IgE when run in six separate affiliated laboratories. On two separate occasions, duplicate samples of 15 different sera pools were independently evaluated by each laboratory in a single blinded fashion. The average intra-assay variance among reactive assay calibrators in all laboratories was 6.2% (range 2.6-18.2%), while the average intra-laboratory inter-assay variance was 12.1% (range 8.0-17.1%). The overall inter-assay inter-laboratory variance was consistent among laboratories and averaged 15.6% (range 15.1-16.6%). All laboratories yielded similar profiles and magnitudes of responses for replicate unknown samples; dose-response profiles observed in each of the laboratories were indistinguishable. Considering positive/negative results, inter-assay inter-laboratory concordance of results exceeded 95%. Correlation of OD values between and among all laboratories was strong (r>0.9, p<0.001). Correlation of OD values between the two separate evaluations was also high for all allergens except olive, which was attributed to lot-to-lot differences of allergen coated wells. Collectively, the results demonstrated that the monoclonal antibody based ELISA for measuring allergen specific canine IgE is reproducible, and documents that consistency of results can be achieved not only in an individual laboratory, but between laboratories using the same monoclonal-based ELISA.