Feature-based Transferable Disruption Prediction for future tokamaks using domain adaptation

The high acquisition cost and the significant demand for disruptive discharges for data-driven disruption prediction models in future tokamaks pose an inherent contradiction in disruption prediction research. In this paper, we demonstrated a novel approach to predict disruption in a future tokamak only using a few discharges based on a domain adaptation algorithm called CORAL. It is the first attempt at applying domain adaptation in the disruption prediction task. In this paper, this disruption prediction approach aligns a few data from the future tokamak (target domain) and a large amount of data from the existing tokamak (source domain) to train a machine learning model in the existing tokamak. To simulate the existing and future tokamak case, we selected J-TEXT as the existing tokamak and EAST as the future tokamak. To simulate the lack of disruptive data in future tokamak, we only selected 100 non-disruptive discharges and 10 disruptive discharges from EAST as the target domain training data. We have improved CORAL to make it more suitable for the disruption prediction task, called supervised CORAL. Compared to the model trained by mixing data from the two tokamaks, the supervised CORAL model can enhance the disruption prediction performance for future tokamaks (AUC value from 0.764 to 0.890). Through interpretable analysis, we discovered that using the supervised CORAL enables the transformation of data distribution to be more similar to future tokamak. An assessment method for evaluating whether a model has learned a trend of similar features is designed based on SHAP analysis. It demonstrates that the supervised CORAL model exhibits more similarities to the model trained on large data sizes of EAST. FTDP provides a light, interpretable, and few-data-required way by aligning features to predict disruption using small data sizes from the future tokamak.Comment: 15 pages, 9 figure

Multidrug-resistant Pseudomonas aeruginosa is predisposed to lasR mutation through up-regulated activity of efflux pumps in non-cystic fibrosis bronchiectasis patients

BackgroundMultidrug-resistant (MDR) Pseudomonas aeruginosa is a frequent opportunistic pathogen that causes significant mortality in patients with non-cystic fibrosis bronchiectasis (NCFB). Although the quorum sensing (QS) system is a potential target for treatment, lasR mutants that present with a QS-deficient phenotype have been frequently reported among clinical P. aeruginosa isolates. We aimed to investigate whether antibiotic resistance would select for lasR mutants during chronic P. aeruginosa lung infection and determine the mechanism underlying the phenomenon.MethodsWe prospectively evaluated episodes of chronic P. aeruginosa lung infections in NCFB patients over a 2-year period at two centers of our institution. QS phenotypic assessments and whole-genome sequencing (WGS) of P. aeruginosa isolates were performed. Evolution experiments were conducted to confirm the emergence of lasR mutants in clinical MDR P. aeruginosa cultures.ResultsWe analyzed episodes of P. aeruginosa infection among 97 NCFB patients and found only prior carbapenem exposure independently predictive of the isolation of MDR P. aeruginosa strains. Compared with non-MDR isolates, MDR isolates presented significantly QS-deficient phenotypes, which could not be complemented by the exogenous addition of 3OC12-HSL. The paired isolates showed that their QS-phenotype deficiency occurred after MDR was developed. Whole-genome sequencing analysis revealed that lasR nonsynonymous mutations were significantly more frequent in MDR isolates, and positive correlations of mutation frequencies were observed between genes of lasR and negative-efflux-pump regulators (nalC and mexZ). The addition of the efflux pump inhibitor PAβN could not only promote QS phenotypes of these MDR isolates but also delay the early emergence of lasR mutants in evolution experiments.ConclusionsOur data indicated that MDR P. aeruginosa was predisposed to lasR mutation through the upregulated activity of efflux pumps. These findings suggest that anti-QS therapy combined with efflux pump inhibitors might be a potential strategy for NCFB patients in the challenge of MDR P. aeruginosa infections

Anaerobic Fungi Isolated From Bactrian Camel Rumen Contents Have Strong Lignocellulosic Bioconversion Potential

This study aims to obtain anaerobic fungi from the rumen and fecal samples and investigates their potential for lignocellulosic bioconversion. Multiple anaerobic strains were isolated from rumen contents (CR1–CR21) and fecal samples (CF1–CF10) of Bactrian camel using the Hungate roll tube technique. After screening for fiber degradability, strains from rumen contents (Oontomyces sp. CR2) and feces (Piromyces sp. CF9) were compared with Pecoramyces sp. F1 (earlier isolated from goat rumen, having high CAZymes of GHs) for various fermentation and digestion parameters. The cultures were fermented with different substrates (reed, alfalfa stalk, Broussonetia papyrifera leaves, and Melilotus officinalis) at 39°C for 96 h. The Oontomyces sp. CR2 had the highest total gas and hydrogen production from most substrates in the in vitro rumen fermentation system and also had the highest digestion of dry matter, neutral detergent fiber, acid detergent fiber, and cellulose present in most substrates used. The isolated strains provided higher amounts of metabolites such as lactate, formate, acetate, and ethanol in the in vitro rumen fermentation system for use in various industrial applications. The results illustrated that anaerobic fungi isolated from Bactrian camel rumen contents (Oontomyces sp. CR2) have the highest lignocellulosic bioconversion potential, suggesting that the Bactrian camel rumen could be a good source for the isolation of anaerobic fungi for industrial applications

COVID-19 causes record decline in global CO2 emissions

The considerable cessation of human activities during the COVID-19 pandemic has affected global energy use and CO2 emissions. Here we show the unprecedented decrease in global fossil CO2 emissions from January to April 2020 was of 7.8% (938 Mt CO2 with a +6.8% of 2-{\sigma} uncertainty) when compared with the period last year. In addition other emerging estimates of COVID impacts based on monthly energy supply or estimated parameters, this study contributes to another step that constructed the near-real-time daily CO2 emission inventories based on activity from power generation (for 29 countries), industry (for 73 countries), road transportation (for 406 cities), aviation and maritime transportation and commercial and residential sectors emissions (for 206 countries). The estimates distinguished the decline of CO2 due to COVID-19 from the daily, weekly and seasonal variations as well as the holiday events. The COVID-related decreases in CO2 emissions in road transportation (340.4 Mt CO2, -15.5%), power (292.5 Mt CO2, -6.4% compared to 2019), industry (136.2 Mt CO2, -4.4%), aviation (92.8 Mt CO2, -28.9%), residential (43.4 Mt CO2, -2.7%), and international shipping (35.9Mt CO2, -15%). Regionally, decreases in China were the largest and earliest (234.5 Mt CO2,-6.9%), followed by Europe (EU-27 & UK) (138.3 Mt CO2, -12.0%) and the U.S. (162.4 Mt CO2, -9.5%). The declines of CO2 are consistent with regional nitrogen oxides concentrations observed by satellites and ground-based networks, but the calculated signal of emissions decreases (about 1Gt CO2) will have little impacts (less than 0.13ppm by April 30, 2020) on the overserved global CO2 concertation. However, with observed fast CO2 recovery in China and partial re-opening globally, our findings suggest the longer-term effects on CO2 emissions are unknown and should be carefully monitored using multiple measures

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

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

The genome sequence of the orchid Phalaenopsis equestris

Orchidaceae, renowned for its spectacular flowers and other reproductive and ecological adaptations, is one of the most diverse plant families. Here we present the genome sequence of the tropical epiphytic orchid Phalaenopsis equestris, a frequently used parent species for orchid breeding. P. equestris is the first plant with crassulacean acid metabolism (CAM) for which the genome has been sequenced. Our assembled genome contains 29,431 predicted protein-coding genes. We find that contigs likely to be underassembled, owing to heterozygosity, are enriched for genes that might be involved in self-incompatibility pathways. We find evidence for an orchid-specific paleopolyploidy event that preceded the radiation of most orchid clades, and our results suggest that gene duplication might have contributed to the evolution of CAM photosynthesis in P. equestris. Finally, we find expanded and diversified families of MADS-box C/D-class, B-class AP3 and AGL6-class genes, which might contribute to the highly specialized morphology of orchid flowers. (Résumé d'auteur

Analysis on unsafe emotion evolution of coal mine employees based on multi-agent modeling

Coal mine production is a typical high-risk industry, and the emotional fluctuations of coal mine employees are neglected sources of danger. In order to effectively manage the coal mine employees’ unsafe emotion, the causes and induced mechanisms of unsafe emotion are analyzed. First, the complex system theory and multi-agent modeling method are used to establish coal mine employees’ unsafe emotion induction model. Second, we use the Netlogo simulation platform to dynamically simulate coal mine employees’ unsafe emotion induction process and the relationship between influencing factors. The research shows that individual psychological resilience level, social support level and event intervention strategies are negatively correlated with coal mine employees’ unsafe emotion, and unsafe affective events are positively correlated with coal mine employees’ unsafe emotion. Finally, from the three aspects of pre-job screening, employee psychological construction and safety atmosphere creation, suggestions for preventing unsafe emotion of coal mine employees are proposed