Prospects of the hydraulic air conditioning of mold air on the basis of pulse wave effects

The results of analytical and technical substantiation of promising solutions for the development of tools for hydraulic conditioning of mine air in deep mine conditions that combine cooling and purification of air based on pulse-wave effects created by diffuser and confuser elements are presented

Diagnostic classification of childhood cancer using multiscale transcriptomics

The causes of pediatric cancers’ distinctiveness compared to adult-onset tumors of the same type are not completely clear and not fully explained by their genomes. In this study, we used an optimized multilevel RNA clustering approach to derive molecular definitions for most childhood cancers. Applying this method to 13,313 transcriptomes, we constructed a pediatric cancer atlas to explore age-associated changes. Tumor entities were sometimes unexpectedly grouped due to common lineages, drivers or stemness profiles. Some established entities were divided into subgroups that predicted outcome better than current diagnostic approaches. These definitions account for inter-tumoral and intra-tumoral heterogeneity and have the potential of enabling reproducible, quantifiable diagnostics. As a whole, childhood tumors had more transcriptional diversity than adult tumors, maintaining greater expression flexibility. To apply these insights, we designed an ensemble convolutional neural network classifier. We show that this tool was able to match or clarify the diagnosis for 85% of childhood tumors in a prospective cohort. If further validated, this framework could be extended to derive molecular definitions for all cancer types

Chemokine Binding Protein M3 of Murine Gammaherpesvirus 68 Modulates the Host Response to Infection in a Natural Host

Murine γ-herpesvirus 68 (MHV-68) infection of Mus musculus-derived strains of mice is an attractive model of γ-herpesvirus infection. Surprisingly, however, ablation of expression of MHV-68 M3, a secreted protein with broad chemokine-binding properties in vitro, has no discernable effect during experimental infection via the respiratory tract. Here we demonstrate that M3 indeed contributes significantly to MHV-68 infection, but only in the context of a natural host, the wood mouse (Apodemus sylvaticus). Specifically, M3 was essential for two features unique to the wood mouse: virus-dependent inducible bronchus-associated lymphoid tissue (iBALT) in the lung and highly organized secondary follicles in the spleen, both predominant sites of latency in these organs. Consequently, lack of M3 resulted in substantially reduced latency in the spleen and lung. In the absence of M3, splenic germinal centers appeared as previously described for MHV-68-infected laboratory strains of mice, further evidence that M3 is not fully functional in the established model host. Finally, analyses of M3's influence on chemokine and cytokine levels within the lungs of infected wood mice were consistent with the known chemokine-binding profile of M3, and revealed additional influences that provide further insight into its role in MHV-68 biology

Gap-filling eddy covariance methane fluxes:Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET)

Gap-filling eddy covariance methane fluxes : Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting halfhourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET).Peer reviewe

Effects of Climate and Atmospheric Nitrogen Deposition on Early to Mid-Term Stage Litter Decomposition Across Biomes

open263siWe acknowledge support by the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, funded by the German Research Foundation (FZT 118), Scientific Grant Agency VEGA(GrantNo.2/0101/18), as well as by the European Research Council under the European Union’s Horizon 2020 Research and Innovation Program (Grant Agreement No. 677232)Litter decomposition is a key process for carbon and nutrient cycling in terrestrial ecosystems and is mainly controlled by environmental conditions, substrate quantity and quality as well as microbial community abundance and composition. In particular, the effects of climate and atmospheric nitrogen (N) deposition on litter decomposition and its temporal dynamics are of significant importance, since their effects might change over the course of the decomposition process. Within the TeaComposition initiative, we incubated Green and Rooibos teas at 524 sites across nine biomes. We assessed how macroclimate and atmospheric inorganic N deposition under current and predicted scenarios (RCP 2.6, RCP 8.5) might affect litter mass loss measured after 3 and 12 months. Our study shows that the early to mid-term mass loss at the global scale was affected predominantly by litter quality (explaining 73% and 62% of the total variance after 3 and 12 months, respectively) followed by climate and N deposition. The effects of climate were not litter-specific and became increasingly significant as decomposition progressed, with MAP explaining 2% and MAT 4% of the variation after 12 months of incubation. The effect of N deposition was litter-specific, and significant only for 12-month decomposition of Rooibos tea at the global scale. However, in the temperate biome where atmospheric N deposition rates are relatively high, the 12-month mass loss of Green and Rooibos teas decreased significantly with increasing N deposition, explaining 9.5% and 1.1% of the variance, respectively. The expected changes in macroclimate and N deposition at the global scale by the end of this century are estimated to increase the 12-month mass loss of easily decomposable litter by 1.1-3.5% and of the more stable substrates by 3.8-10.6%, relative to current mass loss. In contrast, expected changes in atmospheric N deposition will decrease the mid-term mass loss of high-quality litter by 1.4-2.2% and that of low-quality litter by 0.9-1.5% in the temperate biome. Our results suggest that projected increases in N deposition may have the capacity to dampen the climate-driven increases in litter decomposition depending on the biome and decomposition stage of substrate.openKwon T.; Shibata H.; Kepfer-Rojas S.; Schmidt I.K.; Larsen K.S.; Beier C.; Berg B.; Verheyen K.; Lamarque J.-F.; Hagedorn F.; Eisenhauer N.; Djukic I.; Caliman A.; Paquette A.; Gutierrez-Giron A.; Petraglia A.; Augustaitis A.; Saillard A.; Ruiz-Fernandez A.C.; Sousa A.I.; Lillebo A.I.; Da Rocha Gripp A.; Lamprecht A.; Bohner A.; Francez A.-J.; Malyshev A.; Andric A.; Stanisci A.; Zolles A.; Avila A.; Virkkala A.-M.; Probst A.; Ouin A.; Khuroo A.A.; Verstraeten A.; Stefanski A.; Gaxiola A.; Muys B.; Gozalo B.; Ahrends B.; Yang B.; Erschbamer B.; Rodriguez Ortiz C.E.; Christiansen C.T.; Meredieu C.; Mony C.; Nock C.; Wang C.-P.; Baum C.; Rixen C.; Delire C.; Piscart C.; Andrews C.; Rebmann C.; Branquinho C.; Jan D.; Wundram D.; Vujanovic D.; Adair E.C.; Ordonez-Regil E.; Crawford E.R.; Tropina E.F.; Hornung E.; Groner E.; Lucot E.; Gacia E.; Levesque E.; Benedito E.; Davydov E.A.; Bolzan F.P.; Maestre F.T.; Maunoury-Danger F.; Kitz F.; Hofhansl F.; Hofhansl G.; De Almeida Lobo F.; Souza F.L.; Zehetner F.; Koffi F.K.; Wohlfahrt G.; Certini G.; Pinha G.D.; Gonzlez G.; Canut G.; Pauli H.; Bahamonde H.A.; Feldhaar H.; Jger H.; Serrano H.C.; Verheyden H.; Bruelheide H.; Meesenburg H.; Jungkunst H.; Jactel H.; Kurokawa H.; Yesilonis I.; Melece I.; Van Halder I.; Quiros I.G.; Fekete I.; Ostonen I.; Borovsk J.; Roales J.; Shoqeir J.H.; Jean-Christophe Lata J.; Probst J.-L.; Vijayanathan J.; Dolezal J.; Sanchez-Cabeza J.-A.; Merlet J.; Loehr J.; Von Oppen J.; Loffler J.; Benito Alonso J.L.; Cardoso-Mohedano J.-G.; Penuelas J.; Morina J.C.; Quinde J.D.; Jimnez J.J.; Alatalo J.M.; Seeber J.; Kemppinen J.; Stadler J.; Kriiska K.; Van Den Meersche K.; Fukuzawa K.; Szlavecz K.; Juhos K.; Gerhtov K.; Lajtha K.; Jennings K.; Jennings J.; Ecology P.; Hoshizaki K.; Green K.; Steinbauer K.; Pazianoto L.; Dienstbach L.; Yahdjian L.; Williams L.J.; Brigham L.; Hanna L.; Hanna H.; Rustad L.; Morillas L.; Silva Carneiro L.; Di Martino L.; Villar L.; Fernandes Tavares L.A.; Morley M.; Winkler M.; Lebouvier M.; Tomaselli M.; Schaub M.; Glushkova M.; Torres M.G.A.; De Graaff M.-A.; Pons M.-N.; Bauters M.; Mazn M.; Frenzel M.; Wagner M.; Didion M.; Hamid M.; Lopes M.; Apple M.; Weih M.; Mojses M.; Gualmini M.; Vadeboncoeur M.; Bierbaumer M.; Danger M.; Scherer-Lorenzen M.; Ruek M.; Isabellon M.; Di Musciano M.; Carbognani M.; Zhiyanski M.; Puca M.; Barna M.; Ataka M.; Luoto M.; H. Alsafaran M.; Barsoum N.; Tokuchi N.; Korboulewsky N.; Lecomte N.; Filippova N.; Hlzel N.; Ferlian O.; Romero O.; Pinto-Jr O.; Peri P.; Dan Turtureanu P.; Haase P.; Macreadie P.; Reich P.B.; Petk P.; Choler P.; Marmonier P.; Ponette Q.; Dettogni Guariento R.; Canessa R.; Kiese R.; Hewitt R.; Weigel R.; Kanka R.; Cazzolla Gatti R.; Martins R.L.; Ogaya R.; Georges R.; Gaviln R.G.; Wittlinger S.; Puijalon S.; Suzuki S.; Martin S.; Anja S.; Gogo S.; Schueler S.; Drollinger S.; Mereu S.; Wipf S.; Trevathan-Tackett S.; Stoll S.; Lfgren S.; Trogisch S.; Seitz S.; Glatzel S.; Venn S.; Dousset S.; Mori T.; Sato T.; Hishi T.; Nakaji T.; Jean-Paul T.; Camboulive T.; Spiegelberger T.; Scholten T.; Mozdzer T.J.; Kleinebecker T.; Runk T.; Ramaswiela T.; Hiura T.; Enoki T.; Ursu T.-M.; Di Cella U.M.; Hamer U.; Klaus V.; Di Cecco V.; Rego V.; Fontana V.; Piscov V.; Bretagnolle V.; Maire V.; Farjalla V.; Pascal V.; Zhou W.; Luo W.; Parker W.; Parker P.; Kominam Y.; Kotrocz Z.; Utsumi Y.Kwon T.; Shibata H.; Kepfer-Rojas S.; Schmidt I.K.; Larsen K.S.; Beier C.; Berg B.; Verheyen K.; Lamarque J.-F.; Hagedorn F.; Eisenhauer N.; Djukic I.; Caliman A.; Paquette A.; Gutierrez-Giron A.; Petraglia A.; Augustaitis A.; Saillard A.; Ruiz-Fernandez A.C.; Sousa A.I.; Lillebo A.I.; Da Rocha Gripp A.; Lamprecht A.; Bohner A.; Francez A.-J.; Malyshev A.; Andric A.; Stanisci A.; Zolles A.; Avila A.; Virkkala A.-M.; Probst A.; Ouin A.; Khuroo A.A.; Verstraeten A.; Stefanski A.; Gaxiola A.; Muys B.; Gozalo B.; Ahrends B.; Yang B.; Erschbamer B.; Rodriguez Ortiz C.E.; Christiansen C.T.; Meredieu C.; Mony C.; Nock C.; Wang C.-P.; Baum C.; Rixen C.; Delire C.; Piscart C.; Andrews C.; Rebmann C.; Branquinho C.; Jan D.; Wundram D.; Vujanovic D.; Adair E.C.; Ordonez-Regil E.; Crawford E.R.; Tropina E.F.; Hornung E.; Groner E.; Lucot E.; Gacia E.; Levesque E.; Benedito E.; Davydov E.A.; Bolzan F.P.; Maestre F.T.; Maunoury-Danger F.; Kitz F.; Hofhansl F.; Hofhansl G.; De Almeida Lobo F.; Souza F.L.; Zehetner F.; Koffi F.K.; Wohlfahrt G.; Certini G.; Pinha G.D.; Gonzlez G.; Canut G.; Pauli H.; Bahamonde H.A.; Feldhaar H.; Jger H.; Serrano H.C.; Verheyden H.; Bruelheide H.; Meesenburg H.; Jungkunst H.; Jactel H.; Kurokawa H.; Yesilonis I.; Melece I.; Van Halder I.; Quiros I.G.; Fekete I.; Ostonen I.; Borovsk J.; Roales J.; Shoqeir J.H.; Jean-Christophe Lata J.; Probst J.-L.; Vijayanathan J.; Dolezal J.; Sanchez-Cabeza J.-A.; Merlet J.; Loehr J.; Von Oppen J.; Loffler J.; Benito Alonso J.L.; Cardoso-Mohedano J.-G.; Penuelas J.; Morina J.C.; Quinde J.D.; Jimnez J.J.; Alatalo J.M.; Seeber J.; Kemppinen J.; Stadler J.; Kriiska K.; Van Den Meersche K.; Fukuzawa K.; Szlavecz K.; Juhos K.; Gerhtov K.; Lajtha K.; Jennings K.; Jennings J.; Ecology P.; Hoshizaki K.; Green K.; Steinbauer K.; Pazianoto L.; Dienstbach L.; Yahdjian L.; Williams L.J.; Brigham L.; Hanna L.; Hanna H.; Rustad L.; Morillas L.; Silva Carneiro L.; Di Martino L.; Villar L.; Fernandes Tavares L.A.; Morley M.; Winkler M.; Lebouvier M.; Tomaselli M.; Schaub M.; Glushkova M.; Torres M.G.A.; De Graaff M.-A.; Pons M.-N.; Bauters M.; Mazn M.; Frenzel M.; Wagner M.; Didion M.; Hamid M.; Lopes M.; Apple M.; Weih M.; Mojses M.; Gualmini M.; Vadeboncoeur M.; Bierbaumer M.; Danger M.; Scherer-Lorenzen M.; Ruek M.; Isabellon M.; Di Musciano M.; Carbognani M.; Zhiyanski M.; Puca M.; Barna M.; Ataka M.; Luoto M.; H. Alsafaran M.; Barsoum N.; Tokuchi N.; Korboulewsky N.; Lecomte N.; Filippova N.; Hlzel N.; Ferlian O.; Romero O.; Pinto-Jr O.; Peri P.; Dan Turtureanu P.; Haase P.; Macreadie P.; Reich P.B.; Petk P.; Choler P.; Marmonier P.; Ponette Q.; Dettogni Guariento R.; Canessa R.; Kiese R.; Hewitt R.; Weigel R.; Kanka R.; Cazzolla Gatti R.; Martins R.L.; Ogaya R.; Georges R.; Gaviln R.G.; Wittlinger S.; Puijalon S.; Suzuki S.; Martin S.; Anja S.; Gogo S.; Schueler S.; Drollinger S.; Mereu S.; Wipf S.; Trevathan-Tackett S.; Stoll S.; Lfgren S.; Trogisch S.; Seitz S.; Glatzel S.; Venn S.; Dousset S.; Mori T.; Sato T.; Hishi T.; Nakaji T.; Jean-Paul T.; Camboulive T.; Spiegelberger T.; Scholten T.; Mozdzer T.J.; Kleinebecker T.; Runk T.; Ramaswiela T.; Hiura T.; Enoki T.; Ursu T.-M.; Di Cella U.M.; Hamer U.; Klaus V.; Di Cecco V.; Rego V.; Fontana V.; Piscov V.; Bretagnolle V.; Maire V.; Farjalla V.; Pascal V.; Zhou W.; Luo W.; Parker W.; Parker P.; Kominam Y.; Kotrocz Z.; Utsumi Y

Effects of climate and atmospheric nitrogen deposition on early to mid-term stage litter decomposition across biomes

Litter decomposition is a key process for carbon and nutrient cycling in terrestrial ecosystems and is mainly controlled by environmental conditions, substrate quantity and quality as well as microbial community abundance and composition. In particular, the effects of climate and atmospheric nitrogen (N) deposition on litter decomposition and its temporal dynamics are of significant importance, since their effects might change over the course of the decomposition process. Within the TeaComposition initiative, we incubated Green and Rooibos teas at 524 sites across nine biomes. We assessed how macroclimate and atmospheric inorganic N deposition under current and predicted scenarios (RCP 2.6, RCP 8.5) might affect litter mass loss measured after 3 and 12 months. Our study shows that the early to mid-term mass loss at the global scale was affected predominantly by litter quality (explaining 73% and 62% of the total variance after 3 and 12 months, respectively) followed by climate and N deposition. The effects of climate were not litter-specific and became increasingly significant as decomposition progressed, with MAP explaining 2% and MAT 4% of the variation after 12 months of incubation. The effect of N deposition was litter-specific, and significant only for 12-month decomposition of Rooibos tea at the global scale. However, in the temperate biome where atmospheric N deposition rates are relatively high, the 12-month mass loss of Green and Rooibos teas decreased significantly with increasing N deposition, explaining 9.5% and 1.1% of the variance, respectively. The expected changes in macroclimate and N deposition at the global scale by the end of this century are estimated to increase the 12-month mass loss of easily decomposable litter by 1.1-3.5% and of the more stable substrates by 3.8-10.6%, relative to current mass loss. In contrast, expected changes in atmospheric N deposition will decrease the mid-term mass loss of high-quality litter by 1.4-2.2% and that of low-quality litter by 0.9-1.5% in the temperate biome. Our results suggest that projected increases in N deposition may have the capacity to dampen the climate-driven increases in litter decomposition depending on the biome and decomposition stage of substrate. © Copyright © 2021 Kwon, Shibata, Kepfer-Rojas, Schmidt, Larsen, Beier, Berg, Verheyen, Lamarque, Hagedorn, Eisenhauer, Djukic and TeaComposition Network

Safety and immunogenicity of the two-dose heterologous Ad26.ZEBOV and MVA-BN-Filo Ebola vaccine regimen in children in Sierra Leone: a randomised, double-blind, controlled trial

Background—Children account for a substantial proportion of cases and deaths from Ebola virus disease. We aimed to assess the safety and immunogenicity of a two-dose heterologous vaccine regimen, comprising the adenovirus type 26 vector-based vaccine encoding the Ebola virus glycoprotein (Ad26.ZEBOV) and the modified vaccinia Ankara vectorbased vaccine, encoding glycoproteins from the Ebola virus, Sudan virus, and Marburg virus, and the nucleoprotein from the Tai Forest virus (MVA-BN-Filo), in a paediatric population in Sierra Leone. Methods—This randomised, double-blind, controlled trial was done at three clinics in Kambia district, Sierra Leone. Healthy children and adolescents aged 1–17 years were enrolled in three age cohorts (12–17 years, 4–11 years, and 1–3 years) and randomly assigned (3:1), via computer-generated block randomisation (block size of eight), to receive an intramuscular injection of either Ad26.ZEBOV (5 × 1010 viral particles; first dose) followed by MVA-BN-Filo (1 × 108 infectious units; second dose) on day 57 (Ebola vaccine group), or a single dose of meningococcal quadrivalent (serogroups A, C, W135, and Y) conjugate vaccine (MenACWY; first dose) followed by placebo (second dose) on day 57 (control group). Study team personnel (except for those with primary responsibility for study vaccine preparation), participants, and their parents or guardians were masked to study vaccine allocation. The primary outcome was safety, measured as the occurrence of solicited local and systemic adverse symptoms during 7 days after each vaccination, unsolicited systemic adverse events during 28 days after each vaccination, abnormal laboratory results during the study period, and serious adverse events or immediate reportable events throughout the study period. The secondary outcome was immunogenicity (humoral immune response), measured as the concentration of Ebola virus glycoprotein-specific binding antibodies at 21 days after the second dose. The primary outcome was assessed in all participants who had received at least one dose of study vaccine and had available reactogenicity data, and immunogenicity was assessed in all participants who had received both vaccinations within the protocol-defined time window, had at least one evaluable post-vaccination sample, and had no major protocol deviations that could have influenced the immune response. This study is registered at ClinicalTrials.gov, NCT02509494. Findings—From April 4, 2017, to July 5, 2018, 576 eligible children or adolescents (192 in each of the three age cohorts) were enrolled and randomly assigned. The most common solicited local adverse event during the 7 days after the first and second dose was injection-site pain in all age groups, with frequencies ranging from 0% (none of 48) of children aged 1–3 years after placebo injection to 21% (30 of 144) of children aged 4–11 years after Ad26.ZEBOV vaccination. The most frequently observed solicited systemic adverse event during the 7 days was headache in the 12–17 years and 4–11 years age cohorts after the first and second dose, and pyrexia in the 1–3 years age cohort after the first and second dose. The most frequent unsolicited adverse event after the first and second dose vaccinations was malaria in all age cohorts, irrespective of the vaccine types. Following vaccination with MenACWY, severe thrombocytopaenia was observed in one participant aged 3 years. No other clinically significant laboratory abnormalities were observed in other study participants, and no serious adverse events related to the Ebola vaccine regimen were reported. There were no treatment-related deaths. Ebola virus glycoprotein-specific binding antibody responses at 21 days after the second dose of the Ebola virus vaccine regimen were observed in 131 (98%) of 134 children aged 12–17 years (9929 ELISA units [EU]/mL [95% CI 8172–12 064]), in 119 (99%) of 120 aged 4–11 years (10 212 EU/mL [8419–12 388]), and in 118 (98%) of 121 aged 1–3 years (22 568 EU/mL [18 426–27 642]). Interpretation—The Ad26.ZEBOV and MVA-BN-Filo Ebola vaccine regimen was well tolerated with no safety concerns in children aged 1–17 years, and induced robust humoral immune responses, suggesting suitability of this regimen for Ebola virus disease prophylaxis in children