Search for heavy neutral lepton production in K+ decays

A search for heavy neutral lepton production in K + decays using a data sample collected with a minimum bias trigger by the NA62 experiment at CERN in 2015 is reported. Upper limits at the 10−7 to 10−6 level are established on the elements of the extended neutrino mixing matrix |Ue4| 2 and |Uμ4| 2 for heavy neutral lepton mass in the ranges 170–448 MeV/c2 and 250–373 MeV/c2, respectively. This improves on the previous limits from HNL production searches over the whole mass range considered for |Ue4|2 and above 300 MeV/c2 for |Uμ4|2

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

The microbial side of soil priming effects

Priming effects (PEs) are defined as short-term changes in the turnover of soil organic matter (SOM) caused by the addition of easily degradable organic compounds. The direction (positive / negative) and magnitude of PEs in response to organic carbon additions are not easy to predict. Yet, PEs are considered to be large enough to influence ecosystem carbon fluxes. The main aim of this thesis is to increase the understanding of the mechanisms involved in soil PEs, with a particular focus on the role of the quantity and quality of added organic substrates, the size of the soil microbial biomass, the soil microbial community structure and mineral nitrogen availability. The major conclusions are: The degree of resemblance of the chemical structure of the added organic compounds to SOM is an important factor in PEs. PEs are more influenced by trigger substrate concentrations than by the size of the microbial biomass. Triggering of PEs in soils by litter is not only influenced by litter quality but also by the ability of the soil microbial communities to decompose it (home field advantage for PEs). High nitrogen availability can stimulate fungal biomass production but has little effect on PEs. The implications of these results for local and global C and N dynamics are discussed. </ol

A simple method for measuring fungal metabolic quotient and comparing carbon use efficiency of different isolates. Application to Mediterranean leaf litter fungi

The metabolic efficiency of different microbial groups in carbon source uses and single species storage efficiency is poorly characterized and not adequately represented in most biogeochemical models. It it is proposed here a simple approach for an estimation of the metabolic quotient of fungal isolates. The method is based on the values of substrate use (respiration) and growth (biomass production) obtainable for single fungal isolates in vitro using the Phenotype MicroArray™ system to test the metabolic performance of fungi on different substrates. As a case study, this carbon-use efficiency method was used to compare a group of leaf litter fungi. The metabolic efforts of single fungal species were measured on 95 different substrates of different complexity. The respiration to biomass ratio showed a high reliability and the possibility of being used as a measurable property of the microorganisms and an indicator of organism’s performance or fitness

Effect of the amount of organic trigger compounds, nitrogen and soil microbial biomass on the magnitude of priming of soil organic matter

Priming effects (PEs) are defined as short-term changes in the turnover of soil organic matter (SOM) caused by the addition of easily degradable organic compounds to the soil. PEs are ubiquitous but the direction (acceleration or retardation of SOM decomposition) and magnitude are not easy to predict. It has been suggested that the ratio between the amount of added PE-triggering substrate to the size of initial soil microbial biomass is an important factor influencing PEs. However, this is mainly based on comparison of different studies and not on direct experimentation. The aim of the current study is to examine the impact of glucose-to-microbial biomass ratios on PEs for three different ecosystems. We did this by adding three different amounts of 13 C-glucose with or without addition of mineral N (NH 4 NO 3 ) to soils collected from arable lands, grasslands and forests. The addition of 13 C-glucose was equivalent to 15%, 50% and 200% of microbial biomass C. After one month of incubation, glucose had induced positive PEs for almost all the treatments, with differences in magnitude related to the soil origin and the amount of glucose added. For arable and forest soils, the primed C increased with increasing amount of glucose added, whereas for grassland soils this relationship was negative. We found positive correlations between glucose-derived C and primed C and the strength of these correlations was different among the three ecosystems considered. Generally, additions of mineral N next to glucose (C:N = 15:1) had little effect on the flux of substrate-derived C and primed C. Overall, our study does not support the hypothesis that the trigger-substrate to microbial biomass ratio can be an important predictor of PEs. Rather our results indicate that the amount of energy obtained from decomposing trigger substrates is an important factor for the magnitude of PEs. </p

Effect of the amount of organic trigger compounds, nitrogen and soil microbial biomass on the magnitude of priming of soil organic matter

Priming effects (PEs) are defined as short-term changes in the turnover of soil organic matter (SOM) caused by the addition of easily degradable organic compounds to the soil. PEs are ubiquitous but the direction (acceleration or retardation of SOM decomposition) and magnitude are not easy to predict. It has been suggested that the ratio between the amount of added PE-triggering substrate to the size of initial soil microbial biomass is an important factor influencing PEs. However, this is mainly based on comparison of different studies and not on direct experimentation. The aim of the current study is to examine the impact of glucose-to-microbial biomass ratios on PEs for three different ecosystems. We did this by adding three different amounts of 13 C-glucose with or without addition of mineral N (NH 4 NO 3 ) to soils collected from arable lands, grasslands and forests. The addition of 13 C-glucose was equivalent to 15%, 50% and 200% of microbial biomass C. After one month of incubation, glucose had induced positive PEs for almost all the treatments, with differences in magnitude related to the soil origin and the amount of glucose added. For arable and forest soils, the primed C increased with increasing amount of glucose added, whereas for grassland soils this relationship was negative. We found positive correlations between glucose-derived C and primed C and the strength of these correlations was different among the three ecosystems considered. Generally, additions of mineral N next to glucose (C:N = 15:1) had little effect on the flux of substrate-derived C and primed C. Overall, our study does not support the hypothesis that the trigger-substrate to microbial biomass ratio can be an important predictor of PEs. Rather our results indicate that the amount of energy obtained from decomposing trigger substrates is an important factor for the magnitude of PEs. </p

Diabetes Affects Antibody Response to SARS-CoV-2 Vaccination in Older Residents of Long-term Care Facilities: Data From the GeroCovid Vax Study

Objective: Type 2 diabetes may affect the humoral immune response after vaccination, but data concerning coronavirus disease 19 (COVID-19) vaccines are scarce. We evaluated the impact of diabetes on antibody response to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination in older residents of long-term care facilities (LTCFs) and tested for differences according to antidiabetic treatment. Research design and methods: For this analysis, 555 older residents of LTCFs participating in the GeroCovid Vax study were included. SARS-CoV-2 trimeric S immunoglobulin G (anti-S IgG) concentrations using chemiluminescent assays were tested before the first dose and after 2 and 6 months. The impact of diabetes on anti-S IgG levels was evaluated using linear mixed models, which included the interaction between time and presence of diabetes. A second model also considered diabetes treatment: no insulin therapy (including dietary only or use of oral antidiabetic agents) and insulin therapy (alone or in combination with oral antidiabetic agents). Results: The mean age of the sample was 82.1 years, 68.1% were women, and 25.2% had diabetes. In linear mixed models, presence of diabetes was associated with lower anti-S IgG levels at 2 (β = -0.20; 95% CI -0.34, -0.06) and 6 months (β = -0.22; 95% CI -0.37, -0.07) after the first vaccine dose. Compared with those without diabetes, residents with diabetes not using insulin had lower IgG levels at 2- and 6-month assessments (β = -0.24; 95% CI -0.43, -0.05 and β = -0.30; 95% CI -0.50, -0.10, respectively), whereas no differences were observed for those using insulin. Conclusions: Older residents of LTCFs with diabetes tended to have weaker antibody response to COVID-19 vaccination. Insulin treatment might buffer this effect and establish humoral immunity similar to that in individuals without diabetes