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Background Infections are common in nursing home (NH) residents with advanced dementia but are often managed inappropriately. Antimicrobials are extensively prescribed, but frequently with insufficient evidence to support a bacterial infection, promoting the emergence of multidrug-resistant organisms. Moreover, the benefits of antimicrobials remain unclear in these seriously ill residents for whom comfort is often the goal of care. Prior NH infection management interventions evaluated in randomized clinical trials (RCTs) did not consider patient preferences and lack evidence to support their effectiveness in ‘real-world’ practice. Methods This report presents the rationale and methodology of TRAIN-AD (Trial to reduce antimicrobial use in nursing home residents with Alzheimer’s disease and other dementias), a parallel group, cluster RCT evaluating a multicomponent intervention to improve infection management for suspected urinary tract infections (UTIs) and lower respiratory tract infections (LRIs) among NH residents with advanced dementia. TRAIN-AD is being conducted in 28 facilities in the Boston, USA, area randomized in waves using minimization to achieve a balance on key characteristics ( N  = 14 facilities/arm). The involvement of the facilities includes a 3-month start-up period and a 24-month implementation/data collection phase. Residents are enrolled during the first 12 months of the 24-month implementation period and followed for up to 12 months. Individual consent is waived, thus almost all eligible residents are enrolled (target sample size, N  = 410). The intervention integrates infectious disease and palliative care principles and includes provider training delivered through multiple modalities (in-person seminar, online course, management algorithms, and prescribing feedback) and an information booklet for families. Control facilities employ usual care. The primary outcome, abstracted from the residents’ charts, is the number of antimicrobial courses prescribed for UTIs and LRIs per person-year alive. Discussion TRAIN-AD is the first cluster RCT testing a multicomponent intervention to improve infection management in NH residents with advanced dementia. Its findings will provide an evidence base to support the benefit of a program addressing the critical clinical and public health problem of antimicrobial misuse in these seriously ill residents. Moreover, its hybrid efficacy-effectiveness design will inform the future conduct of cluster RCTs evaluating nonpharmacological interventions in the complex NH setting in a way that is both internally valid and adaptable to the ‘real-world’. Trial registration ClinicalTrials.gov, NCT03244917 . Registered on 10 August 2017.

A low-energy electronic recoil calibration of XENON1T, a dual-phase xenon time projection chamber, with an internal$^{37}$$37 Ar source was performed. This calibration source features a 35-day half-life and provides two mono-energetic lines at 2.82 keV and 0.27 keV. The photon yield and electron yield at 2.82 keV are measured to be ($$32.3\\,\\pm \\,0.3$$32.3 ± 0.3 ) photons/keV and ($$40.6\\,\\pm \\,0.5$$40.6 ± 0.5 ) electrons/keV, respectively, in agreement with other measurements and with NEST predictions. The electron yield at 0.27 keV is also measured and it is ($$68.0^{+6.3}_{-3.7}$$68 . 0 - 3.7 + 6.3 ) electrons/keV. The$^{37}$$37 Ar calibration confirms that the detector is well-understood in the energy region close to the detection threshold, with the 2.82 keV line reconstructed at ($$2.83\\,\\pm \\,0.02$$2.83 ± 0.02 ) keV, which further validates the model used to interpret the low-energy electronic recoil excess previously reported by XENON1T. The ability to efficiently remove argon with cryogenic distillation after the calibration proves that$^{37}$$37 Ar can be considered as a regular calibration source for multi-tonne xenon detectors.

To report a prospective epidemiological, virological and serological investigation of a SARS-CoV-2 outbreak in a primary school. As part of a longitudinal, prospective, school-based surveillance study, this investigation involved repeated testing of 73 pupils, 9 teachers, 13 non-teaching staff and 26 household members of participants who tested positive, with rapid antigen tests and/or RT-PCR (Day 0-2 and Day 5-7), serologies on dried capillary blood samples (Day 0-2 and Day 30), contact tracing interviews and SARS-CoV-2 whole genome sequencing. We identified 20 children (aged 4 to 6 years from 4 school classes), 2 teachers and a total of 4 household members who were infected by the Alpha variant during this outbreak. Infection attack rates were between 11.8 and 62.0% among pupils from the 4 school classes, 22.2% among teachers and 0% among non-teaching staff. Secondary attack rate among household members was 15.4%. Symptoms were reported by 63% of infected children, 100% of teachers and 50% of household members. All analysed sequences but one showed 100% identity. Serological tests detected 8 seroconversions unidentified by SARS-CoV-2 virological tests. This study confirmed child-to-child and child-to-adult SARS-CoV-2 transmission and introduction into households. Effective measures to limit transmission in schools have the potential to reduce the overall community circulation.

We present a deep learning pipeline to perform a model-independent, likelihood-free search for anomalous (i.e., non-background) events in the proposed next-generation multi-ton scale liquid xenon-based direct detection experiment, DARWIN. We train an anomaly detector comprising a variational autoencoder (VAE) and a classifier on high-dimensional simulated detector response data and construct a 1D anomaly score to reject the background-only hypothesis in the presence of an excess of non-background-like events. We use simulated validation data to determine the power of the method to reject the background-only hypothesis in the presence of a WIMP dark matter signal, without any model-dependent assumption about the nature of the signal. We show that our neural networks learn relevant features of the events from low-level, high-dimensional detector outputs, avoiding lossy and computationally expensive compression into lower-dimensional observables. Our approach is complementary to the usual likelihood-based analysis, in that it reduces the reliance on many of the corrections and cuts that are traditionally part of the analysis chain, with the potential of achieving higher accuracy and significant reduction of analysis time. We envisage the methodology presented in this work augmenting or complementing likelihood-based and other data-driven methods currently utilized in the DARWIN (and in the future, XLZD) analysis pipeline.

Previous direct observations of the sediment surface in Vidy Bay, Lake Geneva (Switzerland), revealed a range of sediment characteristics in terms of colour, texture and morphology. Dives with the MIR submersibles during the éLEMO project permitted the exploration of a large portion of Vidy Bay. It is the most contaminated part of Lake Geneva, due to inputs of treated and untreated waters from a large wastewater treatment plant (WWTP). To evaluate the influence of WWTP effluent on mercury contamination and sediment characteristics, 14 sediment cores were retrieved in the vicinity of the wastewater treatment plant effluent. Total mercury concentrations in sediments ranged between 0.32 and 10.1 mg/kg. Inorganic mercury and monomethylmercury concentrations in overlying and pore waters were also measured. The total partition coefficients of mercury (logK d) ranged from 3.6 to 5.8. The monomethylmercury concentration in pore waters of surface sediments was a large proportion of the total mercury concentration (44 ± 25 %). A Spearman test showed a negative correlation between the distance to the wastewater treatment plant outlet and the concentrations of total mercury in sediments and pore waters. Visual observations from the submersible allowed recognizing six different types of sediment. The areal distribution of these different sediment types clearly showed the influence of the wastewater treatment plant outlet on the sediment surface patterns. However, no relationship with mercury concentrations could be established.

Understanding propagation of scintillation light is critical for maximizing the discovery potential of next-generation liquid xenon detectors that use dual-phase time projection chamber technology. This work describes a detailed optical simulation of the DARWIN detector implemented using Chroma, a GPU-based photon tracking framework. To evaluate the framework and to explore ways of maximizing efficiency and minimizing the time of light collection, we simulate several variations of the conventional detector design. Results of these selected studies are presented. More generally, we conclude that the approach used in this work allows one to investigate alternative designs faster and in more detail than using conventional Geant4 optical simulations, making it an attractive tool to guide the development of the ultimate liquid xenon observatory.