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During the COVID-19 pandemic, the province of Quebec, Canada implemented stringent measures to mitigate virus transmission, which considerably affected the life of people with disabilities (PWD). The objective of this study was to explore the experiences of PWD during the first year of COVID-19 restrictions across the province. Participants who self-identified as having a disability in the Ma Vie et la pandémie study (MAVIPAN) were invited to participate in a semi-structured interview between December 2020 and May 2021. A mixed inductive and deductive approach was used to conduct a thematic analysis using NVivo 12. Forty PWD from Quebec, Canada participated in the interviews (mean [SD] age, 55.4 [15.5] years, 50% women). A deterioration in mental health and a reduction in social contact with loved ones were reported. PWD experienced delays and cessation of health services and reported feeling at risk of contracting severe strains of COVID-19 because of their health condition. Enhanced difficulties experienced by PWD and the lack of consideration specific to PWD by public authorities during COVID-19 was particularly concerning for participants in this study. Future studies should explore the value of implementing social programs specifically targeting PWD to enhance support as the pandemic continues.

Safely navigating our environment is crucial to daily living, but the study of locomotor navigational control in relation to the complex interaction of personal and environmental factors is still in its infancy. Work to now has proposed different proactive control variables for collision avoidance based on visual information. Such control has more recently been shown to be specific to personal (e.g., age, neurological diseases) and environmental (e.g., obstacle type) characteristics. Continued study of the complex person-environment interaction is required along with continued theorization on combined proactive and reactive control factors.

OBJECTIVE Police and emergency services personnel are at an increased risk of developing mental health issues. We sought to compare patterns of exposure to work-related and nonwork stressors and associations with posttraumatic stress symptoms and psychological distress among police and emergency services employees. METHOD A total of 14,868 employees from 33 ambulance, fire and rescue, police, and state emergency service agencies around Australia participated in a survey to assess the prevalence of exposure to work-related and nonwork stressors, and the prevalence of mental health conditions. Associations between work and nonwork stressors and mental health problems were estimated using logistic regression models and population attributable risk (PAR) percentages. RESULTS Traumatic events experienced while working in the police and emergency services sector were the most frequently reported stressor (51%). Stressful events experienced at work in the sector were associated with significantly higher levels of suspected posttraumatic stress symptoms (odds ratio = 4.5, PAR = 65%) and high psychological distress (odds ratio = 2.5, PAR = 40%) compared to stressors experienced away from the sector. CONCLUSIONS Stressors experienced at work are a risk factor for developing posttraumatic stress symptoms and distress in the sector. Organizations should have comprehensive policies and programs to help prevent the development of mental health problems and to support personnel who develop mental health problems. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Accurate and timely forecasts of sea ice conditions are crucial for safe shipping operations in the Canadian Arctic and other ice-infested waters. Given the recent declining trend of Arctic sea ice extent in past decades, seasonal forecasts are often desired. In this study machine learning (ML) approaches are deployed to provide accurate seasonal forecasts based on ERA5 data as input. This study, unlike previous ML approaches in the sea ice forecasting domain, provides daily spatial maps of sea ice presence probability in the study domain for lead times up to 90 d using a novel spatiotemporal forecasting method based on sequence-to-sequence learning. The predictions are further used to predict freeze-up/breakup dates and show their capability to capture these events within a 7 d period at specific locations of interest to shipping operators and communities. The model is demonstrated in hindcasting mode to allow for evaluation of forecasted predication. However, the design allows for the approach to be used as a forecasting tool. The proposed method is capable of predicting sea ice presence probabilities with skill during the breakup season in comparison to both Climate Normal and sea ice concentration forecasts from a leading subseasonal-to-seasonal forecasting system.

Debris-covered glaciers are an important component of the mountain cryosphere and influence the hydrological contribution of glacierized basins to downstream rivers. This study examines the potential to make estimates of debris thickness, a critical variable to calculate the sub-debris melt, using ground-based thermal infrared radiometry (TIR) images. Over four days in August 2019, a ground-based, time-lapse TIR digital imaging radiometer recorded sequential thermal imagery of a debris-covered region of Peyto Glacier, Canadian Rockies, in conjunction with 44 manual excavations of debris thickness ranging from 10 to 110 cm, and concurrent meteorological observations. Inferring the correlation between measured debris thickness and TIR surface temperature as a base, the effectiveness of linear and exponential regression models for debris thickness estimation from surface temperature was explored. Optimal model performance (R2 of 0.7, RMSE of 10.3 cm) was obtained with a linear model applied to measurements taken on clear nights just before sunrise, but strong model performances were also obtained under complete cloud cover during daytime or nighttime with an exponential model. This work presents insights into the use of surface temperature and TIR observations to estimate debris thickness and gain knowledge of the state of debris-covered glacial ice and its potential hydrological contribution.

Although lactococcal phages have been observed in cheese production settings for almost a century, few longitudinal studies have been performed. This 20-year study describes the close monitoring of dairy lactococcal phages in a cheddar cheese factory.

Objective: Assess the impact of allocation concealment and blinding on the results of COVID-19 trials. Data sources: World Health Organization (WHO) COVID-19 database (up to February 2022) Methods: We included randomized trials that compared drug therapeutics with placebo or standard care in patients with COVID-19. We performed random-effects meta-regressions comparing the results of trials with and without allocation concealment and blinding of healthcare providers and patients. Results: We identified 488 trials. We found that, compared to trials with allocation concealment, trials without allocation concealment may estimate treatments to be more beneficial for mortality, mechanical ventilation, hospital admission, duration of hospitalization, and duration of mechanical ventilation, but results were imprecise. We did not find compelling evidence that, compared to trials with blinding, trials without blinding produce consistently different results for mortality, mechanical ventilation, and duration of hospitalization. We found that trials without blinding may estimate treatments to be more beneficial for hospitalizations and duration of mechanical ventilation. Conclusion: We did not find compelling evidence that COVID-19 trials in which healthcare providers and patients are blinded produce different results from trials without blinding but trials without allocation concealment estimate treatments to be more beneficial compared to trials with allocation concealment. What’s new? Additional information: For decades, allocation concealment (the concealment of the randomization sequence from personnel enrolling participants) and blinding (the concealment of the arm to which participants have been randomized from one or more individuals involved in a trial) have been important considerations in the assessment of risk of bias of trials. Previous studies have produced conflicting results with regards to the associations of blinding and allocation concealment and none have investigated the associations of allocation concealment and blinding in the context of COVID-19. Implications: Our study suggests that lack of blinding may not always bias results but that evidence users should remain skeptical of trials without allocation concealment.

BACKGROUND Trials and study-level meta-analyses have failed to resolve the role of corticosteroids in the management of patients with septic shock. Patient-level meta-analyses may provide more precise estimates of treatment effects, particularly subgroup effects. METHODS We pooled individual patient data from septic shock trials investigating the adjunctive use of intravenous hydrocortisone. The primary outcome was 90-day all-cause mortality, and it was also analyzed across predefined subgroups. Secondary outcomes included mortality at intensive care unit and hospital discharge, at 28 and 180 days, and vasopressor-, ventilator-, and organ failure–free days. Adverse events included superinfection, muscle weakness, hyperglycemia, hypernatremia, and gastroduodenal bleeding. RESULTS Of 24 eligible trials (n=8528), 17 (n=7882) provided individual patient data, and 7 (n=5929) provided 90-day mortality. The marginal relative risk (RR) for 90-day mortality of hydrocortisone versus placebo was 0.93 (95% confidence interval [CI], 0.82 to 1.04; P=0.22; moderate certainty). It was 0.86 (9% CI, 0.79 to 0.92) for hydrocortisone with fludrocortisone and 0.96 (95% CI, 0.82 to 1.12) without fludrocortisone. There was no significant differential treatment effect across subgroups. Hydrocortisone was associated with little to no difference in any of the secondary outcomes except vasopressor-free days (mean difference, 1.24 days; 95% CI, 0.74 to 1.73; high certainty). Hydrocortisone may not be associated with an increase in the risk of superinfection (RR, 1.04; 95% CI, 0.95 to 1.15; low certainty), hyperglycemia (RR, 1.05; 95% CI, 0.98 to 1.12; low certainty), or gastroduodenal bleeding (RR, 1.11; 95% CI, 0.83 to 1.48; low certainty). Hydrocortisone may be associated with an increase in the risk of hypernatremia (RR, 2.01; 95% CI, 1.56 to 2.60; low certainty) and muscle weakness (n=2647; RR, 1.73; 95% CI, 1.49 to 1.99; low certainty). CONCLUSIONS In this patient-level meta-analysis, hydrocortisone compared with placebo was not associated with reduced mortality for patients with septic shock. (Funded by “Programme d’Investissements d’Avenir,” a research Professorship from the National Institute of Health and Care Research, Leadership Fellowships from the National Health and Medical Research Council of Australia, and Emerging Leaders Fellowship from the National Health and Medical Research Council of Australia; PROSPERO registration number, CRD42017062198.)

Pakistan is a water-based economy and suffers from severe water scarcity in its primary river system, the Indus River Basin (IRB). The assessment of interactions among rising agricultural demand, socio-economic development and climate change is crucial to assess water scarcity in the IRB. Given the multiplicity of risks and the physical and social mechanisms that interact with them, estimating the future usage of the IRB requires models that represent plausible futures defined by a broad range of factors.The Global Change Analysis Model (GCAM), an Integrated Assessment Model (IAM), is used to assess the complex connection and interactions between energy, water, land, climate, and the economy. GCAM divides the globe into 235 water basins, including the IRB, and 384 land use regions which are modelled based on combinations of 32 energy regions and overlapping water basins. Dolan et al. (2021) used GCAM to generate a large ensemble of 3,000 plausible future scenarios, varying parameters related to future socioeconomic conditions, climate impacts, and water supply. Each scenario represents a possible future from now until the end of the century, with detailed socio-economic, water supply and demand and land-use results at the basin level. Yet, while these experiments generate large databases, there is a need for specialised methods that extract useful information from that data.Using the example of the IRB, we develop a methodology to leverage this type of database and (1) discover critical scenarios, i.e., scenarios with an outsized impact on water scarcity and economic costs, and (2) learn more about their characteristics, including what makes them critical. Here, we seek to identify outlier patterns by proposing a methodology that combines a machine learning technique, clustering, with dimensionality reduction. With clustering, we aim to identify hidden structures among scenarios and describe the clusters by a set of factors. Dimensionality reduction then assists us in determining which factors have the greatest impact on the critical scenarios that clustering identified.Preliminary results suggest that our methodology is able to identify outlier scenarios for the IRB’s irrigated crops mix (dominated by cotton, wheat, rice, and sugarcane), understand the factors that make them outliers, and evaluate whether they could be critical. The analysis is also able to identify when an ensemble of scenarios becomes an outlier, and indicates that according to GCAM, the crop mix is susceptible to bifurcating in several contrasting directions after 2040. Thus, this methodology helps us to characterise the socio-economic uncertainties associated with the IRB’s water resources and their interaction with climate, land, food, and energy sectors under critical scenarios. It is being developed to have broad applicability in extracting valuable insights from a large ensemble of IAM simulations. Dolan, F., Lamontagne, J., Link, R., Hejazi, M., Reed, P. & Edmonds, J. 2021. Evaluating the economic impact of water scarcity in a changing world. Nat Commun, 12, 1915.

The prevalence of heavy tailed (HT) populations in hydrology is becoming increasingly commonplace due in part to the increasing need and use of high frequency and high-resolution data. In addition to the impact of HT on extremes, HT populations can have a profound impact on a wide range of other hydrologic statistics and methods associated with planning, management and design for extremes. We review the known impacts of HT populations on the instability and bias in a wide range of commonly used hydrologic statistics. Experiments reveal that HT distributions result in the degradation of many commonly used statistical methods including the bootstrap, probability plots, the central limit theorem, and the law of large numbers. We document the gross instability of perhaps the best-behaved statistic of all, the sample mean (SM) when computed from HT distributions. The SM is ubiquitous because it is a component of and related to a myriad of statistical methods, thus its unstable behavior provides a window into future challenges faced by the hydrologic community. We outline many challenges associated with HT data, for example, upper product moments are often infinite for HT populations, yet upper L-moment always exist, so that the theory of L-moments is uniquely suited to HT distributions and data. We introduce a magnification factor for evaluating the impact of HT distributions on the behavior of extreme quantiles

The ongoing global transition to a deeply decarbonized electricity system represents a complex problem. Deep uncertainty in the future pathways of power system capacity expansion and interactions across sectors has led stakeholders to seek out robust methods capable of informing multi-scale, multi-sector tradeoffs among policy pathways within the energy-water-food nexus. In this study, scenario discovery is applied to a large scenario ensemble generated using a global-scale integrated assessment model with a regional focus on Latin America. Scenario discovery is a powerful method for identifying robust, policy-relevant scenarios from large, many-dimensional ensembles of model realizations. Here, ten uncertain sensitivity factors consistent with previous analyses are varied within the model configuration, representing technological costs and efficiencies, advanced electrification, institutional factors, and national climate pledges, among others. The resulting scenario ensemble maps out the impacts of a combinatorial time-evolving uncertainty space defined by these sensitivity factors, using generation mix, electricity cost, energy burden, and energy intensity as power system performance metrics. Additional metrics are utilized to explore cross-sectoral implications of scenarios. The scenario discovery analysis identifies the key global drivers of regional outcomes in Latin America, as well as tradeoffs and synergies regarding climate change mitigation and the future evolution of the Latin American electric power system. Our results underscore the importance of considering coupled systems and the advantages of large-scale scenario ensembles in capacity expansion analyses.

This study investigates the capability of sequence-to-sequence machine learning (ML) architectures in an effort to develop streamflow forecasting tools for Canadian watersheds. Such tools are useful to inform local and region-specific water management and flood forecasting related activities. Two powerful deep-learning variants of the Recurrent Neural Network were investigated, namely the standard and attention-based encoder-decoder long short-term memory (LSTM) models. Both models were forced with past hydro-meteorological states and daily meteorological data with a look-back time window of several days. These models were tested for 10 different watersheds from the Ottawa River watershed, located within the Great Lakes Saint-Lawrence region of Canada, an economic powerhouse of the country. The results of training and testing phases suggest that both models are able to simulate overall hydrograph patterns well when compared to observational records. Between the two models, the attention model significantly outperforms the standard model in all watersheds, suggesting the importance and usefulness of the attention mechanism in ML architectures, not well explored for hydrological applications. The mean performance accuracy of the attention model on unseen data, when assessed in terms of mean Nash–Sutcliffe Efficiency and Kling-Gupta Efficiency is, respectively, found to be 0.985 and 0.954 for these watersheds. Streamflow forecasts with lead times of up to 5 days with the attention model demonstrate overall skillful performance with well above the benchmark accuracy of 70%. The results of the study suggest that the encoder–decoder LSTM, with attention mechanism, is a powerful modelling choice for developing streamflow forecasting systems for Canadian watersheds.

For British Catholic women, conversion was an empowering choice for oneself, rather than a path towards gaining institutionalized power. Lay female converts at the turn of the century were generally privileged, with a worldly understanding of the role of women in British society. Many converts drew on the spirit of female independence at the end of the 19th century to contest their place in British society. For some, their social and financial capital offered an additional position of power from which to push on notions of traditional Britishness and femininity. To have the freedom to choose conversion at all exemplifies this feeling of bodily and mental autonomy rarely exhibited by many women during the late 19th century and early 20th century. This article sheds new light on the expansiveness of the lived, lay Catholic experience in Britain in the late 19th and early 20th centuries through the examples of Mabel Batten (1857–1916) and Radclyffe Hall (1880–1943).

International audience; BACKGROUND Studies that have evaluated the use of intravenous vitamin C in adults with sepsis who were receiving vasopressor therapy in the intensive care unit (ICU) have shown mixed results with respect to the risk of death and organ dysfunction. METHODS In this randomized, placebo-controlled trial, we assigned adults who had been in the ICU for no longer than 24 hours, who had proven or suspected infection as the main diagnosis, and who were receiving a vasopressor to receive an infusion of either vitamin C (at a dose of 50 mg per kilogram of body weight) or matched placebo administered every 6 hours for up to 96 hours. The primary outcome was a composite of death or persistent organ dysfunction (defined by the use of vasopressors, invasive mechanical ventilation, or new renal-replacement therapy) on day 28. RESULTS A total of 872 patients underwent randomization (435 to the vitamin C group and 437 to the control group). The primary outcome occurred in 191 of 429 patients (44.5%) in the vitamin C group and in 167 of 434 patients (38.5%) in the control group (risk ratio, 1.21; 95% confidence interval [CI], 1.04 to 1.40; P = 0.01). At 28 days, death had occurred in 152 of 429 patients (35.4%) in the vitamin C group and in 137 of 434 patients (31.6%) in the placebo group (risk ratio, 1.17; 95% CI, 0.98 to 1.40) and persistent organ dysfunction in 39 of 429 patients (9.1%) and 30 of 434 patients (6.9%), respectively (risk ratio, 1.30; 95% CI, 0.83 to 2.05). Findings were similar in the two groups regarding organ-dysfunction scores, biomarkers, 6-month survival, health-related quality of life, stage 3 acute kidney injury, and hypoglycemic episodes. In the vitamin C group, one patient had a severe hypoglycemic episode and another had a serious anaphylaxis event. CONCLUSIONS In adults with sepsis receiving vasopressor therapy in the ICU, those who received intravenous vitamin C had a higher risk of death or persistent organ dysfunction at 28 days than those who received placebo. (Funded by the Lotte and John Hecht Memorial Foundation; LOVIT ClinicalTrials.gov number, NCT03680274.).

Multiple clinical practice guidelines recommend minimizing radiation in trauma patients but there is a knowledge gap on the importance of this problem for trauma transfers. We aimed to estimate the incidence of pretransfer and repeat posttransfer computed tomography (CT) overall and in patients with an indication for immediate transfer, to assess interhospital practice variation, to identify predictors, and to quantify the influence of pretransfer CT on time to transfer. Methods We conducted a retrospective multicenter cohort study on patients transferred to major trauma centers from 2013 to 2019. Multilevel generalized linear regression was used to generate intraclass correlation coefficients (ICCs) to assess interhospital variation, multilevel logistic regression to generate odds ratios for each predictor, and geometric mean ratios to quantify the influence of CT on time to transfer. Results Of 18,244 patients included, 8501 (47%) had a pretransfer CT and one-quarter (26%) had a repeat posttransfer CT. Interhospital variation was moderate for pretransfer CT (5%-66%, ICC 12.5%) and for repeat posttransfer CT (7%-44%, ICC 14.7%). Pretransfer imaging was more frequent in elders and in males and repeat posttransfer imaging decreased over the study period but was more frequent in patients transferred in from Level III/IV centers than nondesignated hospitals. Time to transfer was doubled in patients who had a pretransfer CT.Results suggest that pretransfer CT and repeat posttransfer CT are frequent and are subject to significant practice variation. In addition, pretransfer CT is associated with increased times to transfer though additional studies are needed to demonstrate causation. These results highlight potential opportunities to reduce low-value imaging for trauma transfers.

To estimate gait variability, several methods have been routinely used which provide a measure of global variability. A recent study introduced a group waveform variability method which provides a point-by-point measurement of data variance equality. This can identify where in the gait cycle the significant differences in variability exist.Do waveform differences exist in equality of variance and group means in lower limb biomechanical variables between healthy younger and older adults during a gait task?Twenty healthy younger (19-44 years old, age=29.9(7.0) years, body mass index= 24.6(3.2)kg/mNo difference in walking speed existed between the younger or older groups (P .05). The older group had greater variability (P .05) in sagittal hip angles, as well as greater frontal ankle angle and moment variability. The younger group had significantly greater mean (P .05) ankle power generation prior to toe-off.This study provided a baseline of temporal differences in variance between healthy younger and older individuals. Its findings warrant the use of the equality of variance test to compare temporal differences for a variety of populations and tasks. Older adults generally had more variability than the younger adults, with many differences occurring near the transition from double- to single-limb support. The statistical parametric mapping analysis showed that the older adults could not generate as much ankle power as the younger adults prior to toe-off.

Sepsis awareness and understanding are important aspects of prevention, recognition, and clinical management of sepsis. We conducted a scoping review to identify and map the literature related to sepsis awareness, general knowledge, and information-seeking behaviors with a goal to inform future sepsis research and knowledge translation campaigns.Scoping review.Using Arksey and O'Malley's methodological framework, we conducted a systematic search on May 3, 2021, across four databases (MEDLINE, EMBASE, CINAHL, and Education Research Complete). Title/abstract and full-text screening was done in duplicate. One researcher extracted the data for each included article, and a second researcher checked data accuracy. The protocol was registered on Open Science Framework ( https://doi.org/10.17605/OSF.IO/YX7AU ).Articles related to sepsis awareness, knowledge, and information seeking behaviors among patients, public, and healthcare professionals.None.Of 5,927 unique studies, 80 reported on patient ( n = 13/80;16.3%), public ( n = 15/80;18.8%), or healthcare professional (nurses, physicians, emergency medical technicians) ( n = 48/80; 60%) awareness and knowledge of sepsis. Healthcare professional awareness and knowledge of sepsis is high compared with patients/public. The proportion of patients/public who had heard of the term sepsis ranged from 2% (Japan) to 88.6% (Germany). The proportions of patients/public who correctly identified the definition of sepsis ranged from 4.2% (Singapore) to 92% (Sweden). The results from the included studies appear to suggest that patient/public awareness of sepsis gradually improved over time. We found that the definition of sepsis was inconsistent in the literature and that few studies reported on patient, public, or healthcare professional knowledge of sepsis risk factors. Most patient/public get their sepsis information from the internet, whereas healthcare professionals get it from their role in healthcare through job training or educational training.Patient, public, and healthcare professional awareness and knowledge of sepsis vary globally. Future research may benefit from a consistent definition as well as country-specific data to support targeted public awareness campaigns.

Climate change and environmental degradation caused by human activities are having an irrefutable impact on human health, particularly mental health. People working in the environment sector are confronted with these impacts daily. This exploratory study was conducted as a response to concern in the sector about rising levels of worry and distress, and a need for organizational knowledge about effective workplace mental health strategies. Using evidenced-based frameworks for workplace mental health and wellbeing, the study focused on the relationship between climate change, environmental degradation and mental health issues for this sector. This Australian-based exploratory qualitative study was guided by participatory research approaches. Maximum variation and criterion sampling strategies were applied to engage environmental sector senior managers ( n = 8) in individual/paired interviews, followed by online focus group sessions with frontline employees ( n = 9). Qualitative thematic analysis techniques were used in an iterative process, combining inductive and deductive strategies. Data was triangulated and interpretation was finalized with reference to literature and a workplace mental health promotion framework. Interview data provided new perspectives on the interconnectivity between risk and protective factors for mental health. Workers were motivated by commitment and values to continue their work despite experiencing increasing levels of trauma, ecological grief, and stress due to overwork and ecological and climate change crises. The findings highlight the need for integrated health promotion approaches that acknowledge the complex interactions between risk and supportive factors that influence mental health in this sector.

Day-neutral (DN) strawberry cultivars are increasingly grown in Canada because they produce flowers and fruits continuously until October. Appropriate artificial lighting conditions during preparation of high-quality transplants is critical. Unfortunately, systematic evaluation of appropriate artificial lighting conditions during transplant production is limited. The objective of this study was to determine how an extended photoperiod supplemented with different light quality affects the vegetative and reproductive growth of a day-neutral cultivar during transplant production. In the first trial, we investigated the photoperiodic nature of the DN cultivar ‘Albion’ under low intensity incandescent light. Transplants were grown under three light combinations with different far-red : blue ratios (1:5, 5:1 and 1:1), supplemented for long day (LD; 24 h), short day (SD; 10 h) photoperiods and during a night interruption (NI) for 2 h. ‘Albion’ cultivar exhibited similar degree of flowering sensitivity regardless of photoperiod duration when incandescent light was used as predominant light source. In case of light emitting diodes (LEDs), dominant blue (1:5) LEDs prompted a significant increase in flower bud induction (FBI), more explicitly under the LD photoperiod. Furthermore, transplants grown under dominant blue light (1:5) supplied during NI produced eight flower buds per plant, the highest among all treatments, and promoted flower development outside the crown. Based on the results, it appears that lower wavelengths advance flowering and higher wavelengths contribute towards the morphological traits especially during transplant production. Results suggest that combination of far-red and blue LEDs at 1:5 ratio could be a potential light source to improve flower bud induction and floral development to subsequently increase fruit production.

Background Zimbabwe has one of the highest incidence rates of cervical cancer in the world – 61.7 per 100,000 women. The government of Zimbabwe introduced bivalent HPV vaccine with a 0,12 month schedule to all 10–14 year old girls using a pulsed-campaign approach in May 2018 (dose 1) and May 2019 (dose 2). Methods In August 2019, we conducted a population-based, two-stage cluster survey of households with girls who were eligible for the national HPV vaccination program to determine two-dose HPV vaccination coverage in three districts of Zimbabwe. All households with girls currently aged 11 to 15 years were line-listed through a census conducted in the pre-selected clusters from each district prior to survey administration. A simple random sample of eligible households was selected from these lists to estimate HPV vaccine coverage at sufficient power with a margin of error of +/- 5%. Criteria for district selection included estimated vaccine uptake (low, medium, high), rural/urban/peri-urban, geographic area, estimated number of girls not in school, and recent natural disasters or disease outbreaks. We oversampled households with girls aged 13 or 14 years at the time of dose 1. Results On-time dose 1 uptake ranged from 88 to 94% and two-dose HPV vaccine coverage ranged from 75 to 86% across the three districts. Nearly all vaccinations occurred in schools, and less than 2% of girls did not attend school. There were challenges assessing ages of girls at schools prior to vaccination – 9% of girls vaccinated were less than 10 years old at time of dose 1. Discussion Zimbabwe has demonstrated that high uptake and successful completion of 2-dose HPV vaccination can be achieved with an annual dosing schedule. Efforts going forward will need to focus on minimizing dropout between doses and routinizing annual vaccinations in schools for every subsequent new cohort of eligible girls in the country.

Writing on social media often departs from prescriptive norms through the use of non-standard words, spellings and punctuation. Amongst these traits is the repetition of letters (e.g. for oui ‘yes’). In this study, we draw upon a corpus of over 65 million tweets from three dialects of French (Laurentian, Metropolitan and Midi) to test phonological motivations for the choice of repeated letter in a word with repetition. Using mixed-effects multinomial regression, we compare dialectal differences in whether repetition targets final consonants (silent or pronounced), word-final orthographic corresponding to phonological schwa, and prosodically accented penults. We demonstrate that repetition covertly signals phonological properties. We conclude that prosody mediates morphological and phonological effects and that grapheme-to-phoneme correspondences vary between regions, thereby producing phonological patterns that writers likely did not intend to convey at the time of writing. We also propose that orthographic repetition on Twitter has two prosodic sources: the default pitch accent in French (shifted or not) and focus.

This dataset includesdatathat were used in:Huang et al. Effects of global climate mitigation on regional air quality and health. Nature Sustainability (2023). For any questions regarding this dataset, please contact Dr. Wei Peng atweipeng@psu.edu.

Stochastic Watershed Models (SWMs) are emerging tools in hydrologic modeling used to propagate uncertainty into model predictions by adding samples of model error to deterministic simulations. One of the most promising uses of SWMs is uncertainty propagation for hydrologic simulations under climate change. However, a core challenge with this approach is that the predictive uncertainty inferred from hydrologic model errors in the historical record may not correctly characterize the error distribution under future climate. For example, the frequency of physical processes (e.g., snow accumulation and melt, droughts and hydrologic recessions) may change under climate change, and so too may the frequency of errors associated with those processes. In this work, we explore for the first time non-stationarity in hydrologic model errors under climate change in an idealized experimental design. We fit one hydrologic model to historical observations, and then fit a second model to the simulations of the first, treating the first model as the true hydrologic system. We then force both models with climate change impacted meteorology and investigate changes to the error distribution between the models in historical and future periods. We develop a hybrid machine learning method that maps model input and state variables to predictive errors, allowing for non-stationary error distributions based on changes in the frequency of internal state variables. We find that this procedure provides an internally consistent methodology to overcome stationarity assumptions in error modeling and offers an important path forward in developing stochastic hydrologic simulations under climate change.

IMPORTANCE: Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. OBJECTIVE: To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS: In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non-critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS: Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES: The primary outcome was organ support-free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS: On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support-free days among critically ill patients was 10 (-1 to 16) in the ACE inhibitor group (n = 231), 8 (-1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support-free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE: In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02735707.