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The Green Edge project was designed to investigate the onset, life, and fate of a phytoplankton spring bloom (PSB) in the Arctic Ocean. The lengthening of the ice-free period and the warming of seawater, amongst other factors, have induced major changes in Arctic Ocean biology over the last decades. Because the PSB is at the base of the Arctic Ocean food chain, it is crucial to understand how changes in the Arctic environment will affect it. Green Edge was a large multidisciplinary, collaborative project bringing researchers and technicians from 28 different institutions in seven countries together, aiming at understanding these changes and their impacts on the future. The fieldwork for the Green Edge project took place over two years (2015 and 2016) and was carried out from both an ice camp and a research vessel in Baffin Bay, in the Canadian Arctic. This paper describes the sampling strategy and the dataset obtained from the research cruise, which took place aboard the Canadian Coast Guard ship (CCGS) Amundsen in late spring and early summer 2016. The sampling strategy was designed around the repetitive, perpendicular crossing of the marginal ice zone (MIZ), using not only ship-based station discrete sampling but also high-resolution measurements from autonomous platforms (Gliders, BGC-Argo floats …) and under-way monitoring systems. The dataset is available at https://doi.org/10.17882/86417 (Bruyant et al., 2022).

The Green Edge initiative was developed to investigate the processes controlling the primary productivity and fate of organic matter produced during the Arctic phytoplankton spring bloom (PSB) and to determine its role in the ecosystem. Two field campaigns were conducted in 2015 and 2016 at an ice camp located on landfast sea ice southeast of Qikiqtarjuaq Island in Baffin Bay (67.4797∘ N, 63.7895∘ W). During both expeditions, a large suite of physical, chemical and biological variables was measured beneath a consolidated sea-ice cover from the surface to the bottom (at 360 m depth) to better understand the factors driving the PSB. Key variables, such as conservative temperature, absolute salinity, radiance, irradiance, nutrient concentrations, chlorophyll a concentration, bacteria, phytoplankton and zooplankton abundance and taxonomy, and carbon stocks and fluxes were routinely measured at the ice camp. Meteorological and snow-relevant variables were also monitored. Here, we present the results of a joint effort to tidy and standardize the collected datasets, which will facilitate their reuse in other Arctic studies. The dataset is available at https://doi.org/10.17882/59892 (Massicotte et al., 2019a).

Influenza causes a substantial number of respiratory illnesses among young children in Kenya (1). The best time to vaccinate against seasonal influenza is before influenza viruses circulate, but because there [...]

In Kenya, influenza virus circulates year-round, raising questions about optimum strategies for vaccination. Given national interest in introducing influenza vaccination for young children 6–23 months of age, we modeled total influenza-associated illnesses (inclusive of hospitalizations, outpatient illnesses, and non‒medically attended illnesses) averted by multiple potential vaccination strategies: year-round versus seasonal-campaign vaccination, and vaccination starting in April (Southern Hemisphere influenza vaccine availability) versus October (Northern Hemisphere availability). We modeled average vaccine effectiveness of 50% and annual vaccination coverage of 60%. In the introduction year, year-round vaccination averted 6,410 total illnesses when introduced in October and 7,202 illnesses when introduced in April, whereas seasonal-campaign vaccination averted 10,236 (October) to 11,612 (April) illnesses. In the year after introduction, both strategies averted comparable numbers of illnesses (10,831–10,868 for year-round, 10,175–11,282 for campaign). Campaign-style vaccination would likely have a greater effect during initial pediatric influenza vaccine introduction in Kenya; however, either strategy could achieve similar longer-term effects.

Scientific writing is a rewarding, challenging, and necessary part of research. Building skills in scientific writing is critical in graduate student training and provides the foundation for scientific communication at all professional levels. Despite the importance of writing in research, this skill remains a barrier to success for many students. We have developed a practical approach to minimize scientific writing barriers using student-driven, peer-exchange in combination with concrete project goals and dedicated writing group times for a graduate Scientific Writing course. Course topics were drawn from self-identified student challenge areas and presented using in-class activities and discussion. Students selected their semester-long project on which to work, including research proposals, thesis chapters, and manuscripts. Students exchanged two rounds of peer review that provided overview comments and detailed editorial feedback. At each stage of their project, students built on their previous work in response to peer and instructor feedback. All students made progress on their individual projects and the majority improved their skills in at least one challenge area. This student-directed approach to scientific writing offers an opportunity to empower students to improve their writing skills with an emphasis on establishing consistent writing habits that will benefit students throughout their professional careers.

Abstract Animal morphology is influenced by several factors, including gonadal development and gametogenesis. Although their effects are well documented in male/female differentiation, much less is known about same-sex effects, such as those caused by their mode of reproduction. Here, using geometric morphometric analyses, we compare two groups of all-female triploid hybrid fish Chrosomus eos × eos-neogaeus, that differ only by their sexual and asexual reproductive strategies. We demonstrate that morphological differences arise from factors inherently associated with their mode of reproduction, with results replicated in two distinct lineages and in natural and common garden environments. Such differences provide additional insight about the costs and benefits of both reproductive strategies, which have mostly been of a demographic, population genetic, or genetic nature. In particular, these findings have important implications for the ecology of asexual organisms and contribute to the study of sex evolution by adding complexity to the paradox of sex theory.

The exponential growth of therapeutic ultrasound applications demonstrates the power of the technology to leverage the combinations of transducer technology and treatment monitoring techniques to effectively control the preferred bioeffect to elicit the desired clinical effect.Objective: This review provides an overview of the most commonly used bioeffects in therapeutic ultrasound and describes existing transducer technologies and monitoring techniques to ensure treatment safety and efficacy.Methods and materials: Literature reviews were conducted to identify key choices that essential in terms of transducer design, treatment parameters and procedure monitoring for therapeutic ultrasound applications. Effective combinations of these options are illustrated through descriptions of several clinical indications, including uterine fibroids, prostate disease, liver cancer, and brain cancer, that have been successful in leveraging therapeutic ultrasound to provide effective patient treatments.Results: Despite technological constraints, there are multiple ways to achieve a desired bioeffect with therapeutic ultrasound in a target tissue. Visualizations of the interplay of monitoring modality, bioeffect, and applied acoustic parameters are presented that demonstrate the interconnectedness of the field of therapeutic ultrasound. While the clinical indications explored in this review are at different points in the clinical evaluation path, based on the ever expanding research being conducted in preclinical realms, it is clear that additional clinical applications of therapeutic ultrasound that utilize a myriad of bioeffects will continue to grow and improve in the coming years.Conclusions: Therapeutic ultrasound will continue to improve in the next decades as the combination of transducer technology and treatment monitoring techniques will continue to evolve and be translated in clinical settings, leading to more personalized and efficient therapeutic ultrasound mediated therapies.

Abstract Aim Guided by Benner's framework, From Novice to Expert, this study aimed to identify (1) critical soft skills to be evaluated in nurses and (2) levels of nursing behaviour indicating achievement of soft skills to provide a framework for developing a soft skills rubric. Background/Introduction Nurse shortages are often attributable to high turnover rates among nurses. To improve this situation, healthcare facilities implement transition programmes and continuing education with a primary focus on developing and maintaining nurses' knowledge and performance (hard skills). However, balancing hard and soft skills (beliefs, values and attitudes) is key to nurses' quality of care and ultimately to their retention. Despite the importance of soft skills, their intangible nature creates problems in evaluating nurses' attainment of these skills and in providing constructive feedback to help them set concrete goals for improving their practice. Methods This qualitative descriptive study described critical soft skills in the nursing context. A purposeful sampling strategy was used to enrol 13 clinical nurse educators from multiple hospital units; each had more than 2 years of clinical nurse educator experience and had completed preceptor training. One‐to‐one interviews with these individuals were audio recorded, transcribed and subjected to direct content analysis using NVivo 12. The study follows the Consolidated criteria for reporting qualitative research (COREQ) guidelines for qualitative studies. Findings Five main competencies, comprising 20 subcompetencies, were identified: personal growth, effective interactions, professionalism, teamwork and the caring role. For each subcompetency, four skill levels were delineated using clinical indicators. Conclusion This study's findings can enhance understanding of clinical nurse educators' perceptions regarding soft skills required of nurses. Implications for the profession and/or patient care The soft skills identified can be incorporated into a rubric to be used by clinical nurse educators to evaluate and guide nurses' professional development and contribute to improving quality of care. No patient or public contribution is required for this study.

Background: Influenza is a significant contributor to acute respiratory infections (ARI), and children

Background and purpose: Magnetic resonance imaging (MRI)-to-computed tomography (CT) synthesis is essential in MRI-only radiotherapy workflows, particularly through deep learning techniques known for their accuracy. However, current supervised methods are limited to specific center’s learnings and depend on registration precision. The aim of this study was to evaluate the accuracy of unsupervised and supervised approaches in the context of prostate MRI-to-CT generation for radiotherapy dose calculation. Methods: CT/MRI image pairs from 99 prostate cancer patients across three different centers were used. A comparison between supervised and unsupervised conditional Generative Adversarial Networks (cGAN) was conducted. Unsupervised training incorporates a style transfer method with. Content and Style Representation for Enhanced Perceptual synthesis (CREPs) loss. For dose evaluation, the photon prescription dose was 60 Gy delivered in volumetric modulated arc therapy (VMAT). Imaging endpoint for sCT evaluation was Mean Absolute Error (MAE). Dosimetric endpoints included absolute dose differences and gamma analysis between CT and sCT dose calculations. Results: The unsupervised paired network exhibited the highest accuracy for the body with a MAE at 33.6 HU, the highest MAE was 45.5 HU obtained with unsupervised unpaired learning. All architectures provided clinically acceptable results for dose calculation with gamma pass rates above 94 % (1 % 1 mm 10 %). Conclusions: This study shows that multicenter data can produce accurate sCTs via unsupervised learning, eliminating CT-MRI registration. The sCTs not only matched HU values but also enabled precise dose calculations, suggesting their potential for wider use in MRI-only radiotherapy workflows.

Abstract Background Horizontal gene transfer inference approaches are usually based on gene sequences: parametric methods search for patterns that deviate from a particular genomic signature, while phylogenetic methods use sequences to reconstruct the gene and species trees. However, it is well-known that sequences have difficulty identifying ancient transfers since mutations have enough time to erase all evidence of such events. In this work, we ask whether character-based methods can predict gene transfers. Their advantage over sequences is that homologous genes can have low DNA similarity, but still have retained enough important common motifs that allow them to have common character traits, for instance the same functional or expression profile. A phylogeny that has two separate clades that acquired the same character independently might indicate the presence of a transfer even in the absence of sequence similarity. Our contributions We introduce perfect transfer networks, which are phylogenetic networks that can explain the character diversity of a set of taxa under the assumption that characters have unique births, and that once a character is gained it is rarely lost. Examples of such traits include transposable elements, biochemical markers and emergence of organelles, just to name a few. We study the differences between our model and two similar models: perfect phylogenetic networks and ancestral recombination networks. Our goals are to initiate a study on the structural and algorithmic properties of perfect transfer networks. We then show that in polynomial time, one can decide whether a given network is a valid explanation for a set of taxa, and show how, for a given tree, one can add transfer edges to it so that it explains a set of taxa. We finally provide lower and upper bounds on the number of transfers required to explain a set of taxa, in the worst case.

Les 30 et 31 janvier 2024, la seconde édition du Sommet de la santé durable, organisée par l’Association pour la santé publique du Québec, a eu lieu, et ce, simultanément à Québec et à Montréal. Ce texte vise à décrire cet évènement et à porter un regard éthique sur celui-ci afin de souligner des forces de ce Sommet, de même que des points à prendre en compte en vue d’une éventuelle troisième édition.

Abstract Redox processes, aqueous and solid‐phase chemistry, and pH dynamics are key drivers of subsurface biogeochemical cycling and methanogenesis in terrestrial and wetland ecosystems but are typically not included in terrestrial carbon cycle models. These omissions may introduce errors when simulating systems where redox interactions and pH fluctuations are important, such as wetlands where saturation of soils can produce anoxic conditions and coastal systems where sulfate inputs from seawater can influence biogeochemistry. Integrating cycling of redox‐sensitive elements could therefore allow models to better represent key elements of carbon cycling and greenhouse gas production. We describe a model framework that couples the Energy Exascale Earth System Model (E3SM) Land Model (ELM) with PFLOTRAN biogeochemistry, allowing geochemical processes and redox interactions to be integrated with land surface model simulations. We implemented a reaction network including aerobic decomposition, fermentation, sulfate reduction, sulfide oxidation, methanogenesis, and methanotrophy as well as pH dynamics along with iron oxide and iron sulfide mineral precipitation and dissolution. We simulated biogeochemical cycling in tidal wetlands subject to either saltwater or freshwater inputs driven by tidal hydrological dynamics. In simulations with saltwater tidal inputs, sulfate reduction led to accumulation of sulfide, higher dissolved inorganic carbon concentrations, lower dissolved organic carbon concentrations, and lower methane emissions than simulations with freshwater tidal inputs. Model simulations compared well with measured porewater concentrations and surface gas emissions from coastal wetlands in the Northeastern United States. These results demonstrate how simulating geochemical reaction networks can improve land surface model simulations of subsurface biogeochemistry and carbon cycling.

Verticillium dahliae is an important soilborne pathogen causing Verticillium wilt. It is also the primary causal agent of potato early dying, a disease complex involving the root-lesion nematode. Here, we report the whole-genome sequencing of 192 isolates of V. dahliae originating from the major potato production areas across Canada. Our results yielded a resource of 277,010 genetic variations that will be useful for genetic analyses and revealed the presence of two major lineages, both present in all provinces but exhibiting differences in regional prevalence. [Figure: see text] Copyright © 2023 His Majesty the King in Right of Canada, as represented by the Minister of Agriculture and Agri-Food Canada. This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Abstract Background Evolutionary scenarios describing the evolution of a family of genes within a collection of species comprise the mapping of the vertices of a gene tree T to vertices and edges of a species tree S. The relative timing of the last common ancestors of two extant genes (leaves of T) and the last common ancestors of the two species (leaves of S) in which they reside is indicative of horizontal gene transfers (HGT) and ancient duplications. Orthologous gene pairs, on the other hand, require that their last common ancestors coincides with a corresponding speciation event. The relative timing information of gene and species divergences is captured by three colored graphs that have the extant genes as vertices and the species in which the genes are found as vertex colors: the equal-divergence-time (EDT) graph, the later-divergence-time (LDT) graph and the prior-divergence-time (PDT) graph, which together form an edge partition of the complete graph. Results Here we give a complete characterization in terms of informative and forbidden triples that can be read off the three graphs and provide a polynomial time algorithm for constructing an evolutionary scenario that explains the graphs, provided such a scenario exists. While both LDT and PDT graphs are cographs, this is not true for the EDT graph in general. We show that every EDT graph is perfect. While the information about LDT and PDT graphs is necessary to recognize EDT graphs in polynomial-time for general scenarios, this extra information can be dropped in the HGT-free case. However, recognition of EDT graphs without knowledge of putative LDT and PDT graphs is NP-complete for general scenarios. In contrast, PDT graphs can be recognized in polynomial-time. We finally connect the EDT graph to the alternative definitions of orthology that have been proposed for scenarios with horizontal gene transfer. With one exception, the corresponding graphs are shown to be colored cographs.

BackgroundLimited data exists regarding risk factors for adverse outcomes in older adults hospitalized with Community-Acquired Pneumonia (CAP) in low- and middle-income countries such as India. This multisite study aimed to assess outcomes and associated risk factors among adults aged ≥60 years hospitalized with pneumonia.MethodsBetween December 2018 and March 2020, we enrolled ≥60-year-old adults admitted within 48 hours for CAP treatment across 16 public and private facilities in four sites. Clinical data and nasal/oropharyngeal specimens were collected by trained nurses and tested for influenza, respiratory syncytial virus (RSV), and other respiratory viruses (ORV) using the qPCR. Participants were evaluated regularly until discharge, as well as on the 7th and 30th days post-discharge. Outcomes included ICU admission and in-hospital or 30-day post-discharge mortality. A hierarchical framework for multivariable logistic regression and Cox proportional hazard models identified risk factors (e.g., demographics, clinical features, etiologic agents) associated with critical care or death.FindingsOf 1,090 CAP patients, the median age was 69 years; 38.4% were female. Influenza viruses were detected in 12.3%, RSV in 2.2%, and ORV in 6.3% of participants. Critical care was required for 39.4%, with 9.9% in-hospital mortality and 5% 30-day post-discharge mortality. Only 41% of influenza CAP patients received antiviral treatment. Admission factors independently associated with ICU admission included respiratory rate >30/min, blood urea nitrogen>19mg/dl, altered sensorium, anemia, oxygen saturation ConclusionHigh ICU admission and 30-day mortality rates were observed among older adults with pneumonia, with a significant proportion linked to influenza and RSV infections. Comprehensive guidelines for CAP prevention and management in older adults are needed, especially with the co-circulation of SARS-CoV-2.

Abstract As interfaces connecting terrestrial and ocean ecosystems, coastal wetlands develop temporally and spatially complex redox conditions, which drive uncertainties in greenhouse gas emission as well as the total carbon budget of the coastal ecosystem. To evaluate the role of complex redox reactions in methane emission from coastal wetlands, a coupled reactive‐transport model was configured to represent subsurface biogeochemical cycles of carbon, nitrogen, and sulfur, along with production and transport of multiple gas species through diffusion and ebullition. This model study was conducted at multiple sites along a salinity gradient in the Barataria Basin at the Mississippi River Deltaic Plain. Over a freshwater to saline gradient, simulated total flux of methane was primarily controlled by its subsurface production and consumption, which were determined by redox reactions directly (e.g., methanogenesis, methanotrophy) and indirectly (e.g., competition with sulfate reduction) under aerobic and/or anaerobic conditions. At fine spatiotemporal scales, surface methane fluxes were also strongly dependent on transport processes, with episodic ebullitive fluxes leading to higher spatial and temporal variability compared to the gradient‐driven diffusion flux. Ebullitive methane fluxes were determined by methane fraction in total ebullitive gas and the frequency of ebullitive events, both of which varied with subsurface methane concentrations and other gas species. Although ebullition thresholds are constrained by local physical factors, this study indicates that redox interactions not only determine gas composition in ebullitive fluxes but can also regulate ebullition frequency through gas production.

BackgroundHistorically, lack of data on cost-effectiveness of influenza vaccination has been identified as a barrier to vaccine use in low- and middle-income countries. We conducted a systematic review of economic evaluations describing (1) costs of influenza illness; (2) costs of influenza vaccination programs; and (3) vaccination cost-effectiveness from low- and middle-income countries to assess if gaps persist that could hinder global implementation of influenza vaccination programs.Methods and findingsWe performed a systematic search in Medline, Embase, Cochrane Library, CINAHL, and Scopus in January 2022 and October 2023 using a combination of the following key words: "influenza" AND "cost" OR "economic." The search included studies with publication years 2012 through 2022. Studies were eligible if they (1) presented original, peer-reviewed findings on cost of illness, cost of vaccination program, or cost-effectiveness of vaccination for seasonal influenza; and (2) included data for at least 1 low- or middle-income country. We abstracted general study characteristics and data specific to each of the 3 study types. Of 54 included studies, 26 presented data on cost-effectiveness, 24 on cost-of-illness, and 5 on program costs. Represented countries were classified as upper-middle income (UMIC; n = 12), lower-middle income (LMIC; n = 7), and low-income (LIC; n = 3). The most evaluated target groups were children (n = 26 studies), older adults (n = 17), and persons with chronic medical conditions (n = 12); fewer studies evaluated pregnant persons (n = 9), healthcare workers (n = 5), and persons in congregate living settings (n = 1). Costs-of-illness were generally higher in UMICs than in LMICs/LICs; however, the highest national economic burden, as a percent of gross domestic product and national health expenditure, was reported from an LIC. Among studies that evaluated the cost-effectiveness of influenza vaccine introduction, most (88%) interpreted at least 1 scenario per target group as either cost-effective or cost-saving, based on thresholds designated in the study. Key limitations of this work included (1) heterogeneity across included studies; (2) restrictiveness of the inclusion criteria used; and (3) potential for missed influenza burden from use of sentinel surveillance systems.ConclusionsThe 54 studies identified in this review suggest an increased momentum to generate economic evidence about influenza illness and vaccination from low- and middle-income countries during 2012 to 2022. However, given that we observed substantial heterogeneity, continued evaluation of the economic burden of influenza illness and costs/cost-effectiveness of influenza vaccination, particularly in LICs and among underrepresented target groups (e.g., healthcare workers and pregnant persons), is needed. Use of standardized methodology could facilitate pooling across settings and knowledge sharing to strengthen global influenza vaccination programs.

UAV-mounted sensors can be used to estimate crop biophysical traits, offering an alternative to traditional field scouting. However, the high temporal resolution offered by UAV platforms, critical for identifying small differences in crop conditions, is rarely exploited throughout the entire growing season. This limits growers' ability to obtain timely information for real-time interventions. New findings support that it is possible to parametrize an entire crop growth cycle under different conditions by accumulating sufficient data over time and using logistic growth models to highlight growth patterns. A step forward would be to model crop growth cycle at the plant-level in order to anticipate the optimal harvest dates in each plot or quickly identify growth problematics. Individual plant monitoring can be achieved by combining high spatial resolution images with accurate segmentation algorithms. The main objective of the study was therefore to develop and validate an integrated pipeline based on multidimensional data to extract predictive growth metrics for crop monitoring at the plant-level under various field conditions. The plant growth monitoring workflow was based on a three-step design ultimately leading to decision-making and reporting. Lettuce (Lactuca sativa L.) was chosen as a model plant due to its simple geometry, rapid growth and simple cultivation method. Treatments were composed of contrasting cover crops. Overall, correlation analysis showed that UAV-derived morphological metrics are reliable proxies for harvested biomass throughout the growing season, especially in later stages (Spearman's ρ > 0.9) and can be used as growth indicators. Therefore, Logistic Growth Curves (LGCs) were fitted to Crop Object Area (COA) values for each individual lettuce, using data up to 26 (generating G26 LGCs), 30 (G30) and 37 (G37) Days After Transplant (DAT). To assess the quality of their projections, G26 and G30 were compared to the reference LGC G37. The results indicated that Mean Absolute Percentage Error (MAPE) of projected COA was 9.6% and 6.8% for G26 and G30 respectively. Overall, the LGC parameters were close to the reference and highly correlated with the harvested biomass. The study also demonstrated the potential of having very good insight on plant maturity level by modeling the LGC 13 days before harvest. Furthermore, a dashboard was proposed to monitor current and projected maturity level, highlighting areas for further investigation. This novel integrated pipeline has the potential to become a valuable tool for research, on-farm decision making, and field interventions by providing data on plant biomass, maturity, and growth stages under different conditions, used as crop growth indicators.