Search

Implemented in Python, CoverPy enables automated estimation of plant area index (PAI), the fraction of vegetation cover (FCOVER), crown cover (CC), and crown porosity (CP) from digital cover photography (DCP). When compared to available alternatives, a key strength of CoverPy is the incorporation of end-to-end uncertainty propagation, enabling uncertainties due to within-plot variability and the user-specified extinction coefficient to be quantified. CoverPy is made available to the community on an open-source basis via GitHub, and should prove useful for researchers and citizen scientists interested in quantifying vegetation structure using inexpensive, non-specialist hardware.

Post-wildfire vegetation cover damage and loss can escalate the risks of secondary disasters such as flood, landslide, and water contamination, particularly in a major wildfire affected region where human settlements are situated. In assessments of the secondary disaster risks, the post-wildfire vegetation cover change plays a key role in influencing the distribution and intensity of the risks. In this work, a processing framework for mapping post-wildfire vegetation cover changes through information fusion has been generated and tested using Landsat8 and WorldView imagery data. The test site was the boreal forest region surrounding Fort McMurray, Alberta, Canada, affected by a massive wildfire in May 2016. The derived map results indicate that the fusion process in the framework is effective for generation of post-wildfire vegetation cover change information. The use of WorldView data revealed more variation details in distribution of the vegetation cover burn damages than use of Landsat data. Moreover, the uncertainty in vegetation burn severity using Landsat-based Differenced Normalized Burn Ratio (dNBR) index exists in the areas with low dNBR reading values due to the sub-pixel effect. The forest burn severity measured with dNBR index can be underestimated due to the quick herbaceous cover recovery after wildfire. These uncertainties in the post-wildfire vegetation cover mapping should be taken into consideration when the derived information is being used for risk assessments.

This study investigates the frequency of a clinically reported variant in PMS2, NM_000535.7:c.2523G>A p.(W841∗), from next-generation sequencing studies in 2 racially diverse cohorts. We identified clinical reports of the PMS2 c.2523G>A p.(W841∗) variant in the National Precision Oncology Program’s somatic testing database (n = 25,168). We determined frequency of the variant in germline exome sequencing from the Penn Medicine BioBank (n = 44,256) and in gnomAD. The PMS2 c.2523G>A p.(W841∗) was identified as a homozygous variant on tumor testing in an adult patient of self-identified Black race/ethnicity with no evidence of constitutional mismatch repair deficiency. The variant was clinically reported on 35 total tumor and liquid biopsy tests (0.1%), and all individuals with the variant were of self-identified Black race/ethnicity (0.6% of n = 5787). In individuals of African genetic ancestry (AFR), the variant's germline frequency was reported to be 0.2% and 1.3% in the Penn Medicine BioBank (PMBB) and gnomAD, respectively. The variant cannot be found in any individuals of European genetic ancestry (EUR) from either of the databases. The variant is found in a region of PMS2 with 100% homology to the PMS2CL pseudogene. PMS2 c.2523G>A p.(W841∗), when identified, is typically an African-ancestry-specific PMS2CL pseudogene variant, which should be recognized to prevent misdiagnosis of Lynch syndrome in Blacks.

Abstract Background There are a variety of costs associated with publication of scientific findings. The purpose of this work was to estimate the cost of peer review in scientific publishing per reviewer, per year and for the entire scientific community. Methods Internet-based self-report, cross-sectional survey, live between June 28, 2021 and August 2, 2021 was used. Participants were recruited via snowball sampling. No restrictions were placed on geographic location or field of study. Respondents who were asked to act as a peer-reviewer for at least one manuscript submitted to a scientific journal in 2020 were eligible. The primary outcome measure was the cost of peer review per person, per year (calculated as wage-cost x number of initial reviews and number of re-reviews per year). The secondary outcome was the cost of peer review globally (calculated as the number of peer-reviewed papers in Scopus x median wage-cost of initial review and re-review). Results A total of 354 participants completed at least one question of the survey, and information necessary to calculate the cost of peer-review was available for 308 participants from 33 countries (44% from Canada). The cost of peer review was estimated at $US1,272 per person, per year ($US1,015 for initial review and $US256 for re-review), or US$1.1–1.7 billion for the scientific community per year. The global cost of peer-review was estimated at US$6 billion in 2020 when relying on the Dimensions database and taking into account reviewed-but-rejected manuscripts. Conclusions Peer review represents an important financial piece of scientific publishing. Our results may not represent all countries or fields of study, but are consistent with previous estimates and provide additional context from peer reviewers themselves. Researchers and scientists have long provided peer review as a contribution to the scientific community. Recognizing the importance of peer-review, institutions should acknowledge these costs in job descriptions, performance measurement, promotion packages, and funding applications. Journals should develop methods to compensate reviewers for their time and improve transparency while maintaining the integrity of the peer-review process.

Effective plot-based field sampling involves a trade-off between implementation efficiency and sample error. Optimal field sampling therefore requires quantifying the sample error under various sampling designs. For remote sensing applications, it is also important to understand how field sample error and training sample size (the number of pixels) affect the retrieval of surface properties. In this research, drone imagery was used to simulate field plots and investigate plot sampling error for forage lichen cover in relation to plot size, number of plots, and sampling strategy. The effect of this error on remote sensing-based lichen cover retrieval was evaluated using varying training sampling sizes in two different study regions in northern Canada. Results showed that cover with high spatial variability increased the number of plots or plot size required to achieve a specified level of error. For lichen cover retrieval at moderate spatial resolution (10–30 m), field sampling (plot size and number of plots) did not have as significant of an effect as regional differences (spectral separability of cover types), sensor, and the number of pixels used for model training. This plot simulation approach using drone images can be applied to other surface properties and regions to provide field sampling guidance.

High-quality ground-truth data are critical for developing reliable Earth Observation (EO) based geospatial products. Conventional methods of collecting these data are either subject to an unknown amount of human error and bias or require extended time in the field to complete (i.e., point-intercept assessments). Digital photograph classification (DPC) may address these drawbacks. In this study, we first assess the performance of a DPC method developed through licensed software to estimate ground cover percentage (%) of bright lichens, a critical caribou forage in fall and winter when other food resources are scarce. We then evaluate the feasibility of replicating this workflow in an open-source environment with a modified U-net model to improve processing time and scalability. Our results indicate that DPC is appropriate for generating ground-truth data in support of large-scale EO-based lichen mapping within the boreal forests of eastern Canada. Our final open-sourced classification model, Lichen Convolutional Neural Network (LiCNN), is comparably accurate yet more efficient than the licensed workflow. Therefore, the LiCNN approach successfully addresses the mentioned shortcomings of conventional ground-truth data collection methods efficiently and without the need for specialized software.

Fat distribution is associated with cardiometabolic disease, although it has been less well studied than overall obesity. In a multiancestry exome-sequencing study, the authors identified predicted loss-of-function mutations in INHBE associated with favorable fat distribution and protection from type 2 diabetes.

Measuring ground elevation changes plays a crucial role in several environmental applications. For instance, permafrost soils undergo seasonal active layer freezing and thawing that causes cyclic elevation changes. Permafrost thaw can result in unidirectional ground subsidence, which may be gradual and uniform, or rapid and irregular in the case of thermokarst landforms such as slumps and degrading ice-wedges. Photogrammetric drone surveys have effectively characterized large (> 0.1 m) ground elevation changes resulting from thermokarst, yet many permafrost processes of interest lead to more subtle elevation changes. In this study, we assessed various drone-based surveying strategies for their precision to measure smaller (

Abstract The club cell, a small airway epithelial (SAE) cell, plays a central role in human lung host defense. We hypothesized that subpopulations of club cells with distinct functions may exist. The SAE of healthy nonsmokers and healthy cigarette smokers were evaluated by single-cell RNA sequencing, and unsupervised clustering revealed subpopulations of SCGCB1A1+KRT5loMUC5AC− club cells. Club cell heterogeneity was supported by evaluations of SAE tissue sections, brushed SAE cells, and in vitro air–liquid interface cultures. Three subpopulations included: (1) progenitor; (2) proliferating; and (3) effector club cells. The progenitor club cell population expressed high levels of mitochondrial, ribosomal proteins, and KRT5 relative to other club cell populations and included a differentiation branch point leading to mucous cell production. The small proliferating population expressed high levels of cyclins and proliferation markers. The effector club cell cluster expressed genes related to host defense, xenobiotic metabolism, and barrier functions associated with club cell function. Comparison of smokers vs. nonsmokers demonstrated that smoking limited the extent of differentiation of all three subclusters and altered SAM pointed domain-containing Ets transcription factor (SPDEF)-regulated transcription in the effector cell population leading to a change in the location of the branch point for mucous cell production, a potential explanation for the concomitant reduction in effector club cells and increase in mucous cells in smokers. These observations provide insights into both the makeup of human SAE club cell subpopulations and the smoking-induced changes in club cell biology.

Abstract Background The human small airway epithelium (SAE) plays a central role in the early events in the pathogenesis of most inherited and acquired lung disorders. Little is known about the molecular phenotypes of the specific cell populations comprising the SAE in humans, and the contribution of SAE specific cell populations to the risk for lung diseases. Methods Drop-seq single-cell RNA-sequencing was used to characterize the transcriptome of single cells from human SAE of nonsmokers and smokers by bronchoscopic brushing. Results Eleven distinct cell populations were identified, including major and rare epithelial cells, and immune/inflammatory cells. There was cell type-specific expression of genes relevant to the risk of the inherited pulmonary disorders, genes associated with risk of chronic obstructive pulmonary disease and idiopathic pulmonary fibrosis and (non-mutated) driver genes for lung cancers. Cigarette smoking significantly altered the cell type-specific transcriptomes and disease risk-related genes. Conclusions This data provides new insights into the possible contribution of specific lung cells to the pathogenesis of lung disorders.

L’itinérance est une problématique grandissante au Canada. Elle est engendrée par les inégalités et les injustices sociales envers les populations vulnérables telles que les personnes aînées. Les bénéfices du sport et de l’activité physique pour contrer l’itinérance sont mal compris autant par les intervenants en santé que de la classe élue. Les politiques en place actuellement ne comportent pas de solutions claires et permanentes à ce phénomène. C’est pourquoi nous proposons une solution innovatrice qui permet de toucher le cœur du problème de l’itinérance. L’activité physique et le sport (APS) a des répercussions considérables sur toutes les sphères du bien-être en plus d’être un véhicule de valeurs et d’habiletés essentielles. Ces apprentissages favorisent le développement personnel, limitent l’utilisation de substances illicites et permettent de construire un réseau de soutien de qualité, ce qui contribue à la réintégration efficace à la société et l’accès à un logement abordable. Ce sont les Homeless Games, un évènement sportif annuel qui permet aux personnes vivant dans l’itinérance (PVI) de pratiquer un sport compétitif, qui a inspiré cette proposition. De plus, bien que les ressources soient limitées, l’intérêt des PVI et des organismes de bienfaisance est énorme. Les résultats sont positifs, l’implantation d’un programme d’activités physiques et sportives communautaires est possible et augmente le capital social. Il est possible de mettre un terme à l’itinérance en utilisant l’activité physique et le sport comme un outil d’intervention au sein des équipes interdisciplinaires qui s’attardent à réduire ou du moins ralentir l’itinérance.

RNA sequencing (RNAseq) has been widely used to generate bulk gene expression measurements collected from pools of cells. Only relatively recently have single-cell RNAseq (scRNAseq) methods provided opportunities for gene expression analyses at the single-cell level, allowing researchers to study heterogeneous mixtures of cells at unprecedented resolution. Tumors tend to be composed of heterogeneous cellular mixtures and are frequently the subjects of such analyses. Extensive method developments have led to several protocols for scRNAseq but, owing to the small amounts of RNA in single cells, technical constraints have required compromises. For example, the majority of scRNAseq methods are limited to sequencing only the 3′ or 5′ termini of transcripts. Other protocols that facilitate full-length transcript profiling tend to capture only polyadenylated mRNAs and are generally limited to processing only 96 cells at a time. Here, we address these limitations and present a novel protocol that allows for the high-throughput sequencing of full-length, total RNA at single-cell resolution. We demonstrate that our method produced strand-specific sequencing data for both polyadenylated and non-polyadenylated transcripts, enabled the profiling of transcript regions beyond only transcript termini, and yielded data rich enough to allow identification of cell types from heterogeneous biological samples.

In 1999, the Government of Canada, along with the provinces and territories, established the National Diabetes Surveillance System (NDSS) to track rates of diabetes in Canada. The NDSS used a novel method to systematically collect and report national diabetes data using linked administrative health databases. The NDSS has since evolved to become the Canadian Chronic Disease Surveillance System (CCDSS) and provides information on over 20 chronic conditions. This At-a-glance report provides the most up-to-date CCDSS information on diabetes rates in Canada. Currently, 8.8% of Canadians (9.4% male, 8.1% female, aged one year and older) live with diabetes, and approximately 549 new cases are diagnosed each day. Since 2000, the age-standardized prevalence rate has increased by an average of 3.3% per year. The age-standardized incidence rate has remained relatively stable, and all-cause mortality rates among those with diabetes have decreased by an average of 2.1% per year. This suggests that people are living longer with a diabetes diagnosis.

En 1999, le gouvernement du Canada, en collaboration avec les provinces et les territoires, met sur pied le Système national de surveillance du diabète (SNSD) afin de suivre l’évolution des taux de diabète au Canada. Le SNSD utilise alors une nouvelle méthode pour recueillir et communiquer les données nationales sur le diabète de façon systématique, au moyen de bases de données administratives sur la santé reliées entre elles. Depuis, il a évolué pour devenir le Système canadien de surveillance des maladies chroniques (SCSMC) et fournit de l’information sur plus de 20 maladies chroniques. Cet article présente les données les plus à jour du SCSMC sur les taux de diabète au Canada. Actuellement, 8,8 % des Canadiens (9,4 % d’hommes et 8,1 % de femmes d’un an et plus) sont atteints de diabète, et environ 549 nouveaux cas sont diagnostiqués chaque jour. Depuis 2000, le taux de prévalence normalisé selon l’âge a augmenté en moyenne de 3,3 % par année, le taux d’incidence normalisé selon l’âge est demeuré relativement stable et les taux de mortalité toutes causes confondues chez les personnes atteintes de diabète ont diminué en moyenne de 2,1 % par année. Cela laisse entendre que l’on vit plus longtemps avec un diagnostic de diabète.

Differencing of digital surface models derived from structure from motion (SfM) processing of airborne imagery has been used to produce snow depth (SD) maps with between ∼ 2 and ∼ 15 cm horizontal resolution and accuracies of ±10 cm over relatively flat surfaces with little or no vegetation and over alpine regions. This study builds on these findings by testing two hypotheses across a broader range of conditions: (i) that the vertical accuracy of SfM processing of imagery acquired by commercial low-cost unmanned aerial vehicle (UAV) systems can be adequately modelled using conventional photogrammetric theory and (ii) that SD change can be more accurately estimated by differencing snow-covered elevation surfaces rather than differencing a snow-covered and snow-free surface. A total of 71 UAV missions were flown over five sites, ranging from short grass to a regenerating forest, with ephemeral snowpacks. Point cloud geolocation performance agreed with photogrammetric theory that predicts uncertainty is proportional to UAV altitude and linearly related to horizontal uncertainty. The root-mean-square difference (RMSD) over the observation period, in comparison to the average of in situ measurements along ∼ 50 m transects, ranged from 1.58 to 10.56 cm for weekly SD and from 2.54 to 8.68 cm for weekly SD change. RMSD was not related to microtopography as quantified by the snow-free surface roughness. SD change uncertainty was unrelated to vegetation cover but was dominated by outliers corresponding to rapid in situ melt or onset; the median absolute difference of SD change ranged from 0.65 to 2.71 cm. These results indicate that the accuracy of UAV-based estimates of weekly snow depth change was, excepting conditions with deep fresh snow, substantially better than for snow depth and was comparable to in situ methods.

Illumination variations in non-atmospherically corrected high-resolution satellite (HRS) images acquired at different dates/times/locations pose a major challenge for large-area environmental mapping and monitoring. This problem is exacerbated in cases where a classification model is trained only on one image (and often limited training data) but applied to other scenes without collecting additional samples from these new images. In this research, by focusing on caribou lichen mapping, we evaluated the potential of using conditional Generative Adversarial Networks (cGANs) for the normalization of WorldView-2 (WV2) images of one area to a source WV2 image of another area on which a lichen detector model was trained. In this regard, we considered an extreme case where the classifier was not fine-tuned on the normalized images. We tested two main scenarios to normalize four target WV2 images to a source 50 cm pansharpened WV2 image: (1) normalizing based only on the WV2 panchromatic band, and (2) normalizing based on the WV2 panchromatic band and Sentinel-2 surface reflectance (SR) imagery. Our experiments showed that normalizing even based only on the WV2 panchromatic band led to a significant lichen-detection accuracy improvement compared to the use of original pansharpened target images. However, we found that conditioning the cGAN on both the WV2 panchromatic band and auxiliary information (in this case, Sentinel-2 SR imagery) further improved normalization and the subsequent classification results due to adding a more invariant source of information. Our experiments showed that, using only the panchromatic band, F1-score values ranged from 54% to 88%, while using the fused panchromatic and SR, F1-score values ranged from 75% to 91%.

Arctic temperatures have increased at almost twice the global average rate since the industrial revolution. Some studies also reported a further amplified rate of climate warming at high elevations; namely, the elevation dependency of climate change. This elevation-dependent climate change could have important implications for the fate of glaciers and ecosystems at high elevations under climate change. However, the lack of long-term climate data at high elevations, especially in the Arctic, has hindered the investigation of this question. Because of the linkage between climate warming and plant phenology changes and remote sensing’s ability to detect the latter, remote sensing provides an alternative way for investigating the elevation dependency of climate change over Arctic mountains. This study investigated the elevation-dependent changes to plant phenology using AVHRR (Advanced Very High Resolution Radiometer) time series from 1985 to 2013 over five study areas in Canada’s Arctic. We found that the start of the growing season (SOS) became earlier faster with an increasing elevation over mountainous study areas (i.e., Sirmilik, the Torngat Mountains, and Ivvavik National Parks). Similarly, the changes rates in the end of growing season (EOS) and the growing season length (GSL) were also higher at high elevations. One exception was SOS in the Ivvavik National Park: “no warming trend” with the May-June temperature at a nearby climate station decreased slightly during 1985–2013, and so no elevation-dependent amplification.

Relating ground photographs to UAV orthomosaics is a key linkage required for accurate multi-scaled lichen mapping. Conventional methods of multi-scaled lichen mapping, such as random forest models and convolutional neural networks, heavily rely on pixel DN values for classification. However, the limited spectral range of ground photos requires additional characteristics to differentiate lichen from spectrally similar objects, such as bright logs. By applying a neural network to tiles of a UAV orthomosaics, additional characteristics, such as surface texture and spatial patterns, can be used for inferences. Our methodology used a neural network (UAV LiCNN) trained on ground photo mosaics to predict lichen in UAV orthomosaic tiles. The UAV LiCNN achieved mean user and producer accuracies of 85.84% and 92.93%, respectively, in the high lichen class across eight different orthomosaics. We compared the known lichen percentages found in 77 vegetation microplots with the predicted lichen percentage calculated from the UAV LiCNN, resulting in a R2 relationship of 0.6910. This research shows that AI models trained on ground photographs effectively classify lichen in UAV orthomosaics. Limiting factors include the misclassification of spectrally similar objects to lichen in the RGB bands and dark shadows cast by vegetation.

After a major earthquake in a dense urban area, the spatial distribution of heavily damaged buildings is indicative of the impact of the event on public safety. Timely assessment of the locations of severely damaged buildings and their damage morphologies using remote sensing approaches is critical for search and rescue actions. Detection of damaged buildings that did not suffer collapse can be highly challenging from aerial or satellite optical imagery, especially those structures with height-reduction or inclination damage and apparently intact roofs. A key information cue can be provided by a comparison of predicted building shadows based on pre-event building models with shadow estimates extracted from post-event imagery. This paper addresses the detection of damaged buildings in dense urban areas using the information of building shadow changes based on shadow simulation, analysis, and image processing in order to improve real-time damage detection and analysis. A novel processing framework for the rapid detection of damaged buildings without collapse is presented, which includes (a) generation of building digital surface models (DSMs) from pre-event LiDAR data, (b) building shadow detection and extraction from imagery, (c) simulation of predicted building shadows utilizing building DSMs, and (d) detection and identification of shadow areas exhibiting significant pre- and post-event differences that can be attributed to building damage. The framework is demonstrated through two simulated case studies. The building damage types considered are those typically observed in earthquake events and include height-reduction, over-turn collapse, and inclination. Total collapse cases are not addressed as these are comparatively easy to be detected using simpler algorithms. Key issues are discussed including the attributes of essential information layers and sources of error influencing the accuracy of building damage detection.

Lichen is an important food source for caribou in Canada. Lichen mapping using remote sensing (RS) images could be a challenging task, however, as lichens generally appear in unevenly distributed, small patches, and could resemble surficial features. Moreover, collecting lichen labeled data (reference data) is expensive, which restricts the application of many robust supervised classification models that generally demand a large quantity of labeled data. The goal of this study was to investigate the potential of using a very-high-spatial resolution (1-cm) lichen map of a small sample site (e.g., generated based on a single UAV scene and using field data) to train a subsequent classifier to map caribou lichen over a much larger area (~0.04 km2 vs. ~195 km2) and a lower spatial resolution image (in this case, a 50-cm WorldView-2 image). The limited labeled data from the sample site were also partially noisy due to spatial and temporal mismatching issues. For this, we deployed a recently proposed Teacher-Student semi-supervised learning (SSL) approach (based on U-Net and U-Net++ networks) involving unlabeled data to assist with improving the model performance. Our experiments showed that it was possible to scale-up the UAV-derived lichen map to the WorldView-2 scale with reasonable accuracy (overall accuracy of 85.28% and F1-socre of 84.38%) without collecting any samples directly in the WorldView-2 scene. We also found that our noisy labels were partially beneficial to the SSL robustness because they improved the false positive rate compared to the use of a cleaner training set directly collected within the same area in the WorldView-2 image. As a result, this research opens new insights into how current very high-resolution, small-scale caribou lichen maps can be used for generating more accurate large-scale caribou lichen maps from high-resolution satellite imagery.

"null"

One of the greatest challenges is to reduce malnutrition worldwide while promoting sustainable agricultural and food systems. This is a daunting task due to the constant growth of the population and the increasing demands by consumers for functional foods with higher nutritional values. Cereal grains are the most important dietary energy source globally; wheat, rice, and maize currently provide about half of the dietary energy source of humankind. In addition, the increase of celiac patients worldwide has motivated the development of gluten-free foods using alternative flour types to wheat such as rice, corn, cassava, soybean, and pseudocereals (amaranth, quinoa, and buckwheat). Amaranth and quinoa have been cultivated since ancient times and were two of the major crops of the Pre-Colombian cultures in Latin- America. In recent years and due to their well-known high nutritional value and potential health benefits, these pseudocereals have received much attention as ideal candidates for gluten-free products. The importance of exploiting these grains for the elaboration of healthy and nutritious foods has forced food producers to develop novel adequate strategies for their processing. Fermentation is one of the most antique and economical methods of producing and preserving foods and can be easily employed for cereal processing. The nutritional and functional quality of pseudocereals can be improved by fermentation using Lactic Acid Bacteria (LAB). This review provides an overview on pseudocereal fermentation by LAB emphasizing the capacity of these bacteria to decrease antinutritional factors such as phytic acid, increase the functional value of phytochemicals such as phenolic compounds, and produce nutritional ingredients such as B-group vitamins. The numerous beneficial effects of lactic fermentation of pseudocereals can be exploited to design novel and healthier foods or grain ingredients destined to general population and especially to patients with coeliac disease.

OBJECTIVE:To conduct a time-cost analysis of formatting in scientific publishing. DESIGN:International, cross-sectional study (one-time survey). SETTING:Internet-based self-report survey, live between September 2018 and January 2019. PARTICIPANTS:Anyone working in research, science, or academia and who submitted at least one peer-reviewed manuscript for consideration for publication in 2017. Completed surveys were available for 372 participants from 41 countries (60% of respondents were from Canada). MAIN OUTCOME MEASURE:Time (hours) and cost (wage per hour x time) associated with formatting a research paper for publication in a peer-reviewed academic journal. RESULTS:The median annual income category was US$61,000-80,999, and the median number of publications formatted per year was four. Manuscripts required a median of two attempts before they were accepted for publication. The median formatting time was 14 hours per manuscript, or 52 hours per person, per year. This resulted in a median calculated cost of US$477 per manuscript or US$1,908 per person, per year. CONCLUSIONS:To our knowledge, this is the first study to analyze the cost of manuscript formatting in scientific publishing. Our results suggest that scientific formatting represents a loss of 52 hours, costing the equivalent of US$1,908 per researcher per year. These results identify the hidden and pernicious price associated with scientific publishing and provide evidence to advocate for the elimination of strict formatting guidelines, at least prior to acceptance.

Background The purpose of this review was to determine the most valid and reliable questions for targeting key modes of sedentary behaviour (SB) in a broad range of national and international health surveillance surveys. This was done by reviewing the SB modules currently used in population health surveys, as well as examining SB questionnaires that have performed well in psychometric testing. Methods Health surveillance surveys were identified via scoping review and contact with experts in the field. Previous systematic reviews provided psychometric information on pediatric questionnaires. A comprehensive search of four bibliographic databases was used to identify studies reporting psychometric information for adult questionnaires. Only surveys/studies published/used in English or French were included. Results The review identified a total of 16 pediatric and 18 adult national/international surveys assessing SB, few of which have undergone psychometric testing. Fourteen pediatric and 35 adult questionnaires with psychometric information were included. While reliability was generally good to excellent for questions targeting key modes of SB, validity was poor to moderate, and reported much less frequently. The most valid and reliable questions targeting specific modes of SB were combined to create a single questionnaire targeting key modes of SB. Discussion Our results highlight the importance of including SB questions in survey modules that are adaptable, able to assess various modes of SB, and that exhibit adequate reliability and validity. Future research could investigate the psychometric properties of the module we have proposed in this paper, as well as other questionnaires currently used in national and international population health surveys.

Tuberculosis (TB) remains a major threat throughout the world and in 2015 it caused the death of 1.4 million people. The Bacillus Calmette-Guérin is the only existing vaccine against this ancient disease; however, it does not provide complete protection in adults. New vaccines against TB are eminently a global priority. The use of bacteria as vehicles for delivery of vaccine plasmids is a promising vaccination strategy. In this study, we evaluated the use of, an engineered invasive Lactococcus lactis (expressing Fibronectin-Binding Protein A from Staphylococcus aureus) for the delivery of DNA plasmid to host cells, especially to the mucosal site as a new DNA vaccine against tuberculosis. One of the major antigens documented that offers protective responses against Mycobacterium tuberculosis is the Ag85A. L. lactis FnBPA+ (pValac:Ag85A) which was obtained and used for intranasal immunization of C57BL/6 mice and the immune response profile was evaluated. In this study we observed that this strain was able to produce significant increases in the amount of pro-inflammatory cytokines (IFN-γ, TNF-α, and IL-6) in the stimulated spleen cell supernatants, showing a systemic T helper 1 (Th1) cell response. Antibody production (IgG and sIgA anti-Ag85A) was also significantly increased in bronchoalveolar lavage, as well as in the serum of mice. In summary, these findings open new perspectives in the area of mucosal DNA vaccine, against specific pathogens using a Lactic Acid Bacteria such as L. lactis.

A multiple sensor payload for a multi-rotor based UAV platform was developed and tested for measuring land surface albedo and spectral measurements at user-defined spatial, temporal, and spectral resolutions. The system includes a Matrice 600 UAV with an RGB camera and a set of four downward pointing radiation sensors including a pyranometer, quantum sensor, and VIS and NIR spectrometers, measuring surface reflected radiation. A companion ground unit consisting of a second set of identical sensors simultaneously measure downwelling radiation. The reflected and downwelling radiation measured by the four sensors are used for calculating albedo for the total shortwave broadband, visible band and any narrowband at a 1.5 nm spectral resolution within the range of 350–1100 nm. The UAV-derived albedo was compared with those derived from Landsat 8 and Sentinel-2 satellite observations. Results show the agreement between total shortwave albedo from UAV pyranometer and Landsat 8 (R2 = 0.73) and Sentinel-2 (R2 = 0.68). Further, total shortwave albedo was estimated from spectral measurements and compared with the satellite-derived albedo. This UAV-based sensor system promises to provide high-resolution multi-sensors data acquisition. It also provides maximal flexibility for data collection at low cost with minimal atmosphere influence, minimal site disturbance, flexibility in measurement planning, and ease of access to study sites (e.g., wetlands) in contrast with traditional data collection methods.

In order to verify if the full moon is associated with sleep and activity behaviors, we used a 12-country study providing 33710 24-hour accelerometer recordings of sleep and activity. The present observational, cross-sectional study included 5812 children ages 9-11 years from study sites that represented all inhabited continents and wide ranges of human development (Australia, Brazil, Canada, China, Colombia, Finland, India, Kenya, Portugal, South Africa, United Kingdom and United States). Three moon phases were used in this analysis: full moon (±4 days; reference), half moon (±5-9 days) and new moon (±10-14 days) from nearest full moon. Nocturnal sleep duration, moderate-to-vigorous physical activity (MVPA), light-intensity physical activity (LPA) and total sedentary time (SED) were monitored over 7 consecutive days using a waist-worn accelerometer worn 24 hours a day. Only sleep duration was found to significantly differ between moon phases (~5 min per night shorter during full moon compared to new moon). Differences in MVPA, LPA and SED between moon phases were negligible and non-significant (

Recent studies report a link between common environmental exposures, such as particulate matter air pollution and tobacco smoke, and decline in cognitive function. The purpose of this study was to assess the association between exposure to polycyclic aromatic hydrocarbons (PAHs), a selected group of chemicals present in particulate matter and tobacco smoke, and measures of cognitive performance among elderly in the general population. This cross-sectional analysis involved data from 454 individuals aged 60 years and older from the 2001-2002 National Health and Nutrition Examination Survey. The association between PAH exposures (as measured by urinary biomarkers) and cognitive function (digit symbol substitution test (DSST)) was assessed using multiple linear regression analyses. After adjusting for age, socio-economic status and diabetes we observed a negative association between urinary 1-hydroxypyrene, the gold standard of PAH exposure biomarkers, and DSST score. A one percent increase in urinary 1-hydroxypyrene resulted in approximately a 1.8 percent poorer performance on the digit symbol substitution test. Our findings are consistent with previous publications and further suggest that PAHs, at least in part may be responsible for the adverse cognitive effects linked to tobacco smoke and particulate matter air pollution.

To target populations at greatest risk ofdeveloping cancer, it is vital to understand the patterns ofrisk factors and screening behaviors along with cancer surveillance data. This study provides data on the prevalence of self-reported cancer history, can- cer risk factors, and cancer screening behaviors in Florida and compares them to rates in the rest ofthe U.S. Data were obtained from the National Health Interview Survey (NHIS), an annual, cross-sectional household survey of the U.S. civilian non-institutionalized population. We analyzed pooled data from the 2000 and 2005 Cancer Control Modules. Compared to the rest of the U.S., Floridians reported a lower prevalence of current smoking, risky drinking, and obesity, and a greater pre- valence of Prostate Specific Antigen (PSA) testing. However, Floridians reported a greater prevalence ofcancer history and a lower prevalence of physical activity, concern for sun protection, lifetime colorectal cancer screening, lifetime breast exam, life- time mammography screening, and lifetime and past year Pap test. The data indicate that Florida lags behind the rest of the U.S. in several areas, particularly cancer screening, and falls short ofthe Healthy People 2020 objectives for health behaviors and screening. These results provide information for key stakeholders and public health policy makers to effectively target Flor- ida residents at greatest risk for cancer and those not receiving recommended cancer screenings. This study also represents a model ofvaluable state-level evaluations that can be conducted using NHIS data. These types of analyses can provide a great- er understanding of state-level variations and lead to more effective public health interventions aimed at reducing cancer burden.

Objectives: To report the clinical outcomes (early death, late death, and rate of reintervention) and performance of the Contegra conduit as a right ventricle outflow tract implant and to determine the risk factors for early reintervention. Methods: Forty-nine Contegra conduits were implanted between January 2002 and June 2009. Data collection was retrospective. The mean age and follow-up duration of Contegra recipients was 3.5 ± 4.6 years and 4.2 ± 2.0 years, respectively. Results: There were three deaths (two early, one late), giving a survival rate of 93.9%. The rate of conduit-related reintervention was 19.6% and was most often due to distal conduit stenosis. Age at implantation of 22 mm) remains the first choice of implant in older children. The rates of reintervention are significantly higher with a diagnosis of truncus arteriosus, age at implantation of