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Abstract BackgroundThere is evidence that changes to the midface and lower third of the face in isolation contribute significantly to one’s perception of the overall facial age. Since the spread of the coronavirus disease 2019 (COVID-19), mask wearing has become commonplace. To date, there have been no studies that explore how covering the lower third of the face impacts the perception of age. ObjectivesThe authors hypothesized that covering the lower third of the face with a mask will make a person appear younger. MethodsOne hundred consecutive plastic surgery patients were photographed in a standardized fashion, both masked and unmasked. A questionnaire for factors known to contribute to facial aging was administered. These photographs were randomized to 6 judges who estimated the patients’ age and also quantified facial rhytids with the validated Lemperle wrinkle assessment score of 6. Data were analyzed using PROC MIXED analysis. ResultsMasked patients on average appeared 6.17% younger (mean difference = 3.16 years, PPPP ConclusionsThe mask group appeared younger and had lower overall and visible wrinkle assessment scores when compared with the unmasked group. This has implications for our understanding of the contributions of the lower third of the face to overall perceived facial age.

The effect of mesh type on the accuracy and computational demands of a two-dimensional Godunov-type flood inundation model is critically examined. Cartesian grids, constrained and unconstrained triangular grids, constrained quadrilateral grids, and mixed meshes are considered, with and without local time stepping (LTS), to determine the approach that maximizes computational efficiency defined as accuracy relative to computational effort. A mixed-mesh numerical scheme is introduced so all grids are processed by the same solver. Analysis focuses on a wide range of dam-break type test cases, where Godunov-type flood models have proven very successful. Results show that different mesh types excel under different circumstances. Cartesian grids are 2-3 times more efficient with relatively simple terrain features such as rectilinear channels that call for a uniform grid resolution, while unstructured grids are about twice as efficient in complex domains with irregular terrain features that call for localized refinements. The superior efficiency of locally refined, unstructured grids in complex terrain is attributable to LTS; the locally refined unstructured grid becomes less efficient using global time stepping. These results point to mesh-type tradeoffs that should be considered in flood modeling applications. A mixed mesh model formulation with LTS is recommended as a general purpose solver because the mesh type can be adapted to maximize computational efficiency. © 2014 Elsevier Ltd.

The Seogu (western) portion of Daegu, Korea experiences chronic urban flooding and there is a need to increase flood detention and storage to reduce flood impacts. Since the site is densely developed, use of an underground car park as a cistern has been proposed. The stormwater management model (SWMM) is applied to study alternative hydraulic designs and overall performance, and it is shown that by linking SWMM to a two-dimensional flood inundation model, SWMM parameters can be calibrated from observations of flood extent. Calibration would otherwise not be possible because storm sewer flows are not internally monitored. This study reveals a significant sensitivity in SWMM relative to surcharge prediction, demonstrates a creative repurposing of urban infrastructure to manage extreme flood events and shows the importance of flood documentation relative to drainage infrastructure analysis and design.

Wheat powdery mildew (Blumeria graminis f. sp. tritici, Bgt) is a disease of increasing importance globally due to the adoption of high yielding varieties and modern sustainable farming technologies. Growing resistant cultivars is a preferred approach to managing this disease, and novel powdery mildew resistance genes are urgently needed for new cultivar development. A genome-wide association study was performed on a panel of 1292 wheat landraces and historical cultivars using 5011 single nucleotide polymorphism (SNP) markers. The association panel was evaluated for reactions to three Bgt inoculants, OKS(14)-B-3-1, OKS(14)-C-2-1, and Bgt15. Linkage disequilibrum (LD) analysis indicated that genome-wide LD decayed to 0.1 at 23 Mb, and population structure analysis revealed seven subgroups in the panel. Association analysis using a mixed linear model (MLM) identified three loci for powdery mildew resistance on chromosome 2B, designated QPm.stars-2BL1, QPm.stars-2BL2, and QPm.stars-2BL3. To evaluate the efficacy of GWAS in gene discovery, QPm.stars-2BL2 was validated using F2 and F2:3 populations derived from PI 420646 × OK1059060-126135-3. Linkage analysis delimited the powdery mildew resistance gene in PI 420646 to an interval where QPm.stars-2BL2 was located, lending credence to the GWAS results. QPm.stars-2BL1 and QPm.stars-2BL3, which were associated with four SNPs located at 457.7–461.7 Mb and two SNPs located at 696.6–715.9 Mb in the Chinese Spring reference IWGSC RefSeq v1.0, respectively, are likely novel loci for powdery mildew resistance and can be used in wheat breeding to improve powdery mildew resistance. Keywords: Wheat, Powdery mildew, GWAS, Linkage mapping, Genome-wide linkage disequilibrium

Airborne transmission is predicted to be a prevalent route of human exposure with SARS-CoV-2. Aside from African green monkeys, nonhuman primate models that replicate airborne transmission of SARS-CoV-2 have not been investigated. A comparative evaluation of COVID-19 in African green monkeys, rhesus macaques, and cynomolgus macaques following airborne exposure to SARS-CoV-2 was performed to determine critical disease parameters associated with disease progression, and establish correlations between primate and human COVID-19. Respiratory abnormalities and viral shedding were noted for all animals, indicating successful infection. Cynomolgus macaques developed fever, and thrombocytopenia was measured for African green monkeys and rhesus macaques. Type II pneumocyte hyperplasia and alveolar fibrosis were more frequently observed in lung tissue from cynomolgus macaques and African green monkeys. The data indicate that, in addition to African green monkeys, macaques can be successfully infected by airborne SARS-CoV-2, providing viable macaque natural transmission models for medical countermeasure evaluation.

Abstract Classical plant breeding has been instrumental in changing the genetic makeup of crop plants for better ecological adaptation and improved quality. This paper provides insights of the genomic changes effected in hard winter wheat (Triticum aestivum L.) through decades of breeding and selection in the Great Plains of the United States. Population structure and differentiation analyses were conducted on 185 wheat cultivars released from 1943 to 2013. Cultivars were grouped into four distinct clusters using discriminant analysis of principal components (DAPC). One of the clusters was unique in that 15 out of the 18 individuals were recent releases (2000–2010), while 12 of the 18 shared the cultivar ‘Jagger’ in their genetic background. Jagger carries a 2NS/2AS translocation segment from Aegilops ventricosa, an important segment for resistance to several foliar diseases. Using the outlier approach, Wright's population fixation index (Fst) identified 450 loci that were directionally selected. The largest signature of selection was found on chromosome 2A. Genetic diversity was high while the inbreeding coefficient was low, indicating extensive hybridization and germplasm exchange among breeding programs within the region. Foliar disease pressure and selection for resistance helped shape the microevolution of wheat in the southern Great Plains. The results showed that high genetic diversity remains in hard winter wheat cultivars adapted to the Great Plains of the USA, and modern plant breeding did not cause any sizable reduction in genetic diversity of the crop in this region.

Humans create vast quantities of wastewater through inefficiencies and poor management of water systems. The wasting of water poses sustainability challenges, depletes energy reserves, and undermines human water security and ecosystem health. Here we review emerging approaches for reusing wastewater and minimizing its generation. These complementary options make the most of scarce freshwater resources, serve the varying water needs of both developed and developing countries, and confer a variety of environmental benefits. Their widespread adoption will require changing how freshwater is sourced, used, managed, and priced.

Abstract Existing needs to manage flood risk in the United States are underserved by available flood hazard information. This contributes to an alarming escalation of flood impacts amounting to hundreds of billions of dollars per year and countless disrupted lives and affected communities. Making information about flood hazards useful for the range of decisions that dictate the consequences of flooding poses many challenges. Here, we describe collaborative flood modeling, whereby researchers and end‐users at two coastal sites co‐develop fine‐resolution flood hazard models and maps responsive to decision‐making needs. We find, first of all, that resident perception and awareness of flooding are enhanced more by fine‐resolution depth contour maps than Federal Emergency Management Agency (FEMA) flood hazard classification maps and that viewing fine‐resolution depth contour maps helps to minimize differences in flood perception across subgroups within the community, generating a shared understanding. We also find that collaborative flood modeling supports the engagement of a wide range of end‐users in contemplating the risks of flooding and provides strong evidence that the co‐produced knowledge can be readily adopted and applied for Flood Risk Management (FRM). Overall, collaborative flood modeling advances FRM by providing multiple points of entry for diverse groups of end‐users to contemplate the spatial extent, intensity, timing, chance, and consequences of flooding, thus enabling the web of decision‐making related to flooding to be better informed with the best available science. This transdisciplinary approach emphasizes vulnerability reduction and is complementary to FEMA Flood Insurance Rate Maps used for flood insurance administration.

Abstract Background Single nucleotide polymorphisms (SNPs) are ideally suited for the construction of high-resolution genetic maps, studying population evolutionary history and performing genome-wide association mapping experiments. Here, we used a genome-wide set of 1536 SNPs to study linkage disequilibrium (LD) and population structure in a panel of 478 spring and winter wheat cultivars (Triticum aestivum) from 17 populations across the United States and Mexico. Results Most of the wheat oligo pool assay (OPA) SNPs that were polymorphic within the complete set of 478 cultivars were also polymorphic in all subpopulations. Higher levels of genetic differentiation were observed among wheat lines within populations than among populations. A total of nine genetically distinct clusters were identified, suggesting that some of the pre-defined populations shared significant proportion of genetic ancestry. Estimates of population structure (FST) at individual loci showed a high level of heterogeneity across the genome. In addition, seven genomic regions with elevated FST were detected between the spring and winter wheat populations. Some of these regions overlapped with previously mapped flowering time QTL. Across all populations, the highest extent of significant LD was observed in the wheat D-genome, followed by lower LD in the A- and B-genomes. The differences in the extent of LD among populations and genomes were mostly driven by differences in long-range LD ( > 10 cM). Conclusions Genome- and population-specific patterns of genetic differentiation and LD were discovered in the populations of wheat cultivars from different geographic regions. Our study demonstrated that the estimates of population structure between spring and winter wheat lines can identify genomic regions harboring candidate genes involved in the regulation of growth habit. Variation in LD suggests that breeding and selection had a different impact on each wheat genome both within and among populations. The higher extent of LD in the wheat D-genome versus the A- and B-genomes likely reflects the episodes of recent introgression and population bottleneck accompanying the origin of hexaploid wheat. The assessment of LD and population structure in this assembled panel of diverse lines provides critical information for the development of genetic resources for genome-wide association mapping of agronomically important traits in wheat.

Dense cities represent biogeochemical hot spots along the shoreline, concentrating fixed nitrogen that is subsequently discharged into adjacent coastal receiving waters. Thus, the ecosystem services provided by natural systems in highly urban environments can play a particularly important role in the global nitrogen cycle. In this paper, we review the recent literature on nitrogen regulation by temperate coastal ecosystems, with a focus on how the distinct physical and biogeochemical features of the urban landscape can affect the provision of this ecosystem service. We use Jamaica Bay, an ultra-urbanized coastal lagoon in the United States of America, as a demonstrative case study. Based on simple areal and tidal-based calculations, the natural systems of Jamaica Bay remove ~ 24% of the reactive nitrogen discharged by wastewater treatment plants. However, this estimate does not represent the dynamic nature of urban nitrogen cycling represented in the recent literature and highlights key research needs and opportunities. Our review reveals that ecosystem-facilitated denitrification may be significant in even the most densely urbanized coastal landscapes, but critical uncertainties currently limit incorporation of this ecosystem service in environmental management.

Core Ideas Prediction performance for winter wheat grain yield and end‐use quality traits. Prediction accuracies evaluated by cross‐validations are significantly overestimated. Nonparametric algorithms outperform the parametric alternatives in cross‐year predictions. Strategically designing training population improves response to selection. Response to selection varies across growing seasons and environments. Considering the practicality of applying genomic selection (GS) in the line development stage of a hard red winter (HRW) wheat (Triticum aestivum L.) variety development program (VDP), the effectiveness of GS was evaluated by prediction accuracy and by the response to selection across field seasons that demonstrated challenges for crop improvement under significant climate variability. Important breeding targets for wheat improvement in the southern Great Plains of the United States, including grain yield, kernel weight, wheat protein content, and sodium dodecyl sulfate (SDS) sedimentation volume as a rapid test for predicting bread‐making quality, were used to estimate the effectiveness of GS across harvest years from 2014 (drought) to 2016 (normal). In general, nonparametric algorithms reproducing kernel Hilbert space (RKHS) and random forest (RF) produced higher accuracies in both same‐year cross‐validations (CVs) and cross‐year predictions for the purpose of line selection. Further, the stability of GS performance was greatest for SDS sedimentation volume but least for wheat protein content. To ensure long‐term genetic gain, our study on selection response suggested that across this sample of environmental variability, and though there are cases where phenotypic selection (PS) might be still preferred, training conducted under drought or in suboptimal conditions could provide an encouraging prediction outcome when selection decisions were made in normal conditions. However, it is not advisable to use training information collected from a normal season to predict trait performance under drought conditions. Finally, the superiority of response to selection was most evident if the training population (TP) can be optimized.

The impacts of climate change on natural methane (CH4) emissions via ebullition are unclear. Here, using published and experimental multi-seasonal CH4 ebullition data, the authors find a strong relationship between CH4 ebullition and temperature across a wide range of freshwater ecosystems globally.

Biosafety, biosecurity, logistical, political, and technical considerations can delay or prevent the wide dissemination of source material containing viable virus from the geographic origin of an outbreak to laboratories involved in developing medical countermeasures (MCMs). However, once virus genome sequence information is available from clinical samples, reverse-genetics systems can be used to generate virus stocks de novo to initiate MCM development. In this study, we developed a reverse-genetics system for natural isolates of Ebola virus (EBOV) variants Makona, Tumba, and Ituri, which have been challenging to obtain. These systems were generated starting solely with in silico genome sequence information and have been used successfully to produce recombinant stocks of each of the viruses for use in MCM testing. The antiviral activity of MCMs targeting viral entry varied depending on the recombinant virus isolate used. Collectively, selecting and synthetically engineering emerging EBOV variants and demonstrating their efficacy against available MCMs will be crucial for answering pressing public health and biosecurity concerns during Ebola disease (EBOD) outbreaks.

There is growing interest in the application of nature-based solutions to adapt to climate change and promote resilience, yet barriers exist to their implementation. These include a perceived lack of evidence of their functioning in comparison to conventional solutions and an inability for existing design, policy, and assessment processes to capture the multiple benefits of these solutions. Positing this as a challenge of operationalizing and measuring resilience, we argue that the concept of resilience needs to be given concrete meaning in applied management contexts. Starting with shoreline vulnerability as a policy problem and natural and nature-based shoreline features as a promising solution, we present a case study of a co-creative process to produce an interdisciplinary and locally relevant approach to understanding and capturing the benefits of natural and nature-based solutions. We develop the notion of resilience service to enable a concreteness to resilience that simultaneously takes into account ecological, technical, and social dimensions. Through the co-creative process, our researcher-practitioner network developed a monitoring framework for shoreline features in New York State to facilitate the comparison of natural and nature-based features with conventional shoreline approaches. We describe the process and assess the advantages and drawbacks of integrating scientific input and local knowledge. We present the monitoring framework, showing how the co-creative character of the process is consequential in the formulation of the final framework through the selection of parameters, indicators, and protocols. We argue that interdisciplinarity, co-creation, pragmatism, multi-scalar applicability, and policy relevance are critical principles to understand the functioning and facilitate the implementation of nature-based solutions, while recognizing that this work necessitates compromise and as such will lead to continued deliberation. We posit this is a strength of the process for it acknowledges the creation of resilience as a social process in which values are central and subject to change.