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We apply a hierarchical clustering algorithm to the Parameter-elevation Relationships on Independent Slopes Model (PRISM) database. The method employs linkage clustering while forcing spatial contiguity. We apply it to the lower-48 United States, deriving regions that are based on temperature and precipitation averages and anomalies, as well as statistical parameters underlying several drought and intense precipitation measures. Resulting regions make intuitive sense from the perspective of driving influences on temperature and precipitation averages and anomalies, and are compatible with results from another empirically derived clustering scheme. Regions selected for individual variables show high similarity across different time frames. There is slightly less similarity when comparing regions created for different monthly or daily hydroclimate variables, and relatively low similarity between monthly vs. daily measures. It is unlikely that any one regionalization solution could summarize hydroclimate extremes given the wide range of variables used to describe them, but geographically sensitive datasets like PRISM and flexible algorithms provide useful methods for regionalization that can aid in drought monitoring and forecasting, and with impacts and planning associated with heavy precipitation. [ABSTRACT FROM AUTHOR]

The serovars of Salmonella enterica display dramatic differences in pathogenesis and host preferences. We developed a process (patent pending) for grouping Salmonella isolates and serovars by their public health risk. We collated a curated set of 12,337 S. enterica isolate genomes from human, beef, and bovine sources in the US. After annotating a virulence gene catalog for each isolate, we used unsupervised random forest methods to estimate the proximity (similarity) between isolates based upon the genomic presentation of putative virulence traits We then grouped isolates (virulence clusters) using hierarchical clustering (Ward's method), used non-parametric bootstrapping to assess cluster stability, and externally validated the clusters against epidemiological virulence measures from FoodNet, the National Outbreak Reporting System (NORS), and US federal sampling of beef products. We identified five stable virulence clusters of S. enterica serovars. Cluster 1 (higher virulence) serovars yielded an annual incidence rate of domestically acquired sporadic cases roughly one and a half times higher than the other four clusters combined (Clusters 2–5, lower virulence). Compared to other clusters, cluster 1 also had a higher proportion of infections leading to hospitalization and was implicated in more foodborne and beef-associated outbreaks, despite being isolated at a similar frequency from beef products as other clusters. We also identified subpopulations within 11 serovars. Remarkably, we found S. Infantis and S. Typhimurium subpopulations that significantly differed in genome length and clinical case presentation. Further, we found that the presence of the pESI plasmid accounted for the genome length differences between the S. Infantis subpopulations. Our results show that S. enterica strains associated with highest incidence of human infections share a common virulence repertoire. This work could be updated regularly and used in combination with foodborne surveillance information to prioritize serovars of public health concern. [ABSTRACT FROM AUTHOR]

Objectives: Blunted cardiovascular reactivity is associated with a distinct behavioural profile of greater exposure to early life adversity, coupled with higher levels of behavioural disengagement and symptoms of depression. The present study sought to extend on this work by investigating if behavioural clusters with distinct patterns of reactivity were related to health and behavioural outcomes at baseline and at a 4‐year follow‐up. Methods: Hierarchical cluster analyses were conducted using longitudinal data drawn from the Midlife Development in the United States (MIDUS 2) Biomarker Project and the MIDUS 3 follow‐up 4 years later. During MIDUS, 2 participants (N = 513) underwent a standardized stress testing protocol and had their blood pressure and heart rate monitored throughout. In addition, hierarchical cluster analyses were conducted on responses from measures of early life adversity, behavioural disengagement and depression. Binary logistic regressions were conducted to determine whether cluster membership was related to health and behavioural outcomes which were taken at both time points. Results: Three behavioural clusters emerged with statistically different blood pressure reactivity patterns. The cluster characterized by greater exposure to early life adversity, higher levels of behavioural disengagement and depressive symptoms, had relatively lower blood pressure reactivity patterns compared with both the exaggerated reactivity cluster and the cluster similar to the sample mean. In fully adjusted models, this cluster was associated with hypertension (p =.050) and depressed affect (p =.033), while Cluster 1 characteristic of an exaggerated blood pressure reactivity profile was associated with depressed affect (p <.001). Cluster membership did not significantly predict future health status. Conclusion: This study extends research on behavioural clusters characteristic of reactivity profiles to demonstrate how they relate to health and behavioural outcomes during MIDUS 2. [ABSTRACT FROM AUTHOR]

The Ogallala Aquifer, located in the Central Plains of the United States, is essential for agricultural irrigation and public water supply. Indiscriminate pumping from the aquifer has caused several negative impacts, such as deterioration of water quality and depletion of groundwater levels, which urgently demand better management. This paper applies hierarchical cluster analysis (HCA) and artificial neural networks (ANNs) for predicting annual groundwater levels in 403 wells of the Ogallala Aquifer. First, the methodology employed HCA to cluster homogeneous wells based on the time series of groundwater levels. Then, the study calibrated an ANN model for each cluster (composed of one or more wells) using previous annual values of groundwater levels as input. The HCA results showed a particular pattern in the spatial distribution of the 30 found clusters, revealing that the Ogallala Aquifer holds higher groundwater levels in the western part, which gradually decrease, advancing to the east. The ANN models provided proper predictions even for wells outside of the calibration data set. This investigation concludes that the integration of HCA and ANN enabled single models to accurately forecast annual groundwater levels for sets of wells in the Ogallala Aquifer. [ABSTRACT FROM AUTHOR]

This paper uses agglomerative hierarchical cluster analysis to group 2778 farming-defined counties into six clusters, revealing farm patterns across the contiguous 48 states of the United States. To understand the differences in economic performance and improve farm households' well-being, economists have endeavored to identify patterns in US farming. The US is a leading global producer and exporter of many agricultural and food products. Our primary objective is to construct a policy-relevant farm clustering to characterize agricultural homogeneity in US farms' production potential. We identify six clusters that are relatively homogeneous in five dimensions: farm size, farm assets, farm labor, farm output, degree of mechanization, and government programs. Minimizing diversity within a cluster allows for analysis of public policy changes on specific clusters and comparison of differential effects of the change across clusters. [ABSTRACT FROM AUTHOR]

The main goal of this analysis was to identify mortality patterns apparent when many drug classes are analyzed together. The Drug Involved Mortality database is a registry of drug terms mentioned on death certificates of all drug-related deaths in the United States. Means of total number of drugs involved and percentages of specific drug combinations were calculated. Dimensionality reduction using multiple correspondence analysis and hierarchical clustering identified clusters of drugs listed on death certificates. An average of 2.4 specific drugs were listed on death certificates in 2017. For 9 of the top 10 drugs involved, over 80% of deaths involved at least one other drug. As expected, opioid drugs and psychostimulants clustered together, but other psychoactive substances (non-opioid analgesics, sedatives, antidepressants, antipsychotics) clustered together into multi-class groups. Other drugs (e.g., acetaminophen, oxymorphone) were frequently involved in polysubstance death, but did not cluster with any other specific drug. Deaths involving illicit drugs listed fewer drugs than deaths involving prescription drugs. While individual drug substances might contribute to many deaths (e.g., fentanyl), polysubstance mortality is more common than single substance mortality. Multidimensional analyses integrating all drugs involved are useful to identify uncommon patterns of overdose and changing trends. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study was to explore body shapes among overweight and obese men and examine fit issues based on the current ASTM sizing standards related to the categorized body shapes. Design/methodology/approach: The SizeUSA data and the additional data extracted using the ImageTwin (TC2-19) software were used. To categorize body shapes, principal component (PC) analysis with varimax rotation, hierarchical cluster analysis for an elbow method and K-mean cluster analysis were employed. Comparing the categorized body shapes and ASTM sizing charts, a cross-tabulation was performed to test associations between fit analyses for top and bottom for the body shape groups. Furthermore, an analysis of variance and pairwise comparison were performed to identify differences in mean values of size drops between two body parts across the different body shape groups. Findings: Using a three-dimensional (3D) body scanning technology and 3D virtual avatars, three body shapes for overweight and obese men emerged: Rectangle-curvy, bottom hourglass-hip tilt and top hourglass-straight shapes. Further, overweight and obese male consumers are not likely to find a perfect fit from apparel companies who developed their sizing charts based on the men's and big men's ASTM standards. Notably, the big men's ASTM sizing standard did not work for most overweight and obese men the United States. Originality/value: Despite the notable increase in the US population that is overweight and obese, most overweight and obese men have had fit problems due to the differences in their body shapes as compared to the standardized body shape used in the current sizing system. The results of this study suggest apparel companies who are targeting overweight and obese male consumers in the United States updating their sizing systems in order to solve fit problems. [ABSTRACT FROM AUTHOR]

Barley stripe rust is a relatively new disease in the United States. The pathogen, Puccinia striiformis f. sp. hordei (Psh), was first observed in Texas in 1991 and has spread north and westwards and mainly caused epidemics in the western United States. A total of 447 isolates collected from 1993 to 2017 were identified as 382 multilocus genotypes (MLGs) using 14 simple sequence repeat markers. The MLGs were clustered into six molecular groups (MGs) using the discriminant analysis of principal components and the hierarchical cluster analysis, and the MGs had significant differences in frequency in different years. MGI was present in the population prior to the year 2000. MG2, MG3, and MG4 became predominate after 2000. MG5 was detected in all 24 years but more frequent from 2010 to 2017. MG6 was the most recent group detected mainly from 2011 to 2017 and had the highest correlation coefficient with the virulence phenotypes among the MGs. The heterozygosity and genotypic diversity of the Psh populations increased from 2000 to 2017, even more from 2010 to 2017. The results indicate rapid genetic changes from year to year, with major molecular group changes around 2000 and 2010. The possible mechanisms underlying the population changes are discussed. [ABSTRACT FROM AUTHOR]

In recent decades, wildfires in many areas of the United States (U.S.) have become larger and more frequent with increasing anthropogenic pressure, including interactions between climate, land-use change, and human ignitions. We aimed to characterize the spatiotemporal patterns of contemporary fire characteristics across the contiguous United States (CONUS). We derived fire variables based on frequency, fire radiative power (FRP), event size, burned area, and season length from satellite-derived fire products and a government records database on a 50 km grid (1984–2020). We used k-means clustering to create a hierarchical classification scheme of areas with relatively homogeneous fire characteristics, or modern 'pyromes,' and report on the model with eight major pyromes. Human ignition pressure provides a key explanation for the East-West patterns of fire characteristics. Human-dominated pyromes (85% mean anthropogenic ignitions), with moderate fire size, area burned, and intensity, covered 59% of CONUS, primarily in the East and East Central. Physically dominated pyromes (47% mean anthropogenic ignitions) characterized by relatively large (average 439 mean annual ha per 50 km pixel) and intense (average 75 mean annual megawatts/pixel) fires occurred in 14% of CONUS, primarily in the West and West Central. The percent of anthropogenic ignitions increased over time in all pyromes (0.5–1.7% annually). Higher fire frequency was related to smaller events and lower FRP, and these relationships were moderated by vegetation, climate, and ignition type. Notably, a spatial mismatch between our derived modern pyromes and both ecoregions and historical fire regimes suggests other major drivers for modern U.S. fire patterns than vegetation-based classification systems. This effort to delineate modern U.S. pyromes based on fire observations provides a national-scale framework of contemporary fire regions and may help elucidate patterns of change in an uncertain future. [ABSTRACT FROM AUTHOR]

To find the trend and hot points of deafness gene research in the nearly 10 years, a bibliometric analysis of literature in the fields from Web of Science was performed. Python 3.7, Gephi 0.9.2, and SPSS 22.0 were used for cleaning, restructuring, and analysis. A total of 2828 article documents were collected, which were from 651 Science Citation Index Expanded journals, 93 countries/regions, 18 199 authors, and 3148 organizations. PLoS One published more papers than other journals. Among the journals that published more than 30 papers, 10 journals were included in the genetics & heredity class. The top country/region with highest number of papers was United States. However, the top author and top affiliation was Dai Pu (China) and People's Liberate Army General Hospital (China), respectively. The results of co-occurrence network, hierarchical clustering, and multidimensional scale analysis of keywords showed that 2 groups were classified. The 2 groups focused on the gene mutation of deafness and the research objects, methods, factors, and gene locus, respectively. Multidimensional scale analysis told the research character and some research details. The gene mutation and variant of family and population diseases were the hot points. [ABSTRACT FROM AUTHOR]

Higher education has historically focused on demographics to target prospective students in recruitment and retention efforts. This study focuses on the effect of psychographic and behavioral elements at the learner level to identify student segments and to influence the outcomes that lead to retention and ultimately graduation. Psychographics that include several motivation, resource, and demographic variables were used to segment 245 undergraduate college students from a four year medium size AACSB accredited state school in the United States. Results from a Hierarchical Cluster Analysis identify four segments that differ significantly in terms of not only motivation, resource, and demographic variables but also outcome variables such as academic achievement, satisfaction, and university loyalty. Findings suggest students to be heterogeneous needing different interventions targeted to different student groups. Discussion includes implications for students, instructors, and administrators, as well as actions that may positively influence retention, and graduation efforts among different student segments. [ABSTRACT FROM AUTHOR]

Commercial buildings account for one third of the total electricity consumption in the United States and a significant amount of this energy is wasted. Therefore, there is a need for "virtual" energy audits, to identify energy inefficiencies and their associated savings opportunities using methods that can be non-intrusive and automated for application to large populations of buildings. Here we demonstrate virtual energy audits applied to large populations of buildings' time-series smart-meter data using a systematic approach and a fully automated Building Energy Analytics (BEA) Pipeline that unifies, cleans, stores and analyzes building energy datasets in a non-relational data warehouse for efficient insights and results. This BEA pipeline is based on a custom compute job scheduler for a high performance computing cluster to enable parallel processing of Slurm jobs. Within the analytics pipeline, we introduced a data qualification tool that enhances data quality by fixing common errors, while also detecting abnormalities in a building's daily operation using hierarchical clustering. We analyze the HVAC scheduling of a population of 816 buildings, using this analytics pipeline, as part of a cross-sectional study. With our approach, this sample of 816 buildings is improved in data quality and is efficiently analyzed in 34 minutes, which is 85 times faster than the time taken by a sequential processing. The analytical results for the HVAC operational hours of these buildings show that among 10 building use types, food sales buildings with 17.75 hours of daily HVAC cooling operation are decent targets for HVAC savings. Overall, this analytics pipeline enables the identification of statistically significant results from population based studies of large numbers of building energy time-series datasets with robust results. These types of BEA studies can explore numerous factors impacting building energy efficiency and virtual building energy audits. This approach enables a new generation of data-driven buildings energy analysis at scale. [ABSTRACT FROM AUTHOR]

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Predicting how forest carbon cycling will change in response to climate change and management depends on the collective knowledge from measurements across environmental gradients, ecosystem manipulations of global change factors, and mathematical models. Formally integrating these sources of knowledge through data assimilation, or model-data fusion, allows the use of past observations to constrain model parameters and estimate prediction uncertainty. However, the influence of different experimental treatments on those predictions depends on the exact methods and techniques used for data assimilation. Here, we introduce a hierarchical Bayesian DA approach (Data Assimilation of Pine Plantation Ecosystem Research, DAPPER) that uses observations of carbon stocks, carbon fluxes, water fluxes, and vegetation dynamics from loblolly pine plantation ecosystems across the Southeastern U.S. to constrain parameters in a modified version of the 3-PG forest growth model. The observations included major experiments that manipulated atmospheric carbon dioxide (CO2) concentration, water, and nutrients, along with non-experimental studies that spanned environmental gradients across an 8.6 × 105 km2 region. We optimized regionally representative posterior distributions for the most sensitive model parameters, which dependably predicted data from plots withheld from the data assimilation. The posterior distributions of parameters associated with ecosystem responses to CO2, precipitation, and nutrient addition, along with the corresponding regional changes in production associated with nutrient fertilization and drought, depended on how the experimental data were assimilated. In particular, assimilating nutrient addition experiments reduced the predicted sensitivity to nutrient fertilization while assimilated water manipulation experiments increased the sensitivity to drought. Further, it was necessary to assimilate data from the CO2 experimental enrichment site before other studies to constrain the parameters associated with the influence of CO2 on canopy photosynthesis. The ambient CO2 plots were numerous and had a large contribution to the cost function compared to the low number of elevated CO2 plots (289 ambient vs. 5 elevated plots). Overall, we demonstrated how three decades of research in southeastern U.S. planted pine forests can be used to develop data assimilation techniques that use multiple locations, multiple data streams, and multiple ecosystem experiment types to optimize parameters. This approach allows for future predictions to be consistent with a rich history of ecosystem research across a region. [ABSTRACT FROM AUTHOR]

Interpretation of regional scale, multivariate geochemical data is aided by a statistical technique called “clustering.” We investigate a particular clustering procedure by applying it to geochemical data collected in the State of Colorado, United States of America. The clustering procedure partitions the field samples for the entire survey area into two clusters. The field samples in each cluster are partitioned again to create two subclusters, and so on. This manual procedure generates a hierarchy of clusters, and the different levels of the hierarchy show geochemical and geological processes occurring at different spatial scales. Although there are many different clustering methods, we use Bayesian finite mixture modeling with two probability distributions, which yields two clusters. The model parameters are estimated with Hamiltonian Monte Carlo sampling of the posterior probability density function, which usually has multiple modes. Each mode has its own set of model parameters; each set is checked to ensure that it is consistent both with the data and with independent geologic knowledge. The set of model parameters that is most consistent with the independent geologic knowledge is selected for detailed interpretation and partitioning of the field samples. [ABSTRACT FROM AUTHOR]

Warm season dry spells over the central and eastern United States are classified into three canonical types via a hierarchical cluster analysis for the period 1950-2005. Four CMIP5 models exhibit diverging skill in representing the observed behavior, ranging from southern Great Plains dry spells that are reasonably simulated by all four models to southeastern U.S. dry spells that are only accurately captured by one model. A model's skill in representing a particular dry spell cluster is positively correlated with the model's ability to simulate the large-scale meteorological patterns (LMPs) accompanying the dry spell. The interannual variability and overall observed decreasing trend in dry spell days are represented with varying degrees of accuracy by the four models. The results 1) highlight existing shortcomings in the climate model representation of regional dry spells and 2) illustrate the importance of properly simulating the observed spectrum of LMPs in minimizing these shortcomings. [ABSTRACT FROM AUTHOR]

Objective: This study seeks to better understand the relationship between neighborhood disability concentration and police calls for assault with a deadly weapon. Is this relationship the result of neighborhood concentrated disadvantage, or does disability act independently of other ecological characteristics associated with high crime rates? Methods: The authors combine Census and other neighborhoodlevel data from Washington, DC to test a one-level random intercept hierarchical multiple regression model using Census tracts as a grouping variable. Disability concentration is measured by the percent of disabled residents living in a block group. Concentrated disadvantage is a composite measure including percent households below poverty line, percent families on public assistance, percent African American, percent female-headed households with children, and percent unemployed. Assault with a deadly weapon is a rate per 1,000 of police calls for assault in 2005-2006. Results: The effect of disability concentration is partially mediated by other ecological factors, but remains a significant predictor of neighborhood rates of reported assault. Each one-unit increase in percent disabled increased police calls for assault by 0.14 %. Conclusions: The results of the analyses suggest that although concentrated disadvantage does affect the relationship between disability concentration and crime, it exerts an independent effect on neighborhood rates of assault with a deadly weapon. [ABSTRACT FROM AUTHOR]

In a context of debate over the future of the US Federal Aviation Administration's (FAA) funding model, this paper revisits the current system of airport classification used for the allocation of public funding for capacity developments. Previous papers have already addressed the limitations of the FAA's uni-dimensional method, and proposed new approaches that take into account the two dimensions of “hubbing” activity, i.e., traffic generation and connectivity. However, these studies are biased by the lack of detailed demand data on international connections. Using an MIDT dataset comprising a sample of domestic and international markets served by US airports during the first quarter of 2013, this paper aims at providing a full picture on the pitfalls of the existing FAA method, as well as addressing the impact of international connectivity in characterising the airports' hubbing profiles. Hierarchical clustering is used to provide alternative criteria for hub classification within the context of US National Plan of Integrated Airport Systems (NPIAS). This new typology of primary US airports can help to optimize AIP funding by allowing for further differentiation in the FAA allocation criteria. [ABSTRACT FROM AUTHOR]

• Community structure concept is used for catchment classification. • A modularity density-based edge betweenness method (MDEB) is proposed. • The method is applied to large catchment networks in Australia and the United States. • Different sizes of networks of catchments are analyzed to examine the scale problem. • The MDEB method performs better than the traditional EB method. Catchment classification is useful for a variety of purposes in hydrologic, environmental, and ecosystem studies. In the context of classification, the concept of community structure , within the realm of complex networks, is particularly attractive and gaining attention in catchment classification studies. Among the many community structure methods, the edge betweenness (EB) method, which applies a hierarchical clustering concept, is one of the most widely used. The EB method, however, is susceptible to the issue of scale (or size) of the network, essentially due to the modularity function that is used to measure the strength of the community structure. To overcome this limitation, the present study proposes an improvement to the EB method. The proposed method, termed as the Modularity Density-based Edge Betweenness (MDEB) method, uses a modularity density function (or D value) by maximization, to obtain the best split of the network. The effectiveness of the MDEB method is evaluated through its application for catchment classification using streamflow data from two large networks: 218 stations from Australia and 639 stations from the United States (US). For each network, three different scenarios in network sizes are studied: (1) the entire network; (2) smaller network sizes based on 100 random realizations, with each realization having 100 and 300 stations for Australia and the US, respectively; and (3) smaller network sizes based on nine different drainage division regions in Australia and 18 different hydrologic units in the US. The classification outcomes from the MDEB method for these three scenarios are compared with those from the EB method. The results indicate that the MDEB method generally performs better than the EB method, for both Australia and the US. The superiority of the MDEB method is evaluated in terms of the number of communities identified and the number of stations that change communities when different network sizes are considered. The catchment communities are also interpreted in terms of the distance-correlation relationship. The results from the present study offer further evidence as to the usefulness and effectiveness of the community structure concept for catchment classification, especially the proposed MDEB method. [ABSTRACT FROM AUTHOR]

Accurate clustering of airports enables airport authorities and operators to precisely position themselves in the competitive market, facilitates airlines to efficiently allocate resources, and empowers the research community with credible data for new discoveries. Existing clustering studies largely use traffic volume, network connectivity, or operational efficiency to group airports into hierarchical clusters or parallel partitions. Developing a different perspective, this study focuses upon a key bottleneck of outbound passenger movements in airport terminals, the security checkpoints, and uses the Transportation Security Administration (TSA) Customer Throughput/Wait Times Reports to cluster hub airports of the United States. With the input data structured as univariate time-series, k-shape , a clustering algorithm that is robust to time axis distortion and computationally efficient, is selected to analyze the similarity of time-series using shape-based distance. The clustering results are validated by examining the raw and z-normalized data of selected airport clusters on six sampled dates. Analysis results indicate that k-shape is competent and efficient to process and cluster time-series data used for this specific research. This study offers a fresh perspective to cluster commercial airports using an infrequently employed dataset. The clustering results reveal how the geographical location, hub status in airlines' operational network, and destination type of an airport affect the movement of outbound passengers through terminals [ABSTRACT FROM AUTHOR]

• We examined mixed-conifer forest structure resulting from an active fire regime. • The diversity of forest structures present relied on a range of fire and biophysical variables. • Time since high-severity fire is a primary driver of forest structure. • Repeat fires and interactions with biophysical variables further modify structure. • A process-based approach using managed wildfire could improve adaptive capacity of mixed-conifer systems. Nearly a century of fire suppression in most forested land of the United States has limited researchers' ability to construct and rigorously test conceptual models of forest structural development in mixed-conifer ecosystems. As a result, land managers must rely on conceptual models of forest development that may overemphasize idealized stand structures and developmental pathways, which ultimately hampers management of many forest systems for resilience to future climate change impacts. We sought to determine the relative importance of fire history (frequency, severity, and time since fire) and biophysical variables on forest structural diversity and development. Importantly, we conducted our study in an ecosystem with a contemporary active fire regime where wildland fire has been managed as an ecosystem process for four decades. Using data from unburned (≥80 years since fire), once-burned and twice-burned mixed-conifer forests in the Bob Marshall Wilderness of Northwest Montana, we conducted a hierarchical clustering analysis to identify forest stand structure classes. We then used a Classification and Regression Tree analysis, combined with other post-hoc analyses, to elucidate the biophysical and disturbance history drivers that lead to each structure class. The cluster analysis revealed six forest structure classes. The CART analysis indicated that time since fire plays a large role in determining forest structure, but at intermediate time scales structure is further shaped by repeat fires and interactions with biophysical variables. The CART and post-hoc analyses did not, however, indicate a singular fire history or biophysical pathway to any one structure class. We synthesize our results in a conceptual model of forest structural development under an active fire regime. This model supports existing theory that succession following severe fire plays a large role in shaping forest structure. It also recognizes the role of fire at variable severities and frequencies, the physical environment, and tree community composition in influencing forest structural development. The complexity of forest structure and development generated by an active fire regime points to the need to incorporate a process-based view of wildfire if the goal is to manage for improved resiliency and adaptive capacity to future climate change impacts. [ABSTRACT FROM AUTHOR]

Background Pertussis is a highly contagious, vaccine-preventable respiratory disease. Historically, pertussis incidence was cyclic with peaks in disease every 3–5 years. In the United States, reported pertussis has increased over the past few decades despite high vaccination coverage; however, there is currently no clear national spatiotemporal pattern. We aimed to assess whether geographically distinct areas in the United States: (1) share similar temporal patterns (trend and periodicity), and (2) were synchronous in peaks in pertussis incidence. Methods We used pertussis cases reported through the National Notifiable Diseases Surveillance System, and CDC Wonder bridged-race county population estimates, for 2000–2017. County-level pertussis case counts were aggregated by month, and counties were aggregated into population areas of ≥300,000 persons. For each population area, trend and dominant periods across the study period were extracted using wavelet analysis. Common temporal patterns were identified using hierarchical cluster analysis of trend and periodicity. Synchrony between population area pairs, and between each area and the country as a whole, were assessed using wavelet coherence and phase difference. Results There was substantial variability in temporal patterns, though geographically distinct population areas clustered by trend and similar dominant periods of 8 to <19 months, 19 to <38 months, and 38 to 71 months, with three main patterns accounting for 79% (400/506) of all population areas (Figures 1 and 2). The majority of areas had a background period of at least 38 months, and 87% (439/506) of population areas experienced a positive trend. However, only 37% (185/506) of areas were synchronous with the national time series at any time during 2000–2017. Conclusion Spatiotemporal patterns in pertussis incidence are complex, and are heterogeneous across the United States. Although a background period of at least 38 months was identified in the majority of areas, similar to the historic perception of a 3–5-year cycle, higher frequency components were also identified. A better understanding of the current spatiotemporal patterns of pertussis will allow us to better characterize current epidemiology and improve prediction of future outbreaks. Disclosures All authors: No reported disclosures. [ABSTRACT FROM AUTHOR]

Maize (Zea mays L.) grain yield and compositional quality are interrelated and are highly influenced by environmental factors such as temperature, total precipitation, and soil water storage. Our aim was to develop a regression model to account for this relationship among grain yield and compositional quality traits across a large geographical region. Three key growth periods were used to develop algorithms based on the week of emergence, the week of 50% silking, and the week of maturity that enabled collection and modeling of the effect of weather and climatic variables across the major maize growing region of the United States. Principal component analysis (PCA), stepwise linear regression models, and hierarchical clustering analyses were used to evaluate the multivariate relationship between weather, grain quality, and yield. Two PCAs were found that could identify superior grain compositional quality as a result of ideal environmental factors as opposed to low-yielding conditions. Above-average grain protein and oil levels were favored by less nitrogen leaching during early vegetative growth and higher temperatures at flowering, while greater oil than protein concentrations resulted from lower temperatures during flowering and grain fill. Water availability during flowering and grain fill was highly explanatory of grain yield and compositional quality. [ABSTRACT FROM AUTHOR]

Meteorological processes play a vital role in air quality simulations by affecting atmospheric chemical kinetics, emissions, advection, diffusion, and deposition. We quantify the impact of meteorological variability on the Community Multiscale Air Quality (CMAQ) model simulated particulate matter of aerodynamic diameter 2.5 µm or smaller (PM2.5) over the contiguous United States (CONUS). The meteorological variability is represented using the Short- Range Ensemble Forecast (SREF) produced operationally by the National Oceanic and Atmospheric Administration (NOAA). A hierarchical cluster analysis technique is applied to down-select a subset of the SREF members that objectively account for the overall meteorological forecast variability of SREF. Three SREF members are selected to drive offline CMAQ simulations during January, April, July, and October 2016. Model results show that the meteorological variability affects all the processes that control PM2.5, but has a stronger influence on emissions, vertical diffusion, aerosol processes, cloud processes, and dry deposition compared to horizontal advection, vertical advection, and horizontal diffusion. Averaged over CONUS, CMAQ simulations driven by all three meteorological configurations capture the observed daytime low and nighttime high PM2.5 mass concentrations but underestimated the observed concentrations. PM2.5 levels across the three simulations agreed well during daytime but showed larger variability during nighttime. The integrated process analysis shows that vertical diffusion is the main driver for larger nighttime variability except during October when both the emissions and vertical diffusion play essential roles. The meteorology-induced variability in PM2.5 is estimated to be 0.08–24 µg/m3 over the CONUS with more substantial variability over the eastern U.S. [ABSTRACT FROM AUTHOR]