SSN2: The next generation of spatial stream network modeling in R.

The SSN2 R package provides tools for spatial statistical modeling, parameter estimation, and prediction on stream (river) networks. SSN2 is the successor to the SSN R package (Ver Hoef, Peterson, Clifford, & Shah, 2014), which was archived alongside broader changes in the R-spatial ecosystem (Nowosad, 2023) that included 1) the retirement of rgdal (Bivand, Keitt, & Rowlingson, 2021), rgeos (Bivand & Rundel, 2020), and maptools (Bivand & Lewin-Koh, 2021) and 2) the lack of active development of sp (Bivand, Pebesma, & Gómez-Rubio, 2013). SSN2 maintains compatibility with the input data file structures used by the SSN R package but leverages modern R-spatial tools like sf (Pebesma, 2018). SSN2 also provides many useful features that were not available in the SSN R package, including new modeling and helper functions, enhanced fitting algorithms, and simplified syntax consistent with other R generic functions.

Household Transmission of Mpox to Children and Adolescents, California, 2022.

BACKGROUND: In California, the 2022 mpox outbreak cumulated 5572 cases, 20% of US cases, as of November 28, 2022; 0.3% of cases were among children <16 years old. The secondary attack rate (SAR) for children sharing households with infected adults is unknown. METHODS: A line list of pediatric mpox household contacts aged <16 years reported through August 31, 2022 was created. It included demographic and clinical information on the contacts. Pediatric contact lists were crossmatched with the state vaccination database to identify those who received postexposure prophylaxis (PEP) with the JYNNEOS vaccine. RESULTS: We identified 129 pediatric household contacts with median age of 7 years (range, 0-15 years). Among 18 symptomatic contacts, 12 (66.7%) underwent mpox testing; 5 (41.2%) were confirmed cases, 6 (50%) were negative, and 1 (0.8%) had an indeterminate result. Six symptomatic children were not tested for mpox (33.3%). Overall, 6 infected contacts were identified, resulting in a SAR of 4.7% (6 of 129). The majority of pediatric household contacts and 4 of 6 infected children identified as Hispanic/Latino. Only 18 children (14%) reported receiving PEP. CONCLUSIONS: The SAR was overall low among pediatric household contacts; none had severe disease. This may be underestimated given low testing rates.

Indexing and partitioning the spatial linear model for large data sets.

We consider four main goals when fitting spatial linear models: 1) estimating covariance parameters, 2) estimating fixed effects, 3) kriging (making point predictions), and 4) block-kriging (predicting the average value over a region). Each of these goals can present different challenges when analyzing large spatial data sets. Current research uses a variety of methods, including spatial basis functions (reduced rank), covariance tapering, etc, to achieve these goals. However, spatial indexing, which is very similar to composite likelihood, offers some advantages. We develop a simple framework for all four goals listed above by using indexing to create a block covariance structure and nearest-neighbor predictions while maintaining a coherent linear model. We show exact inference for fixed effects under this block covariance construction. Spatial indexing is very fast, and simulations are used to validate methods and compare to another popular method. We study various sample designs for indexing and our simulations showed that indexing leading to spatially compact partitions are best over a range of sample sizes, autocorrelation values, and generating processes. Partitions can be kept small, on the order of 50 samples per partition. We use nearest-neighbors for kriging and block kriging, finding that 50 nearest-neighbors is sufficient. In all cases, confidence intervals for fixed effects, and prediction intervals for (block) kriging, have appropriate coverage. Some advantages of spatial indexing are that it is available for any valid covariance matrix, can take advantage of parallel computing, and easily extends to non-Euclidean topologies, such as stream networks. We use stream networks to show how spatial indexing can achieve all four goals, listed above, for very large data sets, in a matter of minutes, rather than days, for an example data set.

Understanding links between water-quality variables and nitrate concentration in freshwater streams using high frequency sensor data.

Real-time monitoring using in-situ sensors is becoming a common approach for measuring water-quality within watersheds. High-frequency measurements produce big datasets that present opportunities to conduct new analyses for improved understanding of water-quality dynamics and more effective management of rivers and streams. Of primary importance is enhancing knowledge of the relationships between nitrate, one of the most reactive forms of inorganic nitrogen in the aquatic environment, and other water-quality variables. We analysed high-frequency water-quality data from in-situ sensors deployed in three sites from different watersheds and climate zones within the National Ecological Observatory Network, USA. We used generalised additive mixed models to explain the nonlinear relationships at each site between nitrate concentration and conductivity, turbidity, dissolved oxygen, water temperature, and elevation. Temporal auto-correlation was modelled with an auto-regressive-moving-average (ARIMA) model and we examined the relative importance of the explanatory variables. Total deviance explained by the models was high for all sites (99%). Although variable importance and the smooth regression parameters differed among sites, the models explaining the most variation in nitrate contained the same explanatory variables. This study demonstrates that building a model for nitrate using the same set of explanatory water-quality variables is achievable, even for sites with vastly different environmental and climatic characteristics. Applying such models will assist managers to select cost-effective water-quality variables to monitor when the goals are to gain a spatial and temporal in-depth understanding of nitrate dynamics and adapt management plans accordingly.

Impact of age at endoscopic metopic synostosis repair on anthropometric outcomes.

OBJECTIVE: Endoscopic strip craniectomy for metopic craniosynostosis relies on rapid growth and postoperative helmeting for correction. Endoscopic repair is generally performed before patients reach 4 months of age, and outcomes in older patients have yet to be quantified. Here, the authors examined a cohort of patients treated with endoscopic repair before or after 4 months of age to determine aesthetic outcomes of delayed repairs. METHODS: Data from eligible patients were retrospectively assessed and aggregated in a dedicated metopic synostosis database. Inclusion criteria were radiographically confirmed metopic synostosis and endoscopic treatment. Patients were dichotomized into two groups: those younger than 4 months and those 4 months or older at the time of repair. The frontal width and interfrontal divergence angle (IFDA) were measured on reconstructed CT images. These measurements, alongside operative time, estimated blood loss, and transfusion rates, were compared between groups using the Student t-test or chi-square test. RESULTS: The study population comprised 28 patients treated before 4 months of age and 8 patients treated at 4-6 months of age. Patient sex and perioperative complications did not differ by age group. Older age at repair was not significantly associated with 1-year postoperative IFDA (140° ± 4.2° vs 142° ± 5.0°, p = 0.28) or frontal width (84 ± 5.2 vs 83 ± 4.4 mm, p = 0.47). CONCLUSIONS: One-year postoperative IFDA and frontal width do not differ significantly between patients treated before and after 4 months of age. Further study with longer follow-up is necessary to confirm the longevity of these results at skeletal maturity.

Increasing Incidence of Invasive Group A Streptococcus Disease, Idaho, USA, 2008-2019.

We investigated invasive group A Streptococcus epidemiology in Idaho, USA, during 2008-2019 using surveillance data, medical record review, and emm (M protein gene) typing results. Incidence increased from 1.04 to 4.76 cases/100,000 persons during 2008-2019. emm 1, 12, 28, 11, and 4 were the most common types, and 2 outbreaks were identified. We examined changes in distribution of clinical syndrome, patient demographics, and risk factors by comparing 2008-2013 baseline with 2014-2019 data. Incidence was higher among all age groups during 2014-2019. Streptococcal toxic shock syndrome increased from 0% to 6.4% of cases (p = 0.02). We identified no differences in distribution of demographic or risk factors between periods. Results indicated that invasive group A Streptococcus is increasing among the general population of Idaho. Ongoing surveillance of state-level invasive group A Streptococcus cases could help identify outbreaks, track regional trends in incidence, and monitor circulating emm types.

Regulating Singlet-Triplet Energy Gaps through Substituent-Driven Modulation of the Exchange and Coulomb Interactions.

Control of the singlet-triplet energy gap (ΔEST) is central to realizing productive energy conversion reactions, photochemical reaction trajectories, and emergent applications that exploit molecular spin physics. Despite this, no systematic methods have been defined to tune ΔEST in simple molecular frameworks, let alone by an approach that also holds chromophore size and electronic structural parameters (such as the HOMO-LUMO gap) constant. Using a combination of molecular design, photophysical and potentiometric experiments, and quantum chemical analyses, we show that the degree of electron-electron repulsion in excited singlet and triplet states may be finely controlled through the substitution pattern of a simple porphyrin absorber, enabling regulation of relative electronically excited singlet and triplet state energies by the designed restriction of the electron-electron Coulomb (J) and exchange (K) interaction magnitudes. This approach modulates the ΔEST magnitude by controlling the densities of state in the occupied and virtual molecular orbital manifolds, natural transition orbital polarization, and the relative contributions of one electron transitions involving select natural transition orbital pairs. This road map, which regulates electron density overlaps in the occupied and virtual states that define the singlet and triplet wave functions of these chromophores, enables new approaches to preserve excitation energy despite intersystem crossing.

Morphometric and Genetic Description of Trophic Adaptations in Cichlid Fishes.

Since Darwin, biologists have sought to understand the evolution and origins of phenotypic adaptations. The skull is particularly diverse due to intense natural selection on feeding biomechanics. We investigated the genetic and molecular origins of trophic adaptation using Lake Malawi cichlids, which have undergone an exemplary evolutionary radiation. We analyzed morphological differences in the lateral and ventral head shape among an insectivore that eats by suction feeding, an obligate biting herbivore, and their F2 hybrids. We identified variation in a series of morphological traits-including mandible width, mandible length, and buccal length-that directly affect feeding kinematics and function. Using quantitative trait loci (QTL) mapping, we found that many genes of small effects influence these craniofacial adaptations. Intervals for some traits were enriched in genes related to potassium transport and sensory systems, the latter suggesting co-evolution of feeding structures and sensory adaptations for foraging. Despite these indications of co-evolution of structures, morphological traits did not show covariation. Furthermore, phenotypes largely mapped to distinct genetic intervals, suggesting that a common genetic basis does not generate coordinated changes in shape. Together, these suggest that craniofacial traits are mostly inherited as separate modules, which confers a high potential for the evolution of morphological diversity. Though these traits are not restricted by genetic pleiotropy, functional demands of feeding and sensory structures likely introduce constraints on variation. In all, we provide insights into the quantitative genetic basis of trophic adaptation, identify mechanisms that influence the direction of morphological evolution, and provide molecular inroads to craniofacial variation.

Polygenic sex determination produces modular sex polymorphism in an African cichlid fish.

For many vertebrates, a single genetic locus initiates a cascade of developmental sex differences in the gonad and throughout the organism, resulting in adults with two phenotypically distinct sexes. Species with polygenic sex determination (PSD) have multiple interacting sex determination alleles segregating within a single species, allowing for more than two genotypic sexes and scenarios where sex genotype at a given locus can be decoupled from gonadal sex. Here we investigate the effects of PSD on secondary sexual characteristics in the cichlid fish Metriaclima mbenjii, where one female (W) and one male (Y) sex determination allele interact to produce siblings with four possible sex classes: ZZXX females, ZWXX females, ZWXY females, and ZZXY males. We find that PSD in M. mbenjii produces an interplay of sex linkage and sex limitation resulting in modular variation in morphological and behavioral traits. Further, the evolution or introgression of a newly acquired sex determiner creates additional axes of phenotypic variation for varied traits, including genital morphology, craniofacial morphology, gastrointestinal morphology, and home tank behaviors. In contrast to single-locus sex determination, which broadly results in sexual dimorphism, polygenic sex determination can induce higher-order sexual polymorphism. The modularity of secondary sexual characteristics produced by PSD provides context for understanding the evolutionary causes and consequences of maintenance, gain, or loss of sex determination alleles in populations.

Systematic review on quantifying pedestrian injury when evaluating changes to the built environment.

Modifying the built environment to make communities more walkable remains one strategy to promote physical activity. These modifications may have the added benefit of reducing the risk of pedestrian injury; however, there is a gap in the physical activity literature regarding how best to measure pedestrian injury. Examining the measures that have been used and related data sources can help inform the use of pedestrian injury data to evaluate whether safety is optimized as walking increases. We conducted a systematic review of the literature to identify studies that evaluated changes to the built environment that support walking and measures impacts on pedestrian injury as a measure of safety. We searched PubMed, PsycInfo, and Web of Science to identify peer-review studies and websites of fifteen organizations to document studies from the grey literature published in English between January 1, 2010 and December 31, 2018. Our search identified twelve studies that met the inclusion criteria. The few studies that measured changes in pedestrian injury used crash data from police reports. Injury frequency was often reported, but not injury severity, and no studies reported injury risk based on walking exposure. We conclude that few studies have measured pedestrian injury in the context of creating more walkable communities. Future research would benefit from using well-characterized measures from existing studies to support consistency in measurement, and from more longitudinal and evaluation research to strengthen the evidence on additional benefits of walkability. Increased collaborations with injury prevention professionals could bolster use of valid and reliable measures.

Supporting conscious competency: Validation of the Generic Supervision Assessment Tool (GSAT).

OBJECTIVES: Clinical supervision is essential for ensuring effective service delivery. International imperatives to demonstrate professional competence has increased attention on the role of supervision in enhancing client outcomes. Although supervisor competency tools are recognised as important components in effective supervision, there remains a shortage of tools that are evidenced-based, applicable across workforces and freely accessible. DESIGN: An expert multidisciplinary group developed the Generic Supervision Assessment Tool (GSAT) to assess supervisor competencies across a range of professions. Initially the GSAT consisted of 32 items responded to by either a supervisor (GSAT-SR) or supervisee (GSAT-SE). The current study, using surveys, employed a cross-sectional design to test the reliability and construct validity of the GSAT. METHODS: The study consisted of two phases and included 12 professional groups across Australasia. In 2018, exploratory factor analysis (EFA) was undertaken with survey data from 479 supervisors and 447 supervisees. In 2019 survey data from 182 supervisors and 186 supervisees were used to conduct confirmatory factor analysis (CFA). The results were used to refine and validate the GSAT. RESULTS: The final GSAT-SR has four factors with 26 competency items. The final GSAT-SE has two factors with 21 competency items. The EFA and CFA confirmed that the GSAT-SR and the GSAT-SE are psychometrically valid tools that supervisors and supervisees can utilise to assess competencies. CONCLUSION: As a non-discipline specific supervision tool, the GSAT is a validated, freely available tool for benchmarking the competencies of clinical supervisors across professions, potentially optimising supervisory evaluation processes and strengthening supervision effectiveness. PRACTITIONER POINTS: Supervisor competency tools are recognised as important components of safe and effective supervision provision yet there is a dearth of valid, reliable and effective measures. The Generic Supervision Assessment Tool (GSAT-SR and GSAT-SE) are unique psychometrically valid, and reliable measures of supervisor competence. The GSAT-SR and the GSAT-SE can enhance translation of evidence-based supervision competency skills into regular practice. Validated with a broad cross section of professionals in diverse practice settings the GSAT provides a comprehensive conceptualization of supervisor competence.

Reconstructing Missing and Anomalous Data Collected from High-Frequency In-Situ Sensors in Fresh Waters.

In situ sensors that collect high-frequency data are used increasingly to monitor aquatic environments. These sensors are prone to technical errors, resulting in unrecorded observations and/or anomalous values that are subsequently removed and create gaps in time series data. We present a framework based on generalized additive and auto-regressive models to recover these missing data. To mimic sporadically missing (i) single observations and (ii) periods of contiguous observations, we randomly removed (i) point data and (ii) day- and week-long sequences of data from a two-year time series of nitrate concentration data collected from Arikaree River, USA, where synoptically collected water temperature, turbidity, conductance, elevation, and dissolved oxygen data were available. In 72% of cases with missing point data, predicted values were within the sensor precision interval of the original value, although predictive ability declined when sequences of missing data occurred. Precision also depended on the availability of other water quality covariates. When covariates were available, even a sudden, event-based peak in nitrate concentration was reconstructed well. By providing a promising method for accurate prediction of missing data, the utility and confidence in summary statistics and statistical trends will increase, thereby assisting the effective monitoring and management of fresh waters and other at-risk ecosystems.

Associations between Perceptions and Measures of Weather and Walking, United States-2015.

INTRODUCTION: Weather can be a barrier to walking. Understanding how perceptions of weather as a barrier and measured temperature are associated with walking can inform monitoring and promotion strategies. The objective of this study is to examine the association between perceptions of weather as a barrier to walking and measured weather with the volume of leisure and transportation walking. METHODS: The 2015 National Health Interview Survey (NHIS) assessed participation in and volume of walking (transportation, leisure) in the past week and frequency of reporting weather as a barrier to walking. Data were collected over the entire year. In 2019, we merged month-specific temperature data from the PRISM climate group with individual NHIS records. We examined associations using logistic (participation) and linear regression models (volume). RESULTS: Participation in walking increased as frequency of reporting weather as a barrier to walking decreased, from 'almost always' (transportation: 23%, leisure: 42%) to 'a little of the time' (transportation: 40%, leisure: 67%). Among adults reporting walking, walking volume increased as frequency of reporting weather as a barrier decreased from 'almost always' (transportation: 51 min/week, leisure: 64 min/week) to 'never' (transportation: 69 min/week, leisure: 98 min/week). Month-specific temperature was significantly associated with leisure walking with lower participation at the lowest and highest temperature quintiles, although the strength of the association differed by frequency of reporting weather as a barrier. CONCLUSIONS: In general, prevalence and volume of leisure and transportation walking decreased as the perception of weather as a barrier increased. Low and high temperature conditions were also associated with leisure walking participation, particularly among adults with increased perceptions of weather as a barrier. Our findings highlight the importance of including strategies to help adults overcome perceived and actual weather-related barriers in walking promotion efforts.

Connecting virtual reality and ecology: a new tool to run seamless immersive experiments in R.

Virtual reality (VR) technology is an emerging tool that is supporting the connection between conservation research and public engagement with environmental issues. The use of VR in ecology consists of interviewing diverse groups of people while they are immersed within a virtual ecosystem to produce better information than more traditional surveys. However, at present, the relatively high level of expertise in specific programming languages and disjoint pathways required to run VR experiments hinder their wider application in ecology and other sciences. We present R2VR, a package for implementing and performing VR experiments in R with the aim of easing the learning curve for applied scientists including ecologists. The package provides functions for rendering VR scenes on web browsers with A-Frame that can be viewed by multiple users on smartphones, laptops, and VR headsets. It also provides instructions on how to retrieve answers from an online database in R. Three published ecological case studies are used to illustrate the R2VR workflow, and show how to run a VR experiments and collect the resulting datasets. By tapping into the popularity of R among ecologists, the R2VR package creates new opportunities to address the complex challenges associated with conservation, improve scientific knowledge, and promote new ways to share better understanding of environmental issues. The package could also be used in other fields outside of ecology.

A cluster of Achromobacter xylosoxidans led to identification of Pseudomonas aeruginosa and Serratia marcescens contamination at a long-term-care facility.

A cluster of Achromobacter xylosoxidans, an emerging multidrug-resistant aquaphilic bacterium, was identified in 3 long-term-care facility residents. As Pseudomonas aeruginosa and Serratia marcescens were also present in clinical specimens, we conducted an investigation of all 3 water-associated species and identified P. aerguniosa and S. marcescens contamination at the facility. Sequencing analysis linked P. aeruginosa to a clinical isolate. Findings highlight the need for precautionary measures to prevent transmission of water-associated multidrug-resistant bacteria in long-term-care facilities.

Algorithm for Management of Mandibular Condylar Base Fractures.

BACKGROUND: Increasing evidence suggests that open reduction and internal fixation of condylar base fractures in adults results in improved outcomes in regard to interincisal opening, jaw movement, pain, and malocclusion. However, most of the condylar fractures are managed by maxillomandibular fixation alone due to the need for specialized training and equipment. Our aim was to present an algorithm for condylar base fractures to simplify surgical management. METHODS: A retrospective review was performed of patients (n = 22) with condylar base fractures treated from 2016 to 2020. Patients who presented with operative fractures that require open treatment underwent 1 of 2 different techniques depending on the fracture type: a preauricular approach with a transoral approach if the condyle was dislocated (n = 2) or a transoral only approach (n = 20) in nondislocated cases. Operative time, occlusion, range of motion, and postoperative complications were assessed. RESULTS: Condylar base fractures were combined with other mandibular fractures in 16 of 22 patients. Patients with condylar dislocation were managed with a preauricular approach with a secondary transoral incision (n = 2, median 147 minutes). Those without dislocation were treated with a transoral approach (n = 20, median 159 minutes). Most patients were restored to their preoperative occlusion without long-term complications. CONCLUSIONS: We present a simplified algorithm for treating condylar base fractures. Our case series suggests that reduction in operative time and clinical success can be achieved with open reduction and internal fixation using a transoral approach alone or in combination with a preauricular approach for dislocated fractures.

A stochastic model of jaguar abundance in the Peruvian Amazon under climate variation scenarios.

The jaguar (Panthera onca) is the dominant predator in Central and South America, but is now considered near-threatened. Estimating jaguar population size is difficult, due to uncertainty in the underlying dynamical processes as well as highly variable and sparse data. We develop a stochastic temporal model of jaguar abundance in the Peruvian Amazon, taking into account prey availability, under various climate change scenarios. The model is calibrated against existing data sets and an elicitation study in Pacaya Samiria. In order to account for uncertainty and variability, we construct a population of models over four key parameters, namely three scaling parameters for aquatic, small land, and large land animals and a hunting index. We then use this population of models to construct probabilistic evaluations of jaguar populations under various climate change scenarios characterized by increasingly severe flood and drought events and discuss the implications on jaguar numbers. Results imply that jaguar populations exhibit some robustness to extreme drought and flood, but that repeated exposure to these events over short periods can result in rapid decline. However, jaguar numbers could return to stability-albeit at lower numbers-if there are periods of benign climate patterns and other relevant factors are conducive.

Preparing GIS data for analysis of stream monitoring data: The R package openSTARS.

Stream monitoring data provides insights into the biological, chemical and physical status of running waters. Additionally, it can be used to identify drivers of chemical or ecological water quality, to inform related management actions, and to forecast future conditions under land use and global change scenarios. Measurements from sites along the same stream may not be statistically independent, and the R package SSN provides a way to describe spatial autocorrelation when modelling relationships between measured variables and potential drivers. However, SSN requires the user to provide the stream network and sampling locations in a certain format. Likewise, other applications require catchment delineation and intersection of different spatial data. We developed the R package openSTARS that provides the functionality to derive stream networks from a digital elevation model, delineate stream catchments and intersect them with land use or other GIS data as potential predictors. Additionally, locations for model predictions can be generated automatically along the stream network. We present an example workflow of all data preparation steps. In a case study using data from water monitoring sites in Southern Germany, the resulting stream network and derived site characteristics matched those constructed using STARS, an ArcGIS custom toolbox. An advantage of openSTARS is that it relies on free and open-source GRASS GIS and R functions, unlike the original STARS toolbox which depends on proprietary ArcGIS. openSTARS also comes without a graphical user interface, to enhance reproducibility and reusability of the workflow, thereby harmonizing and simplifying the data pre-processing prior to statistical modelling. Overall, openSTARS facilitates the use of spatial regression and other applications on stream networks and contributes to reproducible science with applications in hydrology, environmental sciences and ecology.

SSNdesign-An R package for pseudo-Bayesian optimal and adaptive sampling designs on stream networks.

Streams and rivers are biodiverse and provide valuable ecosystem services. Maintaining these ecosystems is an important task, so organisations often monitor the status and trends in stream condition and biodiversity using field sampling and, more recently, autonomous in-situ sensors. However, data collection is often costly, so effective and efficient survey designs are crucial to maximise information while minimising costs. Geostatistics and optimal and adaptive design theory can be used to optimise the placement of sampling sites in freshwater studies and aquatic monitoring programs. Geostatistical modelling and experimental design on stream networks pose statistical challenges due to the branching structure of the network, flow connectivity and directionality, and differences in flow volume. Geostatistical models for stream network data and their unique features already exist. Some basic theory for experimental design in stream environments has also previously been described. However, open source software that makes these design methods available for aquatic scientists does not yet exist. To address this need, we present SSNdesign, an R package for solving optimal and adaptive design problems on stream networks that integrates with existing open-source software. We demonstrate the mathematical foundations of our approach, and illustrate the functionality of SSNdesign using two case studies involving real data from Queensland, Australia. In both case studies we demonstrate that the optimal or adaptive designs outperform random and spatially balanced survey designs implemented in existing open-source software packages. The SSNdesign package has the potential to boost the efficiency of freshwater monitoring efforts and provide much-needed information for freshwater conservation and management.