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BACKGROUND CONTEXT Minimally invasive transforaminal lumbar interbody fusion (MI-TLIF) is the workhorse minimally invasive treatment for patients with degenerative lumbar stenosis and spondylolisthesis refractory to nonoperative care. While the basic tenets of surgical treatment do not differ between minimally invasive and open techniques, surgeons performing MI-TLIF are more likely to attempt reduction of spondylolisthesis in order to optimize restoration of canal dimensions due to a greater reliance on indirect decompression, which has been shown to be effective. While intraoperative reduction is often successful, postoperative loss of reduction and slip recurrence can be seen. PURPOSE To determine the rate and clinical impact of slip recurrence after MI-TLIF with expandable cage technology. STUDY DESIGN/SETTING Retrospective cohort study. PATIENT SAMPLE Patients undergoing MI-TLIF for degenerative spondylolisthesis using an articulating, expandable cage OUTCOME MEASURES Patient-reported outcome measures (PROMs), including the Oswestry Disability Index (ODI), visual analog scale (VAS) for back/leg pain, Short Form-12 (SF-12) and PROMIS Physical Function (PF) METHODS Patients undergoing MI-TLIF for degenerative spondylolisthesis using articulating, expandable cages from 2017-2019 were retrospectively studied. Lateral radiographs were reviewed and evaluated for the presence or absence of spondylolisthesis preoperatively, intraoperatively, and at follow-up times including 2 weeks, 6 weeks, 12 weeks, 6 months and 1-year postoperatively. Spondylolisthesis was measured from the posterior inferior corner of the cephalad vertebra to the posterior superior corner of the caudal vertebral, with any measurement >1mm classified as spondylolisthesis, and Meyerding grade was noted. Intraoperative reduction was measured and loss of reduction was defined as >1mm increase in spondylolisthesis comparing follow-up imaging to intraoperative films. PROMs were recorded at the preoperative and follow-up time points. Fusion was assessed 1-year postoperatively via CT. RESULTS A total of 63 patients and 70 levels were included, with mean age 59.8 years (SD,13.8). Nineteen levels (27.1%) had complete reduction intraoperatively, 40 (57.1%) had partial reduction and 11 (15.7%) had no reduction. Of the 30 levels with loss of reduction (50.8%), 20 (66.7%) occurred by 2 weeks postoperatively and 28 (93.3%) occurred by 12 weeks postoperatively. At 6 months, there were significant differences between those who had loss of reduction and those who did not in VAS back pain (3.0 vs 0.9, p=0.017) and SF-12 PCS (41.5 vs 50.0, p=0.035), but no differences were found between the groups for any instruments at any other time points. The overall fusion rate was 89.5% (34/38) at 1 year postoperatively. There was no significant difference in fusion rate between the loss of reduction (16/18) and no loss of reduction (18/20) groups. Patients with loss of reduction had no difference in reoperation rate (1/28) compared to those without loss of reduction (2/24). CONCLUSIONS While a majority of patients demonstrated reduction intraoperatively, 51% had loss of reduction, most commonly in the acute postoperative period. There were few differences in PROMs between patients who had loss of reduction and those who did not, suggesting that radiographic loss of reduction after MI-TLIF in the setting of degenerative spondylolisthesis may not be clinically meaningful. FDA DEVICE/DRUG STATUS This abstract does not discuss or include any applicable devices or drugs.

Retrospective cohort study.The aim of this study was to examine sagittal alignment over time in adult spinal deformity (ASD) and to understand whether these changes vary by choice of upper instrumented vertebra (UIV).Recent ASD literature has focused on specific alignment goals. Less is known about how sagittal parameters evolve over time after surgical correction and whether these changes differ by choice of UIV.This was a retrospective review of ASD patients from a single institution. Routine 36″ sagittal x-rays were obtained preoperatively, before hospital discharge, and at 6 months, 1 year, and 2 years and sagittal parameters were measured. Patients with UIV T6 and above were classified as upper thoracic (UT) and T7 and below as lower thoracic (LT).A total of 102 patients with mean age 66.0 years (±7.7) were included in the analysis (49 UT, 53 LT). All sagittal and coronal alignment parameters demonstrated significant improvement from preoperatively to any postoperative time point. Although multiple parameters maintained correction over time, others (TK, TPA, and PT) demonstrated significant increase from discharge to 2 years postoperatively, with changes occurring relatively early after surgery, whereas overall global alignment was maintained. Both UT/LT groups demonstrated significantly greater TK from preoperatively to discharge to 6 months (P 0.05), stabilizing at that time point out to 2 years, whereas TLK preferentially increased in the LT group. There was significant improvement in sagittal vertical axis after surgery, which was maintained out to 2years of follow-up (P 0.05).Our data suggest that although several key parameters are maintained over time out to 2years postopera- tively, TK tends to worsen over time for all patients, whereas TLK preferentially increases in the LT group. Nevertheless, despite these trends, compensatory changes are seen in PT such that global alignment is relatively maintained.Level of Evidence: 3.

Over the past decade, several review articles have evaluated the techniques and outcomes of robotics vs traditional methods in spine surgery. Recently, robot-assisted pedicle screw placement has emerged, representing an important milestone in the evolution of spine surgery. In the present article, the authors aim to provide the historical context regarding the use and growth of spinal robotics through the lens of the Industrial Revolution and the personal computer revolution. While the former provides insight into the current implications of robotics in spine surgery, the latter predicts future steps in this arena.

Tick paralysis is an uncommon cause of neuromuscular paralysis affecting 0.12% of wild birds presented to Currumbin Wildlife Sanctuary, Queensland, with a strong seasonal predilection towards spring and summer. Clinical signs and progression of paralysis showed similarities to companion animals and were consistent across 20 species. Tick location, number of engorged ticks and number of clinical signs did not affect the outcome; however, all mortalities occurred within 4 days of admission. Treatment with canine-derived tick antiserum resulted in clinical improvement within 24 h and a recovery rate of 73%. Average time to resolution of clinical signs was 4.3 days, with juvenile birds recovering more quickly than adults. The treatment and release of wild birds affected by tick paralysis are both achievable and rewarding, further research is required to establish treatment guidelines in birds.

Background That foot infections are predominately polymicrobial has long been recognized, but it is not clear if the various species co-occur randomly or in patterns. We sought nonrandom species co-occurrence patterns that might help better predict prognosis or guide antimicrobial selection. Methods We analyzed tissue (bone, skin, and other soft tissue), fluid, and swab specimens collected from initial foot infection episodes during a 10-year period using a hospital registry. Nonrandom co-occurrence of microbial species was identified using simple pairwise co-occurrence rates adjusted for multiple comparisons, Markov and conditional random fields, and factor analysis. A historical cohort was used to validate pattern occurrence and identify clinical significance. Results In total, 156 unique species were identified among the 727 specimens obtained from initial foot infection episodes in 694 patients. Multiple analyses suggested that Staphylococcus aureus is negatively associated with other staphylococci. Another pattern noted was the co-occurrence of alpha-hemolytic Streptococcus, Enterococcus fecalis, Klebsiella, Proteus, Enterobacter, or Escherichia coli, and absence of both Bacteroides and Corynebacterium. Patients in a historical cohort with this latter pattern had significantly higher risk-adjusted rates of treatment failure. Conclusions Several nonrandom microbial co-occurrence patterns are frequently seen in foot infection specimens. One particular pattern with many Proteobacteria species may denote a higher risk for treatment failure. Staphylococcus aureus rarely co-occurs with other staphylococci.

The 2019 coronavirus disease (COVID-19) disrupted economic and social systems on an unprecedented scale. Organizational leaders faced unstructured problems that required novel analysis and evidenced-based decision-making approaches. This paper explains several analytical tools and problem-solving methodologies used at the United States Military Academy at West Point to support decision-making related to operational activities and future planning. While many of the tools apply basic analytical methods, the novelty of this paper lies in the unique application of the tools, visual presentation of data analytics, and the explanation of the contextual circumstances that prompted the development of these tools.

Recent work modeling the rheological behavior of human blood indicates that it has all the hallmark features of a complex material, including shear-thinning, viscoelastic behavior, a yield stress, and thixotropy. After decades of modeling steady state blood data, and the development of simple steady state models, like the Casson and Herschel-Bulkley the advancement and evolution of blood modeling to incorporate more thixo-elasto-visco-plastic (TEVP) features to accurately capture transient flow has renewed interest. With recently collected steady state and oscillatory shear flow rheological data from a DHR-3 using human blood, we show modeling efforts with a contemporary thixo-elasto-visco-plastic (TEVP) model. Best fit rheological model parameters are used to determine values for normal, healthy blood and corroborate correlations from literature. Series of physical processes (SPP) analysis is incorporated to illustrate how mechanical properties are tied to the transient, evolving microstructure of human blood and physiological parameters. Using LAOS data predictions of the structure parameter, λ is compared, and correlated with the transient elastic modulus, G t ′ .

Quick identification of building and roads is critical for execution of tactical US military operations in an urban environment. To this end, a gridded, referenced, satellite images of an objective, often referred to as a gridded reference graphic or GRG, has become a standard product developed during intelligence preparation of the environment. At present, operational units identify key infrastructure by hand through the work of individual intelligence officers. Recent advances in Convolutional Neural Networks, however, allows for this process to be streamlined through the use of object detection algorithms. In this paper, we describe an object detection algorithm designed to quickly identify and label both buildings and road intersections present in an image. Our work leverages both the U-Net architecture as well the SpaceNet data corpus to produce an algorithm that accurately identifies a large breadth of buildings and different types of roads. In addition to predicting buildings and roads, our model numerically labels each building by means of a contour finding algorithm. Most importantly, the dual U-Net model is capable of predicting buildings and roads on a diverse set of test images and using these predictions to produce clean GRGs.

Canine-mediated rabies kills tens of thousands of people annually in lesser-developed communities of Asia, Africa, and the Americas, primarily through bites from infected dogs. Multiple rabies outbreaks have been associated with human deaths in Nigeria. However, the lack of quality data on human rabies hinders advocacy and resource allocation for effective prevention and control. We obtained 20 years of dog bite surveillance data across 19 major hospitals in Abuja, incorporating modifiable and environmental covariates. To overcome the challenge of missing information, we used a Bayesian approach with expert-solicited prior information to jointly model missing covariate data and the additive effects of the covariates on the predicted probability of human death after rabies virus exposure. Only 1155 cases of dog bites were recorded throughout the study period, out of which 4.2% (N = 49) died of rabies. The odds for risk of human death were predicted to decrease among individuals who were bitten by owned dogs compared to those bitten by free-roaming dogs. Similarly, there was a predicted decrease in the probability of human death among victims bitten by vaccinated dogs compared to those bitten by unvaccinated dogs. The odds for the risk of human death after bitten individuals received rabies prophylaxis were predicted to decrease compared to no prophylaxis. We demonstrate the practical application of a regularised Bayesian approach to model sparse dog bite surveillance data to uncover risk factors for human rabies, with broader applications in other endemic rabies settings with similar profiles. The low reporting observed in this study underscores the need for community engagement and investment in surveillance to increase data availability. Better data on bite cases will help to estimate the burden of rabies in Nigeria and would be important to plan effective prevention and control of this disease.

Generalised Additive Models (GAMs) are increasingly popular for describing smooth nonlinear relationships between predictors and response variables. GAMs are particularly relevant in ecology for representing hierarchical functions for discrete responses that encompass complex features including zero-inflation, bounding and uneven sampling. However, GAMs are less useful for producing forecasts as their smooth functions provide unstable predictions outside the range of training data.We introduce Dynamic Generalised Additive Models (DGAMs), where the GAM linear predictor is jointly estimated with unobserved dynamic components to model time series that evolve as a function of nonlinear predictor associations and latent temporal processes. These models are particularly useful for multivariate series, as they can estimate hierarchical smooths while learning complex temporal associations via dimension-reduced latent factor processes. We implement our models in the mvgam R package, which uses the mgcv and rjags packages to construct smoothing splines and estimate unobserved parameters in a probabilistic framework.Using simulations, we illustrate how our models outperform competing formulations in realistic ecological forecasting tasks while identifying important smooth predictor functions. We use a real-world case study to highlight some of mvgam’s key features, which include functions for: calculating correlations among series’ latent trends, performing model selection using rolling window forecasts, online data augmentation via a recursive particle filter, and visualising probabilistic uncertainties for smooths and predictions.Dynamic GAMs (DGAM) offer a solution to the challenge of forecasting discrete time series while estimating ecologically relevant nonlinear predictor associations. Our Bayesian latent factor approach will be particularly useful for exploring competing dynamic ecological models that encompass hierarchical smoothing structures while providing forecasts with robust uncertainties, tasks that are becoming increasingly important in applied ecology.

Background Ankylosing spondylitis (AS) is a common inflammatory spondyloarthropathy with hip involvement in 40% of patients. With the recent interest in the hip-spine interplay, the purpose of this study was to define the long-term outcomes of revision total hip arthroplasty (THA) in the setting of AS. Methods 174 hips in patients with AS treated with revision THA from 1969 to 2016 were identified. Mean age at revision THA was 53 years and 76% were male. Cumulative incidences of any re-revision, reoperation, and dislocation were calculated using a competing risk analysis. Mean follow-up was 13 years. Results The cumulative incidence of any re-revision after index revision THA was 7% at 5 years and 36% at 20 years. Cumulative incidence of any reoperation was 9% at 5 years and 38% at 20 years. Cumulative incidence of dislocation was 6% at 5 years and 8% at 20 years. Revision THAs performed with contemporary implants (2000-2016) had a lower but statistically nonsignificant cumulative incidence of any re-revision when compared with historical implants (before 2000) at 5 years (5% vs 8%), 10 years (11% vs 18%), and 15 years (11% vs 38%) (hazard ratio, 0.47; 95% confidence interval, 0.17-1.33; P = .016). Conclusion In this large series of 174 revision THAs in patients with AS, the cumulative incidence of dislocation was 8% at 20 years. The 20-year cumulative incidence of any re-revision was 36%, which is similar to reported rates in patients with comparable demographic features without AS. Level of Evidence Level IV.

Analytics and professional sports have become linked over the past several years, but little attention has been paid to the growing field of esports within the sports analytics community. We seek to apply an Adjusted Plus Minus (APM) model, an accepted analytic approach used in traditional sports like hockey and basketball, to one particular esports game: Defense of the Ancients 2 (Dota 2). As with traditional sports, we show how APM metrics developed with Bayesian hierarchical regression can be used to quantify individual player contributions to their teams and, ultimately, use this player-level information to predict game outcomes. In particular, we first provide evidence that gold can be used as a continuous proxy for wins to evaluate a team’s performance, and then use a Bayesian APM model to estimate how players contribute to their team’s gold differential. We demonstrate that this APM model outperforms models based on common team-level statistics (often referred to as “box score statistics”). Beyond the specifics of our modeling approach, this paper serves as an example of the potential utility of applying analytical methodologies from traditional sports analytics to esports.

Marine ecosystems are under increasing threat from warming waters. Winter warming is occurring at a faster rate than summer warming for ecosystems around the world, but most studies focus on the summer. Here, we show that winter warming could affect coastal fish community compositions in the Mediterranean Sea using a model that captures how biotic associations change with sea surface temperature to influence species’ distributions for 215 fish species. Species’ associations control how communities are formed, but the effect of winter warming on associations will be on average four times greater than that of summer warming. Projections using future climate scenarios show that 60% of coastal Mediterranean grid cells are expected to lose fish species by 2040. Heavily fished areas in the west will experience diversity losses that exacerbate regime shifts linked to overexploitation. Incorporating seasonal differences will therefore be critical for developing effective coastal fishery and marine ecosystem management. The rate of warming in many marine ecosystems is faster in winter than in summer. Winter warming will impact fish species’ associations in the Mediterranean more than summer warming, and this has implications for how communities form and for future biodiversity, particularly in heavily fished areas.

Background Resection arthroplasty of the hip is considered a viable option after multiple failed attempts to eradicate a prosthetic joint infection (PJI). However, much less information about resection arthroplasty of the knee is available. The goals of this study were to determine the success of infection eradication with a resection arthroplasty of the knee and subsequent functional outcomes in this group. Methods We retrospectively identified 25 knees (23 patients) treated with resection arthroplasty of the knee for PJI performed at a single institution between 1974 and 2016. The mean age at resection arthroplasty was 65 years. The mean body mass index was 37 kg/m2, and the mean Charleston Comorbidity Index was 5. Patients had a mean of 5 operations on the knee (mean of 3 operations for infection) before the resection arthroplasty. Failure to eradicate the infection was defined as any reoperation for infection. Clinical outcomes were assessed via ambulatory status, use of gait aids, and ongoing pain. The mean follow-up was 4 years. Results At most recent follow-up, 84% (21 of 25) of knees were free of infection. Three patients had recurrent infection within the first year, and 1 patient had a late infection at 4 years postoperatively. However, only 1 patient required a subsequent amputation. Forty-five percent were community ambulators, 35% were household ambulators, and 20% were only able to transfer. All patients required knee bracing and assistive devices. Fifteen percent of patients were using long-term narcotics. Conclusion This large series demonstrates the results of selected use of resection arthroplasty as a treatment for recalcitrant periprosthetic knee infections that have failed multiple attempts to eradicate an ongoing PJI. The resection definitively solved the infection in 84% of patients. Functional results were variable but surprisingly good in some. All patients required bracing and assistive devices.

Over the last two decades, statistics educators have made important changes to introductory courses. Current guidelines emphasize developing statistical thinking in students and exposing them to the entire investigative process in the context of interesting research questions and real data. As a result, many concepts (confounding, multivariable models, study design, etc.) previously reserved only for higher-level courses now appear in introductory courses. Despite these changes, causality is rarely discussed in introductory courses, except for warning students “correlation does not imply causation” or covering the special case of randomized controlled experiments. In this article, we argue causal inference concepts align well with statistics education guidelines for introductory courses by developing statistical and multivariable thinking, exposing students to many aspects of the investigative process, and fostering active learning. We discuss how to integrate causal inference concepts into introductory courses using causal diagrams and provide an illustrative example with youth smoking data. Through our website, we also provide a guided student activity and instructor resources. Supplementary materials for this article are available online.

The occurrence and clinical significance of the protozoal parasite reported as Hepatozoon tachyglossi in wild short-beaked echidnas (Tachyglossis aculeatus) have long been uncertain, as has its potential as a prognostic indicator. This retrospective survey of free-ranging short-beaked echidnas admitted to a wildlife hospital used morphological evidence to identify a H. tachyglossi prevalence of 56%, with parasitaemias affecting 0%–36% of monocytes. There was no statistical association between H. tachyglossi intensity and clinical status (P-value = 0.12; 95% confidence interval = 0.1 to 1.3), nor between the presence of H. tachyglossi and age, reason for admission, outcome, season or location. Piroplasms, presumed to be Theileria tachyglossi, were concurrently identified in the erythrocytes of 88% of short-beaked echidnas with no association between age, outcome, season or location, but a statistical association with the location where the animal was found (either on a road, airport runway, exposed urban area, or entangled). Given the current results, intracellular parasitism due to H. tachyglossi may be considered as an incidental finding on haematologic examination of short-beaked echidnas and is likely not an effective prognostic indicator. Further research using molecular tools is required to resolve the uncertain identity of H. tachyglossi which has been based on morphologic characteristics alone.

Aim: Macroecological analyses provide valuable insights into factors that influence how parasites are distributed across space and among hosts. Amid large uncertainties that arise when generalizing from local and regional findings, hierarchical approaches applied to global datasets are required to determine whether drivers of parasite infection patterns vary across scales. We assessed global patterns of haemosporidian infections across a broad diversity of avian host clades and zoogeographical realms to depict hotspots of prevalence and to identify possible underlying drivers. Location: Global. Time period: 1994–2019. Major taxa studied: Avian haemosporidian parasites (genera Plasmodium, Haemoproteus, Leucocytozoon and Parahaemoproteus). Methods: We amalgamated infection data from 53,669 individual birds representing 2,445 species world-wide. Spatio-phylogenetic hierarchical Bayesian models were built to disentangle potential landscape, climatic and biotic drivers of infection probability while accounting for spatial context and avian host phylogenetic relationships. Results: Idiosyncratic responses of the three most common haemosporidian genera to climate, habitat, host relatedness and host ecological traits indicated marked variation in host infection rates from local to global scales. Notably, host ecological drivers, such as migration distance for Plasmodium and Parahaemoproteus, exhibited predominantly varying or even opposite effects on infection rates across regions, whereas climatic effects on infection rates were more consistent across realms. Moreover, infections in some low-prevalence realms were disproportionately concentrated in a few local hotspots, suggesting that regional-scale variation in habitat and microclimate might influence transmission, in addition to global drivers. Main conclusions: Our hierarchical global analysis supports regional-scale findings showing the synergistic effects of landscape, climate and host ecological traits on parasite transmission for a cosmopolitan and diverse group of avian parasites. Our results underscore the need to account for such interactions, in addition to possible variation in drivers across regions, to produce the robust inference required to predict changes in infection risk under future scenarios.

We introduce a new R package "MrIML" ("Mister iml"; Multi-response Interpretable Machine Learning). MrIML provides a powerful and interpretable framework that enables users to harness recent advances in machine learning to quantify multilocus genomic relationships, to identify loci of interest for future landscape genetics studies, and to gain new insights into adaptation across environmental gradients. Relationships between genetic variation and environment are often nonlinear and interactive; these characteristics have been challenging to address using traditional landscape genetic approaches. Our package helps capture this complexity and offers functions that fit and interpret a wide range of highly flexible models that are routinely used for single-locus landscape genetics studies but are rarely extended to estimate response functions for multiple loci. To demonstrate the package's broad functionality, we test its ability to recover landscape relationships from simulated genomic data. We also apply the package to two empirical case studies. In the first, we model genetic variation of North American balsam poplar (Populus balsamifera, Salicaceae) populations across environmental gradients. In the second case study, we recover the landscape and host drivers of feline immunodeficiency virus genetic variation in bobcats (Lynx rufus). The ability to model thousands of loci collectively and compare models from linear regression to extreme gradient boosting, within the same analytical framework, has the potential to be transformative. The MrIML framework is also extendable and not limited to modelling genetic variation; for example, it can quantify the environmental drivers of microbiomes and coinfection dynamics.

The International Civil Aviation Organization’s 2020 mandate for all aircraft to have Automatic Dependent Surveillance-Broadcast (ADS-B) equipment installed poses a unique challenge to the military. With the use of cheap sensors, adversaries now can collect real-time data on flights transporting troops. In this paper, we outline a statistical model that can be leveraged both defensively and offensively. Our model, a time-series spatial process model with a first-order Markov assumption, can be used to develop a flight path that could help a military aircraft seem like ordinary air traffic to an observer. This would prevent adversaries from identifying and tracking these flights as easily. The model can also be used to predict the destination of flights based on their observed location. These real-time flight predictions use Bayesian updating to improve their accuracy as more information about the flight is observed.

Ninety-six wrists (56 right and 40 left) in 96 patients (36 males and 60 females, mean age 38, range 15–77 years) underwent repair of ulnotriquetral ligament split tears between 2007 and 2016. Mayo wrist scores, visual analogue scale pain scores, and objective measures including grip strength and range of motion were obtained. Patients were assessed after a mean follow-up of 21 months (range 6–112 months). Ulnotriquetral split tear repair resulted in substantial improvements in pain and function. The mean Mayo wrist score improved from 57 preoperatively to 81 postoperatively, with 84% of patients achieving a good or excellent outcome. Pain scores decreased from 5.8 to 1.2. Grip improved from 25 kg to 29 kg. There was no significant change in range of motion of the wrist. Complications were noted in eight patients, with three experiencing continued pain, four with dysaesthesia of the dorsal sensory ulnar nerve, and one superficial infection. Arthroscopic ulnotriquetral split tear repair significantly reduced pain and improved Mayo wrist scores.Level of evidence: IV

Our study aimed to report the short-term outcomes of patients who underwent partial ulnar head replacement and distal radial ulnar joint interposition arthroplasty. From 2012 to 2016, nine patients underwent this procedure with mean follow-up of 27 months (range 12–55). Seven of the nine patients had previously undergone surgical intervention on the ipsilateral wrist. The procedure resulted in substantial improvements in pain and function. Mayo Wrist Score improved from 37 preoperatively to 73 postoperatively, and six patients achieved a good or excellent outcome. Visual analogue pain scores decreased from 7 preoperatively to 1 postoperatively. Grip improved from 20 kg preoperatively to 30 kg postoperatively. There was no significant change in wrist range of motion. Two patients underwent revision surgery to improve wrist motion. We conclude that over short-term follow-up the procedure provides a feasible option for distal radial ulnar joint arthritis.Level of evidence: IV

Within host-parasite communities, viral co-circulation and co-infections of hosts are the norm, yet studies of significant emerging zoonoses tend to focus on a single parasite species within the host. Using a multiplexed paramyxovirus bead-based PCR on urine samples from Australian flying foxes, we show that multi-viral shedding from flying fox populations is common. We detected up to nine bat paramyxoviruses shed synchronously. Multi-viral shedding infrequently coalesced into an extreme, brief and spatially restricted shedding pulse, coinciding with peak spillover of Hendra virus, an emerging fatal zoonotic pathogen of high interest. Such extreme pulses of multi-viral shedding could easily be missed during routine surveillance yet have potentially serious consequences for spillover of novel pathogens to humans and domestic animal hosts. We also detected co-occurrence patterns suggestive of the presence of interactions among viruses, such as facilitation and cross-immunity. We propose that multiple viruses may be interacting, influencing the shedding and spillover of zoonotic pathogens. Understanding these interactions in the context of broader scale drivers, such as habitat loss, may help predict shedding pulses of Hendra virus and other fatal zoonoses.

Host specificity has a major influence on a parasite's ability to shift between human and animal host species. Yet there is a dearth of quantitative approaches to explore variation in host specificity across biogeographical scales, particularly in response to the varying community compositions of potential hosts. We built a global dataset of intermediate host associations for nine of the world's most widespread helminth parasites (all of which infect humans). Using hierarchical models, we asked if realised parasite host specificity varied in response to regional variation in the phylogenetic and functional diversities of potential host species. Parasites were recorded in 4–10 zoogeographical regions, with some showing considerable geographical variation in observed versus expected host specificity. Parasites generally exhibited the lowest phylogenetic host specificity in regions with the greatest variation in prospective host phylogenetic diversity, namely the Neotropical, Saharo-Arabian and Australian regions. Globally, we uncovered notable variation in parasite host shifting potential. Observed host assemblages for Hydatigera taeniaeformis and Hymenolepis diminuta were less phylogenetically diverse than expected, suggesting limited potential to spillover into unrelated hosts. Host assemblages for Echinococcus granulosus, Mesocestoides lineatus and Trichinella spiralis were less functionally diverse than expected, suggesting limited potential to shift across host ecological niches. By contrast, Hyd. taeniaeformis infected a higher functional diversity of hosts than expected, indicating strong potential to shift across hosts with different ecological niches. We show that the realised phylogenetic and functional diversities of infected hosts are determined by biogeographical gradients in prospective host species pools. These findings emphasise the need to account for underlying species diversity when assessing parasite host specificity. Our framework to identify variation in realised host specificity is broadly applicable to other host–parasite systems and will provide key insights into parasite invasion potential at regional and global scales.

In the aftermath of radiation or chemical accidents, responders must rapidly map out regions of contamination as quickly and accurately as possible. One important and relevant statistical method for this kind of disaster response is spatial kriging, which makes predictions based on incomplete knowledge of spatially referenced observations. Given an Unmanned Aerial System (UAS) equipped with radiation sensors, we develop a spatial statistics-based approach to optimally map out a contamination field over a geographic region. In this article, we evaluate three approaches to UAS mapping: a Variance Driven Sampling (VDS) approach that minimizes kriging variance, a more computationally intensive Hybrid Entropy Search (HES), and a baseline Levy Flight search. Considering limited UAS range, we also implement a restricted version of these approaches that only considers nearby points. We find that HES is optimal for small numbers of sampled points with the restricted versions of HES and VDS becoming optimal for larger samples. Ultimately, the best method is dependent on the number of samples to be taken, with each method providing clear benefits over a random search in terms of both mean squared error and path length. We demonstrate the advantages of our methodology using actual radiation field test data from the Idaho National Lab.

Canine companion animals can carry a number of zoonotic parasites which can adversely impact both human and animal health. Previous studies in Australia indicated that while parasitic infections in dogs are still common and there is variability in the awareness and perception of zoonotic risks among pet owners, the likely contribution of sociodemographic factors to the variation in awareness and perception needs to be further explored. The primary objective of this study is to quantify the relationship between dog owners' knowledge and beliefs about dog parasites and their sociodemographic characteristics. In this study, we surveyed a total of 281 dog owners in SE Queensland between April 2019 to March 2020 and the relationship between dog owners' perception of gastrointestinal parasite infection was assessed using an adaptation of the Health Belief Model, social cognitive framework for health protection. The model looked into the role of dog owners' demography on their perceived severity and susceptibility to zoonotic canine parasites and their likelihood of performing actions associated with worm control of their pets. Our results indicate that owners perceptions about parasitic disease severity in their pets was 26% higher in female dog owners compared to males, in respondents owning dogs over 10 years (27% higher than those owning a dog

The System Usability Scale (SUS) is a short, survey-based approach used to determine the usability of a system from an end user perspective once a prototype is available for assessment. Individual scores are gathered using a 10-question survey with the survey results reported in terms of central tendency (sample mean) as an estimate of the system's usability (the SUS study score), and confidence intervals on the sample mean are used to communicate uncertainty levels associated with this point estimate. When the number of individuals surveyed is large, the SUS study scores and accompanying confidence intervals relying upon the central limit theorem for support are appropriate. However, when only a small number of users are surveyed, reliance on the central limit theorem falls short, resulting in confidence intervals that suffer from parameter bound violations and interval widths that confound mappings to adjective and other constructed scales. These shortcomings are especially pronounced when the underlying SUS score data is skewed, as it is in many instances. This paper introduces an empirically-based remedy for such small-sample circumstances, proposing a set of decision rules that leverage either an extended bias-corrected accelerated (BCa) bootstrap confidence interval or an empirical Bayesian credibility interval about the sample mean to restore and bolster subsequent confidence interval accuracy. Data from historical SUS assessments are used to highlight shortfalls in current practices and to demonstrate the improvements these alternate approaches offer while remaining statistically defensible. A freely available, online application is introduced and discussed that automates SUS analysis under these decision rules, thereby assisting usability practitioners in adopting the advocated approaches.

We introduce a new R package ‘MrIML’ (Multi-response Interpretable Machine Learning). MrIML provides a powerful and interpretable framework that enables users to harness recent advances in machine learning to map multi-locus genomic relationships, to identify loci of interest for future landscape genetics studies and to gain new insights into adaptation across environmental gradients. Relationships between genetic change and environment are often non-linear, interactive and autocorrelated. Our package helps capture this complexity and offers functions that construct, fit and conduct inference on a wide range of highly flexible models that are routinely used for single-locus landscape genetics studies but are rarely extended to estimate response functions for multiple loci. To demonstrate the package’s broad functionality, we test its ability to recover landscape relationships from simulated genomic data. We also apply the package to two empirical case studies. In the first we estimate variation in the population-level genetic composition of North American balsam poplar (Populus balsamifera, Salicaceae) and in the second we recover individual-level landscapes while estimating host drivers of feline immunodeficiency virus genetic spread in bobcats (Lynx rufus). The ability to model thousands of loci collectively and compare models from linear regression to extreme gradient boosting, within the same analytical framework, has the potential to be transformative. The MrIML framework is also extendable and not limited to mapping genetic change, for example, it can be used to quantify the environmental driver sof microbiomes and coinfection dynamics.

Background Ankylosing spondylitis (AS) is a common inflammatory spondyloarthropathy with hip involvement in 40% of patients. With the renewed interest in the hip-spine interplay, this study aimed to define long-term outcomes of primary total hip arthroplasty (THA) in the setting of AS. Methods We identified 309 hips (219 patients) with AS treated with primary THA from 1969 to 2018. Mean age was 49 years, 80% were males, and mean body mass index was 28 kg/m2. Cumulative incidences of any revision, reoperation, and dislocation were calculated utilizing a competing risk analysis. Harris Hip Scores and complications were also reported. Mean follow-up was 16 years. Results The cumulative incidence of any revision after primary THA was 2.3% at 5 years and 17.5% at 20 years. The most common reasons for revision (n = 73) were aseptic loosening (41%), osteolysis/polyethylene (PE) wear (30%, all with conventional PE), and femoral component fracture (8%). The cumulative incidence of dislocation was 1.9% at 5 years and 2.9% at 20 years. Younger age was associated with increased risk of revision (hazard ratio (HR) = 1.3, P Conclusion In this series of 309 primary THAs in patients with AS, the 20-year cumulative incidence of any revision after primary THA was 17.5%. Aseptic loosening, osteolysis/PE wear, and femoral component fracture were the most common reasons for revision. Notably, the cumulative incidence of dislocation at 20 years was only 2.9%. Level of Evidence Level IV.

Q fever is a zoonotic disease caused by the intracellular bacterium, Coxiella burnetii. Its primary mode of transmission is by inhalation of aerosols originating from infected animals and contaminated environments. The organism has a very low infective dose, can persist in the environment for long periods of time and large outbreaks fuelled by windborne spread have been previously reported. Detection of C. burnetii in the environment is therefore important during human and animal outbreak investigations and for the control and prevention of Q fever. This study aimed to systematically review and critically appraise the published literature on sampling methods used to detect C. burnetii from different environmental samples. A search of four electronic databases with subsequent hand searching identified 47 eligible articles published since 1935. These articles described sampling of dust, air, soil and liquids in attempts to detect C. burnetii during 19 Q fever outbreaks and in 28 endemic settings. Environmental positivity was most commonly associated with ruminant livestock populations. Evidence describing spatio-temporal characteristics and associated geographical dispersion gradients was limited. The most commonly tested sample type was dust which also yielded the highest bacterial loads of >108 bacteria/cloth. The MD8 (Sartorius) air sampler was used widely for air sampling. Soil was the only sample type for which a validated laboratory protocol was established specifically for C. burnetii. Each environmental sample type has its advantages and limitations which are discussed in detail and a simplified framework to guide decisions around environmental sampling for C. burnetii is provided. In any type of environmental sampling, it is recommended to use standardized and validated methods and to match the most ideal sampling strategy and timing with the research context. These conditions are essential to be considered when designing future Q fever management plans that involve environmental sampling for C. burnetii.

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2 or COVID‐19) disease has led to a wide‐spread global pandemic (1). COVID‐19 symptoms and mortality are disproportionately more severe in people with obesity and obesity related comorbidities (2, 3). This is of concern for the United States, where ~42% have obesity and of these, 85% have type 2 diabetes.

Identifying robust environmental predictors of infection probability is central to forecasting and mitigating the ongoing impacts of climate change on vector-borne disease threats. We applied phylogenetic hierarchical models to a data set of 2,171 Western Palearctic individual birds from 47 species to determine how climate and landscape variation influence infection probability for three genera of haemosporidian blood parasites (Haemoproteus, Leucocytozoon, and Plasmodium). Our comparative models found compelling evidence that birds in areas with higher vegetation density (captured by the normalized difference vegetation index [NDVI]) had higher likelihoods of carrying parasite infection. Magnitudes of this relationship were remarkably similar across parasite genera considering that these parasites use different arthropod vectors and are widely presumed to be epidemiologically distinct. However, we also uncovered key differences among genera that highlighted complexities in their climate responses. In particular, prevalences of Haemoproteus and Plasmodium showed strong but contrasting relationships with winter temperatures, supporting mounting evidence that winter warming is a key environmental filter impacting the dynamics of host-parasite interactions. Parasite phylogenetic community diversities demonstrated a clear but contrasting latitudinal gradient, with Haemoproteus diversity increasing towards the equator and Leucocytozoon diversity increasing towards the poles. Haemoproteus diversity also increased in regions with higher vegetation density, supporting our evidence that summer vegetation density is important for structuring the distributions of these parasites. Ongoing variation in winter temperatures and vegetation characteristics will probably have far-reaching consequences for the transmission and spread of vector-borne diseases.