Evaluation of a novel, sensitive thyroid-stimulating hormone assay as a diagnostic test for thyroid disease in cats.

OBJECTIVE: Clinicians commonly use thyroid-stimulating hormone (TSH) concentrations to diagnose thyroid disorders in humans and dogs. In cats, canine TSH chemiluminescent immunoassays (CLIA) assays are commonly used to measure TSH, but these TSH-CLIAs cannot measure low TSH concentrations (< 0.03 ng/mL) and therefore cannot distinguish between low-normal concentrations and truly low TSH concentrations (characteristic of hyperthyroidism). Our aim was to evaluate a novel TSH assay based on bulk acoustic wave (BAW) technology that has lower functional sensitivity (0.008 ng/mL) than TSH-CLIAs. ANIMALS: 169 untreated hyperthyroid cats, 53 cats treated with radioiodine (131I), 12 cats with chronic kidney disease (CKD), and 78 clinically healthy cats. METHODS: Serum concentrations of T4, TSH-CLIA, and TSH-BAW were measured in all cats. Untreated hyperthyroid cats were divided into 4 severity groups (subclinical, mild, moderate, and severe), whereas 131I-treated cats were divided into euthyroid and hypothyroid groups. RESULTS: Test sensitivity, specificity, and positive predictive value for identifying hyperthyroidism were higher for TSH-BAW (90.5%, 98.9%, and 86.9%) than TSH-CLIA (79.9%, 76.7%, and 21.7%; P < .001). Test sensitivity for identifying 131I-induced hypothyroidism was only 45.5% for T4 versus 100.0% for both TSH-CLIA and TSH-BAW (P = .03), whereas TSH-BAW had a higher positive predictive value (100%) than did either TSH-CLIA (81.2%) or T4 (71.9%). CLINICAL RELEVANCE: Serum TSH-BAW alone or together with T4 is a highly sensitive and specific diagnostic test for evaluating feline hyperthyroidism and iatrogenic hypothyroidism. Finding low serum TSH-BAW concentrations is most useful for diagnosing subclinical and mild hyperthyroidism, in which serum T4 remains within or only slightly above the reference interval.

A framework for integrating inferred movement behavior into disease risk models.

Movement behavior is an important contributor to habitat selection and its incorporation in disease risk models has been somewhat neglected. The habitat preferences of host individuals affect their probability of exposure to pathogens. If preference behavior can be incorporated in ecological niche models (ENMs) when data on pathogen distributions are available, then variation in such behavior may dramatically impact exposure risk. Here we use data from the anthrax endemic system of Etosha National Park, Namibia, to demonstrate how integrating inferred movement behavior alters the construction of disease risk maps. We used a Maximum Entropy (MaxEnt) model that associated soil, bioclimatic, and vegetation variables with the best available pathogen presence data collected at anthrax carcass sites to map areas of most likely Bacillus anthracis (the causative bacterium of anthrax) persistence. We then used a hidden Markov model (HMM) to distinguish foraging and non-foraging behavioral states along the movement tracks of nine zebra (Equus quagga) during the 2009 and 2010 anthrax seasons. The resulting tracks, decomposed on the basis of the inferred behavioral state, formed the basis of step-selection functions (SSFs) that used the MaxEnt output as a potential predictor variable. Our analyses revealed different risks of exposure during different zebra behavioral states, which were obscured when the full movement tracks were analyzed without consideration of the underlying behavioral states of individuals. Pathogen (or vector) distribution models may be misleading with regard to the actual risk faced by host animal populations when specific behavioral states are not explicitly accounted for in selection analyses. To more accurately evaluate exposure risk, especially in the case of environmentally transmitted pathogens, selection functions could be built for each identified behavioral state and then used to assess the comparative exposure risk across relevant states. The scale of data collection and analysis, however, introduces complexities and limitations for consideration when interpreting results.

Responding to COVID-19 Among U.S. Military Units in South Korea: The U.S. Forces Korea's Operation Kill the Virus.

INTRODUCTION: The coronavirus disease 2019 (COVID-19) is a viral respiratory illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and has led to one of the world's largest infectious disease outbreaks. COVID-19 first emerged in Wuhan, Hubei, China, in December 2019, and the emergence was especially concerning to the U.S. Forces Korea (USFK) stationed in the Republic of Korea (ROK, South Korea), which remains vital to peace and security of the East Asian region. The first wave of cases emerged in South Korea from China before a globally established response, which forced USFK into a challenging position to combat a novel virus with countless unknowns regarding effective control and portended impact. MATERIALS AND METHODS: As cases began to emerge in South Korea, USFK in early February began to proactively formulate peninsula-wide preventative health measures to protect the force. Eventually, USFK spearheaded a uniquely proactive Operation Kill the Virus that targeted COVID-19 as an enemy that must be rigorously defended against. Through the operation, USFK systematically employed eight key principles to successfully combat the pandemic, which are documented in this article. RESULTS: The operation's eight principles focused on (1) Treat it like a combat operation, (2) Protect the force to protect the mission, (3) Stay one step ahead of the curve by exercising an abundance of caution, (4) Use predictive analysis, (5) Maintain open and transparent dialog with the community every day, (6) Be empathetic but prepare the community for lifestyle and culture changes, (7) Follow and enforce rules, and finally (8) Keep your foot on the gas and fight complacency. By closely collaborating with the ROK government, especially the Korean Centers for Disease Control and Prevention, USFK effectively limited the number of locally acquired cases, including service members, families, and civilians, to 24 by April 2020. Vital to that success was ensuring a sufficient capability and capacity to test, trace, treat, and logistically support with personal protective equipment and sufficient infrastructure for quarantine and isolation. As the pandemic shifted to the USA and Europe, new cases in the ROK shifted from locally acquired to imported from international travelers. Fundamental to USFK's sustained preservation of readiness and training included aggressive quarantine and testing of all arrivals from the United States of America (USA), identification of hotspots in all installations, and perpetual fine-tuning of the operation's principles in collaboration with the ROK's aggressive approach to eradicate COVID-19 from the peninsula. CONCLUSIONS: In successfully executing the operation, USFK imparts three main lessons for future outbreaks. First, a military command should execute a health response similar to how it executes combat operations against a battlefield enemy. Second, the command should maintain flexibility to new changes or risks that alter courses of action. And finally, engagement with the local community, host nation, and international partners should not be compromised when formulating strategies. The USFK's immediate recognition of the public health threat by all levels of leadership and medical personnel enabled a unique and highly effective Operation Kill the Virus that engaged all members of the community, both local and international.

Serum thyroxine and thyrotropin concentrations decrease with severity of nonthyroidal illness in cats and predict 30-day survival outcome.

BACKGROUND: In cats, nonthyroidal illness affects serum thyroid hormone concentrations. Serum thyroxine (T4 ) and triiodothyronine (T3 ) concentrations commonly decrease, whereas free T4 (fT4 ) concentrations vary unpredictably. Limited information exists regarding effects on serum thyrotropin (thyroid-stimulating hormone [TSH]) concentrations in cats with nonthyroidal illness syndrome (NTIS). OBJECTIVES: To characterize alterations in thyroid function that develop in cats with NTIS and to correlate these alterations with severity and outcome of the nonthyroidal illness. ANIMALS: Two hundred and twenty-two cats with NTIS and 380 clinically normal cats of similar age and sex. METHODS: Prospective, cross-sectional study. All cats had serum T4 , T3 , free T4 , and TSH concentrations measured. Cats were grouped based on illness severity and 30-day survival. RESULTS: Cats with NTIS had lower serum T4 and T3 concentrations than did normal cats (P < .001). Serum fT4 and TSH concentrations did not differ between groups. Serum T4 , T3 , and fT4 concentrations progressively decreased with increasing disease severity (P < .001). The 56 cats that died had lower T4 , T3 , and TSH concentrations than did the 166 survivors, with no difference in fT4 concentration. Multivariable logistic regression modeling indicated that serum T4 and TSH concentrations both predicted survival (P < .02). CONCLUSIONS AND CLINICAL IMPORTANCE: Cats with NTIS commonly develop low serum T4 , T3 , and TSH concentrations, the prevalence and extent of which increases with disease severity. Clinicians should consider evaluating thyroid function in cats with severe NTIS, because doing so could help determine probability of successful treatment responses before investing considerable time, effort, and finances in addressing the underlying disease.

Keeping pace with climate change in global terrestrial protected areas.

Protected areas (PAs) are essential to biodiversity conservation, but their static boundaries may undermine their potential for protecting species under climate change. We assessed how the climatic conditions within global terrestrial PAs may change over time. By 2070, protection is expected to decline in cold and warm climates and increase in cool and hot climates over a wide range of precipitation. Most countries are expected to fail to protect >90% of their available climate at current levels. The evenness of climatic representation under protection-not the amount of area protected-positively influenced the retention of climatic conditions under protection. On average, protection retention would increase by ~118% if countries doubled their climatic representativeness under protection or by ~102% if countries collectively reduced emissions in accordance with global targets. Therefore, alongside adoption of mitigation policies, adaptation policies that improve the complementarity of climatic conditions within PAs will help countries safeguard biodiversity.

The Changing Face of Water: A Dynamic Reflection of Antibiotic Resistance Across Landscapes.

Little is known about the role of surface water in the propagation of antibiotic resistance (AR), or the relationship between AR and water quality declines. While healthcare and agricultural sectors are considered the main contributors to AR dissemination, few studies have been conducted in their absence. Using linear models and Bayesian kriging, we evaluate AR among Escherichia coli water isolates collected bimonthly from the Chobe River in Northern Botswana (n = 1997, n = 414 water samples; July 2011-May 2012) in relation to water quality dynamics (E. coli, fecal coliform, and total suspended solids), land use, season, and AR in wildlife and humans within this system. No commercial agricultural or large medical facilities exist within this region. Here, we identify widespread AR in surface water, with land use and season significant predicators of AR levels. Mean AR was significantly higher in the wet season than the dry season (p = 0.003), and highest in the urban landscape (2.15, SD = 0.098) than the protected landscape (1.39, SD = 0.051). In-water E. coli concentrations were significantly positively associated with mean AR in the wet season (p < 0.001) but not in the dry season (p = 0.110), with TSS negatively associated with mean AR across seasons (p = 0.016 and p = 0.029), identifying temporal and spatial relationships between water quality variables and AR. Importantly, when human, water, and wildlife isolates were examined, similar AR profiles were identified (p < 0.001). Our results suggest that direct human inputs are sufficient for extensive dispersal of AR into the environment, with landscape features, season, and water quality variables influencing AR dynamics. Focused and expensive efforts to minimize pollution from agricultural sources, while important, may only provide incremental benefits to the management of AR across complex landscapes. Controlling direct human AR inputs into the environment remains a critical and pressing challenge.

Commentary to: a cross-validation-based approach for delimiting reliable home range estimates.

BACKGROUND: Continued exploration of the performance of the recently proposed cross-validation-based approach for delimiting home ranges using the Time Local Convex Hull (T-LoCoH) method has revealed a number of issues with the original formulation. MAIN TEXT: Here we replace the ad hoc cross-validation score with a new formulation based on the total log probability of out-of-sample predictions. To obtain these probabilities, we interpret the normalized LoCoH hulls as a probability density. The application of the approach described here results in optimal parameter sets that differ dramatically from those selected using the original formulation. The derived metrics of home range size, mean revisitation rate, and mean duration of visit are also altered using the corrected formulation. CONCLUSION: Despite these differences, we encourage the use of the cross-validation-based approach, as it provides a unifying framework governed by the statistical properties of the home ranges rather than subjective selections by the user.

Consensus and conflict among ecological forecasts of Zika virus outbreaks in the United States.

Ecologists are increasingly involved in the pandemic prediction process. In the course of the Zika outbreak in the Americas, several ecological models were developed to forecast the potential global distribution of the disease. Conflicting results produced by alternative methods are unresolved, hindering the development of appropriate public health forecasts. We compare ecological niche models and experimentally-driven mechanistic forecasts for Zika transmission in the continental United States. We use generic and uninformed stochastic county-level simulations to demonstrate the downstream epidemiological consequences of conflict among ecological models, and show how assumptions and parameterization in the ecological and epidemiological models propagate uncertainty and produce downstream model conflict. We conclude by proposing a basic consensus method that could resolve conflicting models of potential outbreak geography and seasonality. Our results illustrate the usually-undocumented margin of uncertainty that could emerge from using any one of these predictions without reservation or qualification. In the short term, ecologists face the task of developing better post hoc consensus that accurately forecasts spatial patterns of Zika virus outbreaks. Ultimately, methods are needed that bridge the gap between ecological and epidemiological approaches to predicting transmission and realistically capture both outbreak size and geography.

Going through the motions: incorporating movement analyses into disease research.

Though epidemiology dates back to the 1700s, most mathematical representations of epidemics still use transmission rates averaged at the population scale, especially for wildlife diseases. In simplifying the contact process, we ignore the heterogeneities in host movements that complicate the real world, and overlook their impact on spatiotemporal patterns of disease burden. Movement ecology offers a set of tools that help unpack the transmission process, letting researchers more accurately model how animals within a population interact and spread pathogens. Analytical techniques from this growing field can also help expose the reverse process: how infection impacts movement behaviours, and therefore other ecological processes like feeding, reproduction, and dispersal. Here, we synthesise the contributions of movement ecology in disease research, with a particular focus on studies that have successfully used movement-based methods to quantify individual heterogeneity in exposure and transmission risk. Throughout, we highlight the rapid growth of both disease and movement ecology and comment on promising but unexplored avenues for research at their overlap. Ultimately, we suggest, including movement empowers ecologists to pose new questions, expanding our understanding of host-pathogen dynamics and improving our predictive capacity for wildlife and even human diseases.

Genetic responsiveness of African buffalo to environmental stressors: A role for epigenetics in balancing autosomal and sex chromosome interactions?

In the African buffalo (Syncerus caffer) population of the Kruger National Park (South Africa) a primary sex-ratio distorter and a primary sex-ratio suppressor have been shown to occur on the Y chromosome. A subsequent autosomal microsatellite study indicated that two types of deleterious alleles with a negative effect on male body condition, but a positive effect on relative fitness when averaged across sexes and generations, occur genome-wide and at high frequencies in the same population. One type negatively affects body condition of both sexes, while the other acts antagonistically: it negatively affects male but positively affects female body condition. Here we show that high frequencies of male-deleterious alleles are attributable to Y-chromosomal distorter-suppressor pair activity and that these alleles are suppressed in individuals born after three dry pre-birth years, likely through epigenetic modification. Epigenetic suppression was indicated by statistical interactions between pre-birth rainfall, a proxy for parental body condition, and the phenotypic effect of homozygosity/heterozygosity status of microsatellites linked to male-deleterious alleles, while a role for the Y-chromosomal distorter-suppressor pair was indicated by between-sex genetic differences among pre-dispersal calves. We argue that suppression of male-deleterious alleles results in negative frequency-dependent selection of the Y distorter and suppressor; a prerequisite for a stable polymorphism of the Y distorter-suppressor pair. The Y distorter seems to be responsible for positive selection of male-deleterious alleles during resource-rich periods and the Y suppressor for positive selection of these alleles during resource-poor periods. Male-deleterious alleles were also associated with susceptibility to bovine tuberculosis, indicating that Kruger buffalo are sensitive to stressors such as diseases and droughts. We anticipate that future genetic studies on African buffalo will provide important new insights into gene fitness and epigenetic modification in the context of sex-ratio distortion and infectious disease dynamics.

Discrete stochastic analogs of Erlang epidemic models.

Erlang differential equation models of epidemic processes provide more realistic disease-class transition dynamics from susceptible (S) to exposed (E) to infectious (I) and removed (R) categories than the ubiquitous SEIR model. The latter is itself is at one end of the spectrum of Erlang SE[Formula: see text]I[Formula: see text]R models with [Formula: see text] concatenated E compartments and [Formula: see text] concatenated I compartments. Discrete-time models, however, are computationally much simpler to simulate and fit to epidemic outbreak data than continuous-time differential equations, and are also much more readily extended to include demographic and other types of stochasticity. Here we formulate discrete-time deterministic analogs of the Erlang models, and their stochastic extension, based on a time-to-go distributional principle. Depending on which distributions are used (e.g. discretized Erlang, Gamma, Beta, or Uniform distributions), we demonstrate that our formulation represents both a discretization of Erlang epidemic models and generalizations thereof. We consider the challenges of fitting SE[Formula: see text]I[Formula: see text]R models and our discrete-time analog to data (the recent outbreak of Ebola in Liberia). We demonstrate that the latter performs much better than the former; although confining fits to strict SEIR formulations reduces the numerical challenges, but sacrifices best-fit likelihood scores by at least 7%.

Ecological metrics and methods for GPS movement data.

The growing field of movement ecology uses high resolution movement data to analyze animal behavior across multiple scales: from individual foraging decisions to population-level space-use patterns. These analyses contribute to various subfields of ecology-inter alia behavioral, disease, landscape, resource, and wildlife-and facilitate facilitate novel exploration in fields ranging from conservation planning to public health. Despite the growing availability and general accessibility of animal movement data, much potential remains for the analytical methods of movement ecology to be incorporated in all types of geographic analyses. This review provides for the Geographical Information Sciences (GIS) community an overview of the most common movement metrics and methods of analysis employed by animal ecologists. Through illustrative applications, we emphasize the potential for movement analyses to promote transdisciplinary GIS/wildlife-ecology research.

Correction to: A cross-validation-based approach for delimiting reliable home range estimates.

[This corrects the article DOI: 10.1186/s40462-017-0110-4.].

Parasite biodiversity faces extinction and redistribution in a changing climate.

Climate change is a well-documented driver of both wildlife extinction and disease emergence, but the negative impacts of climate change on parasite diversity are undocumented. We compiled the most comprehensive spatially explicit data set available for parasites, projected range shifts in a changing climate, and estimated extinction rates for eight major parasite clades. On the basis of 53,133 occurrences capturing the geographic ranges of 457 parasite species, conservative model projections suggest that 5 to 10% of these species are committed to extinction by 2070 from climate-driven habitat loss alone. We find no evidence that parasites with zoonotic potential have a significantly higher potential to gain range in a changing climate, but we do find that ectoparasites (especially ticks) fare disproportionately worse than endoparasites. Accounting for host-driven coextinctions, models predict that up to 30% of parasitic worms are committed to extinction, driven by a combination of direct and indirect pressures. Despite high local extinction rates, parasite richness could still increase by an order of magnitude in some places, because species successfully tracking climate change invade temperate ecosystems and replace native species with unpredictable ecological consequences.

A cross-validation-based approach for delimiting reliable home range estimates.

BACKGROUND: With decreasing costs of GPS telemetry devices, data repositories of animal movement paths are increasing almost exponentially in size. A series of complex statistical tools have been developed in conjunction with this increase in data. Each of these methods offers certain improvements over previously proposed methods, but each has certain assumptions or shortcomings that make its general application difficult. In the case of the recently developed Time Local Convex Hull (T-LoCoH) method, the subjectivity in parameter selection serves as one of the primary impediments to its more widespread use. While there are certain advantages to the flexibility it offers for question-driven research, the lack of an objective approach for parameter selection may prevent some users from exploring the benefits of the method. METHODS: Here we present a cross-validation-based approach for selecting parameter values to optimize the T-LoCoH algorithm. We demonstrate the utility of the approach using a case study from the Etosha National Park anthrax system. RESULTS: Utilizing the proposed algorithm, rather than the guidelines in the T-LoCoH documentation, results in significantly different values for derived site fidelity metrics. CONCLUSIONS: Due to its basis in principles of cross-validation, the application of this method offers a more objective approach than the relatively subjective guidelines set forth in the T-LoCoH documentation and enables a more accurate basis for the comparison of home ranges among individuals and species, as well as among studies.

Suite of simple metrics reveals common movement syndromes across vertebrate taxa.

BACKGROUND: Because empirical studies of animal movement are most-often site- and species-specific, we lack understanding of the level of consistency in movement patterns across diverse taxa, as well as a framework for quantitatively classifying movement patterns. We aim to address this gap by determining the extent to which statistical signatures of animal movement patterns recur across ecological systems. We assessed a suite of movement metrics derived from GPS trajectories of thirteen marine and terrestrial vertebrate species spanning three taxonomic classes, orders of magnitude in body size, and modes of movement (swimming, flying, walking). Using these metrics, we performed a principal components analysis and cluster analysis to determine if individuals organized into statistically distinct clusters. Finally, to identify and interpret commonalities within clusters, we compared them to computer-simulated idealized movement syndromes representing suites of correlated movement traits observed across taxa (migration, nomadism, territoriality, and central place foraging). RESULTS: Two principal components explained 70% of the variance among the movement metrics we evaluated across the thirteen species, and were used for the cluster analysis. The resulting analysis revealed four statistically distinct clusters. All simulated individuals of each idealized movement syndrome organized into separate clusters, suggesting that the four clusters are explained by common movement syndrome. CONCLUSIONS: Our results offer early indication of widespread recurrent patterns in movement ecology that have consistent statistical signatures, regardless of taxon, body size, mode of movement, or environment. We further show that a simple set of metrics can be used to classify broad-scale movement patterns in disparate vertebrate taxa. Our comparative approach provides a general framework for quantifying and classifying animal movements, and facilitates new inquiries into relationships between movement syndromes and other ecological processes.

Parasite vulnerability to climate change: an evidence-based functional trait approach.

Despite the number of virulent pathogens that are projected to benefit from global change and to spread in the next century, we suggest that a combination of coextinction risk and climate sensitivity could make parasites at least as extinction prone as any other trophic group. However, the existing interdisciplinary toolbox for identifying species threatened by climate change is inadequate or inappropriate when considering parasites as conservation targets. A functional trait approach can be used to connect parasites' ecological role to their risk of disappearance, but this is complicated by the taxonomic and functional diversity of many parasite clades. Here, we propose biological traits that may render parasite species particularly vulnerable to extinction (including high host specificity, complex life cycles and narrow climatic tolerance), and identify critical gaps in our knowledge of parasite biology and ecology. By doing so, we provide criteria to identify vulnerable parasite species and triage parasite conservation efforts.

An Agent-Based Model of School Closing in Under-Vacccinated Communities During Measles Outbreaks.

The winter 2014-15 measles outbreak in the US represents a significant crisis in the emergence of a functionally extirpated pathogen. Conclusively linking this outbreak to decreases in the measles/mumps/rubella (MMR) vaccination rate (driven by anti-vaccine sentiment) is critical to motivating MMR vaccination. We used the NOVA modeling platform to build a stochastic, spatially-structured, individual-based SEIR model of outbreaks, under the assumption that R0 ≈ 7 for measles. We show this implies that herd immunity requires vaccination coverage of greater than approximately 85%. We used a network structured version of our NOVA model that involved two communities, one at the relatively low coverage of 85% coverage and one at the higher coverage of 95%, both of which had 400-student schools embedded, as well as students occasionally visiting superspreading sites (e.g. high-density theme parks, cinemas, etc.). These two vaccination coverage levels are within the range of values occurring across California counties. Transmission rates at schools and superspreading sites were arbitrarily set to respectively 5 and 15 times background community rates. Simulations of our model demonstrate that a 'send unvaccinated students home' policy in low coverage counties is extremely effective at shutting down outbreaks of measles.

An Ecological Assessment of the Pandemic Threat of Zika Virus.

The current outbreak of Zika virus poses a severe threat to human health. While the range of the virus has been cataloged growing slowly over the last 50 years, the recent explosive expansion in the Americas indicates that the full potential distribution of Zika remains uncertain. Moreover, many studies rely on its similarity to dengue fever, a phylogenetically closely related disease of unknown ecological comparability. Here we compile a comprehensive spatially-explicit occurrence dataset from Zika viral surveillance and serological surveys based in its native range, and construct ecological niche models to test basic hypotheses about its spread and potential establishment. The hypothesis that the outbreak of cases in Mexico and North America are anomalous and outside the native ecological niche of the disease, and may be linked to either genetic shifts between strains, or El Nino or similar climatic events, remains plausible at this time. Comparison of the Zika niche against the known distribution of dengue fever suggests that Zika is more constrained by the seasonality of precipitation and diurnal temperature fluctuations, likely confining autochthonous non-sexual transmission to the tropics without significant evolutionary change. Projecting the range of the diseases in conjunction with three major vector species (Aedes africanus, Ae. aegypti, and Ae. albopictus) that transmit the pathogens, under climate change, suggests that Zika has potential for northward expansion; but, based on current knowledge, our models indicate Zika is unlikely to fill the full range its vectors occupy, and public fear of a vector-borne Zika epidemic in the mainland United States is potentially informed by biased or limited scientific knowledge. With recent sexual transmission of the virus globally, we caution that our results only apply to the vector-borne transmission route of the pathogen, and while the threat of a mosquito-carried Zika pandemic may be overstated in the media, other transmission modes of the virus may emerge and facilitate naturalization worldwide.