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Human cases of tick-borne diseases have been increasing in the United States. In particular, the incidence of Lyme disease, the major vector-borne disease in Rhode Island, has risen, along with cases of babesiosis and anaplasmosis, all vectored by the blacklegged tick. These increases might relate, in part, to climate change, although other environmental changes in the northeastern U.S. (land use as it relates to habitat; vertebrate host populations for tick reproduction and enzootic cycling) also contribute. Lone star ticks, formerly southern in distribution, have been spreading northward, including expanded distributions in Rhode Island. Illnesses associated with this species include ehrlichiosis and alpha-gal syndrome, which are expected to increase. Ranges of other tick species have also been expanding in southern New England, including the Gulf Coast tick and the introduced Asian longhorned tick. These ticks can carry human pathogens, but the implications for human disease in Rhode Island are unclear.

Deep learning is a powerful approach for distinguishing classes of images, and there is a growing interest in applying these methods to delimit species, particularly in the identification of mosquito vectors. Visual identification of mosquito species is the foundation of mosquito-borne disease surveillance and management, but can be hindered by cryptic morphological variation in mosquito vector species complexes such as the malaria-transmitting Anopheles gambiae complex. We sought to apply Convolutional Neural Networks (CNNs) to images of mosquitoes as a proof-of-concept to determine the feasibility of automatic classification of mosquito sex, genus, species, and strains using whole-body, 2D images of mosquitoes. We introduce a library of 1, 709 images of adult mosquitoes collected from 16 colonies of mosquito vector species and strains originating from five geographic regions, with 4 cryptic species not readily distinguishable morphologically even by trained medical entomologists. We present a methodology for image processing, data augmentation, and training and validation of a CNN. Our best CNN configuration achieved high prediction accuracies of 96.96% for species identification and 98.48% for sex. Our results demonstrate that CNNs can delimit species with cryptic morphological variation, 2 strains of a single species, and specimens from a single colony stored using two different methods. We present visualizations of the CNN feature space and predictions for interpretation of our results, and we further discuss applications of our findings for future applications in malaria mosquito surveillance., Author summary Rapid and accurate identification of mosquitoes that transmit human pathogens is an essential part of mosquito-borne disease surveillance. Such identification can be difficult for mosquitoes that transmit malaria, as many are morphologically indistinguishable, including those in the Anopheles gambiae species complex. We photographed 1, 709 individual mosquitoes from 16 laboratory colonies housed at the Centers for Disease Control and Prevention to create a database of whole-body mosquito images. We present a methodology for image processing, data augmentation, and training and validation of a convolutional neural network (CNN). We applied this method to our mosquito image database, finding a 96.96% prediction accuracy for class identification and 98.48% for sex. Further, our best model accurately predicted images between 2 strains of a single species and between 2 storage methods of mosquitoes from the same colony. These results demonstrate that image classification with deep learning can be a useful method for malaria mosquito identification, even among species with cryptic morphological variation. We discuss the application of deep learning to mosquito identification in malaria mosquito surveillance.

Large enrollment foundational courses are perceived as "high stakes" because of their potential to act as barriers for progression to the next course or admittance to a program. The nature of gateway courses makes them ideal settings to explore the relationship between anxiety, pedagogical interventions, and student performance. Here, two-stage collaborative examinations were implemented to improve test-taking skills and address widespread test anxiety in an introductory human anatomy course. Test anxiety data were collected (using the Motivated Strategies for Learning Questionnaire) before the first examination and last examination. Most students experienced decreased test anxiety over the course of the semester; however, some students may have experienced performance limiting conditions due to test anxiety at the end of the semester based on academic ability in the course (in "C" students when compared to "A" students: P

Dengue Fever (DF) is a mosquito vector transmitted flavivirus and a reemerging global public health threat. Although several studies have addressed the relation between climatic and environmental factors and the epidemiology of DF, or looked at purely spatial or time series analysis, this article presents a joint spatio-temporal epidemiological analysis. Our approach accounts for both temporal and spatial autocorrelation in DF incidence and the effect of temperatures and precipitation by using a hierarchical Bayesian approach. We fitted several space-time areal models to predict relative risk at the municipality level and for each month from 1990 to 2014. Model selection was performed according to several criteria: the preferred models detected significant effects for temperature at time lags of up to four months and for precipitation up to three months. A boundary detection analysis is incorporated in the modeling approach, and it was successful in detecting municipalities with historically anomalous risk.

Integrated Pest Management (IPM) is an approach to minimizing economic and environmental harm caused by pests, and Integrated Vector Management (IVM) uses similar methods to minimize pathogen transmission by vectors. The risk of acquiring a vector-borne infection is often quantified using the density of infected vectors. The relationship between vector numbers and risk of human infection is more or less linear when both vector numbers and pathogen prevalence in vectors are low, but the relationship is nonlinear when vector density and/or infection prevalence are high. Therefore, the density of infected vectors often does not accurately predict risk of human exposure to pathogens, and traditional estimates of the percent control often overestimate the level of protection from infection resulting from management programs. We suggest a modified estimator, percent protection, which more accurately quantifies protection against human infection resulting from a management intervention. Cost-effectiveness of a management program is critical to protection of both public health and the environment, because the more efficiently available resources and funding are used, the fewer people get sick, and well-targeted efficient management programs minimize the need for poorly targeted, expensive environmental interventions (e.g., broadscale pesticide applications) that tend to damage nontarget organisms and natural systems. Design of an efficient, cost-effective IVM program requires knowledge of the cost-effectiveness functions (the effectiveness of control methods at lowering vector bites and/or infection prevalence with different levels of application) of the various control methods to be applied. Alternative programs can be designed that optimize percent protection by integrating different control methods at different levels of investment, and environmental effects of these alternatives can be compared, allowing environmental considerations to be included explicitly in the decision process. IPM, IVM, and Adaptive Management share the characteristic that management decisions must be made with incomplete knowledge of the functioning of natural systems or the efficacies of interventions. IVM surveillance programs that assess the effects of individual control methods and of combinations of control methods on the numbers of vector bites and on infection prevalence in vectors can increase knowledge of pathogen transmission dynamics and provide information to improve program effectiveness in subsequent applications.

Meta-analysis of 33 studies of developmental timing of Culex pipiens s.l. Linnaeus demonstrates that development rate, or the rate of progression through immature life stadia, is primarily driven by temperature, whereas immature survival is driven by temperature, density, and variability in the environmental conditions. As expected, the linear relationship of temperature and development rate is positive for the larval period as well as development to adult emergence. However, the strength of this association varies significantly. Variation in development rate can be explained using additional environmental factors of intraspecific rearing density, sex, and study methodology. Heterogeneity in development rates even once temperature has been considered emphasizes the need for further research of multiple environmental factors and in changing environments. Immature survival is also significantly impacted by variability in environmental conditions. Development rates vary between subspecies of Cx. pipiens, but these population differences are no longer significant once an environmental factor of temperature is considered. Thus, variability in development rate of these insects appears to be primarily driven by response to certain environmental conditions rather than differences between populations. Broad patterns of phenotypic variation across latitude and 96 yr of empirical estimates were not significant once environmental rearing conditions had been considered.

Large enrollment foundational courses are perceived as "high stakes" because of their potential to act as barriers for progression to the next course or admittance to a program. The nature of gateway courses makes them ideal settings to explore the relationship between anxiety, pedagogical interventions, and student performance. Here, two-stage collaborative examinations were implemented to improve test-taking skills and address widespread test anxiety in an introductory human anatomy course. Test anxiety data were collected (using the Motivated Strategies for Learning Questionnaire) before the first examination and last examination. Most students experienced decreased test anxiety over the course of the semester; however, some students may have experienced performance limiting conditions due to test anxiety at the end of the semester based on academic ability in the course (in "C" students when compared to "A" students: P 0.00006 and "B" students: P 0.05), overall academic ability (in academically weaker students: P 0.025), and demographic factors (in women: P 0.025). The strongest performances on examinations were primarily observed in already academically strong students (mean individual performance: P 0.000, mean group performance: P 0.000). Furthermore, changes in test anxiety were not significantly associated with the group portion of the examinations. Patterns of changes in test anxiety over the course of the semester underscore a complex interaction between test anxiety, student background, and student performance. Results suggest that pathways for test anxiety in "high stakes" courses may be separate from the mechanisms responsible for the benefits of collaborative testing. Anat Sci Educ 10: 409-422. © 2017 American Association of Anatomists.

Many environmental factors, biotic and abiotic interact to influence organismal development. Given the importance of Aedes aegypti as a vector of human pathogens including dengue and yellow fever, understanding the impact of environmental factors such as temperature, resource availability, and intraspecific competition during development is critical for population control purposes. Despite known associations between developmental traits and factors of diet and density, temperature has been considered the primary driver of development rate and survival. To determine the relative importance of these critical factors, wide gradients of conditions must be considered. We hypothesize that 1) diet and density, as well as temperature influence the variation in development rate and survival, 2) that these factors interact, and this interaction is also necessary to understand variation in developmental traits. Temperature, diet, density, and their two-way interactions are significant factors in explaining development rate variation of the larval stages of Ae. aegypti mosquitoes. These factors as well as two and three-way interactions are significantly associated with the development rate from hatch to emergence. Temperature, but not diet or density, significantly impacted juvenile mortality. Development time was heteroskedastic with the highest variation occurring at the extremes of diet and density conditions. All three factors significantly impacted survival curves of experimental larvae that died during development. Complex interactions may contribute to variation in development rate. To better predict variation in development rate and survival in Ae. aegypti, factors of resource availability and intraspecific density must be considered in addition, but never to the exclusion of temperature.

Background Oviposition-site choice is an essential component of the life history of all mosquito species. According to the oviposition-preference offspring-performance (P-P) hypothesis, if optimizing offspring performance and fitness ensures high overall reproductive fitness for a given species, the female should accurately assess details of the heterogeneous environment and lay her eggs preferentially in sites with conditions more suitable to offspring. Methods We empirically tested the P-P hypothesis using the mosquito species Aedes albopictus by artificially manipulating two habitat conditions: diet (measured as mg of food added to a container) and conspecific density (CD; number of pre-existing larvae of the same species). Immature development (larval mortality, development time to pupation and time to emergence) and fitness (measured as wing length) were monitored from first instar through adult emergence using a factorial experimental design over two ascending gradients of diet (2.0, 3.6, 7.2 and 20 mg food/300 ml water) and CD (0, 20, 40 and 80 larvae/300 ml water). Treatments that exerted the most contrasting values of larval performance were recreated in a second experiment consisting of single-female oviposition site selection assay. Results Development time decreased as food concentration increased, except from 7.2 mg to 20.0 mg (Two-Way CR ANOVA Post-Hoc test, P > 0.1). Development time decreased also as conspecific density increased from zero to 80 larvae (Two-Way CR ANOVA Post-Hoc test, P ). Combined, these results support the role of density-dependent competition for resources as a limiting factor for mosquito larval performance. Oviposition assays indicated that female mosquitoes select for larval habitats with conspecifics and that larval density was more important than diet in driving selection for oviposition sites. Conclusions This study supports predictions of the P-P hypothesis and provides a mechanistic understanding of the underlying factors driving mosquito oviposition site selection.

Large gaps exist in our knowledge about common patterns and variability in the endocrinology of immature nonhuman primates, and even normal hormonal profiles during that life stage are lacking for wild populations. In the present study we present steroid profiles for a wild population of baboons (Papio cynocephalus) from infancy through reproductive maturation, obtained by noninvasive fecal analyses. Fecal concentrations of glucocorticoid (fGC) and testosterone (fT) metabolites for males, and of fGC, estrogen (fE), and progestin (fP) metabolites for females were measured by radioimmunoassay (RIA). In males, infancy was characterized by high and declining levels of fGC and fT, whereas steroid concentrations were low during the juvenile years. During the months immediately prior to testicular enlargement, fT (but not fGC) concentration tended to increase. Males that matured early consistently had higher fT and fGC concentrations than those that matured late, but not significantly so at any age. Individual differences in fT concentrations were stable across ages, and average individual fT and fGC concentrations were positively correlated. For females, high and declining levels of fE characterized infancy, and values increased again after 3.5 years of age, as some females reached menarche by that age. Both fP and fGC were relatively low and constant throughout infancy and the juvenile period. During the months immediately prior to menarche, fGC concentration significantly decreased, while no changes were observed for fE levels. fP exhibited a complicated pattern of decrease that was subsequently followed by a more modest and nonsignificant increase as menarche approached. Early- (EM) and late-maturing (LM) females differed only in fP concentration; the higher fP concentrations in EM females reached significance at 4-4.5 years of age. Maternal rank at offspring conception did not predict concentrations of any hormone for either sex. Our results demonstrate the presence of individual endocrine variability, which could have important consequences for the timing of sexual maturation and subsequently for individual reproductive success. Further evaluation of the factors that affect hormone concentrations during the juvenile and adolescent periods should lead to a better understanding of mechanisms of life-history variability.

Background Development rates of Aedes aegypti are known to vary with respect to many abiotic and biotic factors including temperature, resource availability, and intraspecific competition. The relative importance of these factors and their interactions are not well established across populations. We performed meta-analysis on a dataset of development rate estimates from 49 studies. Results Meta-analytic results indicated that the environmental factor of temperature is sufficient to explain development rate variability in Ae. aegypti. While diet and density may greatly impact other developmental phenotypes, these results suggest that for development rate these factors should never be considered to the exclusion of temperature. The effect of temperature on development rate is not homogenous or constant. The sources of heterogeneity of the effect of temperature are difficult to analyze due to lack of consistent reporting of larval rearing methods. Conclusions Temperature is the most important ecological determinant of development rate in Ae. aegypti, but its effect is heterogeneous. Ignoring this heterogeneity is problematic for models of vector population and vector-borne disease transmission.