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We report on the use of leaf diffuse reflectance spectroscopy for plant disease detection. A smartphone-operated, compact diffused reflectance spectrophotometer is used for field collection of leaf diffuse reflectance spectra to enable pre-symptomatic detection of the progression of potato late blight disease post inoculation with oomycete pathogen Phytophthora infestans. Neural-network-based analysis predicts infection with >96% accuracy, only 24 h after inoculation with the pathogen, and nine days before visual late blight symptoms appear. Our study demonstrates the potential of using portable optical spectroscopy in tandem with machine learning analysis for early diagnosis of plant diseases.

In most of the developing countries, the economy is largely based on agriculture. The poor availability of skilled personnel and of appropriate supporting infrastructure, make crop fields vulnerable to the outbreak of plant diseases, possibly due to spreading viruses and fungi, or to adverse environmental conditions, such as drought. The mobile application PlantVillage Nuru, provides an invaluable tool for early detection of plant diseases and sustainable food production. A mobile device endowed with Nuru is a powerful mobile sensor: it analyzes plant images and uses an AI engine to recognize health issues. In this paper we propose a crowd-sensing framework, where Nuru is adopted at large scale in the farmer population. We tackle the device deployment problem, where device mobility is only partially controllable, mostly in an indirect manner, through incentives. We propose two problem formulations, and related algorithms, to minimize the number of required smartphones while providing sufficient geographical coverage. We study the proposed models in simulated as well as real scenarios, showing that they outperform current solutions in terms of monitoring accuracy and completeness, with lower cost. Then we describe the test-bed implementation, confirming the applicability of the proposed crowd-sensing framework in a real scenario in Kenya.

Determining how ant colonies optimize foraging while mitigating pathogen and predator risks provides insight into how the ants have achieved ecological success. Ants must respond to changing resource conditions, but exploration comes at a cost of higher potential exposure to threats. Fungal infected cadavers surround the main foraging trails of the carpenter ant Camponotus rufipes, offering a system to study how foragers behave given the persistent occurrence of disease threats. Studies on social insect foraging behavior typically require many hours of human labor due to the high density of individuals. To overcome this, we developed deep learning based computer vision algorithms to track foraging ants, frame-by-frame, from video footage shot under the natural conditions of a tropical forest floor at night. We found that most foragers walk in straight lines overlapping the same areas as other ants, but there is a subset of foragers with greater exploration. Consistency in walking behavior may protect most ants from infection, while foragers that explore unique portions of the trail may be more likely to encounter fungal spores implying a trade-off between resource discovery and risk avoidance.

Ant feeding interactions (i.e., trophallaxis events) are thought to regulate the flow of nutrients and disease within a colony. Consequently, there is great interest in learning which environmental and behavioral factors drive ant trophallaxis. In this paper, we analyze ant trophallaxis behavior in a colony of 73 carpenter ants, observed at 1-s intervals over a period of 4 h. The data represent repeated observations from a dynamic contact network; however, traditional statistical analyses of network models are ill-suited for data observed at such high temporal resolution. We present a model for high-resolution longitudinal network data, where the network is assumed to be a time inhomogeneous, continuous-time Markov chain, with transition rates modeled as a function of time-varying individual and pairwise biological covariates. In particular, the high temporal resolution of the data leads to a tractable likelihood function, and likelihood-based inference procedures are utilized to explain which biological factors drive contact. Our results reveal how differences in ant social castes and individual behaviors, such as ant speed and activity levels, influence patterns of ant trophallaxis in the colony. Supplementary materials accompanying this paper appear online.

Many organisms are able to elicit behavioral change in other organisms. Examples include different microbes (e.g., viruses and fungi), parasites (e.g., hairworms and trematodes), and parasitoid wasps. In most cases, the mechanisms underlying host behavioral change remain relatively unclear. There is a growing body of literature linking alterations in immune signaling with neuron health, communication, and function; however, there is a paucity of data detailing the effects of altered neuroimmune signaling on insect neuron function and how glial cells may contribute toward neuron dysregulation. It is important to consider the potential impacts of altered neuroimmune communication on host behavior and reflect on its potential role as an important tool in the “neuro-engineer” toolkit. In this review, we examine what is known about the relationships between the insect immune and nervous systems. We highlight organisms that are able to influence insect behavior and discuss possible mechanisms of behavioral manipulation, including potentially dysregulated neuroimmune communication. We close by identifying opportunities for integrating research in insect innate immunity, glial cell physiology, and neurobiology in the investigation of behavioral manipulation.

The fall armyworm, Spodoptera frugiperda (FAW), first invaded Africa in 2016 and has since become established in many areas across the continent where it poses a serious threat to food and nutrition security. We re-parameterized the existing CLIMEX model to assess the FAW global invasion threat, emphasizing the risk of transient and permanent population establishment in Africa under current and predicted future climates, considering irrigation patterns. FAW can establish itself in almost all countries in eastern and central Africa and a large part of western Africa under the current climate. Climatic barriers, such as heat and dry stresses, may limit the spread of FAW to North and South Africa. Future predictions suggest that FAW invasive range will retract from both northern and southern regions towards the equator. However, a large area in eastern and central Africa is predicted to have an optimal climate for FAW persistence. These areas will serve as FAW ‘hotspots’ from where it may migrate to the north and south during winter seasons and then pose an economic threat. Our projections can be used to identify countries at risk for permanent and transient FAW-population establishment and inform timely integrated pest management interventions under present and future climate in Africa.

Manual data annotation for training custom object detection can be a time-consuming and error-prone process. In this paper, we propose an automatic approach to generating synthetic, annotated images using differentiable neural rendering and 3D object models. We also investigate the possibility of using 3D adversarial object models to improve object detection accuracy. The experimental results show that the object detection models trained using both synthetic examples rendered from 3D object models and real data outperform the baseline model trained on only real data.

Humans and other group-living animals tend to distribute their social effort disproportionately. Individuals predominantly interact with a small number of close companions while maintaining weaker social bonds with less familiar group members. By incorporating this behavior into a mathematical model, we find that a single parameter, which we refer to as social fluidity, controls the rate of social mixing within the group. Large values of social fluidity correspond to gregarious behavior, whereas small values signify the existence of persistent bonds between individuals. We compare the social fluidity of 13 species by applying the model to empirical human and animal social interaction data. To investigate how social behavior influences the likelihood of an epidemic outbreak, we derive an analytical expression of the relationship between social fluidity and the basic reproductive number of an infectious disease. For species that form more stable social bonds, the model describes frequency-dependent transmission that is sensitive to changes in social fluidity. As social fluidity increases, animal-disease systems become increasingly density-dependent. Finally, we demonstrate that social fluidity is a stronger predictor of disease outcomes than both group size and connectivity, and it provides an integrated framework for both density-dependent and frequency-dependent transmission.

Fungi develop structures that interact with their surroundings and evolve adaptively in the presence of geometrical constraints, finding optimal solutions for complex combinatorial problems. The pathogenic fungus Ophiocordyceps constitutes a perfect model for the study of constrained interactive networks. Modeling these networks is challenging due to the highly coupled physics involved and their interaction with moving boundaries. In this work, we develop a computational phase-field model to elucidate the mechanics of the emerging properties observed in fungal networks. We use a variational approach to derive the equations governing the evolution in time of the mycelium biomass and the nutrients in the medium. We present an extensive testing of our model, reproduce growing and decaying phenomena, and capture spatial and temporal scales. We explore the variables interplay mechanism that leads to different colony morphologies, and explain abrupt changes of patterns observed in the laboratory. We apply our model to simulate analogous processes to the evolution of Ophiocordyceps as it grows through confined geometry and depletes available resources, demonstrating the suitability of the formulation to study this class of biological networks.

Ophiocordyceps species infecting ants are globally distributed, with diversity concentrated in the tropics and decreasing with increasing latitude. Among these myrmecophilous species, the ones exhibiting the ability to manipulate host behavior, the so-called "zombie-ant fungi" of the O. unilateralis clade, have been studied progressively over the last decade. However, we know very little about other myrmecophilous groups, such as species within the Ophiocordyceps subgenus Neocordyceps. Species within this group exhibit Hymenostilbe asexual morphs with the ascospores readily breaking into part-spores and regularly kill their hosts on the forest floor, with few records of behavioral manipulation. Here, we describe five new species of Ophiocordyceps belonging to the subgenus Neocordyceps infecting ants in the rainforests of the Brazilian Amazon and Ghana and analyze their ability to manipulate host behavior. We also propose a new status for a species previously described as a variety, providing its phylogenetic placement for the first time. The species proposed herein can readily be separated using classic taxonomic criteria, and this is further supported by ecological and molecular multiloci data.

Fall armyworm (FAW) is a polyphagus pest with a preference for young maize leaves that relocates to the cob during cob development and can devastate maize yields. Time series anomaly change detection and first derivative growth pattern analyses were conducted under the hypothesis that, if unimpeded and occurring during the vegetativegrowth stage, FAW presence will result in a reduction of the LAI or total green biomass (NDVI) of the crop. We have conducted observations NDVI/LAI at three different scales from continental to field scales., (i) Sentinel-2 a+b and (ii) micro-satellite Planet Scope, (iii) multispectral camera. Correlations between LAI and (i) NDVI of (i, ii, iii) were R2 0.60, 0.66 and 0.35, respectively. The NDVI time series and first derivative were then compared to the FAW damage recorded using a mobile app.

Ophiocordyceps species infecting ants – the so-called zombie-ant fungi – comprise one of the most intriguing and fascinating relationships between microbes and animals. They are widespread within tropical forests worldwide, with relatively few reports from temperate ecosystems. These pathogens possess the ability to manipulate host behaviour in order to increase their own fitness. Depending on the fungal species involved the infected ants are manipulated either to leave the nest to ascend understorey shrubs, to die biting onto vegetation, or descend from the canopy to die at the base of trees. Experimental evidence has demonstrated that the behavioural change aids spore dispersal and thus increases the chances of infection, because of the existing behavioural immunity expressed inside ant colonies that limits fungal development and transmission. Despite their undoubted importance for ecosystem functioning, these fungal pathogens are still poorly documented, especially regarding their diversity, ecology and evolutionary relationships. Here, we describe 15 new species of Ophiocordyceps with hirsutella-like asexual morphs that exclusively infect ants. These form a monophyletic group that we identified in this study as myrmecophilous hirsutelloid species. We also propose new combinations for species previously described as varieties and provide for the first time important morphological and ecological information. The species proposed herein were collected in Brazil, Colombia, USA, Australia and Japan. All species could readily be separated using classic taxonomic criteria, in particular ascospore and asexual morphology. Key words: Behaviour manipulation, Camponotini, Entomopathogenic fungi, Host association, Hypocreales, Insect pathogen, Multigene phylogeny, Ophiocordyceps, Ophiocordyceps unilateralis, Zombie-ant fungi, Taxonomic novelties: new combinations: Ophiocordyceps dolichoderi (H.C. Evans & Samson) Araújo, H.C. Evans & D.P. Hughes, O. monacidis (H.C. Evans & Samson) Araújo, H.C. Evans & D.P. Hughes, new species: O. albacongiuae Araújo, H.C. Evans & D.P. Hughes, O. blakebarnesii Araújo, H.C. Evans & D.P. Hughes, O. camponoti-chartificis Araújo, H.C. Evans & D.P. Hughes, O. camponoti-femorati Araújo, H.C. Evans & D.P. Hughes, O. camponoti-floridani Araújo, H.C. Evans & D.P. Hughes, O. camponoti-hippocrepidis Araújo, H.C. Evans & D.P. Hughes, O. camponoti-nidulantis Araújo, H.C. Evans & D.P. Hughes, O. camponoti-renggeri Araújo, H.C. Evans & D.P. Hughes, O. camponoti-sexguttati Araújo, H.C. Evans & D.P. Hughes, O. daceti Araújo, H.C. Evans & D.P. Hughes, O. kimflemingiae Araújo, H.C. Evans & D.P. Hughes, O. naomipierceae Araújo, H.C. Evans & D.P. Hughes, O. oecophyllae Araújo, S. Abell, T. Marney, R. Shivas H.C. Evans & D.P. Hughes, O. ootakii Araújo, H.C. Evans & D.P. Hughes., O. satoi Araújo, H.C. Evans & D.P. Hughes

The type of Ophiocordyceps unilateralis (Ophiocordycipitaceae, Hypocreales, Ascomycota) is based on an immature specimen collected on an ant in Brazil. The host was identified initially as a leaf-cutting ant (Atta cephalotes, Attini, Myrmicinae). However, a critical examination of the original illustration reveals that the host is the golden carpenter ant, Camponotus sericeiventris (Camponotini, Formicinae). Because the holotype is no longer extant and the original diagnosis lacks critical taxonomic information - specifically, on ascus and ascospore morphology - a new type from Minas Gerais State of south-east Brazil is designated herein. A re-description of the fungus is provided and a new phylogenetic tree of the O. unilateralis clade is presented. It is predicted that many more species of zombie-ant fungi remain to be delimited within the O. unilateralis complex worldwide, on ants of the tribe Camponotini.

Parasites can alter the behavior of animals. Such alterations could be a byproduct of infection or actively controlled and directed by the parasite. Ants infected with zombie ant fungi (Ophiocordyceps sp.) show behavioral changes culminating in the ant dying while biting into vegetation. To investigate the influence of the parasite on behavioral changes, we created an agent-based model that provides a prediction of how fungal infected ants move before death. The model shows how alterations in movement, such as an increased turning rate, within the normal range of ant behavior, can lead a host from the nest to the underside of a leaf. This demonstrates the simplicity in how such behavioral changes could evolve, as the fungal parasite could benefit from the natural behavior of the host, contesting a hypothesis of highly directed manipulation.

Significance Microbial parasites may behave collectively to manipulate their host’s behavior. We examine adaptations of a microbial parasite in its natural environment: the body of its coevolved and manipulated host. Electron microscopy and 3D reconstructions of host and parasite tissues reveal that this fungus invades muscle fibers throughout the ant’s body but leaves the brain intact, and that the fungal cells connect to form extensive networks. The connections are likened to structures that aid in transporting nutrients and organelles in several plant-associated fungi. These findings alter the current view of parasite-extended phenotypes by demonstrating that behavior control does not require the parasite to physically invade the host brain and that parasite cells may coordinate to change host behavior., Some microbes possess the ability to adaptively manipulate host behavior. To better understand how such microbial parasites control animal behavior, we examine the cell-level interactions between the species-specific fungal parasite Ophiocordyceps unilateralis sensu lato and its carpenter ant host (Camponotus castaneus) at a crucial moment in the parasite’s lifecycle: when the manipulated host fixes itself permanently to a substrate by its mandibles. The fungus is known to secrete tissue-specific metabolites and cause changes in host gene expression as well as atrophy in the mandible muscles of its ant host, but it is unknown how the fungus coordinates these effects to manipulate its host’s behavior. In this study, we combine techniques in serial block-face scanning-electron microscopy and deep-learning–based image segmentation algorithms to visualize the distribution, abundance, and interactions of this fungus inside the body of its manipulated host. Fungal cells were found throughout the host body but not in the brain, implying that behavioral control of the animal body by this microbe occurs peripherally. Additionally, fungal cells invaded host muscle fibers and joined together to form networks that encircled the muscles. These networks may represent a collective foraging behavior of this parasite, which may in turn facilitate host manipulation.

Animal movement often exhibits changing behavior because animals often alternate between exploring, resting, feeding, or other potential states. Changes in these behavioral states are often driven by environmental conditions or the behavior of nearby individuals. We propose a model for dependence among individuals’ behavioral states. We couple this state switching with complex discrete-time animal movement models to analyze a large variety of animal movement types. To demonstrate this method of capturing dependence, we study the movements of ants in a nest. The behavioral interaction structure is combined with a spatially varying stochastic differential equation model to allow for spatially and temporally heterogeneous collective movement of all ants within the nest. Our results reveal behavioral tendencies that are related to nearby individuals, particularly the queen, and to different locations in the nest.

Premise of the studyNuru is an artificial intelligence system for diagnosis of plant diseases and pests developed as a public good by PlantVillage (Penn State University), FAO, IITA and CIMMYT. It provides a simple, inexpensive and robust means of conducting in-field diagnosis without requiring internet connection and provides real-time results and advice. The present work evaluates the effectiveness of Nuru as an in-field diagnostic tool by comparing the diagnosis capability of Nuru to that of cassava experts (researchers trained on cassava pests and diseases), agricultural extension agents and farmers.MethodsThe diagnosis capability of Nuru and that of the assessed individuals was determined by inspecting cassava plants in-field and by using the cassava symptom recognition assessment tool (CaSRAT) to score images of cassava leaves.ResultsNuru’s accuracy for symptom recognition when using six leaves (74 - 88%, depending on the condition) was similar to that of experts, 1.5-times higher than agricultural extension agents and two-times higher than farmers.DiscussionThese findings suggests that Nuru can be an effective tool for in-field diagnosis of cassava diseases and has a potential of being a quick and cost-effective means of disseminating knowledge from researchers to agricultural extension agents and farmers.

There are numerous examples of parasites that manipulate the behavior of the hosts that they infect. One such host–pathogen relationship occurs between the ‘zombie-ant fungus’ Ophiocordyceps unilateralis sensu lato and its carpenter ant host. Infected ants climb to elevated locations and bite onto vegetation where they remain permanently affixed well after death. The mandibular muscles, but not the brain, of infected ants are extensively colonized by the fungus. We sought to investigate the mechanisms by which O. unilateralis s.l. may be able to influence mandibular muscle contraction despite widespread muscle damage. We found that infected muscles show evidence of hypercontraction. Despite the extensive colonization, both motor neurons and neuromuscular junctions appear to be maintained. Infection results in sarcolemmal damage, but this is not specific to the death grip. We found evidence of precise penetration of muscles by fungal structures and the presence of extracellular vesicle-like particles, both of which may contribute to mandibular hypercontraction.

Parasite transmission plays a central role in disease dynamics, but little is known about the extent to which direct and indirect transmission contributes to disease dynamics in group-living animals. Quantifying transmission by contact or exposure is challenging, as direct measurements of pathogen transmission are often impractical and individual behaviour is largely unknown. Here, we attempt to tackle these twin problems through the use of a generalist entomopathogenic fungus Metarhizium robertsii to study direct and indirect transmission in the black carpenter ant Camponotus pennsylvanicus. We provide evidence that large quantities of fungal conidia (spores) can be acquired over time on the ant’s cuticle and significant amounts are shed into the environment, allowing indirect transmission. Unexposed ants in the nest can be infected either through direct (i.e. social contacts) or indirect (i.e. acquisition from the environment) transmission, potentially leading to the same mortality as if all individuals of a nest would have been directly exposed to the pathogen. Our findings highlight the importance of indirect transmission routes in a species of social living organisms.

Interactions lie at the heart of social organization, particularly in ant societies. Interaction rates are presumed to increase with density, but there is little empirical evidence for this. We manipulated density within carpenter ant colonies of the species Camponotus pennsylvanicus by quadrupling nest space and by manually tracking 6.9 million ant locations and over 3200 interactions to study the relationship between density, spatial organization and interaction rates. Colonies divided into distinct spatial regions on the basis of their underlying spatial organization and changed their movement patterns accordingly. Despite a reduction in both overall and local density, we did not find the expected concomitant reduction in interaction rates across all colonies. Instead, we found divergent effects across colonies. Our results highlight the remarkable organizational resilience of ant colonies to changes in density, which allows them to sustain two key basic colony life functions, that is food and information exchange, during environmental change.

Some parasites can manipulate the behavior of their animal hosts to increase transmission. An interesting area of research is understanding how host neurobiology is manipulated by microbes to the point of displaying such aberrant behaviors. Here, we characterize the metabolic profile of the brain of an insect at the moment of the behavioral manipulation by a parasitic microbe. Our model system are ants infected with the parasitic fungus Ophiocordyceps kimflemingiae (=unilateralis), which manipulates ants to climb and bite into plant substrates, before killing the host (i.e. zombie ants). At the moment of the behavioral manipulation by the fungus, the host's brain is not invaded by the fungus which is known to extensively invade muscle tissue. We found that, despite not being invaded by the parasite, the brains of manipulated ants are notably different, showing alterations in neuromodulatory substances, signs of neurodegeneration, changes in energy use, and antioxidant compound that signal stress reactions by the host. Ergothionine, a fungal derived compound with known neuronal cytoprotection functions was found to be highly elevated in zombie ant brains suggesting the fungus, which does not invade the central nervous system, is preserving the brain.

Some parasites have evolved the ability to precisely control the behavior of animals in ways that enhance the transmission of parasite genes into the next generation. This is the concept of the 'extended phenotype' first conceived by Richard Dawkins in 1982. It states that the behavior we observe in animals is due not only to the expression of their genes, but also to the genes of parasites infecting them. In such cases, the behavior is an extended phenotype of the parasite.

Background Therapeutic efficacy studies in uncomplicated Plasmodium falciparum malaria are confounded by new infections, which constitute competing risk events since they can potentially preclude/pre-empt the detection of subsequent recrudescence of persistent, sub-microscopic primary infections. Methods Antimalarial studies typically report the risk of recrudescence derived using the Kaplan–Meier (K–M) method, which considers new infections acquired during the follow-up period as censored. Cumulative Incidence Function (CIF) provides an alternative approach for handling new infections, which accounts for them as a competing risk event. The complement of the estimate derived using the K–M method (1 minus K–M), and the CIF were used to derive the risk of recrudescence at the end of the follow-up period using data from studies collated in the WorldWide Antimalarial Resistance Network data repository. Absolute differences in the failure estimates derived using these two methods were quantified. In comparative studies, the equality of two K–M curves was assessed using the log-rank test, and the equality of CIFs using Gray’s k-sample test (both at 5% level of significance). Two different regression modelling strategies for recrudescence were considered: cause-specific Cox model and Fine and Gray’s sub-distributional hazard model. Results Data were available from 92 studies (233 treatment arms, 31,379 patients) conducted between 1996 and 2014. At the end of follow-up, the median absolute overestimation in the estimated risk of cumulative recrudescence by using 1 minus K–M approach was 0.04% (interquartile range (IQR): 0.00–0.27%, Range: 0.00–3.60%). The overestimation was correlated positively with the proportion of patients with recrudescence [Pearson’s correlation coefficient (ρ): 0.38, 95% Confidence Interval (CI) 0.30–0.46] or new infection [ρ: 0.43; 95% CI 0.35–0.54]. In three study arms, the point estimates of failure were greater than 10% (the WHO threshold for withdrawing antimalarials) when the K–M method was used, but remained below 10% when using the CIF approach, but the 95% confidence interval included this threshold. Conclusions The 1 minus K–M method resulted in a marginal overestimation of recrudescence that became increasingly pronounced as antimalarial efficacy declined, particularly when the observed proportion of new infection was high. The CIF approach provides an alternative approach for derivation of failure estimates in antimalarial trials, particularly in high transmission settings. Electronic supplementary material The online version of this article (10.1186/s12936-019-2837-4) contains supplementary material, which is available to authorized users.

Infectious disease outbreaks are a common constraint of group living organisms. Ants (Hymenoptera: Formicidae) live in large, dense colonies and are host to a diverse range of parasites and pathogens, facilitating the possibility of epidemic-induced collapse. However, the majority of parasites infecting ants require a period of development outside of the nest before they can transmit to their next ant host (‘ex-nido’ transmission) and the impact of these parasites on colony dynamics is unknown. Here we develop a mathematical model to assess ant colony dynamics in the presence of such parasites. We find that under field-realistic model conditions, such parasites are unlikely to cause the epidemic collapse of mature ant colonies, unless colony birth rate drops below 0.2328 ants/day. The preponderance ofex-nidotransmitting parasites infecting ants and their limited epidemiological impact on colony dynamics may partly explain why collapsed ant colonies are rarely, if ever, observed in natural populations.

Determining how ant colonies optimize foraging while mitigating disease risk provides insight into how the ants have achieved ecological success. Fungal infected cadavers surround the main foraging trails of the carpenter antCamponotus rufipes, offering a system to study how foragers behave given the persistent occurrence of disease threats. Studies on social insect foraging behavior typically require many hours of human labor due to the high density of individuals. To overcome this, we developed deep learning based computer vision algorithms to track foraging ants, frame-by-frame, from video footage. We found foragers can be divided into behavioral categories based on how straight they walk across the trail. Eighty percent of ants walk directly across the trail, while 20% wander or circle when crossing the trail. Departure from the main trail encourages exploration of new areas and could enhance discovery of new food resources. Conversely, results from our agent-based model simulations suggest deviation from a straight path exposes foragers to more infectious fungal spores. Consistency in walking behavior may protect most ants from infection, while the foragers with increased exposure due to their mode of walking could be a sufficient number of new hosts to sustain disease in this environment.

Convolutional neural network (CNN) models have the potential to improve plant disease phenotyping where the standard approach is visual diagnostics requiring specialized training. In scenarios where a CNN is deployed on mobile devices, models are presented with new challenges due to lighting and orientation. It is essential for model assessment to be conducted in real world conditions if such models are to be reliably integrated with computer vision products for plant disease phenotyping. We train a CNN object detection model to identify foliar symptoms of diseases in cassava (Manihot esculenta Crantz). We then deploy the model in a mobile app and test its performance on mobile images and video of 720 diseased leaflets in an agricultural field in Tanzania. Within each disease category we test two levels of severity of symptoms-mild and pronounced, to assess the model performance for early detection of symptoms. In both severities we see a decrease in performance for real world images and video as measured with the F-1 score. The F-1 score dropped by 32% for pronounced symptoms in real world images (the closest data to the training data) due to a decrease in model recall. If the potential of mobile CNN models are to be realized our data suggest it is crucial to consider tuning recall in order to achieve the desired performance in real world settings. In addition, the varied performance related to different input data (image or video) is an important consideration for design in real world applications.

Stigmas are a primal phenomena, ubiquitous in human societies past and present. Some evolutionary anthropologists have argued that stigmatization in response to disease is an adaptive behavior because stigmatization may help people and communities reduce the risks they face from infectious diseases and increase reproductive success. On the other hand, some cultural anthropologists and social critics argue that stigmatization has strong negative impacts on community health. One recent analysis resolved this conflict by hypothesizing that stigmas had individual and group-evolutionary benefits in the past but are now maladaptive because of intervening societal transitions. Here, we present a quantitative theory of infectious disease stigmatization. Using a four-compartment model of stigmatization against a chronic disease, we show a stigma ratio, being the ratio of net transmissions by stigmatized people to net transmissions by unstigmatized people, predicts the impact of stigmatization on lifetime infection risk. When stigmatized people are segregated from the rest of the population and there are no alternative interventions that reduce transmission, stigmatization can reduce prevalence and infection risk. When stigmas do not lead to segregation but do discourage behavior change and reduce access to medical interventions, stigmatization acts to increases the lifetime risk of infection in the community. We further show that fear of stigmas can create policy resistance to healthcare access. The societal consequences of fear are worse when effective medical treatment is available. We conclude that stigma's can be adaptive, but good healthcare and leaky ostracism can make stigmas against chronic infectious disease maladaptive, and that the deprecation of stigmas is a natural transition in the modern urban societies.

1.AbstractAnts are among the most ecologically successful organisms on Earth, with a global distribution and diverse nesting and foraging ecologies. Ants are also social organisms, living in crowded, dense colonies that can range up to millions of individuals. Understanding the ecological success of the ants requires understanding how they have mitigated one of the major costs of social living-infection by parasitic organisms. Additionally, the ecological diversity of ants suggests that they may themselves harbor a diverse, and largely unknown, assemblage of parasites. As a first step, we need to know the taxonomic and functional diversity of the parasitic organisms infecting ants. To that end, we provide a comprehensive review of the parasitic organisms infecting ants by collecting all extant records. We synthesize major patterns in parasite ecology by categorizing how parasites encounter their ant hosts, whether they require host death as a developmental necessity, and how they transmit to future hosts.We report 1,415 records of parasitic organisms infecting ants, the majority of which come from order Diptera (34.8%), phylum Fungi (25.6%), and order Hymenoptera (25.1%). Most parasitic organisms infecting ants are parasitoids (89.6%), requiring the death of their host as developmental necessity and most initially encounter their hosts in the extranidal environment (68.6%). Importantly, though most parasitic organisms infecting ants only need a single host to complete their life cycle (89.2%), the vast majority need to leave the nest before transmission to the next ant host can occur (88.3%), precluding ant-to-ant transmission within the nest. With respect to the host, we only found records for 9 out of 17 extant ant sub-families, and for 82 out of the currently recognized 334 ant genera. Though there is likely bias in the records reported, both host and parasite ecological traits and evolutionary histories underlie the pattern of ant-parasite association reported here. This work provides a foundation for future work that will begin to untangle the ecological drivers of ant-parasite relationships and the evolutionary implications thereof.

Social insect colonies function cohesively due, in part, to altruistic behaviors performed towards related individuals. These colonies can be affected by parasites in two distinct ways, either at the level of the individual or the entire colony. As such, colonies of social insects can experience conflict with infected individuals reducing the cohesiveness that typifies them. Parasites of social insects therefore offer us a framework to study conflicts within social insect colonies in addition to the traditionally viewed conflicts afforded by groups of low genetic relatedness due to multiple mating for example. In our study, we use the behavior manipulating fungal pathogen, Ophiocordyceps kimflemingiae (= unilateralis) and its host, Camponotus castaneus, to ask if colony members are able to detect infected individuals. Such detection would be optimal for the colony since infected workers die near foraging trails where the fungus develops its external structures and releases spores that infect other colony members. To determine if C. castaneus workers can detect these future threats, we used continuous-time point observations coupled with longer continuous observations to discern any discrimination towards infected individuals. After observing 1,240 hours of video footage we found that infected individuals are not removed from the colony and continuously received food during the course of fungal infection. We also calculated the distances between workers and the nest entrance in a total of 35,691 data points to find infected workers spent more time near the entrance of the nest. Taken together, these results suggest healthy individuals do not detect the parasite inside their nestmates. The colony’s inability to detect infected individuals allows O. kimflemingiae to develop within the colony, while receiving food and protection from natural enemies, which could damage or kill its ant host before the parasite has completed its development.

Few biologists have studied the evolutionary processes at work in indoor environments. Yet indoor environments comprise approximately 0.5% of ice-free land area--an area as large as the subtropical coniferous forest biome. Here we review the emerging subfield of 'indoor biome' studies. After defining the indoor biome and tracing its deep history, we discuss some of its evolutionary dimensions. We restrict our examples to the species found in human houses--a subset of the environments constituting the indoor biome--and offer preliminary hypotheses to advance the study of indoor evolution. Studies of the indoor biome are situated at the intersection of evolutionary ecology, anthropology, architecture, and human ecology and are well suited for citizen science projects, public outreach, and large-scale international collaborations.