Can \u3cem\u3eDarapsa myron\u3c/em\u3e (Lepidoptera: Sphingidae) Successfully Use the Invasive Plant \u3cem\u3eAmpelopsis brevipedunculata\u3c/em\u3e as a Food Resource?

Although biological invasions are generally thought to negatively impact native fauna, native herbivores that can utilize invasive plants may benefit. The East Coast of the United States has been invaded by the vitaceous plant Ampelopsis brevipedunculata. The invaded range of A. brevipedunculata overlaps with that of the native Vitis labrusca, a closely-related species that is a host plant for the native moth Darapsa myron (Lepidoptera: Sphingidae). We reared D. myron larvae on either V. labrusca or A. brevipedunculata to assess whether development and survival differed on the two plant species. Larval growth and survival to pupation was only 5% on A. brevipedunculata compared to 30% on V. labrusca, suggesting that the invasive species is an unsuitable hostplant for D. myron

Datana drexelii (Lepidoptera: Notododontidae) occurrence and larval survival on highbush blueberry cultivars

Plant genotype influences plant suitability to herbivores; domesticated plants selected for properties such as high fruit yield may be particularly vulnerable to herbivory. Cultivated strains of highbush blueberry, Vaccinium corymbosum L. can be high-quality hosts for larvae of the gregariously-feeding notodontid Datana drexelii (Hy. Edwards). We conducted an experiment assessing D. drexelii larval survival and pupal weight when fed foliage from five blueberry cultivars: ‘Bluecrop’, ‘Bluetta’, ‘Blueray’, ‘Lateblue’, and ‘Jersey’. We complemented this experimental work with repeated bush-level surveys of a managed blueberry patch for naturally occurring D. drexelii larval clusters. Larval survival and pupal weight were significantly higher on ‘Lateblue’ foliage than from the ‘Bluecrop’, ‘Bluetta’, and ‘Jersey’ cultivars. The blueberry patch surveys found more D. drexelii larval clusters on ‘Bluehaven’, ‘Collins’, and ‘Darrow’ bushes than on the cultivars ‘Earliblue’ and ‘Jersey’. The low D. drexelii occurrence and performance on the ‘Jersey’ cultivar suggests that this variety may be appropriate for areas where this pest is common; conversely, their high occurrence on ‘Bluehaven’ ‘Collins’, and ‘Darrow’ suggests that these cultivars may be particularly vulnerable. Cultivar-level variation in herbivore vulnerability highlights how understanding plant-pest interactions can help manage agricultural species

Predator Cues Increase Silkmoth Mortality

While prey responses to predators reduce the threat of consumption, the physiological costs of these responses can be considerable. This is especially true for organisms that lack effective anti-predator defenses and must rely on camouflage or mimicry for protection. The luna moth, Actias luna, is a large saturniid native to Eastern North America that is preyed on and parasitized by a wide variety of predators and parasitoids. We report the results of two separate experiments assessing the responses of Actias larvae to predatory wasps (Vespula maculifrons) that were rendered non-lethal but remained able to move freely, as well as in a control (wasp-free) treatment. We determined whether these responses were predator-specific by also testing the response of Actias larvae to a similarly-sized but harmless scavenging fly. In both experiments, (A) Actias larvae in the wasp treatment died at a higher rate than those in the control treatments; and (B) larval survival in the fly and control treatments did not differ. Despite similar Actias survival in the fly and control treatments, fly-treatment larvae that died appeared to respond similarly to flies as other larvae did to wasps. In both years, larvae that died in the fly and wasp treatments gained virtually no weight between the start of the experiment and their death, suggesting that they may have succumbed to starvation. Our results, replicated over 2 years, illustrate the high cost of anti-predator responses and are the first report of lethal risk effects in caterpillars

Facilitation between invasive herbivores: hemlock woolly adelgid increases gypsy moth preference for and performance on eastern hemlock

Interactions between invertebrate herbivores with different feeding modes are common on long‐lived woody plants. In cases where one herbivore facilitates the success of another, the consequences for their shared host plant may be severe. Eastern hemlock (Tsuga canadensis), a canopy‐dominant conifer native to the eastern U.S., is currently threatened with extirpation by the invasive stylet‐feeding hemlock woolly adelgid (Adelges tsugae). The effect of adelgid on invasive hemlock‐feeding folivores remains unknown. This study evaluated the impact of feeding by hemlock woolly adelgid on gypsy moth (Lymantria dispar) larval preference for, and performance on, eastern hemlock. To assess preference, 245 field‐grown hemlocks were surveyed for gypsy moth herbivory damage and laboratory paired‐choice bioassays were conducted. To assess performance, gypsy moth larvae were reared to pupation on adelgid‐infested or uninfested hemlock foliage, and pupal weight, proportional weight gain, and larval period were analysed. Adelgid‐infested hemlocks experienced more gypsy moth herbivory than did uninfested control trees, and laboratory tests confirmed that gypsy moth larvae preferentially feed on adelgid‐infested hemlock foliage. Gypsy moth larvae reared to pupation on adelgid‐infested foliage gained more weight than larvae reared on uninfested control foliage. These results suggest that the synergistic effect of adelgid and gypsy moth poses an additional threat to eastern hemlock that may increase extirpation risk and ecological impact throughout most of its range

Egocentric Image Captioning for Privacy-Preserved Passive Dietary Intake Monitoring

Camera-based passive dietary intake monitoring is able to continuously capture the eating episodes of a subject, recording rich visual information, such as the type and volume of food being consumed, as well as the eating behaviours of the subject. However, there currently is no method that is able to incorporate these visual clues and provide a comprehensive context of dietary intake from passive recording (e.g., is the subject sharing food with others, what food the subject is eating, and how much food is left in the bowl). On the other hand, privacy is a major concern while egocentric wearable cameras are used for capturing. In this paper, we propose a privacy-preserved secure solution (i.e., egocentric image captioning) for dietary assessment with passive monitoring, which unifies food recognition, volume estimation, and scene understanding. By converting images into rich text descriptions, nutritionists can assess individual dietary intake based on the captions instead of the original images, reducing the risk of privacy leakage from images. To this end, an egocentric dietary image captioning dataset has been built, which consists of in-the-wild images captured by head-worn and chest-worn cameras in field studies in Ghana. A novel transformer-based architecture is designed to caption egocentric dietary images. Comprehensive experiments have been conducted to evaluate the effectiveness and to justify the design of the proposed architecture for egocentric dietary image captioning. To the best of our knowledge, this is the first work that applies image captioning to dietary intake assessment in real life settings

Egocentric image captioning for privacy-preserved passive dietary intake monitoring

Camera-based passive dietary intake monitoring is able to continuously capture the eating episodes of a subject, recording rich visual information, such as the type and volume of food being consumed, as well as the eating behaviors of the subject. However, there currently is no method that is able to incorporate these visual clues and provide a comprehensive context of dietary intake from passive recording (e.g., is the subject sharing food with others, what food the subject is eating, and how much food is left in the bowl). On the other hand, privacy is a major concern while egocentric wearable cameras are used for capturing. In this article, we propose a privacy-preserved secure solution (i.e., egocentric image captioning) for dietary assessment with passive monitoring, which unifies food recognition, volume estimation, and scene understanding. By converting images into rich text descriptions, nutritionists can assess individual dietary intake based on the captions instead of the original images, reducing the risk of privacy leakage from images. To this end, an egocentric dietary image captioning dataset has been built, which consists of in-the-wild images captured by head-worn and chest-worn cameras in field studies in Ghana. A novel transformer-based architecture is designed to caption egocentric dietary images. Comprehensive experiments have been conducted to evaluate the effectiveness and to justify the design of the proposed architecture for egocentric dietary image captioning. To the best of our knowledge, this is the first work that applies image captioning for dietary intake assessment in real-life settings

Microbial DNA fingerprinting of human fingerprints: dynamic colonization of fingertip microflora challenges human host inferences for forensic purposes

Human fingertip microflora is transferred to touched objects and may provide forensically relevant information on individual hosts, such as on geographic origins, if endogenous microbial skin species/strains would be retrievable from physical fingerprints and would carry geographically restricted DNA diversity. We tested the suitability of physical fingerprints for revealing human host information, with geographic inference as example, via microbial DNA fingerprinting. We showed that the transient exogenous fingertip microflora is frequently different from the resident endogenous bacteria of the same individuals. In only 54% of the experiments, the DNA analysis of the transient fingertip microflora allowed the detection of defined, but often not the major, elements of the resident microflora. Although we found microbial persistency in certain individuals, time-wise variation of transient and resident microflora within individuals was also observed when resampling fingerprints after 3 weeks. While microbial species differed considerably in their frequency spectrum between fingerprint samples from volunteers in Europe and southern Asia, there was no clear geographic distinction between Staphylococcus strains in a cluster analysis, although bacterial genotypes did not overlap between both continental regions. Our results, though limited in quantity, clearly demonstrate that the dynamic fingerprint microflora challenges human host inferences for forensic purposes including geographic ones. Overall, our results suggest that human fingerprint microflora is too dynamic to allow for forensic marker developments for retrieving human information

Food/Non-Food Classification of Real-Life Egocentric Images in Low- and Middle-Income Countries Based on Image Tagging Features.

Malnutrition, including both undernutrition and obesity, is a significant problem in low- and middle-income countries (LMICs). In order to study malnutrition and develop effective intervention strategies, it is crucial to evaluate nutritional status in LMICs at the individual, household, and community levels. In a multinational research project supported by the Bill & Melinda Gates Foundation, we have been using a wearable technology to conduct objective dietary assessment in sub-Saharan Africa. Our assessment includes multiple diet-related activities in urban and rural families, including food sources (e.g., shopping, harvesting, and gathering), preservation/storage, preparation, cooking, and consumption (e.g., portion size and nutrition analysis). Our wearable device ("eButton" worn on the chest) acquires real-life images automatically during wake hours at preset time intervals. The recorded images, in amounts of tens of thousands per day, are post-processed to obtain the information of interest. Although we expect future Artificial Intelligence (AI) technology to extract the information automatically, at present we utilize AI to separate the acquired images into two binary classes: images with (Class 1) and without (Class 0) edible items. As a result, researchers need only to study Class-1 images, reducing their workload significantly. In this paper, we present a composite machine learning method to perform this classification, meeting the specific challenges of high complexity and diversity in the real-world LMIC data. Our method consists of a deep neural network (DNN) and a shallow learning network (SLN) connected by a novel probabilistic network interface layer. After presenting the details of our method, an image dataset acquired from Ghana is utilized to train and evaluate the machine learning system. Our comparative experiment indicates that the new composite method performs better than the conventional deep learning method assessed by integrated measures of sensitivity, specificity, and burden index, as indicated by the Receiver Operating Characteristic (ROC) curve