Christine Picard

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https://hdl.handle.net/1805/30169

Discovering Nature's Powerful Allies in Sustainability and Justice

Professor Christine Picard is a member of the groundbreaking team that established the world's first research center dedicated to insects as feed and food, known as the Center for Environmental Sustainability through Insect Farming (CEIF), funded in by the National Science Foundation, and other industry members. Their mission involves collaborating with industry members to address the pressing needs of this emerging field through multidisciplinary research.

The insect farming industry is still in its nascent stage, and the growth potential is nothing short of remarkable. What adds to the excitement is the availability of advanced technological tools and the remarkably short generation times of insects, which enable Professor Picard and her research partners to make significant advancements in their work at an accelerated pace.

Her specific focus lies in leveraging genetic and genomic tools to enhance our ability to predict breeding outcomes and optimize production efficiency of these insects. Whether they are used as feed ingredients for livestock or as a sustainable protein source for human consumption, insects possess incredible potential. They are highly efficient protein factories, requiring fewer resources compared to traditional protein sources, and they excel at recycling food waste.

These tiny creatures are projected to have a tremendous impact as a climate solution in the future of food production. Their ability to provide sustainable protein, while minimizing resource usage and contributing to waste reduction, positions them as a crucial element in addressing the environmental challenges associated with traditional protein production methods. As part of The Center for Environmental Sustainability through Insect Farming, the research team is dedicated to pioneering research and driving innovation in this field, ensuring a more sustainable and resilient future for the food industry. Professor Picard's translation of insects into sustainable protein sources for humans and animals is another excellent example of how IUPUI's faculty members are TRANSLATING their RESEARCH INTO PRACTICE.

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Introduction to Christine Picard & Her Work
(Center for Translating Research Into Practice, IU Indianapolis, 2023-07) Picard, Christine
In this video, Professor Christine Picard describes her translational research. Professor Picard and her research team are developing strategies to optimize insect production as a sustainable solution to address the protein needs of a growing global population.
Discovering Nature's Powerful Allies in Sustainability and Justice
(Center for Translating Research Into Practice, IU Indianapolis, 2023-07-28) Picard, Christine
Professor Christine Picard is a member of the groundbreaking team that established the world’s first research center dedicated to insects as feed and food, known as the Center for Environmental Sustainability through Insect Farming (CEIF), funded by the National Science Foundation and other industry members. Professor Picard’s research focuses on unraveling the genetic basis of postmortem-feeding insects in the field of forensic entomology and exploring insects as sustainable protein sources for humans and animals. During this presentation, Christine Picard shares how she and her research team are developing strategies to optimize insect production as a sustainable solution to address the protein needs of a growing global population.
Classifying the Unknown: Identification of Insects by Deep Open-set Bayesian Learning
(bioRxiv, 2021-09-17) Badirli, Sarkhan; Picard, Christine J.; Mohler, George; Akata, Zeynep; Dundar, Murat
Insects represent a large majority of biodiversity on Earth, yet only 20% of the estimated 5.5 million insect species are currently described (1). While describing new species typically requires specific taxonomic expertise to identify morphological characters that distinguish it from other potential species, DNA-based methods have aided in providing additional evidence of separate species (2). Machine learning (ML) is emerging as a potential new approach in identifying new species, given that this analysis may be more sensitive to subtle differences humans may not process. Existing ML algorithms are limited by image repositories that do not include undescribed species. We developed a Bayesian deep learning method for the open-set classification of species. The proposed approach forms a Bayesian hierarchy of species around corresponding genera and uses deep embeddings of images and barcodes together to identify insects at the lowest level of abstraction possible. To demonstrate proof of concept, we used a database of 32,848 insect instances from 1,040 described species split into training and test data. The test data included 243 species not present in the training data. Our results demonstrate that using DNA sequences and images together, insect instances of described species can be classified with 96.66% accuracy while achieving accuracy of 81.39% in identifying genera of insect instances of undescribed species. The proposed deep open-set Bayesian model demonstrates a powerful new approach that can be used for the gargantuan task of identifying new insect species.
New methods for the synthesis of heterocyclic compounds
(De Gruyter, 2004) Caiazzo, Aldo; Dalili, Shadi; Picard, Christine; Sasaki, Mikio; Siu, Tung; Yudin, Andrei K.
Due to frequent occurrence of nitrogen-containing groups among the biologically active compounds, chemoselective functionalization of organic molecules with nitrogen-containing functional groups is an important area of organic synthesis. We have proposed and implemented a new strategy toward design of nitrogen-transfer reactions on inert electrode surfaces with a particular focus on the generation and trapping of highly reactive nitrogen-transfer agents. A wide range of structurally dissimilar olefins can be readily transformed into the corresponding aziridines. The resulting aziridines are precursors to a range of catalysts via nucleophilic ring-opening with diaryl- and dialkyl phosphines. Another strategy explored in the context of oxidative nitrogen transfer is cycloamination of olefins using NH aziridines.
Molecular Genetic Methods for Forensic Entomology
(CRC Press, 2019) Stevens, Jamie R.; Picard, Christine J.; Wells, Jeffrey D.
A preservative solution containing formaldehyde should not be used if it can be avoided, as formalin can interact with deoxyribonucleic acid (DNA), making subsequent molecular analyses difficult. Some forensic entomologists recommend killing maggots by blanching in hot water; this technique does not appear to hinder any subsequent DNA analysis. Maggots found in the absence of a corpse may still have the victim’s tissue in their gut. Such specimens must be killed and preserved immediately, otherwise the evidence may be digested and lost. There is little doubt about the need for accurate specimen identification in forensic entomology. Intraspecific variation in DNA sequence is commonly observed, so an unknown specimen will often not exactly match the genotype of a reference specimen. Ribonucleic acid analysis can reveal the genes that were active within a tissue sample at the time it was processed.
The genomes of a monogenic fly: views of primitive sex chromosomes
(Springer Nature, 2020) Andere, Anne A.; Pimsler, Meaghan L.; Tarone, Aaron M.; Picard, Christine J.
The production of male and female offspring is often determined by the presence of specific sex chromosomes which control sex-specific expression, and sex chromosomes evolve through reduced recombination and specialized gene content. Here we present the genomes of Chrysomya rufifacies, a monogenic blow fly (females produce female or male offspring, exclusively) by separately sequencing and assembling each type of female and the male. The genomes (> 25X coverage) do not appear to have any sex-linked Muller F elements (typical for many Diptera) and exhibit little differentiation between groups supporting the morphological assessments of C. rufifacies homomorphic chromosomes. Males in this species are associated with a unimodal coverage distribution while females exhibit bimodal coverage distributions, suggesting a potential difference in genomic architecture. The presence of the individual-sex draft genomes herein provides new clues regarding the origination and evolution of the diverse sex-determining mechanisms observed within Diptera. Additional genomic analysis of sex chromosomes and sex-determining genes of other blow flies will allow a refined evolutionary understanding of how flies with a typical X/Y heterogametic amphogeny (male and female offspring in similar ratios) sex determination systems evolved into one with a dominant factor that results in single sex progeny in a chromosomally monomorphic system.
Genomic Tools To Reduce Error in PMI Estimates Derived From Entomological Evidence
(2016-07) Tarone, Aaron M.; Picard, Christine; Sze, Sing-Hoi
The rationale for this research is the need to address recent research findings of genetic variation in blow fly development (Gallagher et al., 2010; Tarone et al, 2011; Owings et al., 2014). Currently, little is known about the consequences for PMI estimates of this genetic variation in blow fly traits. In addition, estimates of blow fly age can vary considerably in their accuracy. The current research examined the genetics of development-time variation in blow flies and functional genetics of the development of wild type strain. The project has begun to expand knowledge on the role of genetics in blow fly development, showing that there is ample wild genetic variation that could potentially impact forensic PMI estimates. The project's habitability estimates provide an empirical estimate of the impact of genetic variation on development-time variation. The report notes that the analyses are preliminary and advises that subsequent publications with the data presented in this report may differ from future publications based on the collection of additional data, changes in parameter settings, differences in statistical tests performed, or choices in algorithms applied to the data. Scholarly products of this research are listed.
Early View: The Journal of Forensic Entomology - From the Editors
(North American Forensic Entomology Association (NAFEA), 2022) Picard, Christine; Brundage, Adrienne
Global population genetic structure and demographic trajectories of the black soldier fly, Hermetia illucens
(BMC, 2021) Kaya, Cengiz; Generalovic, Tomas N.; Ståhls, Gunilla; Hauser, Martin; Samayoa, Ana C.; Nunes-Silva, Carlos G.; Roxburgh, Heather; Wohlfahrt, Jens; Ewusie, Ebenezer A.; Kenis, Marc; Hanboonson, Yupa; Orozco, Jesus; Carrejo, Nancy; Nakamura, Satoshi; Gasco, Laura; Rojo, Santos; Tanga, Chrysantus M.; Meier, Rudolf; Rhode, Clint; Picard, Christine J.; Jiggins, Chris D.; Leiber, Florian; Tomberlin, Jeffery K.; Hasselmann, Martin; Blanckenhorn, Wolf U.; Kapun, Martin; Sandrock, Christoph
Background: The black soldier fly (Hermetia illucens) is the most promising insect candidate for nutrient-recycling through bioconversion of organic waste into biomass, thereby improving sustainability of protein supplies for animal feed and facilitating transition to a circular economy. Contrary to conventional livestock, genetic resources of farmed insects remain poorly characterised. We present the first comprehensive population genetic characterisation of H. illucens. Based on 15 novel microsatellite markers, we genotyped and analysed 2862 individuals from 150 wild and captive populations originating from 57 countries on seven subcontinents. Results: We identified 16 well-distinguished genetic clusters indicating substantial global population structure. The data revealed genetic hotspots in central South America and successive northwards range expansions within the indigenous ranges of the Americas. Colonisations and naturalisations of largely unique genetic profiles occurred on all non-native continents, either preceded by demographically independent founder events from various single sources or involving admixture scenarios. A decisive primarily admixed Polynesian bridgehead population serially colonised the entire Australasian region and its secondarily admixed descendants successively mediated invasions into Africa and Europe. Conversely, captive populations from several continents traced back to a single North American origin and exhibit considerably reduced genetic diversity, although some farmed strains carry distinct genetic signatures. We highlight genetic footprints characteristic of progressing domestication due to increasing socio-economic importance of H. illucens, and ongoing introgression between domesticated strains globally traded for large-scale farming and wild populations in some regions. Conclusions: We document the dynamic population genetic history of a cosmopolitan dipteran of South American origin shaped by striking geographic patterns. These reflect both ancient dispersal routes, and stochastic and heterogeneous anthropogenic introductions during the last century leading to pronounced diversification of worldwide structure of H. illucens. Upon the recent advent of its agronomic commercialisation, however, current human-mediated translocations of the black soldier fly largely involve genetically highly uniform domesticated strains, which meanwhile threaten the genetic integrity of differentiated unique local resources through introgression. Our in-depth reconstruction of the contemporary and historical demographic trajectories of H. illucens emphasises benchmarking potential for applied future research on this emerging model of the prospering insect livestock sector.
Classifying the unknown: Insect identification with deep hierarchical Bayesian learning
(Wiley, 2023) Badirli, Sarkhan; Picard, Christine Johanna; Mohler, George; Richert, Frannie; Akata, Zeynep; Dundar, Murat
1. Classifying insect species involves a tedious process of identifying distinctive morphological insect characters by taxonomic experts. Machine learning can harness the power of computers to potentially create an accurate and efficient method for performing this task at scale, given that its analytical processing can be more sensitive to subtle physical differences in insects, which experts may not perceive. However, existing machine learning methods are designed to only classify insect samples into described species, thus failing to identify samples from undescribed species. 2. We propose a novel deep hierarchical Bayesian model for insect classification, given the taxonomic hierarchy inherent in insects. This model can classify samples of both described and undescribed species; described samples are assigned a species while undescribed samples are assigned a genus, which is a pivotal advancement over just identifying them as outliers. We demonstrated this proof of concept on a new database containing paired insect image and DNA barcode data from four insect orders, including 1040 species, which far exceeds the number of species used in existing work. A quarter of the species were excluded from the training set to simulate undescribed species. 3. With the proposed classification framework using combined image and DNA data in the model, species classification accuracy for described species was 96.66% and genus classification accuracy for undescribed species was 81.39%. Including both data sources in the model resulted in significant improvement over including image data only (39.11% accuracy for described species and 35.88% genus accuracy for undescribed species), and modest improvement over including DNA data only (73.39% genus accuracy for undescribed species). 4. Unlike current machine learning methods, the proposed deep hierarchical Bayesian learning approach can simultaneously classify samples of both described and undescribed species, a functionality that could become instrumental in biodiversity monitoring across the globe. This framework can be customized for any taxonomic classification problem for which image and DNA data can be obtained, thus making it relevant for use across all biological kingdoms.

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