1 Introduction

Parasitic insects, particularly parasitoid wasps, represent a fascinating and diverse group within the insect world. These insects exhibit a unique life strategy where they lay their eggs on or within a host, eventually leading to the host's death. This parasitic lifestyle has significant implications for ecological interactions and evolutionary biology.

Parasitic insects, especially those within the order Hymenoptera, have evolved various strategies to exploit their hosts. Parasitoid wasps, a subgroup of these insects, are particularly notable for their role in regulating arthropod populations, including agricultural pests and disease vectors (Peters et al., 2017; Blaimer et al., 2020). The evolutionary history of these insects is marked by complex interactions with their hosts and symbiotic relationships with viruses, which have been integrated into their genomes to aid in parasitism (Coffman and Burke, 2020).

Studying parasitoid wasps is crucial for several reasons. Firstly, they serve as natural biological control agents, reducing the need for chemical pesticides in agriculture (Werren et al., 2010; Kraaijeveld et al., 2018). Secondly, their genomes provide insights into the mechanisms of speciation, host-parasite co-evolution, and the evolution of complex life history traits (Sharanowski et al., 2020). Additionally, the study of parasitoid wasps can reveal the genetic and morphological adaptations that enable their parasitic lifestyle, including the integration of viral elements that disrupt host immune responses (Drezen et al., 2017).

This study aims to synthesize current knowledge on the genetic and morphological evolution of parasitoid wasps. By examining recent genomic and phylogenomic studies, this study understands the evolutionary processes that have shaped these insects' parasitic strategies; specifically, explores the role of viral symbionts in parasitoid evolution, the genetic basis of host-parasite interactions, and the broader implications for biological control and evolutionary biology, and also provides a detailed understanding of the evolutionary dynamics that underpin the success of parasitoid wasps as both parasites and beneficial insects.

2 Traditional Techniques in Insect Structure and Function Research

2.1 Histology and microscopy

Histology and microscopy have long been foundational techniques in the study of insect structure and function. These methods allow researchers to visualize and document the intricate details of insect morphology. Scanning electron microscopy (SEM) and microphotography are particularly valuable for examining external features, with SEM providing detailed information on tiny surface structures and microphotography capturing coloration, transparency, and sclerotization (Wipfler et al., 2016). Advanced imaging techniques such as confocal laser scanning microscopy (CLSM), light-sheet fluorescence microscopy (LSFM), and micro-computed tomography (micro-CT) have further enhanced our ability to study insect anatomy at high resolution. These methods enable the reconstruction of three-dimensional structures, facilitating a deeper understanding of insect morphology in a phylogenetic context.

2.2 Biochemical assays

Biochemical assays are essential for investigating the metabolic and physiological processes in insects. These assays help in understanding the bioavailability and metabolism of compounds, such as insecticides. Traditional biochemical techniques involve the purification of native insect enzymes and the use of radiotracer studies to track the distribution and metabolism of compounds within insect bodies (David, 2017). Modern approaches have integrated high-resolution mass spectrometry to analyze complex biochemical systems, allowing for a more detailed characterization of insecticide metabolism and resistance mechanisms. This combination of classical and modern techniques provides a comprehensive understanding of the biochemical pathways in insects.

2.3 Genetic analysis and classical genetics

Genetic analysis has been a cornerstone of insect research, providing insights into gene function and regulation. Classical genetics, involving techniques such as mutagenesis and cross-breeding, has been instrumental in identifying genes responsible for various traits in insects. Recent advancements have expanded the genetic toolbox available for insect research. RNA interference (RNAi) has emerged as a powerful technique for generating loss-of-function phenotypes, enabling the study of gene function across different insect species (Belles, 2010). Additionally, electroporation-mediated somatic transgenesis offers a rapid and cost-effective method for functional genetic analysis in non-model insects, facilitating the study of gene function in diverse tissues (Ando and Fujiwara, 2013). These genetic techniques, combined with traditional approaches, continue to drive progress in understanding the genetic basis of insect structure and function.

3 Advancements in Imaging Techniques

3.1 Confocal microscopy and 3D imaging

Confocal microscopy has become a cornerstone in the study of insect neuroanatomy, particularly for generating three-dimensional (3D) reconstructions of neurons and neuropils. This technique allows for high-resolution imaging of whole-mount brain preparations, which is crucial for understanding the complex architecture of insect brains. However, pigmentation on the brain surface can pose significant challenges. Recent advancements, such as the use of hydrogen peroxide bleaching, have enabled clearer imaging of pigment-obstructed regions, facilitating more comprehensive 3D reconstructions (Stöckl and Heinze, 2015). Additionally, confocal laser scanning microscopy (CLSM) is widely used for anatomical studies, providing detailed images of internal structures and contributing to the understanding of insect morphology in a phylogenetic context (Wipfler et al., 2016).

3.2 Electron microscopy and cryo-electron tomography

Electron microscopy (EM) has significantly advanced our understanding of insect structures at the molecular and cellular levels. Techniques such as scanning electron microscopy (SEM) and cryo-electron microscopy (cryo-EM) offer high-resolution images that reveal intricate details of insect anatomy. Cryo-EM, in particular, has emerged as a powerful tool for visualizing biological objects in a near-native state. This technique has been instrumental in studying macromolecular assemblies and their spatial organization within cells (Orlova and Saibil, 2011). Cryo-electron tomography (cryo-ET) further enhances this capability by providing 3D reconstructions of cellular structures, enabling researchers to visualize protein complexes in their native environments (Danev et al., 2019). These advancements have made cryo-EM and cryo-ET indispensable tools in structural biology, offering insights into the dynamic processes underlying insect physiology (Shoemaker and Ando, 2018; Chua et al., 2022).

3.3 X-ray computed tomography (CT) scanning in insect morphology

X-ray computed tomography (CT) scanning has revolutionized the field of insect morphology by enabling rapid, high-resolution 3D imaging of biological specimens. This technique allows for the visualization of internal structures without the need for sectioning, preserving the integrity of the samples. Micro-CT, a variant of CT scanning, can image macroscopic volumes at sub-micron resolutions, making it ideal for studying the internal morphology of insects. It has been used to visualize dynamic changes in living tissues and to quantify structural differences in various biological samples (Shearer et al., 2016). Additionally, X-ray microscopy tomography has proven effective in linking optical and electron microscopy, providing valuable information about tissue organization and cellular structures (Auer, 2017). These capabilities make X-ray CT scanning a powerful tool for advancing our understanding of insect morphology and function.

4 Molecular and Genomic Approaches

4.1 Next-generation sequencing (NGS) and transcriptomics

Next-generation sequencing (NGS) has revolutionized the field of genomics by providing rapid, cost-effective, and high-throughput sequencing capabilities. In insect research, NGS has been instrumental in uncovering the genetic basis of various physiological and developmental processes. For instance, NGS has enabled the detection of previously undetectable viral pathogens in plants, which can be extrapolated to similar applications in insect virology and genomics (Simón et al., 2018; Shahid et al., 2021). The integration of NGS with transcriptomics allows for comprehensive analysis of gene expression patterns, providing insights into the functional genomics of insects. This approach has been particularly useful in identifying key regulatory genes and pathways involved in insect development and adaptation (Schulze and Lammers, 2020; Mushtaq et al., 2021).

4.2 CRISPR-Cas9 and gene editing techniques

The CRISPR-Cas9 system has emerged as a powerful tool for precise genome editing in insects. This technology allows for targeted mutagenesis, gene knockout, and gene knock-in, facilitating functional genomics studies. For example, CRISPR-Cas9 has been successfully used to edit multiple genes in the fall armyworm, Spodoptera frugiperda, resulting in phenotypic changes that elucidate gene function (Zhu et al., 2020).

The versatility and efficiency of CRISPR-Cas9 make it a preferred method over traditional gene editing techniques such as zinc finger nucleases (ZnF) and transcription activator-like effector nucleases (TALENs) (Taning et al., 2017). Additionally, advancements in CRISPR technology, such as the development of CRISPR/Cpf1 and CRISPR/C2c2 systems, hold promise for even more precise and efficient genome editing in insects (Sun et al., 2017).

4.3 Proteomics and metabolomics in insect functional studies

Proteomics and metabolomics are essential for understanding the functional aspects of insect biology at the molecular level. These approaches involve the large-scale study of proteins and metabolites, respectively, providing a comprehensive view of the biochemical pathways and networks within an organism. Proteomics has been used to identify and quantify proteins involved in various physiological processes, such as development, immunity, and metabolism. Metabolomics, on the other hand, offers insights into the metabolic changes and adaptations in response to environmental stresses or genetic modifications. Together, these techniques complement genomic and transcriptomic data, offering a holistic understanding of insect structure and function (Barrangou and Doudna, 2016).

5 Integrating Computational Modeling and Bioinformatics

5.1 Systems biology and network analysis

Systems biology and network analysis have become pivotal in understanding the complex interactions within biological systems, including insects. By leveraging computational-intelligence methods, researchers can construct and analyze structural-interaction networks (SINs) of protein-protein interactions, which are crucial for understanding the biological functions and interactions at a molecular level. These methods facilitate the prediction and analysis of biological systems through statistical and visualized models, providing insights into the organization and complexity of these networks (Chen et al., 2018). The integration of machine learning techniques with bioinformatics frameworks further enhances the ability to study complex biological systems by enabling automatic feature extraction, selection, and generation of predictive models (Auslander et al., 2021).

5.2 Structural bioinformatics in predicting insect protein functions

Structural bioinformatics plays a critical role in predicting the functions of insect proteins by analyzing their three-dimensional structures. Recent advancements in deep learning methods, such as AlphaFold, have significantly improved the accuracy of protein structure predictions, even in cases where no homologous structures are available. These methods incorporate physical and biological knowledge into deep learning algorithms, enabling the prediction of protein structures with atomic accuracy (Jumper et al., 2021). Additionally, machine learning approaches have been developed to assess the quality of protein models, helping to select the most accurate candidates for further research (Chen and Siu, 2020). The integration of multiple data sources and the use of transductive multi-label learning algorithms have also enhanced the performance of protein function prediction algorithms, providing more accurate and confident predictions (Meng et al., 2016).

5.3 Machine learning and AI applications in insect research

Machine learning and artificial intelligence (AI) have revolutionized insect research by providing powerful tools for data analysis and prediction. Deep learning methods, such as convolutional neural networks and recurrent neural networks, have been employed to predict protein structural information at various levels of detail, significantly impacting the field of structural bioinformatics (Torrisi et al., 2020). These techniques have been applied to various bioinformatics problems, including protein structure prediction and sequence alignments, demonstrating their generalization and pattern recognition capabilities (Zamani and Kremer, 2013). Moreover, new deep learning methods for protein loop modeling have shown promising performance, improving the accuracy of predicted protein structures (Nguyen et al., 2019). The integration of machine learning techniques with established bioinformatics approaches offers unique opportunities to address emerging problems in insect research and enhance our understanding of insect biology (Suh et al., 2021).

6 Case Study

6.1 Overview of selected insect species or functional aspect

The Mediterranean fruit fly (Ceratitis capitata) and the olive fruit fly (Bactrocera oleae) are two economically significant tephritid pest species. These species are known for their similar morphological characteristics and locomotory patterns, which pose challenges for accurate identification and monitoring in agricultural settings (Tannous et al., 2023).

6.2 Application of multidisciplinary techniques

To address these challenges, a combination of artificial intelligence (AI) and deep learning techniques has been employed. Specifically, convolutional neural networks (CNNs) have been developed to automatically detect and classify these insects in real-time. This approach leverages computer vision to analyze digital images and video feeds, enabling precise identification and monitoring of the pests as they move and change postures in their natural environment (Evans and Kitson, 2020).

6.3 Results and implications of integrated research

The application of CNNs has demonstrated a high precision rate of approximately 93% in distinguishing between Ceratitis capitata and Bactrocera oleae . This level of accuracy is significant given the similar shapes and movement patterns of the two species. The successful implementation of this technology facilitates autonomous pest monitoring, allowing for timely and targeted interventions. This not only enhances integrated pest management (IPM) strategies but also promotes sustainable agricultural practices by reducing the reliance on chemical pesticides.

6.4 Lessons learned and future opportunities

The integration of AI and deep learning in insect monitoring has highlighted the potential for these technologies to revolutionize pest management. Key lessons include the importance of large, well-annotated datasets for training models and the need for adaptable detection architectures to handle diverse environmental conditions and insect behaviors. Future opportunities lie in expanding these techniques to other pest species and further refining the algorithms to improve detection accuracy and efficiency. Additionally, integrating these AI-driven methods with other multidisciplinary approaches, such as molecular ecology and synthetic biology, could provide deeper insights into insect behavior and interactions, ultimately leading to more effective and sustainable pest control solutions (Barah and Bones, 2015).

7 Future Directions in Insect Structure and Function Research

7.1 Emerging technologies and potential breakthroughs

The field of insect structure and function research is on the brink of significant advancements, driven by the integration of cutting-edge technologies. Environmental DNA (eDNA) analysis, CRISPR genome editing, and artificial intelligence (AI) are revolutionizing traditional methodologies. eDNA offers a non-invasive approach to monitor elusive insect species, enriching biodiversity databases (Sharma et al., 2023). CRISPR technology enables precise manipulation of insect genes, providing deeper insights into their physiology and behavior (Leftwich et al., 2015). AI and machine learning facilitate automated species identification and predictive modeling of insect populations, which are invaluable for conservation efforts. Additionally, advances in molecular ecology and DNA metabarcoding are resolving complex plant-insect interactions, offering new perspectives on ecological networks (Evans and Kitson, 2020). These technologies promise to broaden our understanding of insects' roles in ecosystems and their adaptability to environmental changes.

7.2 Challenges and limitations of current approaches

Despite the promising advancements, several challenges and limitations persist in current research methodologies. One major challenge is the variability in RNA interference (RNAi) efficiency among different insect species, which hampers its widespread application in pest management. Additionally, the degradation of double-stranded RNA (dsRNA) and the lack of reliable delivery methods further complicate RNAi-based approaches (Zhu and Palli, 2020). In the realm of genomics, while significant progress has been made in sequencing insect genomes, there remain substantial hurdles in data integration and the development of effective data-mining tools (Li et al., 2019). Moreover, the rapid evolution of technology necessitates continuous training and ethical considerations, as scientists must master new tools and address potential ethical issues arising from their implementation (Giron et al., 2018). These challenges highlight the need for ongoing innovation and refinement of existing techniques.

7.3 Interdisciplinary collaborations and research opportunities

The future of insect structure and function research lies in fostering interdisciplinary collaborations and exploring new research opportunities. Integrative multidisciplinary approaches, combining ecology, genomics, synthetic biology, and other fields, are essential for addressing complex biological questions. For instance, the integration of omics-based high-throughput experimental approaches with ecological data can provide a comprehensive understanding of plant-insect interactions (Barah and Bones, 2015). Collaborative efforts between entomologists, geneticists, ecologists, and technologists can lead to the development of novel pest control strategies and sustainable management practices. Furthermore, the application of insect-inspired principles in architecture and materials science offers exciting prospects for sustainable design and innovation (Gorb and Gorb, 2020). By leveraging the collective expertise of diverse scientific disciplines, researchers can unlock new insights and drive the field forward.

Acknowledgments

Authors sincerely thank Professor Li for thoroughly reviewing the manuscript and offering valuable insights that enhanced the clarity of the manuscript.

Conflict of Interest Disclosure

The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.

References

Ando T., and Fujiwara H., 2013, Electroporation-mediated somatic transgenesis for rapid functional analysis in insects, Development, 140: 454-458.

https://doi.org/10.1242/dev.085241

Auer M., 2017, Cross-scale integrated biomaging by 3D light X-ray and electron microscopy-from organisms organs and tissue to cells organelles and macromolecular complexes, Microscopy and Microanalysis, 23: 338.

https://doi.org/10.1017/S1431927617002379

Auslander N., Gussow A., and Koonin E., 2021, Incorporating machine learning into established bioinformatics frameworks, International Journal of Molecular Sciences, 22(6): 2903.

https://doi.org/10.3390/ijms22062903

Barah P., and Bones A., 2015, Multidimensional approaches for studying plant defence against insects: from ecology to omics and synthetic biology, Journal of Experimental Botany, 66(2): 479-493.

https://doi.org/10.1093/jxb/eru489

Barrangou R., and Doudna J., 2016, Applications of CRISPR technologies in research and beyond, Nature Biotechnology, 34: 933-941.

https://doi.org/10.1038/nbt.3659

Belles X., 2010, Beyond Drosophila: RNAi in vivo and functional genomics in insects, Annual Review of Entomology, 55: 111-128.

https://doi.org/10.1146/annurev-ento-112408-085301

Blaimer B., Gotzek D., Brady S., and Buffington M., 2020, Comprehensive phylogenomic analyses re-write the evolution of parasitism within cynipoid wasps, BMC Evolutionary Biology, 20: 1-22.

https://doi.org/10.1186/s12862-020-01716-2

Chen H., Guo W., Shen J., Wang L., and Song J., 2018, Structural principles analysis of host-pathogen protein-protein interactions: a structural bioinformatics survey, IEEE Access, 6: 11760-11771.

https://doi.org/10.1109/ACCESS.2018.2807881

Chen J., and Siu S., 2020, Machine learning approaches for quality assessment of protein structures, Biomolecule, 10(4): 626.

https://doi.org/10.3390/biom10040626

Chua E., Mendez J., Rapp M., Ilca S., Tan Y., Maruthi K., Kuang H., Zimanyi C., Cheng A., Eng E., Noble A., Potter C., and Carragher B., 2022, Better faster cheaper: recent advances in cryo-electron microscopy, Annual review of Biochemistry, 91(1): 1-32.

https://doi.org/10.1146/annurev-biochem-032620-110705

Coffman K., and Burke G., 2020, Genomic analysis reveals an exogenous viral symbiont with dual functionality in parasitoid wasps and their hosts, PLoS Pathogens, 16(11): e1009069.

https://doi.org/10.1371/journal.ppat.1009069

Danev R., Yanagisawa H., and Kikkawa M., 2019, Cryo-electron microscopy methodology: current aspects and future directions, Trends in Biochemical Sciences, 44(10): 837-848.

https://doi.org/10.1016/j.tibs.2019.04.008

David M., 2017, Insecticide ADME for support of early-phase discovery: combining classical and modern techniques, Pest Management Science, 73(4): 692-699.

https://doi.org/10.1002/ps.4345

Drezen J., Leobold M., Bézier A., Huguet E., Volkoff A., and Herniou E., 2017, Endogenous viruses of parasitic wasps: variations on a common theme, Current Opinion in Virology, 25: 41-48.

https://doi.org/10.1016/j.coviro.2017.07.002

Evans D., and Kitson J., 2020, Molecular ecology as a tool for understanding pollination and other plant-insect interactions, Current Opinion in Insect Science, 38: 26-33.

https://doi.org/10.1016/j.cois.2020.01.005

Giron D., Dubreuil G., Bennett A., Dedeine F., Dicke M., Dyer L., Erb M., Harris M., Huguet E., Kaloshian I., Kawakita A., Lopez-Vaamonde C., Palmer T., Petanidou T., Poulsen M., Sallé A., Simon J., Terblanche J., Thiéry D., Whiteman N., Woods H., and Pincebourde S., 2018, Promises and challenges in insect-plant interactions, Entomologia Experimentalis et Applicata, 166(5): 319-343.

https://doi.org/10.1111/eea.12679

Gorb S., and Gorb E., 2020, Insect-inspired architecture to build sustainable cities, Current Opinion in Insect Science, 40: 62-70.

https://doi.org/10.1016/j.cois.2020.05.013

Jumper J., Evans R., Pritzel A., Green T., Figurnov M., Ronneberger O., Tunyasuvunakool K., Bates R., Zídek A., Potapenko A., Bridgland A., Meyer C., Kohl S., Ballard A., Cowie A., Romera-Paredes B., Nikolov S., Jain R., Adler J., Back T., Petersen S., Reiman D., Clancy E., Zielinski M., Steinegger M., Pacholska M., Berghammer T., Bodenstein S., Silver D., Vinyals O., Senior A., Kavukcuoglu K., Kohli P., and Hassabis D., 2021, Highly accurate protein structure prediction with AlphaFold, Nature, 596: 583-589.

https://doi.org/10.1038/s41586-021-03819-2

Kraaijeveld K., Neleman P., Mariën J., Meijer E., and Ellers J., 2018, Genomic resources for goniozus legneri Aleochara bilineata and Paykullia maculata representing three independent origins of the parasitoid lifestyle in insects, G3: Genes Genomes Genetics, 9: 987-991.

https://doi.org/10.1534/g3.119.300584

Leftwich P., Bolton M., and Chapman T., 2015, Evolutionary biology and genetic techniques for insect control, Evolutionary Applications, 9: 212-230.

https://doi.org/10.1111/eva.12280

Li F., Zhao X., Li M., He K., Huang C., Zhou Y., Li Z., and Walters J., 2019, Insect genomes: progress and challenges, Insect Molecular Biology, 28(6): 739-758.

https://doi.org/10.1111/imb.12599

Meng J., Wekesa J., Shi G., and Luan Y., 2016, Protein function prediction based on data fusion and functional interrelationship, Mathematical Biosciences, 274: 25-32.

https://doi.org/10.1016/j.mbs.2016.02.001

Mushtaq M., Dar A., Basu U., Bhat B., Mir R., Vats S., Dar M., Tyagi A., Ali S., Bansal M., Rai G., and Wani S., 2021, Integrating CRISPR-Cas and next generation sequencing in plant virology, Frontiers in Genetics, 12: 735489.

https://doi.org/10.3389/fgene.2021.735489

Nguyen S., Li Z., Xu D., and Shang Y., 2019, New deep learning methods for protein loop modeling, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 16: 596-606.

https://doi.org/10.1109/TCBB.2017.2784434.

Orlova E., and Saibil H., 2011, Structural analysis of macromolecular assemblies by electron microscopy, Chemical Reviews, 111: 7710-7748.

https://doi.org/10.1021/cr100353t

Peters R., Krogmann L., Mayer C., Donath A., Gunkel S., Meusemann K., Kozlov A., Podsiadlowski L., Petersen M., Lanfear R., Diez P., Heraty J., Kjer K., Klopfstein S., Meier R., Polidori C., Schmitt T., Liu S., Zhou X., Wappler T., Rust J., Misof B., and Niehuis O., 2017, Evolutionary history of the hymenoptera, Current Biology, 27: 1013-1018.

https://doi.org/10.1016/j.cub.2017.01.027

Schulze S., and Lammers M., 2020, The development of genome editing tools as powerful techniques with versatile applications in biotechnology and medicine: CRISPR/Cas9 ZnF and TALE nucleases RNA interference and Cre/loxP, ChemTexts, 7(1): 3.

https://doi.org/10.1007/s40828-020-00126-7

Shahid M., Sattar M., Iqbal Z., Raza A., and Al-Sadi A., 2021, Next-generation sequencing and the CRISPR-Cas nexus: a molecular plant virology perspective, Frontiers in Microbiology, 11: 609376.

https://doi.org/10.3389/fmicb.2020.609376

Sharanowski B., Ridenbaugh R., Piekarski P., Broad G., Burke G., Deans A., Lemmon A., Lemmon E., Diehl G., Whitfield J., and Hines H., 2020, Phylogenomics of ichneumonoidea (Hymenoptera) and implications for evolution of mode of parasitism and viral endogenization, bioRxiv, 156: 107023.

https://doi.org/10.1101/2020.06.17.157719

Sharma R., Boruah A., Khan A., Thilagam P.A., Sivakumar S., Dhapola P., and Singh B., 2023, Exploring the significance of insects in ecosystems: a comprehensive examination of entomological studies, International Journal of Environment and Climate Change, 65(1): 293-311.

https://doi.org/10.9734/ijecc/2023/v13i113276

Shearer T., Bradley R., Hidalgo-Bastida L., Sherratt M., and Cartmell S., 2016, Three-dimensional visualisation of soft biological structures by X-ray computed micro-tomography, Journal of Cell Science, 129: 2483-2492.

https://doi.org/10.1242/jcs.179077

Shoemaker S., and Ando N., 2018, X-rays in the cryo-electron microscopy era: structural biology’s dynamic future, Biochemistry, 57(3): 277-285.

https://doi.org/10.1021/acs.biochem.7b01031

Simón J., Rodríguez Á., and Vispo N., 2018, CRISpr-cas9: a precise approach to genome engineering, Therapeutic Innovation and Regulatory Science, 52: 701-707.

https://doi.org/10.1177/2168479018762798

Stöckl A., and Heinze S., 2015, A clearer view of the insect brain-combining bleaching with standard whole-mount immunocytochemistry allows confocal imaging of pigment-covered brain areas for 3D reconstruction, Frontiers in Neuroanatomy, 9: 121.

https://doi.org/10.3389/fnana.2015.00121

Suh D., Lee J., Choi S., and Lee Y., 2021, Recent applications of deep learning methods on evolution- and contact-based protein structure prediction, International Journal of Molecular Sciences, 22(11): 6032.

https://doi.org/10.3390/ijms22116032

Sun D., Guo Z., Liu Y., and Zhang Y., 2017, Progress and prospects of CRISPR/Cas systems in insects and other arthropods, Frontiers in Physiology, 8: 608.

https://doi.org/10.3389/fphys.2017.00608

Taning C., Eynde B., Yu N., Ma S., and Smagghe G., 2017, CRISPR/Cas9 in insects: Applications best practices and biosafety concerns, Journal of Insect Physiology, 98: 245-257.

https://doi.org/10.1016/j.jinsphys.2017.01.007

Tannous M., Stefanini C., and Romano D., 2023, A deep-learning-based detection approach for the identification of insect species of economic importance, Insects, 14(2): 148.

https://doi.org/10.3390/insects14020148

Torrisi M., Pollastri G., and Le Q., 2020, Deep learning methods in protein structure prediction, Computational and Structural Biotechnology Journal, 18: 1301-1310.

https://doi.org/10.1016/j.csbj.2019.12.011

Werren J., Richards S., Desjardins C., Niehuis O., Gadau J., Colbourne J., Beukeboom L., Desplan C., Elsik C., Grimmelikhuijzen C., Kitts P., Lynch J., Murphy T., Oliveira D., Smith C., Zande L., Worley K., Zdobnov E., Aerts M., Albert S., Anaya V., Anzola J., Barchuk A., Behura S., Bera A., Berenbaum M., Bertossa R., Bitondi M., Bordenstein S., Bork P., Bornberg-Bauer E., Brunain M., Cazzamali G., Chaboub L., Chacko J., Chavez D., Childers C., Choi J., Clark M., Claudianos C., Clinton R., Cree A., Cristino A., Dang P., Darby A., Graaf D., Devreese B., Dinh H., Edwards R., Elango N., Elhaik E., Ermolaeva O., Evans J., Forêt S., Fowler G., Gerlach D., Gibson J., Gilbert D., Graur D., Gründer S., Hagen D., Han Y., Hauser F., Hultmark D., Hunter H., Hurst G., Jhangian S., Jiang H., Johnson R., Jones A., Junier T., Kadowaki T., Kamping A., Kapustin Y., Kechavarzi B., Kim J., Kim J., Kiryutin B., Koevoets T., Kovar C., Kriventseva E., Kucharski R., Lee H., Lee S., Lees K., Lewis L., Loehlin D., Logsdon J., Lopez J., Lozado R., Maglott D., Maleszka R., Mayampurath A., Mazur D., McClure M., Moore A., Morgan M., Muller J., Munoz-Torres M., Muzny D., Nazareth L., Neupert S., Nguyen N., Nunes F., Oakeshott J., Okwuonu G., Pannebakker B., Pejaver V., Peng Z., Pratt S., Predel R., Pu L., Ranson H., Raychoudhury R., Rechtsteiner A., Reese J., Reid J., Riddle M., Robertson H., Romero‐Severson J., Rosenberg M., Sackton T., Sattelle D., Schlüns H., Schmitt T., Schneider M., Schüler A., Schurko A., Shuker D., Simões Z., Sinha S., Smith Z., Solovyev V., Souvorov A., Springauf A., Stafflinger E., Stage D., Stanke M., Tanaka Y., Telschow A., Trent C., Vattathil S., Verhulst E., Viljakainen L., Wanner K., Waterhouse R., Whitfield J., Wilkes T., Williamson M., Willis J., Wolschin F., Wyder S., Yamada T., Yi S., Zecher C., Zhang L., and Gibbs R., 2010, Functional and evolutionary insights from the genomes of three parasitoid nasonia species, Science, 327: 343-348.

https://doi.org/10.1126/science.1178028

Wipfler B., Pohl H., Yavorskaya M., and Beutel R., 2016, A review of methods for analysing insect structures-the role of morphology in the age of phylogenomics, Current Opinion in Insect Science, 18: 60-68.

https://doi.org/10.1016/j.cois.2016.09.004

Zamani M., and Kremer S., 2013, Neural Networks in Bioinformatics, 2013: 565-583.

https://doi.org/10.1007/978-3-642-36657-4_15

Zhu G., Chereddy S., Howell J., and Palli S., 2020, Genome editing in the fall armyworm Spodoptera frugiperda: multiple sgRNA/Cas9 method for identification of knockouts in one generation, Insect Biochemistry and Molecular Biology, 122: 103373.

https://doi.org/10.1016/j.ibmb.2020.103373

Zhu K., and Palli S., 2020, Mechanisms applications and challenges of insect RNA interference, Annual Review of Entomology, 65(1): 293-311.

https://doi.org/10.1146/annurev-ento-011019-025224