Abstract. Gupta YM, Tanasarnpaiboon S, Buddhachat K, Peyachoknagul S, Inthim P, Homchan S. 2020. Development of microsatellite markers for the house cricket, Acheta domesticus (Orthoptera: Gryllidae). Biodiversitas 21: 4094-4099. The house cricket, Acheta domesticus, is one of the species of crickets commonly found in Thailand. Insect breeders in Thailand prefer to breed house cricket as food due to its better taste and popularity among local people. Moreover, largescale breeding industries also breed house cricket to produce cricket-based edible products. Insect breeding industry is growing rapidly and requires primary precaution for sustainable production. To facilitate breeding system to maintain genetic variation in the captive population, we have sequenced the whole genome of A. domesticus to search for simple sequence repeats (SSRs) in order to develop polymorphic microsatellite markers for preliminary population genetic analysis. A total of 112,157 SSRs with primer pairs were identified in our analysis.  Of these, 91 were randomly selected to check for amplification of microsatellite polymorphisms. From these, nine microsatellites were used to check genetic variation in forty-five individuals of A. domesticus from the Phitsanulok population (Thailand).  These microsatellite markers also showed cross-amplification with other three species of edible crickets, specifically Gryllus bimaculatus, Gryllus testaceus, and Brachytrupes portentosus. The microsatellite markers presented herein will facilitate future population genetic analysis of A. domesticus populations. Moreover, the transferability of these makers would also enable researchers to conduct genetic studies for other closely related species.

The parasitoid Psyttalia fletcheri (Silvestri) is an important natural enemy of the melon fly, Bactrocera cucurbitae (Coquillett). Melon fly infestations are responsible for extensive losses of cucurbit production worldwide, and P. fletcheri has been used for some time in biological control programmes attempting to deal with this pest. However, there is a general lack of knowledge of the genetic structure of populations of P. fletcheri, and the development of this information is key to the effective use of this parasitoid. In this study, we isolated several novel microsatellite loci to investigate the genetic structure of P. fletcheri populations from six locations in Thailand. All the loci analysed here were polymorphic, and the mean number of alleles per locus ranged from 4.2-8.6. Heterozygote deficiencies were noticed in most populations. Overall FST estimates showed moderate genetic differentiation among P. fletcheri populations with a jackknife mean of 0.084. However, pairwise FST calculations revealed that 11 out of 15 population comparisons showed genetic differentiation. The greatest level of differentiation was also found for the population that had the lowest value for genetic diversity. In contrast, populations with high levels of genetic variation did not show significant genetic differentiation, nor did they show significant isolation by distance. An unrooted dendrogram constructed from Nei's genetic distance values also confirmed that one population from the south of Thailand can be separated from the others.

Abstract. Homchan S, Gupta YM. 2020. Short communication: Insect detection using a machine learning model. Nusantara Bioscience 13: 69-73. The key step in characterizing any organisms and their gender highly relies on correct identification of specimens. Here we aim to classify insect and their sex by supervised machine learning (ML) model. In the present preliminary study, we used a newly developed graphical user interface (GUI) based platform to create a machine learning model for classifying two economically important cricket species. This study aims to develop ML model for Acheta domesticus and Gryllus bimaculatus species classification and sexing. An experimental investigation was conducted to use Google teachable machine GTM for preliminary cricket species detection and sexing using pre-processed 2646 still images. An alternative method for image processing is used to extract still images from high-resolution video for optimum accuracy. Out of the 2646 images, 2247 were used for training ML model and 399 were used for testing the trained model. The prediction accuracy of trained model had 100 % accuracy to identify both species and their sex. The developed trained model can be integrated into the mobile application for cricket species classification and sexing. The present study may guide professionals in the field of life science to develop ML models based on image classification, and serve as an example for researchers and taxonomists to employ machine learning for species classification and sexing in the preliminary analysis. Apart from our main goals, the paper also intends to provide the possibility of ML models in biological studies and to conduct the preliminary assessment of biodiversity.

The potential of mitochondrial DNA (mtDNA) genes are well-known for species identification and to establish a phylogenetic relationship. The De-novo transcriptome assembly of Acheta domesticus commonly known as house cricket, is provides important segments of DNA fragments from mitochondrial DNA due to higher abundance of its mRNA. When the reference sequence with gene annotation is absent for assembling and aligning desire gene sequences, like in the present case, the most similar sequence is obtained from online insect mitochondrial genome database to find mitochondrial DNA conserved domains of interested gene from high throughput RNA sequencing (RNA-seq) data. The RNA-seq data of Acheta domesticus transcriptome is used to retrieve single nucleotide fragment out of 50,046 assembled contigs to discover three important genes from mtDNA of the house cricket. Present study provides effective workflow to identify genes like cytochrome c oxidase subunit II (COX2), NADH dehydrogenase subunit 2 (ND2), cytochrome c oxidase subunit I (COX1) from mtDNA in large sequence archive of RNA-seq data. These three novel barcode sequences will be useful for genetic identification and evolution investigation of Acheta domesticus. The partial mtDNA sequence with these genes will be important for mitochondrial genome construction.

The COVID‐19 pandemic has forced the Bioinformatics course to switch from on‐site teaching to remote learning. This shift has prompted a change in teaching methods and laboratory activities. Students need to have a basic understanding of DNA sequences and how to analyze them using custom scripts. To facilitate learning, we have modified the course to use Jupyter Notebook, which offers an alternative approach to writing custom scripts for basic DNA sequence analysis. This approach allows students to acquire the necessary skills while working remotely. It is a versatile and user‐friendly platform that can be used to combine explanations, code, and results in a single document. This feature enables students to interact with the code and results, making the learning process more engaging and effective. Jupyter Notebook provides a hybrid approach to learning basic Python scripting and genomics that is effective for remote teaching and learning during the COVID‐19 pandemic.