Predicting the Weathering Time by the Empty Puparium of Sarcophaga peregrina (Diptera: Sarcophagidae) with the ANN Models

Combining spectrum and machine learning algorithms to predict the weathering time of empty puparia of Sarcophaga peregrine (Diptera: Sarcophagidae)

The weathering time of empty puparia could be important in predicting the minimum postmortem interval (PMImin). As corpse decomposition progresses to the skeletal stage, empty puparia often remain the sole evidence of fly activity at the scene. In this study, we used empty puparia of Sarcophaga peregrina (Diptera: Sarcophagidae) collected at ten different time points between January 2019 and February 2023 as our samples. Initially, we used the scanning electron microscope (SEM) to observe the surface of the empty puparia, but it was challenging to identify significant markers to estimate weathering time. We then utilized attenuated total internal reflectance Fourier transform infrared spectroscopy (ATR-FTIR) to detect the puparia spectrogram. Absorption peaks were observed at 1064 cm-1, 1236 cm-1, 1381 cm-1, 1538 cm-1, 1636 cm-1, 2852 cm-1, 2920 cm-1. Three machine learning models were used to regress the spectral data after dimensionality reduction using principal component analysis (PCA). Among them, eXtreme Gradient Boosting regression (XGBR) showed the best performance in the wavenumber range of 1800-600 cm-1, with a mean absolute error (MAE) of 1.20. This study highlights the value of refining these techniques for forensic applications involving entomological specimens and underscores the considerable potential of combining FTIR and machine learning in forensic practice.

Analysis and comparison of machine learning methods for species identification utilizing ATR-FTIR spectroscopy

Accurate identification of insect species holds paramount significance in diverse fields as it facilitates a comprehensive understanding of their ecological habits, distribution range, and impact on both the environment and humans. While morphological characteristics have traditionally been employed for species identification, the utilization of empty pupariums for this purpose remains relatively limited. In this study, ATR-FTIR was employed to acquire spectral information from empty pupariums of five fly species, subjecting the data to spectral pre-processing to obtain average spectra for preliminary analysis. Subsequently, PCA and OPLS-DA were utilized for clustering and classification. Notably, two wavebands (3000-2800 cm-1 and 1800-1300 cm-1) were found to be significant in distinguishing A. grahami. Further, we established three machine learning models, including SVM, KNN, and RF, to analyze spectra from different waveband groups. The biological fingerprint region (1800-1300 cm-1) demonstrated a substantial advantage in identifying empty puparium species. Remarkably, the SVM model exhibited an impressive accuracy of 100 % in identifying all five fly species. This study represents the first instance of employing infrared spectroscopy and machine learning methods for identifying insect species using empty pupariums, providing a robust research foundation for future investigations in this area.

Genome-wide analysis of long noncoding RNAs and their association in regulating the metamorphosis of the Sarcophaga peregrina (Diptera: Sarcophagidae)

BACKGROUND

The flesh fly, Sarcophaga peregrina (Diptera: Sarcophagidae), is an important hygiene pest, that causes myiasis in humans and other mammals, typically livestock, and as a vector for various parasitic agents, including bacteria, viruses, and parasites. The role of long non-coding RNAs (lncRNAs) in regulating gene expression during metamorphosis of the flesh fly has not been well established.

METHODOLOGY/PRINCIPAL FINDINGS

In this study, we performed genome-wide identification and characterization of lncRNAs from the early pupal stage (1-days pupae), mid-term pupal stage (5-days pupae), and late pupal stage (9-days pupae) of S. peregrina by RNA-seq, and a total of 6921 lncRNAs transcripts were identified. RT-qPCR and enrichment analyses revealed the differentially expressed lncRNAs (DE lncRNAs) that might be associated with insect metamorphosis development. Furthermore, functional analysis revealed that the DE lncRNA (SP_lnc5000) could potentially be involved in regulating the metamorphosis of S. peregrina. RNA interference of SP_lnc5000 caused reduced expression of metamorphosis-related genes in 20-hydroxyecdysone (20E) signaling (Br-c, Ftz-F1), cuticle tanning pathway (TH, DOPA), and chitin related pathway (Cht5). Injection of dsSP_lnc5000 in 3rd instar larvae of S. peregrina resulted in deformed pupae, stagnation of pupal-adult metamorphosis, and a decrease in development time of pupal, pupariation rates and eclosion rates. Hematoxylin-eosin staining (H&E), scanning electron microscope (SEM) observation and cuticle hydrocarbons (CHCs) analysis indicated that SP_lnc5000 had crucial roles in the metamorphosis developmental by modulating pupal cuticular development.

CONCLUSIONS/SIGNIFICANCE

We established that the lncRNA SP_lnc5000 potentially regulates the metamorphosis of S. peregrina by putatively affecting the structure and composition of the pupal cuticle. This study enhances our understanding of lncRNAs as regulators of metamorphosis in S. peregrina, and provide valuable insights into the identification of potential targets for vector control and the development of effective strategies for controlling the spread of myiasis and parasitic diseases.

Development of Forensically Important Sarcophaga peregrina (Diptera: Sarcophagidae) and Intra-Puparial Age Estimation Utilizing Multiple Methods at Constant and Fluctuating Temperatures

Sarcophaga peregrina (Robineau-Desvoidy, 1830) has the potential to estimate the minimum postmortem interval (PMI_min). Development data and intra-puparial age estimation are significant for PMI_min estimation. Previous research has focused on constant temperatures, although fluctuating temperatures are a more real scenario at a crime scene. The current study examined the growth patterns of S. peregrina under constant (25.75 °C) and fluctuating temperatures (18-36 °C; 22-30 °C). Furthermore, differentially expressed genes, attenuated total reflectance Fourier-transform infrared spectroscopy, and cuticular hydrocarbons of S. peregrina during the intra-puparial period were used to estimate age. The results indicated that S. peregrina at fluctuating temperatures took longer to develop and had a lower pupariation rate, eclosion rate, and pupal weight than the group at constant temperatures did. Moreover, we found that six DEG expression profiles and ATR-FTIR technology, CHCs detection methods, and chemometrics can potentially estimate the intra-puparial age of S. peregrina at both constant and fluctuating temperatures. The findings of the study support the use of S. peregrina for PMI_min estimation and encourage the use of entomological evidence in forensic practice.

Pupal Age Estimation of Sarcophaga peregrina (Diptera: Sarcophagidae) at Different Constant Temperatures Utilizing ATR-FTIR Spectroscopy and Cuticular Hydrocarbons

Sarcophaga peregrina (Robineau-Desvoidy, 1830) (Diptera: Sarcophagidae) is a forensically important flesh fly that has potential value in estimating the PMImin. The precise pupal age estimation has great implications for PMImin estimation. During larval development, the age determination is straightforward by the morphological changes and variation of length and weight, however, the pupal age estimation is more difficult due to anatomical and morphological changes not being visible. Thus, it is necessary to find new techniques and methods that can be implemented by standard experiments for accurate pupal age estimation. In this study, we first investigated the potential of attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy and cuticular hydrocarbons (CHCs) for the age estimations of S. peregrina pupae at different constant temperatures (20 °C, 25 °C, and 30 °C). The orthogonal projections latent structure discrimination analysis (OPLS-DA) classification model was used to distinguish the pupae samples of different developmental ages. Then, a multivariate statistical regression model, partial least squares (PLS), was established with the spectroscopic and hydrocarbon data for pupal age estimations. We identified 37 CHCs with a carbon chain length between 11 and 35 in the pupae of S. peregrina. The results of the OPLS-DA model show a significant separation between different developmental ages of pupae (R2X > 0.928, R2Y > 0.899, Q2 > 0.863). The PLS model had a satisfactory prediction with a good fit between the actual and predicted ages of the pupae (R2 > 0.927, RMSECV < 1.268). The results demonstrate that the variation tendencies of spectroscopy and hydrocarbons were time-dependent, and ATR-FTIR and CHCs may be optimal for the age estimations of pupae of forensically important flies with implications for PMImin estimation in forensic practice.