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In this paper, we propose a novel and enhanced approach for crowd counting within the domain of manatee monitoring, aiming to significantly improve efficiency and accuracy. The proposed model achieves state-of-the-art results in the challenging task of manatee counting, simplifying the work of scientists and experts in the field. Our model not only facilitates the identification and enumeration of manatees in images and videos but also excels in scenarios that pose considerable challenges for human observers. To enhance accurate counting of the manatee aggregation, we introduce a framework with three key innovations to tackle the challenge: a new approach to generate density maps during the training process, an augmented technique to balance the dataset, and a cross-domain solution to enhance overall performance. The proposed two-dimensional Gaussian kernel offers a refined method for creating density maps, providing a more robust foundation for the training phase. Additionally, we built a balanced and augmented dataset, ensuring that the model is exposed to diverse and representative instances, thus improving its generalization capabilities. Furthermore, we incorporate a cross-domain phase pretraining the model utilizing an image dataset of wild animals to initialize the weights and further improve performance. Experiments and comparisons, with respect to previously established CSRNET model presented in Wang et al. (2023), demonstrate noteworthy improvements. Remarkably, our model achieves a Mean Absolute Error (MAE) of nearly half compared to the rival approach, showcasing the substantial advancements achieved through our refined methodology. This progress boosts the reliability of manatee counting in conservation efforts and ecological research.

Evidence showed that N6-methyladenosine (m6A) modification plays a pivotal role in influencing RNA fate and is strongly associated with cell growth and developmental processes in many species. However, no information regarding m6A modification in Eimeria tenella is currently available. In the present study, we surveyed the transcriptome-wide prevalence of m6A in sporulated oocysts and unsporulated oocysts of E. tenella. Methylated RNA immunoprecipitation sequencing (MeRIP-seq) analysis showed that m6A modification was most abundant in the coding sequences, followed by stop codon. There were 3,903 hypermethylated and 3,178 hypomethylated mRNAs in sporulated oocysts compared with unsporulated oocysts. Further joint analysis suggested that m6A modification of the majority of genes was positively correlated with mRNA expression. The mRNA relative expression and m6A level of the selected genes were confirmed by quantitative reverse transcription PCR (RT-qPCR) and MeRIP-qPCR. GO and KEGG analysis indicated that differentially m6A methylated genes (DMMGs) with significant differences in mRNA expression were closely related to processes such as regulation of gene expression, epigenetic, microtubule, autophagy-other and TOR signaling. Moreover, a total of 96 DMMGs without significant differences in mRNA expression showed significant differences at protein level. GO and pathway enrichment analysis of the 96 genes showed that RNA methylation may be involved in cell biosynthesis and metabolism of E. tenella. We firstly present a map of RNA m6A modification in E. tenella, which provides significant insights into developmental biology of E. tenella.