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Simple Summary: Batocera horsfieldi (Hope) is an important wood-boring pest in China. This pest primarily infests tree trunks by feeding on the woody tissue, creating a network of interconnected tunnels within the trunk. These tunnels become blocked with insect feces and wood debris, causing damage, decay, and even the death of the host plant's tissues. In this study, single-molecule real-time sequencing (SMRT) and Illumina RNA-seq technologies were employed to conduct full-length transcriptome sequencing of male and female adults of B. horsfieldi. A total of 20,356,793 subreads (38.26 G, clean reads) were generated, including 432,091 circular consensus sequences and 395,851 full-length non-chimera reads. Clustering and redundancy removal of the full-length non-chimera reads resulted in 39,912 consensus reads. Additionally, functional annotation was performed on a total of 84,650 transcripts in seven different databases. This study provides an important foundation for future exploration of gene regulation in the interaction between B. horsfieldi and host plants using RNA interference (RNAi), and it offers a scientific basis for the prevention and control of B. horsfieldi. Batocera horsfieldi (Hope) (Coleoptera: Cerambycidae) is an important forest pest in China that mainly infests timber and economic forests. This pest primarily causes plant tissue to necrotize, rot, and eventually die by feeding on the woody parts of tree trunks. To gain a deeper understanding of the genetic mechanism of B. horsfieldi, this study employed single-molecule real-time sequencing (SMRT) and Illumina RNA-seq technologies to conduct full-length transcriptome sequencing of the insect. Total RNA extracted from male and female adults was mixed and subjected to SMRT sequencing, generating a complete transcriptome. Transcriptome analysis, prediction of long non-coding RNA (lncRNA), coding sequences (CDs), analysis of simple sequence repeats (SSR), prediction of transcription factors, and functional annotation of transcripts were performed in this study. The collective 20,356,793 subreads (38.26 G, clean reads) were generated, including 432,091 circular consensus sequences and 395,851 full-length non-chimera reads. The full-length non-chimera reads (FLNC) were clustered and redundancies were removed, resulting in 39,912 consensus reads. SSR and ANGEL software v3.0 were used for predicting SSR and CDs. In addition, four tools were used for annotating 6058 lncRNAs, identifying 636 transcription factors. Furthermore, a total of 84,650 transcripts were functionally annotated in seven different databases. This is the first time that the full-length transcriptome of B. horsfieldi has been obtained using SMRT sequencing. This provides an important foundation for investigating the gene regulation underlying the interaction between B. horsfieldi and its host plants through gene editing in the future and provides a scientific basis for the prevention and control of B. horsfieldi. [ABSTRACT FROM AUTHOR]

The practice of crime risk mapping, enabled by the utilization of geospatial big data such as street view images, has received significant research attention. However, in situations where available data is scarce, mapping models may suffer from underfitting and generate inaccurate spatial pattern estimations of crime risk. The covert nature of pickpocketing crimes results in limited observed areas relevant to such criminal events, leading to insufficient coverage of geospatial data. Moreover, the location of crime is also influenced by socio-economic characteristics that may introduce biases into crime risk estimates. These factors render it challenging for the model to capture a valid crime risk pattern, potentially yielding misleading conclusions. Therefore, effectively extracting crime risk with limited data remains a challenge, especially when relying on easily accessible, widespread, and unbiased geospatial data. To address this challenge, we propose a novel crime risk assessment framework based on deep anomaly detection techniques, assuming that urban landscape anomalies carry deep crime risk information. We take Shenzhen as the study area and map the distribution of pickpocketing risk using street view images, accurately revealing the spatial aggregation of pickpocketing crime risk. Our findings indicate that pickpocketing crime in China is caused by regional economic conditions, built environment factors, and human routine activities. This study provides valuable insights for policing and prevention strategies aimed at addressing pickpocketing crimes in large Chinese cities. By leveraging our proposed crime risk assessment framework, decision-makers can allocate resources more efficiently and develop targeted interventions to mitigate crime risks. • A deep learning-based model for mining pickpocketing crimes from street view images is proposed. • With the idea of anomaly detection, crime risk information can be extracted from a small number of street view images with crime labels. • The reliable parcel-level pickpocketing risk distribution was mapped in China's megacity. • Pickpocketing crime in China is multifactorial and spatially heterogeneous. • Pickpocketing crime risk is aggregated and affected by neighbourhood socioeconomic conditions and the built environment. [ABSTRACT FROM AUTHOR]