Detection and Recognition of the Invasive species, Hylurgus ligniperda, in Traps, Based on a Cascaded Convolution Neural Network.

BACKGROUND: Hylurgus ligniperda, an invasive species originating from Eurasia, is now a major forestry quarantine pest worldwide. In recent years, it has caused significant damage in China. While traps have been effective in monitoring and controlling pests, manual inspections are labor-intensive and require expertise in insect classification. To address this, we applied a two-stage cascade convolutional neural network, YOLOX-MobileNetV2 (YOLOX-Mnet), for identifying H. ligniperda and other pests captured in traps. This method streamlines target and non-target insect detection from trap images, offering a more efficient alternative to manual inspections. RESULTS: Two cascade convolutional neural network models were employed in two stages to detect both target and non-target insects from images captured in the same forest. Initially, You Only Look Once X (YOLOX) served as the target detection model, identifying insects and non-insects from the collected images, with non-insect targets subsequently filtered out. In the second stage, MobileNetV2, a classification network, classified the captured insects. This approach effectively reduced false positives from non-insect objects, enabled the inclusion of additional classification terms for multi-class insect classification models, and utilized sample control strategies to enhance classification performance. CONCLUSION: Application of the cascade convolutional neural network model accurately identified H. ligniperda, and Mean F1-score of all kinds of insects in the trap was 0.98. Compared to traditional insect classification, this method offers great improvement in the identification and early warning of forest pests, as well as provide technical support for the early prevention and control of forest pests. This article is protected by copyright. All rights reserved.

Prechiasmatic Transection of the Unilateral Dodge Class â Optic Pathway Glioma without Neurofibromatosis Type 1: Technical Description and Clinical Prognosis.

OBJECTIVE: For unilateral Dodge Class Ⅰ optic pathway glioma (OPG-uDCⅠ) without neurofibromatosis type 1, unilateral isolated optic nerve gliomas before the optic chiasm have been confirmed to possibly cause visual deterioration and poor prognosis. For this type of highly selective localized tumor, we explored surgery as the only treatment method. This article retrospectively analyzed and summarized the clinical data of this case series, with the aim of exploring the main technical details and clinical prognosis. METHODS: Included were patients with OPG-uDCⅠ without neurofibromatosis type 1 and experiencing vision loss on the affected side. The fronto-orbital approach was used, which was mainly divided into 3 parts: intraorbital, optic canal, and intracranial. All patients underwent prechiasmatic resection without any adjuvant treatments. The follow-up period was 3 months after surgery, and magnetic resonance imaging and contralateral visual acuity were reviewed annually after surgery. RESULTS: All OPG-uDCⅠ cases were completely removed without any adjuvant treatments, and there was no recurrence during the follow-up period. Pathological results showed that, except for 1 adult patient with pilomyxoid astrocytoma (World Health Organization grade Ⅱ), the others all had pilocytic astrocytoma (World Health Organization grade Ⅰ). Five patients experienced transient ptosis, and all recovered 3 months after surgery. CONCLUSIONS: For OPG-uDCⅠ without neurofibromatosis type 1, radical prechiasmatic resection of the tumor is possible, without the need for postoperative radiotherapy and chemotherapy.

The interpretability of the activity signal detection model for wood-boring pests Semanotus bifasciatus in the larval stage.

BACKGROUND: The acoustic detection model of activity signals based on deep learning could detect wood-boring pests accurately and reliably. However, the black-box characteristics of the deep learning model have limited the credibility of the results and hindered its application. Aiming to address the reliability and interpretability of the model, this paper designed an active interpretable model called Dynamic Acoustic Larvae Prototype Network (DalPNet), which used the prototype to assist model decisions and achieve more flexible model explanation through dynamic feature patch computation. RESULTS: In the experiments, the average recognition accuracy of the DalPNet on the simple test set and anti-noise test set for Semanotus bifasciatus larval activity signals reached 99.3% and 98.5%, respectively. The quantitative evaluation of interpretability was measured by the relative area under the curve (RAUC) and the cumulative slope (CS) of the accuracy change curve in this paper. In the experiments, the RAUC and the CS of DalPNet were 0.2923 and -2.0105, respectively. Additionally, according to the visualization results, the explanation results of DalPNet were more accurate in locating the bite pulses of the larvae and could better focus on multiple bite pulses in one signal, which showed better performance compared to the baseline model. CONCLUSION: The experimental results demonstrated that the proposed DalPNet had better explanation while ensuring recognition accuracy. In view of that, it could improve the trust of forestry custodians in the activity signals detection model and aid in the practical application of the model in the forestry field. © 2023 Society of Chemical Industry.

CXC Chemokine Receptor Type 4 Antagonistic Gold Nanorods Induce Specific Immune Responses and Long-Term Immune Memory to Combat Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC) is highly challenging in its treatment because of the lack of the targeted markers. TNBC patients are not able to acquire benefits from endocrine therapy and targeted therapy except for chemotherapy. CXCR4 is highly expressed on TNBC cells that mediated the tumor cell metastasis as well as proliferation by the response of its ligand CXCL12, therefore holding promising potential of a candidate target for the treatment. In this work, a novel conjugate of CXCR4 antagonist peptide E5 and gold nanorods was fabricated (AuNRs-E5), which was applied to murine breast cancer tumor cells and an animal model, aiming to induce endoplasmic reticulum stress by endoplasmic reticulum-targeted photothermal immunological effects. Results showed that AuNRs-E5 could induce much more generation of damage-related molecular patterns in 4T1 cells under laser irradiation than AuNRs, which significantly promoted the maturation of dendritic cells and stimulated systematic anti-tumor immune responses by enhancing the infiltration of CD8+T cells into the tumor and tumor-draining lymph node, downregulating the regulatory T lymphocytes, and upregulating M1 macrophages in tumors, reversing the microenvironment from "cold" tumors to "hot" tumors. The administration of AuNRs-E5 with laser irradiation not only inhibited the tumor growth significantly but also exerted specific long immune responses to the triple-negative breast cancer tumor cells, which led to the prolonged survival of the mice and the specific immunological memory.

A Waveform Mapping-Based Approach for Enhancement of Trunk Borers' Vibration Signals Using Deep Learning Model.

The larvae of some trunk-boring beetles barely leave traces on the outside of trunks when feeding within, rendering the detection of them rather difficult. One approach to solving this problem involves the use of a probe to pick up boring vibrations inside the trunk and distinguish larvae activity according to the vibrations. Clean boring vibration signals without noise are critical for accurate judgement. Unfortunately, these environments are filled with natural or artificial noise. To address this issue, we constructed a boring vibration enhancement model named VibDenoiser, which makes a significant contribution to this rarely studied domain. This model is built using the technology of deep learning-based speech enhancement. It consists of convolutional encoder and decoder layers with skip connections, and two layers of SRU++ for sequence modeling. The dataset constructed for study is made up of boring vibrations of Agrilus planipennis Fairmaire, 1888 (Coleoptera: Buprestidae) and environmental noise. Our VibDenoiser achieves an improvement of 18.57 in SNR, and it runs in real-time on a laptop CPU. The accuracy of the four classification models increased by a large margin using vibration clips enhanced by our model. The results demonstrate the great enhancement performance of our model, and the contribution of our work to better boring vibration detection.

Acoustic Denoising Using Artificial Intelligence for Wood-Boring Pests Semanotus bifasciatus Larvae Early Monitoring.

Acoustic detection technology is a new method for early monitoring of wood-boring pests, and the effective denoising methods are the premise of acoustic detection in forests. This paper used sensors to record Semanotus bifasciatus larval feeding sounds and various environmental noises, and two kinds of sounds were mixed to obtain the noisy feeding sounds with controllable noise intensity. Then, the time domain denoising models and frequency domain denoising models were designed, and the denoising effects were compared using the metrics of a signal-to-noise ratio (SNR), a segment signal-noise ratio (SegSNR), and log spectral distance (LSD). In the experiments, the average SNR increment could achieve 17.53 dB and 11.10 dB using the in the test data using the time domain features and frequency domain features, respectively. The average SegSNR increment achieved 18.59 dB and 12.04 dB, respectively, and the average LSD between pure feeding sounds and denoised feeding sounds were 0.85 dB and 0.84 dB, respectively. The experimental results demonstrated that the denoising models based on artificial intelligence were effective methods for S. bifasciatus larval feeding sounds, and the overall denoising effect was more significant, especially at low SNRs. In view of that, the denoising models using time domain features were more suitable for the forest area and quarantine environment with complex noise types and large noise interference.

CircXRCC5, as a Potential Novel Biomarker, Promotes Glioma Progression via the miR-490-3p/XRCC5/CLC3 Competing Endogenous RNA Network.

Circular RNAs (circRNAs), forming a covalently closed loop, are identified as a special subgroup of non-coding RNAs. Herein, we investigated the function and underlying mechanism of circXRCC5, generated from the XRCC5 gene, in glioma progression. Bioinformatics analysis was employed to determine the genomic information of circXRCC5 derived from XRCC5 pre-mRNA. Quantitative real-time PCR was conducted to examine the expression of circXRCC5 in glioma tissues and cells. Stable knockdown of circXRCC5 in U87 and U251 cells was established to assess its' biological functions. Cell Counting Kit-8, EdU incorporation, flow cytometry and transwell assay were performed to evaluate cell proliferation, apoptosis, migration and invasion, respectively. The circRNA-miRNA-mRNA regulatory network was verified using luciferase reporter assay and RNA immunoprecipitation. The samples were subjected to CHIP to ascertain the transcriptional regulation of XRCC5 at the promoter region of CLC3. Up-regulation of circXRCC5 was observed in glioma tissues and cell lines, and indicated poor prognosis of glioma patients. Knockdown of circXRCC5 suppressed cell proliferation, migration and invasion, while facilitated apoptosis. Mechanistically, circXRCC5 acted as a molecular sponge for miR-490-3p in a sequence-specific manner. There was a reciprocal negative feedback between circXRCC5 and miR-490-3p in an Argonaute2-dependent manner. Moreover, circXRCC5 acted as a sponge of miR-490-3p to regulate the expression of downstream target gene XRCC5, thus activating the transcription of CLC3, which fostered the progression of glioma. Collectively, circXRCC5 promoted glioma progression via the miR-490-3p/XRCC5/CLC3 ceRNA network, providing a novel prognostic biomarker and a prospective target for glioma treatment.