Responses to Host Plant Volatiles and Identification of Odorant Binding Protein and Chemosensory Protein Genes in Bradysia odoriphaga

The chive maggot Bradysia odoriphaga Yang et Zhang (Diptera: Sciaridae) is a devastating agricultural pest that feeds on liliaceous vegetables and edible mushrooms. The shared host plant volatiles and chemosensory genes of B. odoriphaga may together play crucial roles for insects to identify their host. However, the responses of B. odoriphaga to host volatiles remain unclear. Electroantennography (EAG) and behavioral bioassays were performed on 12 volatiles shared in Allium and Pleurotus. Hexanal evoked in both male and female adults extremely significant EAG responses. In behavioral assays, 3rd-instar larvae and female adults can be significantly repelled by methyl propyl disulfide, 1-octen-3-ol, or hexanal at the concentration of 100 mg/mL. Third-instar larvae and female adults were significantly attracted by limonene with a concentration of 10 and 100 mg/mL, respectively. In addition, 57 chemosensory genes, including 51 odorant binding proteins (OBPs) and 6 chemosensory proteins (CSPs), were identified based on transcriptomes of larvae and pupae. Compared with previous adult transcriptomes, 11 BodoOBPs were specifically expressed in adults, and 6 BodoOBPs were specifically expressed in larvae and pupae. BodoCSP2 and BodoCSP3 were exclusively expressed in the adult stage. Our results provided the potential substances in new ecofriendly pest management and the targets for further study of chemosensory gene functions

Data_Sheet_1_Altered topological properties of functional brain networks in patients with first episode, late-life depression before and after antidepressant treatment.docx

ObjectivesTo preliminarily explore the functional activity and information integration of the brains under resting state based on graph theory in patients with first-episode, late-life depression (LLD) before and after antidepressant treatment.MethodsA total of 50 patients with first-episode LLD and 40 non-depressed controls (NCs) were recruited for the present research. Participants underwent the RBANS test, the 17-item Hamilton depression rating scale (HAMD-17) test, and resting-state functional MRI scans (rs-fMRI). The RBANS test consists of 12 sub-tests that contribute to a total score and index scores across the five domains: immediate memory, visuospatial/constructional, language, attention, and delayed memory. Escitalopram or sertraline was adopted for treating depression, and the dosage of the drug was adjusted by the experienced psychiatrists. Of the 50 LLD patients, 27 cases who completed 6-month follow-ups and 27 NCs matched with age, sex, and education level were included for the final statistical analysis.ResultsThere were significant differences in RBANS total score, immediate memory, visuospatial/constructional, language, attention, and delayed memory between LLD baseline group and NCs group (P ConclusionsThe ability to integrate and divide labor of functional brain networks declines in LLD patients and linked with the depression severity. After the relief of depressive symptoms, the small-world attribute of functional brain networks in LLD patients persists. However, the information transmission efficiency and centrality of some brain regions continue to decline over time, perhaps related to their progressive cognitive impairment.</p

DataSheet_1_Multi-Institutional Validation of Two-Streamed Deep Learning Method for Automated Delineation of Esophageal Gross Tumor Volume Using Planning CT and FDG-PET/CT.pdf

BackgroundThe current clinical workflow for esophageal gross tumor volume (GTV) contouring relies on manual delineation with high labor costs and inter-user variability.PurposeTo validate the clinical applicability of a deep learning multimodality esophageal GTV contouring model, developed at one institution whereas tested at multiple institutions.Materials and MethodsWe collected 606 patients with esophageal cancer retrospectively from four institutions. Among them, 252 patients from institution 1 contained both a treatment planning CT (pCT) and a pair of diagnostic FDG-PET/CT; 354 patients from three other institutions had only pCT scans under different staging protocols or lacking PET scanners. A two-streamed deep learning model for GTV segmentation was developed using pCT and PET/CT scans of a subset (148 patients) from institution 1. This built model had the flexibility of segmenting GTVs via only pCT or pCT+PET/CT combined when available. For independent evaluation, the remaining 104 patients from institution 1 behaved as an unseen internal testing, and 354 patients from the other three institutions were used for external testing. Degrees of manual revision were further evaluated by human experts to assess the contour-editing effort. Furthermore, the deep model’s performance was compared against four radiation oncologists in a multi-user study using 20 randomly chosen external patients. Contouring accuracy and time were recorded for the pre- and post-deep learning-assisted delineation process.</p