Search

The Asian honeybee, Apis cerana, is an ecologically and economically important pollinator. Mapping its genetic variation is key to understanding population‐level health, histories and potential capacities to respond to environmental changes. However, most efforts to date were focused on single nucleotide polymorphisms (SNPs) based on a single reference genome, thereby ignoring larger scale genomic variation. We employed long‐read sequencing technologies to generate a chromosome‐scale reference genome for the ancestral group of A. cerana. Integrating this with 525 resequencing data sets, we constructed the first pan‐genome of A. cerana, encompassing almost the entire gene content. We found that 31.32% of genes in the pan‐genome were variably present across populations, providing a broad gene pool for environmental adaptation. We identified and characterized structural variations (SVs) and found that they were not closely linked with SNP distributions; however, the formation of SVs was closely associated with transposable elements. Furthermore, phylogenetic analysis using SVs revealed a novel A. cerana ecological group not recoverable from the SNP data. Performing environmental association analysis identified a total of 44 SVs likely to be associated with environmental adaptation. Verification and analysis of one of these, a 330 bp deletion in the Atpalpha gene, indicated that this SV may promote the cold adaptation of A. cerana by altering gene expression. Taken together, our study demonstrates the feasibility and utility of applying pan‐genome approaches to map and explore genetic feature variations of honeybee populations, and in particular to examine the role of SVs in the evolution and environmental adaptation of A. cerana. [ABSTRACT FROM AUTHOR]

The division of labor is a dominant characteristic of honeybees and is accompanied by behavioral specialization and cognitive enhancement. As the central nervous system to control the labor-specific behaviors of honeybee, the brain is richest in lipid in terms of both diversity and abundance. In this study, an in-depth LC-MS/MS-based lipidomic method was applied to systematically characterize the brain lipid compositions of worker bees with three labor stages: newly emerged bee (NEB), nurse bee (NB), and forager bee (FB). A total number of 337 lipid species that assigned to 20 lipid classes were analyzed. The association of the brain lipidomes with the division of labors was suggested by the results of both the unsupervised and supervised multivariate pattern recognition analysis. More than 68% of the identified lipid species were found to be significantly changed in at least one comparison between NEB, NB, and FB. A total of 81 lipid species were identified as the potential labor-featured molecules with VIP > 1 and p-adj < 0.05. The labor-featured lipids of FA(18:2), FA(18:3), FA(26:0), PC(18:0_18:3), PS(18:1_18:1), SM(d38:1), CoQ10, and CoQ9, as well as their interactions with 12 behavior-related genes, including AmEST-6, AmFABP, AmE75, AmDGAT2, AmLSD1, AmNPC1, AmABCA1, AmNMDAR1, AmHTT, AmNOS, etc., were revealed by the further IPA analysis. These findings demonstrate for the first time that the brain lipidomes of worker bees are associated with the stable differences in their labors, which help understand the function of brain lipids on the labor-dependent behaviors of honeybees. [ABSTRACT FROM AUTHOR]

Background: In clinical practice, some patients might not be able or unwilling to provide a thorough history of medication and poison exposure. The aim of this study was to use toxicological analysis to examine the clinical characteristics of patients with acute poisoning whose exposure history was uncertain from a toxicological analysis perspective., Methods: This was a retrospective and descriptive study from an institute of poisoning. Patient registration information and test reports spanning the period from April 1, 2020 to March 31, 2022, were obtained. Patients with uncertain exposure histories and who underwent toxicological analysis were included. Clinical manifestations and categories of toxics were analyzed., Results: Among the 195 patients with positive toxicological analysis results, the main causes of uncertain exposure history was disturbance of consciousness (62.6%), unawareness (23.6%) and unwillingness or lack of cooperation (13.8%). The predominant clinical manifestations were disturbed consciousness (62.6%), followed by vomiting and nausea (14.4%) and liver function abnormalities (8.7%). A comparison of clinical manifestations between patients with positive and negative ( n =99) toxicological analyses results revealed significantly different proportions of disturbances in consciousness (63% vs. 21%), dizziness (1.5% vs. 5.1%), multi-organ failure (1.5% vs. 7.1%), and local pain (0 vs 4%). The main categories of substances involved were psychiatric medications (23.1%), sedatives (20.5%), insecticides (13.8%), and herbicides (12.8%)., Conclusion: The clinical manifestations of acute poisoning in patients with an uncertain exposure history are diverse and nonspecific, and toxicological analysis plays a pivotal role in the diagnosis and differential diagnosis of such patients., Competing Interests: Conflicts of interest: No conflicts of interest are reported., (Copyright: © World Journal of Emergency Medicine.)

Background: Computed tomography (CT) imaging technology has become an indispensable auxiliary method in medical diagnosis and treatment. In mitigating the radiation damage caused by X-rays, low-dose computed tomography (LDCT) scanning is becoming more widely applied. However, LDCT scanning reduces the signal-to-noise ratio of the projection, and the resulting images suffer from serious streak artifacts and spot noise. In particular, the intensity of noise and artifacts varies significantly across different body parts under a single low-dose protocol., Methods: To improve the quality of different degraded LDCT images in a unified framework, we developed a generative adversarial learning framework with a dynamic controllable residual. First, the generator network consists of the basic subnetwork and the conditional subnetwork. Inspired by the dynamic control strategy, we designed the basic subnetwork to adopt a residual architecture, with the conditional subnetwork providing weights to control the residual intensity. Second, we chose the Visual Geometry Group Network-128 (VGG-128) as the discriminator to improve the noise artifact suppression and feature retention ability of the generator. Additionally, a hybrid loss function was specifically designed, including the mean square error (MSE) loss, structural similarity index metric (SSIM) loss, adversarial loss, and gradient penalty (GP) loss., Results: The results obtained on two datasets show the competitive performance of the proposed framework, with a 3.22 dB peak signal-to-noise ratio (PSNR) margin, 0.03 SSIM margin, and 0.2 contrast-to-noise ratio margin on the Challenge data and a 1.0 dB PSNR margin and 0.01 SSIM margin on the real data., Conclusions: Experimental results demonstrated the competitive performance of the proposed method in terms of noise decrease, structural retention, and visual impression improvement., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-22-1384/coif). The authors have no conflicts of interest to declare., (2023 Quantitative Imaging in Medicine and Surgery. All rights reserved.)