RESUMEN
Objective: This study aimed to investigate the reliability of a deep neural network (DNN) model trained only on contrast-enhanced T1 (T1CE) images for predicting intraoperative cerebrospinal fluid (ioCSF) leaks in endoscopic transsphenoidal surgery (EETS). Methods: 396 pituitary adenoma (PA) cases were reviewed, only primary PAs with Hardy suprasellar Stages A, B, and C were included in this study. The T1CE images of these patients were collected, and sagittal and coronal T1CE slices were selected for training the DNN model. The model performance was evaluated and tested, and its interpretability was explored. Results: A total of 102 PA cases were enrolled in this study, 51 from the ioCSF leakage group, and 51 from the non-ioCSF leakage group. 306 sagittal and 306 coronal T1CE slices were collected as the original dataset, and data augmentation was applied before model training and testing. In the test dataset, the DNN model provided a single-slice prediction accuracy of 97.29%, a sensitivity of 98.25%, and a specificity of 96.35%. In clinical test, the accuracy of the DNN model in predicting ioCSF leaks in patients reached 84.6%. The feature maps of the model were visualized and the regions of interest for prediction were the tumor roof and suprasellar region. Conclusion: In this study, the DNN model could predict ioCSF leaks based on preoperative T1CE images, especially in PAs in Hardy Stages A, B, and C. The region of interest in the model prediction-making process is similar to that of humans. DNN models trained with preoperative MRI images may provide a novel tool for predicting ioCSF leak risk for PA patients.
RESUMEN
To address the issue of not having enough labeled fault data for planetary gearboxes in actual production, this research develops a simulation data-driven deep transfer learning fault diagnosis method that applies fault diagnosis knowledge from a dynamic simulation model to an actual planetary gearbox. Massive amounts of different fault simulation data are collected by creating a dynamic simulation model of a planetary gearbox. A fresh deep transfer learning network model is built by fusing one-dimensional convolutional neural networks, attention mechanisms, and domain adaptation methods. The network model is used to learn domain invariant features from simulated data, thereby enabling fault diagnosis on real data. The fault diagnosis experiment is verified by using the Drivetrain Diagnostics Simulator test bench. The validity of the proposed means is evaluated by comparing the diagnostic accuracy of various means on various diagnostic tasks.
RESUMEN
BACKGROUND: A key to the East Palaearctic and northern Oriental species of Rhysipolis Foerster, 1862 (Hymenoptera: Braconidae: Rhysipolinae) is presented. Rhysipolis longicaudatus Belokobylskij, 1994 (stat. nov.) is redescribed, the first host records are given and it is reported new for China. NEW INFORMATION: Rhysipolis longicaudatus was reared from Taleporia sp. (Lepidoptera: Psychidae) in Inner Mongolia Autonomous Region and from Bazaria turensis Ragonot (Lepidoptera: Pyralidae) in Qinghai Province.
RESUMEN
A new species of Metopiinae, Trieces etuokensis Sheng, sp. n., is described and illustrated. Specimens were reared from two species of Lepidoptera: Bazaria turensis (Ragonot, 1887) (Pyralidae) from Balong, Dulan, Qinghai Province, and an unidentified psychid (Psychidae) from Mukainor, Etuoke, Inner Mongolia Autonomous Region, China. The new species is characterized by a yellow face and clypeus, fore and middle femora and hind tibia mainly black, antennae slightly longer than head and mesosoma combined, with 17 flagellomeres, occipital carina entirely absent, and the hind femur being compressed, 2.5 times as its long as maximum width.