A Novel Deep Learning Framework for Internal Gross Target Volume Definition from 4D Computed Tomography of Lung Cancer Patients

In this paper, we study the reliability of a novel deep learning framework for internal gross target volume (IGTV) delineation from four-dimensional computed tomography (4DCT), which is applied to patients with lung cancer treated by Stereotactic Body Radiation Therapy (SBRT). 77 patients who underwent SBRT followed by 4DCT scans were incorporated in a retrospective study. The IGTV_DL was delineated using a novel deep machine learning algorithm with a linear exhaustive optimal combination framework, for the purpose of comparison, three other IGTVs base on common methods was also delineated, we compared the relative volume difference (RVI), matching index (MI) and encompassment index (EI) for the above IGTVs. Then, multiple parameter regression analysis assesses the tumor volume and motion range as clinical influencing factors in the MI variation. Experimental results demonstrated that the deep learning algorithm with linear exhaustive optimal combination framework has a higher probability of achieving optimal MI compared with other currently widely used methods. For patients after simple breathing training by keeping the respiratory frequency in 10 BMP, the four phase combinations of 0%, 30%, 50% and 90% can be considered as a potential candidate for an optimal combination to synthesis IGTV in all respiration amplitudes

Instant Multi-View Head Capture through Learnable Registration

Existing methods for capturing datasets of 3D heads in dense semantic correspondence are slow, and commonly address the problem in two separate steps; multi-view stereo (MVS) reconstruction followed by non-rigid registration. To simplify this process, we introduce TEMPEH (Towards Estimation of 3D Meshes from Performances of Expressive Heads) to directly infer 3D heads in dense correspondence from calibrated multi-view images. Registering datasets of 3D scans typically requires manual parameter tuning to find the right balance between accurately fitting the scans surfaces and being robust to scanning noise and outliers. Instead, we propose to jointly register a 3D head dataset while training TEMPEH. Specifically, during training we minimize a geometric loss commonly used for surface registration, effectively leveraging TEMPEH as a regularizer. Our multi-view head inference builds on a volumetric feature representation that samples and fuses features from each view using camera calibration information. To account for partial occlusions and a large capture volume that enables head movements, we use view- and surface-aware feature fusion, and a spatial transformer-based head localization module, respectively. We use raw MVS scans as supervision during training, but, once trained, TEMPEH directly predicts 3D heads in dense correspondence without requiring scans. Predicting one head takes about 0.3 seconds with a median reconstruction error of 0.26 mm, 64% lower than the current state-of-the-art. This enables the efficient capture of large datasets containing multiple people and diverse facial motions. Code, model, and data are publicly available at https://tempeh.is.tue.mpg.de.Comment: Conference on Computer Vision and Pattern Recognition (CVPR) 202

EREG is a risk factor for the prognosis of patients with cervical cancer

BackgroundCervical cancer continues to threaten women's health worldwide. Identifying critical oncogenic molecules is important to drug development and prognosis prediction for patients with cervical cancer. Recent studies have demonstrated that epiregulin (EREG) is upregulated in various cancer types, which contributes to cancer progression by triggering the EGFR signaling pathway. However, the role of EREG is still unclear.MethodsIn this study, we first conducted a comprehensive biological analysis to investigate the expression of EREG in cervical cancer. Then, we investigated the correlations between EREG expression level and clinicopathological features. In addition, we validated the effects of EREG expression on the proliferation and apoptosis of cervical cancer cells.ResultsBased on the public database, we found that the expression of EREG was higher in advanced cervical cancer samples. Survival analysis showed that EREG was a risk factor for the prognosis of cervical cancer. In vitro experiments demonstrated that EREG knockdown undermined proliferation and promoted apoptosis in cancer cells.ConclusionEREG plays a vital role in the progression of cervical cancer, which contributes to hyperactive cell proliferation and decreased cell apoptosis. It might be a valuable target for prognosis prediction and drug development for cervical cancer in the future

A large calcium-imaging dataset reveals a systematic V4 organization for natural scenes

The visual system evolved to process natural scenes, yet most of our understanding of the topology and function of visual cortex derives from studies using artificial stimuli. To gain deeper insights into visual processing of natural scenes, we utilized widefield calcium-imaging of primate V4 in response to many natural images, generating a large dataset of columnar-scale responses. We used this dataset to build a digital twin of V4 via deep learning, generating a detailed topographical map of natural image preferences at each cortical position. The map revealed clustered functional domains for specific classes of natural image features. These ranged from surface-related attributes like color and texture to shape-related features such as edges, curvature, and facial features. We validated the model-predicted domains with additional widefield calcium-imaging and single-cell resolution two-photon imaging. Our study illuminates the detailed topological organization and neural codes in V4 that represent natural scenes.Comment: 39 pages, 14 figure

Symmetry breaking induced insulating electronic state in Pb9_{9}Cu(PO4_4)6_6O

The recent experimental claim of room-temperature ambient-pressure superconductivity in a Cu-doped lead-apatite (LK-99) has ignited substantial research interest in both experimental and theoretical domains. Previous density functional theory (DFT) calculations with the inclusion of an on-site Hubbard interaction $U$ consistently predict the presence of flat bands crossing the Fermi level. This is in contrast to DFT plus dynamical mean field theory calculations, which reveal the Mott insulating behavior for the stoichiometric Pb$_{9}$Cu(PO$_4$)$_6$O compound. Nevertheless, the existing calculations are all based on the $P6_3/m$ structure, which is argued to be not the ground-state structure. Here, we revisit the electronic structure of Pb$_{9}$Cu(PO$_4$)$_6$O with the energetically more favorable $P\bar{3}$ structure, fully taking into account electronic symmetry breaking. We examine all possible configurations for Cu substituting the Pb sites. Our results show that the doped Cu atoms exhibit a preference for substituting the Pb2 sites than the Pb1 sites. In both cases, the calculated substitutional formation energies are large, indicating the difficulty in incorporating Cu at the Pb sites. We find that most of structures with Cu at the Pb2 site tend to be insulating, while the structures with both two Cu atoms at the Pb1 sites (except one configuration) are predicted to be metallic by DFT+$U$ calculations. However, when accounting for the electronic symmetry breaking, some Cu-doped configurations previously predicted to be metallic (including the structure studied in previous DFT+$U$ calculations) become insulating. Our work highlights the importance of symmetry breaking in obtaining correct electronic state for Pb$_{9}$Cu(PO$_4$)$_6$O, thereby reconciling previous DFT+$U$ and DFT+DMFT calculations.Comment: 19 pages, 9 figures (including Supplementary Material

Auto-adjustable Spoiler Control System

Veroptimal Solution would like to present the Auto-adjustable Spoiler Control System (ASCS).  This system is a feedback control system which is able to either automatically or manually change the elevation and angle of attack (AOA) of the car spoiler. Research shows that when the spoiler elevation is low, or stays very close to the surface of the boot lid, the spoiler can more effectively spoil out the undesirable flows and reshape airflow streams around the vehicle.  The major benefit to this would be the reduction of drag and the increase of fuel efficiency. When the spoiler elevation is high, the AOA of the spoiler plays a crucial role because the angle determines how much air should be deflected upwards and this generates downforce at the rear of the vehicle