Region Normalization for Image Inpainting

Feature Normalization (FN) is an important technique to help neural network training, which typically normalizes features across spatial dimensions. Most previous image inpainting methods apply FN in their networks without considering the impact of the corrupted regions of the input image on normalization, e.g. mean and variance shifts. In this work, we show that the mean and variance shifts caused by full-spatial FN limit the image inpainting network training and we propose a spatial region-wise normalization named Region Normalization (RN) to overcome the limitation. RN divides spatial pixels into different regions according to the input mask, and computes the mean and variance in each region for normalization. We develop two kinds of RN for our image inpainting network: (1) Basic RN (RN-B), which normalizes pixels from the corrupted and uncorrupted regions separately based on the original inpainting mask to solve the mean and variance shift problem; (2) Learnable RN (RN-L), which automatically detects potentially corrupted and uncorrupted regions for separate normalization, and performs global affine transformation to enhance their fusion. We apply RN-B in the early layers and RN-L in the latter layers of the network respectively. Experiments show that our method outperforms current state-of-the-art methods quantitatively and qualitatively. We further generalize RN to other inpainting networks and achieve consistent performance improvements.Comment: Accepted by AAAI-202

A VMD and LSTM based hybrid model of load forecasting for power grid security

As the basis for the static security of the power grid, power load forecasting directly affects the safety of grid operation, the rationality of grid planning, and the economy of supply-demand balance. However, various factors lead to drastic changes in short-term power consumption, making the data more complex and thus more difficult to forecast. In response to this problem, a new hybrid model based on Vari-ational mode decomposition (VMD) and Long Short-Term Memory (LSTM) with seasonal factors elimination and error correction is proposed in this paper. Comprehensive case studies on four real-world load datasets from Singapore and the United States are employed to demonstrate the effectiveness and practicality of the proposed hybrid model. The experimental results show that the prediction accuracy of the proposed model is significantly higher than that of the contrast models. Index Terms-Power grid security, short-term load forecasting , seasonal factors elimination, error correction

The clinical outcome of pembrolizumab for patients with recurrent or metastatic squamous cell carcinoma of the head and neck: a single center, real world study in China

BackgroundThe KEYNOTE-048 and KEYNOTE-040 study have demonstrated the efficacy of pembrolizumab in recurrent or metastatic squamous cell carcinoma of the head and neck (R/M HNSCC), we conducted this real-world study to investigate the efficacy of pembrolizumab in patients with R/M HNSCC.MethodsThis is a single-center retrospective study conducted in the Shanghai Ninth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (Shanghai, China). Between December 2020 and December 2022, a total of 77 patients with R/M HNSCC were included into analysis. The primary endpoint of the study was overall survival (OS), and the secondary endpoints were progression-free survival (PFS), overall response rate (ORR)and toxicity.Efficacy was assessed according to RECIST version 1.1.SPSS 27.0 and GraphPad Prism 8.0 software were utilized to perform the statistical analysis.ResultsBy the cut-off date (February 28, 2023), the median OS,PFS and ORR were 15.97 months,8.53 months and 48.9% in patients treated with the pembrolizumab regimen in the first line therapy. Among these patients, 17 patients received pembrolizumab with cetuximab,and 18 received pembrolizumab with chemotherapy.We observed no significant differences between two groups neither in median OS (13.9 vs 19.4 months, P=0.3582) nor PFS (unreached vs 8.233 months, P= 0.2807). In the ≥2nd line therapy (n=30), the median OS, PFS and ORR were 5.7 months, 2.58 months and 20% respectively. Combined positive score (CPS) was eligible from 54 patients. For first line therapy, the median OS and PFS were 14.6 and 8.53 months in patients with CPS ≥1, and median OS and PFS were 14.6 and 12.33 months in patients with CPS ≥20. The immune-related adverse events (irAEs) were occurred in the 31 patients (31/77, 40.26%), and the most common potential irAEs were hypothyroidism (25.97%), and pneumonitis (7.79%).ConclusionOur real-world results indicated that pembrolizumab regimen is a promising treatment in patients with R/M HNSC

Sp1 Is Essential for p16(INK4a) Expression in Human Diploid Fibroblasts during Senescence

BACKGROUND: p16 (INK4a) tumor suppressor protein has been widely proposed to mediate entrance of the cells into the senescent stage. Promoter of p16 (INK4a) gene contains at least five putative GC boxes, named GC-I to V, respectively. Our previous data showed that a potential Sp1 binding site, within the promoter region from −466 to −451, acts as a positive transcription regulatory element. These results led us to examine how Sp1 and/or Sp3 act on these GC boxes during aging in cultured human diploid fibroblasts. METHODOLOGY/PRINCIPAL FINDINGS: Mutagenesis studies revealed that GC-I, II and IV, especially GC-II, are essential for p16 (INK4a) gene expression in senescent cells. Electrophoretic mobility shift assays (EMSA) and ChIP assays demonstrated that both Sp1 and Sp3 bind to these elements and the binding activity is enhanced in senescent cells. Ectopic overexpression of Sp1, but not Sp3, induced the transcription of p16 (INK4a). Both Sp1 RNAi and Mithramycin, a DNA intercalating agent that interferes with Sp1 and Sp3 binding activities, reduced p16 (INK4a) gene expression. In addition, the enhanced binding of Sp1 to p16 (INK4a) promoter during cellular senescence appeared to be the result of increased Sp1 binding affinity, not an alteration in Sp1 protein level. CONCLUSIONS/SIGNIFICANCE: All these results suggest that GC- II is the key site for Sp1 binding and increase of Sp1 binding activity rather than protein levels contributes to the induction of p16 (INK4a) expression during cell aging

Raman-Guided Bronchoscopy: Feasibility and Detection Depth Studies Using Ex Vivo Lung Tissues and SERS Nanoparticle Tags

Image-guided and robotic bronchoscopy is currently under intense research and development for a broad range of clinical applications, especially for minimally invasive biopsy and surgery of peripheral pulmonary nodules or lesions that are frequently discovered by CT or MRI scans. Optical imaging and spectroscopic modalities at the near-infrared (NIR) window hold great promise for bronchoscopic navigation and guidance because of their high detection sensitivity and molecular/cellular specificity. However, light scattering and background interference are two major factors limiting the depth of tissue penetration of photons, and diseased lesions such as small tumors buried under the tissue surface often cannot be detected. Here we report the use of a miniaturized Raman device that is inserted into one of the bronchoscope channels for sensitive detection of “phantom” tumors using fresh pig lung tissues and surface-enhanced Raman scattering (SERS) nanoparticle tags. The ex vivo results demonstrate not only the feasibility of using Raman spectroscopy for endoscopic guidance, but also show that ultrabright SERS nanoparticles allow detection through a bronchial wall of 0.85 mm in thickness and a 5 mm-thick layer of lung tissue (approaching the fourth-generation airway). This work highlights the prospects and potential of Raman-guided bronchoscopy for minimally invasive imaging and detection of lung lesions

A Hybrid Excitation Model Based Lightweight Siamese Network for Underwater Vehicle Object Tracking Missions

Performing object tracking tasks and efficiently perceiving the underwater environment in real time for underwater vehicles is a challenging task due to the complex nature of the underwater environment. A hybrid excitation model based lightweight Siamese network is proposed to solve the mismatch between underwater objects with limited characteristics and complex deep learning models. The lightweight neural network is applied to the residual network in the Siamese network to reduce the computational complexity and cost of the model while constructing a deeper network. In addition, to deal with the changeable complex underwater environment and consider the timing of video tracking, the global excitation model (HE module) is introduced. The model adopts the excitation methods of space, channel, and motion to improve the accuracy of the algorithm. Based on the designed underwater vehicle, the underwater target tracking and target grabbing experiments are carried out, and the experimental results show that the proposed tracking algorithm has a high tracking success rate

A Multiparameter Coupled Prediction Model for Annular Cuttings Bed Height in Horizontal Wells

Cuttings accumulation in horizontal drilling is one of the main reasons for high drilling torque and drag and serious backing pressure and consequently influencing the rate of penetration (ROP), so inhibiting the generation of cuttings bed and keeping the hole clean is an important prerequisite to ensure the smooth and safe drilling of horizontal section. In order to master the formation mechanism of cuttings bed and reveal the influence law of influencing factors, based on the three-layer cuttings bed migration model and the two-phase solid-liquid flow theory, this paper establishes a numerical simulation method for cuttings migration characteristics in the annulus of horizontal wells, reveals the cuttings concentration distribution law and migration characteristics in the horizontal well section, and fits the engineering prediction model of cuttings bed height considering many factors such as geology, engineering, and fluid body according to the simulation results. The analysis of sensitive factors shows that the rate of penetrating, cuttings density, and drill pipe eccentricity is not conducive to borehole cleaning, and cuttings are easy to accumulate to form cuttings bed; increasing the drilling fluid density, displacement, and drill pipe speed is conducive to borehole cleaning and reducing the height of cuttings bed. The research shows that within the range of normal construction parameters, the error of the horizontal cuttings height prediction model is less than 10%, which can meet the needs of engineering analysis. The model provides convenience for engineering applications