Percutaneous Aortic Valve ReplacementThe Anatomy of Aortic Root Structures and Postmortem Aortic Valve Stent Implantation

Background: Previous studies have suggested that the close proximity of the coronary orifice to the aortic valve leaflets made percutaneous and transapical aortic valve implantation a major challenge such as coronary flow impairment. In this study, the aortic root structures were observed and measured in human cadavers. Material and Methods: We assessed the diameter of the distal ascending aorta, the proximal ascending aorta, the STJ, the aortic annulus, the coronary ostia, and the distance of the annulus to the coronary ostia, the coronary ostia to the STJ level, each ostium to its bilateral commissure of the aortic leaflet, the height of the aortic leaflets, and the aortic annulus to the STJ level. Meanwhile, the malformation, location, number, and shape of the coronary artery orifices and the presence of accessory orifices were observed and recorded. During the study, the relation of the coronary ostia, the aortic leaflets, and the valved stent were also investigated through the post mortem aortic valved stent implantation. Results: The results demonstrated that most of the coronary ostia were located below the STJ level, only very few coronary ostia were at or above the same. The left coronary ostia cluster near to the central region of the sinus curvature, while the right coronary ostia tend to locate eccentrically, a lower position, and in the right side of the sinus. Most of the coronary ostia, which were located below the STJ, were covered by its corresponding aortic leaflets; while the ostia were rarely covered by the aortic leaflets if they lay at or above the STJ. The right coronary ostia were more frequently covered by its aortic leaflets than the left ones. Conclusion: To perform an orthotopic PAVR, resection of the native leaflets before implantation of the valved stent might be necessary and new stents have to be designed to prevent coverage of the coronary ostia by crimping the leaflets

Multi-wavelength Stellar Polarimetry of the Filamentary Cloud IC5146: I. Dust Properties

We present optical and near-infrared stellar polarization observations toward the dark filamentary clouds associated with IC5146. The data allow us to investigate the dust properties (this paper) and the magnetic field structure (Paper II). A total of 2022 background stars were detected in $R_{c}$-, $i'$-, $H$-, and/or $K$-bands to $A_V \lesssim 25$ mag. The ratio of the polarization percentage at different wavelengths provides an estimate of $\lambda_{max}$, the wavelength of peak polarization, which is an indicator of the small-size cutoff of the grain size distribution. The grain size distribution seems to significantly change at $A_V \sim$ 3 mag, where both the average and dispersion of $P_{R_c}/P_{H}$ decrease. In addition, we found $\lambda_{max}$ $\sim$ 0.6-0.9 $\mu$m for $A_V>2.5$ mag, which is larger than the $\sim$ 0.55 $\mu$m in the general ISM, suggesting that grain growth has already started in low $A_V$ regions. Our data also reveal that polarization efficiency (PE $\equiv P_{\lambda}/A_V$) decreases with $A_V$ as a power-law in $R_c$-, $i'$-, and $K$-bands with indices of -0.71$\pm$0.10, -1.23$\pm$0.10 and -0.53$\pm$0.09. However, $H$-band data show a power index change; the PE varies with $A_V$ steeply (index of -0.95$\pm$0.30) when $A_V < 2.88\pm0.67$ mag but softly (index of -0.25$\pm$0.06) for greater $A_V$ values. The soft decay of PE in high $A_V$ regions is consistent with the Radiative Aligned Torque model, suggesting that our data trace the magnetic field to $A_V \sim 20$ mag. Furthermore, the breakpoint found in $H$-band is similar to the $A_V$ where we found the $P_{R_c}/P_{H}$ dispersion significantly decreased. Therefore, the flat PE-$A_V$ in high $A_V$ regions implies that the power index changes result from additional grain growth.Comment: 31 pages, 17 figures, and 3 tables; accepted for publication in Ap

Evaluation of InSAR and TomoSAR for monitoring deformations caused by mining in a mountainous area with high resolution satellite-based SAR

Interferometric Synthetic Aperture Radar (InSAR) and Differential Interferometric Synthetic Aperture Radar (DInSAR) have shown numerous applications for subsidence monitoring. In the past 10 years, the Persistent Scatterer InSAR (PSI) and Small BAseline Subset (SBAS) approaches were developed to overcome the problem of decorrelation and atmospheric effects, which are common in interferograms. However, DInSAR or PSI applications in rural areas, especially in mountainous regions, can be extremely challenging. In this study we have employed a combined technique, i.e., SBAS-DInSAR, to a mountainous area that is severely affected by mining activities. In addition, L-band (ALOS) and C-band (ENVISAT) data sets, 21 TerraSAR-X images provided by German Aerospace Center (DLR) with a high resolution have been used. In order to evaluate the ability of TerraSAR-X for mining monitoring, we present a case study of TerraSAR-X SAR images for Subsidence Hazard Boundary (SHB) extraction. The resulting data analysis gives an initial evaluation of InSAR applications within a mountainous region where fast movements and big phase gradients are common. Moreover, the experiment of four-dimension (4-D) Tomography SAR (TomoSAR) for structure monitoring inside the mining area indicates a potential near all-wave monitoring, which is an extension of conventional InSAR

Effect of Thymosin on Inflammatory Factor Levels, Immune Function, and Quality of Life in Lung Cancer Patients Undergoing Radical Thoracoscopic Surgery

Purpose. To explore the effect of thymosin on inflammatory factor levels, immune function, and quality of life in patients undergoing radical thoracoscopic lung cancer surgery. Methods. One hundred and twenty patients admitted to the Surgical Oncology Department of the First Hospital of Jiaxing from January 2018 to January 2019 were randomized into the study group and the control group using the random number table method, with 60 cases in each group. The control group was treated with radical thoracoscopic lung cancer surgery, and the study group was treated with radical thoracoscopic lung cancer surgery combined with thymosin. The clinical efficiency, inflammatory factors, immune function, and quality of life between the two groups of patients were compared. Results. There was no significant difference between the two groups in terms of pathological stage, tissue type, maximum tumor diameter, and perioperative indicators such as operative time, intraoperative bleeding, pleural drainage, hospital stay, and the number of intraoperative lymph nodes removed. The levels of CD4 (+%), CD8 (+%), CD4+/CD8+, and natural killer cell (NK) (%) were significantly decreased in both groups after treatment, with significantly higher results in the study group than in the control group. The study group had significantly lower serum interleukin-6 (IL-6) levels and higher interleukin-10 (IL-10) levels than the control group. After treatment, patients in the study group had better postoperative physiological status and overall score than the control group. There was no significant difference in postoperative survival and adverse reactions between the two groups. Conclusion. The use of thymosin treatment in lung cancer patients undergoing radical thoracoscopic surgery significantly improves immune function, mitigates inflammatory response, and enhances the quality of life, which is worthy of clinical application

Positioning rub of the aero-engine based on acoustic emission and Mathematical Morphology in noise background

AbstractAiming at the practical requirement of locating and diagnosis of aero-engine rub fault and the impact of noise on the positioning accuracy, this paper introduces Mathematical Morphology to preprocess the acoustic emission (AE) signals, and uses Matrix Locating Method to confirm rub fault and its position: firstly, the improved Matrix Locating Method and Mathematical Morphology are introduced by referring the rub AE source locating; then, the rub AE signals were collected by sensor arrays on the case of rub fault simulated test-bed, and locating precisions of some typical issues are compared between before and after using morphology filter, and the results show that the introduction of Mathematical Morphology receives favorable effect on AE locating of rub fault in noise background

Dynamic Sampling Network for Semantic Segmentation

Sampling is a basic operation of modern convolutional neural networks (CNN) since down-sampling operators are employed to enlarge the receptive field while up-sampling operators are adopted to increase resolution. Most existing deep segmentation networks employ regular grid sampling operators, which can be suboptimal for semantic segmentation task due to large shape and scale variance. To address this problem, this paper proposes a Context Guided Dynamic Sampling (CGDS) module to obtain an effective representation with rich shape and scale information by adaptively sampling useful segmentation information in spatial space. Moreover, we utilize the multi-scale contextual representations to guide the sampling process. Therefore, our CGDS can adaptively capture shape and scale information according to not only the input feature map but also the multi-scale semantic context. CGDS provides a plug-and-play module which can be easily incorporated in deep segmentation networks. We incorporate our proposed CGDS module into Dynamic Sampling Network (DSNet) and perform extensive experiments on segmentation datasets. Experimental results show that our CGDS significantly improves semantic segmentation performance and achieves state-of-the-art performance on PASCAL VOC 2012 and ADE20K datasets. Our model achieves 85.2% mIOU on PASCAL VOC 2012 test set without MS COCO dataset pre-trained and 46.4% on ADE20K validation set. The codes will become publicly available after publication

BBTv2: Pure Black-Box Optimization Can Be Comparable to Gradient Descent for Few-Shot Learning

Black-Box Tuning (BBT) is a derivative-free approach to optimize continuous prompt tokens prepended to the input of language models. Although BBT has achieved comparable performance to full model tuning on simple classification tasks under few-shot settings, it requires pre-trained prompt embedding to match model tuning on hard tasks (e.g., entailment tasks), and therefore does not completely get rid of the dependence on gradients. In this paper we present BBTv2, a pure black-box optimization approach that can drive language models to achieve comparable results to gradient-based optimization. In particular, we prepend continuous prompt tokens to every layer of the language model and propose a divide-and-conquer algorithm to alternately optimize the prompt tokens at different layers. For the optimization at each layer, we perform derivative-free optimization in a low-dimensional subspace, which is then randomly projected to the original prompt parameter space. Experimental results show that BBTv2 not only outperforms BBT by a large margin, but also achieves comparable or even better performance than full model tuning and state-of-the-art parameter-efficient methods (e.g., Adapter, LoRA, BitFit, etc.) under few-shot learning settings, while maintaining much fewer tunable parameters.Comment: Work in progress. Code is publicly available at https://github.com/txsun1997/Black-Box-Tunin

Multitask Pre-training of Modular Prompt for Chinese Few-Shot Learning

Prompt tuning is a parameter-efficient approach to adapting pre-trained language models to downstream tasks. Although prompt tuning has been shown to match the performance of full model tuning when training data is sufficient, it tends to struggle in few-shot learning settings. In this paper, we present Multi-task Pre-trained Modular Prompt (MP2) to boost prompt tuning for few-shot learning. MP2 is a set of combinable prompts pre-trained on 38 Chinese tasks. On downstream tasks, the pre-trained prompts are selectively activated and combined, leading to strong compositional generalization to unseen tasks. To bridge the gap between pre-training and fine-tuning, we formulate upstream and downstream tasks into a unified machine reading comprehension task. Extensive experiments under two learning paradigms, i.e., gradient descent and black-box tuning, show that MP2 significantly outperforms prompt tuning, full model tuning, and prior prompt pre-training methods in few-shot settings. In addition, we demonstrate that MP2 can achieve surprisingly fast and strong adaptation to downstream tasks by merely learning 8 parameters to combine the pre-trained modular prompts.Comment: Accepted to ACL 2023 (main conference). Code and data are publicly available at https://github.com/Hzfinfdu/MPM