39 research outputs found
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 -, -,
-, and/or -bands to mag. The ratio of the polarization
percentage at different wavelengths provides an estimate of ,
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 3 mag, where both the average and dispersion
of decrease. In addition, we found
0.6-0.9 m for mag, which is larger than the 0.55 m
in the general ISM, suggesting that grain growth has already started in low
regions. Our data also reveal that polarization efficiency (PE ) decreases with as a power-law in -, -, and
-bands with indices of -0.710.10, -1.230.10 and -0.530.09.
However, -band data show a power index change; the PE varies with
steeply (index of -0.950.30) when mag but softly
(index of -0.250.06) for greater values. The soft decay of PE in
high regions is consistent with the Radiative Aligned Torque model,
suggesting that our data trace the magnetic field to mag.
Furthermore, the breakpoint found in -band is similar to the where we
found the dispersion significantly decreased. Therefore, the
flat PE- in high 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
Can AI Assistants Know What They Don't Know?
Recently, AI assistants based on large language models (LLMs) show surprising
performance in many tasks, such as dialogue, solving math problems, writing
code, and using tools. Although LLMs possess intensive world knowledge, they
still make factual errors when facing some knowledge intensive tasks, like
open-domain question answering. These untruthful responses from the AI
assistant may cause significant risks in practical applications. We believe
that an AI assistant's refusal to answer questions it does not know is a
crucial method for reducing hallucinations and making the assistant truthful.
Therefore, in this paper, we ask the question "Can AI assistants know what they
don't know and express them through natural language?" To answer this question,
we construct a model-specific "I don't know" (Idk) dataset for an assistant,
which contains its known and unknown questions, based on existing open-domain
question answering datasets. Then we align the assistant with its corresponding
Idk dataset and observe whether it can refuse to answer its unknown questions
after alignment. Experimental results show that after alignment with Idk
datasets, the assistant can refuse to answer most its unknown questions. For
questions they attempt to answer, the accuracy is significantly higher than
before the alignment.Comment: Work in progres
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
IrrE, a Global Regulator of Extreme Radiation Resistance in Deinococcus radiodurans, Enhances Salt Tolerance in Escherichia coli and Brassica napus
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