Bicuspid aortic valve repair—current techniques, outcomes, challenges, and future perspectives

Bicuspid aortic valve (BAV) is a common congenital heart condition that can lead to some valve-related complications, such as aortic stenosis and/or regurgitation, and is often associated with aortic root dilation. With the development and refinement of BAV repair techniques over the past three decades, surgical repair of BAV has emerged as an effective treatment option, offering symptomatic relief and improved outcomes. This review aims to summarize the current techniques, outcomes, and challenges of BAV repair, and to provide potential future perspectives in the field

Research on Metaverse Technology-enabled Innovation Ecosystem

Traditional innovation systems are facing new dilemmas and challenges, and there is an urgent need for digital technologies to reduce investment risks and fuel technology transformation at scale. The metaverse, as an integrator of multiple emerging technologies, brings new opportunities for the development of innovation systems as it provides a trusted platform for innovative companies and organizations. The metaverse initially started as a game experiment that generated a new form of innovation in a virtual world with a plethora of opportunities and capabilities. However, research on the issue of innovation entrepreneurship in the metaverse and the conceptual framework its core technologies in this process is limited. In this paper,we construct the conceptual framework of the metaverse technologyenabled innovation ecosystem, and summarize the three phases of the metaverse innovation ecosystem towards maturity

Clinical effectiveness of a combination of oxiracetam and traditional Chinese medicine rehabilitation program in the treatment of early stroke patients with hemiplegia

Purpose: To evaluate the efficacy of a combination of oxiracetam and traditional Chinese medicine rehabilitation program on early stroke patients with hemiplegia. Methods: 120 patients with early stroke hemiplegia admitted to Wuhu Fifth People's Hospital from March 2019 to July 2020 were recruited. They were equally and randomly assigned to either a control group or a study group, using the random number table method. The control group received oxiracetam, while the study group received oxiracetam plus a traditional Chinese medicine (TCM) rehabilitation program. Outcome measures included treatment effectiveness, motor function, neurological function, TCM symptom scores, and patient satisfaction. Results: There was significantly higher treatment effectiveness in the study group versus the control group (p < 0.05). The Fugl-Meyer score of the control group was lower than that of the study group (52.49 ± 4.73 vs 74.73 ± 5.92; p < 0.001). After treatment, patients in the study group showed lower neurological function and TCM scores than those in the control group (p < 0.05). Furthermore, the study group showed higher satisfaction than the control group (p < 0.05). Conclusion: The combination of oxiracetam and TCM rehabilitation program produce good treatment effectiveness in early stroke hemiplegia patients, and also boosts motor and neurological functions when compared to the use of oxiracetam alone. However, the combination treatment should be subjected to further clinical trials prior to application in clinical practice. Keywords: Chinese medicine rehabilitation program; Early stroke hemiplegia; Oxiracetam; Motor function; Nerve functio

Efficient Attention: Attention with Linear Complexities

Dot-product attention has wide applications in computer vision and natural language processing. However, its memory and computational costs grow quadratically with the input size. Such growth prohibits its application on high-resolution inputs. To remedy this drawback, this paper proposes a novel efficient attention mechanism equivalent to dot-product attention but with substantially less memory and computational costs. Its resource efficiency allows more widespread and flexible integration of attention modules into a network, which leads to better accuracies. Empirical evaluations demonstrated the effectiveness of its advantages. Efficient attention modules brought significant performance boosts to object detectors and instance segmenters on MS-COCO 2017. Further, the resource efficiency democratizes attention to complex models, where high costs prohibit the use of dot-product attention. As an exemplar, a model with efficient attention achieved state-of-the-art accuracies for stereo depth estimation on the Scene Flow dataset. Code is available at https://github.com/cmsflash/efficient-attention.Comment: To appear at WACV 202

Strategic Considerations for Enhancing Civil Defense Systems in Subways and Underground Utility Tunnels Amid Evolving National Defense Mobilization Needs

This paper is set against the backdrop of new national defense mobilization circumstances, and by reviewing and summarizing the functions of subways and underground utility tunnels during wartime, along with the characteristics of domestic and international subway and utility tunnel projects, it analyzes their critical roles in personnel shelter, evacuation, and pipeline protection. The paper discusses the construction of the civil air defense protection system, innovatively proposing some suggestions that may serve as references and examples for the development of civil defense systems in the urban subway and underground utility tunnels sectors in China

RNA extraction from ten year old formalin-fixed paraffin-embedded breast cancer samples: a comparison of column purification and magnetic bead-based technologies

<p>Abstract</p> <p>Background</p> <p>The development of protocols for RNA extraction from paraffin-embedded samples facilitates gene expression studies on archival samples with known clinical outcome. Older samples are particularly valuable because they are associated with longer clinical follow up. RNA extracted from formalin-fixed paraffin-embedded (FFPE) tissue is problematic due to chemical modifications and continued degradation over time. We compared quantity and quality of RNA extracted by four different protocols from 14 ten year old and 14 recently archived (three to ten months old) FFPE breast cancer tissues. Using three spin column purification-based protocols and one magnetic bead-based protocol, total RNA was extracted in triplicate, generating 336 RNA extraction experiments. RNA fragment size was assayed by reverse transcription-polymerase chain reaction (RT-PCR) for the housekeeping gene glucose-6-phosphate dehydrogenase (G6PD), testing primer sets designed to target RNA fragment sizes of 67 bp, 151 bp, and 242 bp.</p> <p>Results</p> <p>Biologically useful RNA (minimum RNA integrity number, RIN, 1.4) was extracted in at least one of three attempts of each protocol in 86–100% of older and 100% of recently archived ("months old") samples. Short RNA fragments up to 151 bp were assayable by RT-PCR for G6PD in all ten year old and months old tissues tested, but none of the ten year old and only 43% of months old samples showed amplification if the targeted fragment was 242 bp.</p> <p>Conclusion</p> <p>All protocols extracted RNA from ten year old FFPE samples with a minimum RIN of 1.4. Gene expression of G6PD could be measured in all samples, old and recent, using RT-PCR primers designed for RNA fragments up to 151 bp. RNA quality from ten year old FFPE samples was similar to that extracted from months old samples, but quantity and success rate were generally higher for the months old group. We preferred the magnetic bead-based protocol because of its speed and higher quantity of extracted RNA, although it produced similar quality RNA to other protocols. If a chosen protocol fails to extract biologically useful RNA from a given sample in a first attempt, another attempt and then another protocol should be tried before excluding the case from molecular analysis.</p

ImFace++: A Sophisticated Nonlinear 3D Morphable Face Model with Implicit Neural Representations

Accurate representations of 3D faces are of paramount importance in various computer vision and graphics applications. However, the challenges persist due to the limitations imposed by data discretization and model linearity, which hinder the precise capture of identity and expression clues in current studies. This paper presents a novel 3D morphable face model, named ImFace++, to learn a sophisticated and continuous space with implicit neural representations. ImFace++ first constructs two explicitly disentangled deformation fields to model complex shapes associated with identities and expressions, respectively, which simultaneously facilitate the automatic learning of correspondences across diverse facial shapes. To capture more sophisticated facial details, a refinement displacement field within the template space is further incorporated, enabling a fine-grained learning of individual-specific facial details. Furthermore, a Neural Blend-Field is designed to reinforce the representation capabilities through adaptive blending of an array of local fields. In addition to ImFace++, we have devised an improved learning strategy to extend expression embeddings, allowing for a broader range of expression variations. Comprehensive qualitative and quantitative evaluations demonstrate that ImFace++ significantly advances the state-of-the-art in terms of both face reconstruction fidelity and correspondence accuracy.Comment: Project page: https://github.com/MingwuZheng/ImFace/tree/imface%2B%2B. arXiv admin note: text overlap with arXiv:2203.1451

Variational Relational Point Completion Network for Robust 3D Classification

Real-scanned point clouds are often incomplete due to viewpoint, occlusion, and noise, which hampers 3D geometric modeling and perception. Existing point cloud completion methods tend to generate global shape skeletons and hence lack fine local details. Furthermore, they mostly learn a deterministic partial-to-complete mapping, but overlook structural relations in man-made objects. To tackle these challenges, this paper proposes a variational framework, Variational Relational point Completion Network (VRCNet) with two appealing properties: 1) Probabilistic Modeling. In particular, we propose a dual-path architecture to enable principled probabilistic modeling across partial and complete clouds. One path consumes complete point clouds for reconstruction by learning a point VAE. The other path generates complete shapes for partial point clouds, whose embedded distribution is guided by distribution obtained from the reconstruction path during training. 2) Relational Enhancement. Specifically, we carefully design point self-attention kernel and point selective kernel module to exploit relational point features, which refines local shape details conditioned on the coarse completion. In addition, we contribute multi-view partial point cloud datasets (MVP and MVP-40 dataset) containing over 200,000 high-quality scans, which render partial 3D shapes from 26 uniformly distributed camera poses for each 3D CAD model. Extensive experiments demonstrate that VRCNet outperforms state-of-the-art methods on all standard point cloud completion benchmarks. Notably, VRCNet shows great generalizability and robustness on real-world point cloud scans. Moreover, we can achieve robust 3D classification for partial point clouds with the help of VRCNet, which can highly increase classification accuracy.Comment: 12 pages, 10 figures, accepted by PAMI. project webpage: https://mvp-dataset.github.io/. arXiv admin note: substantial text overlap with arXiv:2104.1015

Vibration characteristics of mistuned multistage bladed disks of the aero-engine compressor

In order to analyze the vibration characteristics of mistuned multistage bladed disks of an aero-engine compressor, a finite element reduction model of mistuned multistage bladed disks is established based on substructure modal synthesis method. The accuracy of the substructure model was verified by comparing calculation accuracy of the substructure model and the integral model. The influence of different modal truncation numbers on the calculation results are discussed. The vibration modes of each stage of the bladed disks are obtained, the forced response is analyzed from the perspective of strain energy. The result shows that modal truncation number, rotation softening effect, and speed have significant effects on the dynamic frequency calculation results of the multistage bladed disks. The typical mode shapes of the first 200 orders of multistage bladed disks are obtained. With the increase of mistuning standard deviation, the strain energy of multistage bladed disk system decreases gradually