Effect of Recession on the Re-entry Capsule Aerodynamic Characteristic

AbstractNumerical simulation and analysis of aerodynamic characteristics of Soyuz ablation shape is carried out in this paper for the adverse influence coming from recession. The result indicates that the shape change caused by the recession will increase absolute value of trim angle of attack and trim lift-drag ratio. The conclusion offers reference for the aerodynamic layout design and improve of the Soyuz re-entry capsule

Vitamin E Inhibits Osteoclastogenesis in Protecting Osteoporosis

The most common orthopedic condition affecting senior adults is osteoporosis, which is defined by a decrease in bone mass and strength as well as microstructural degradation that leads to fragility fractures. Bone remodeling is a well-planned, ongoing process that replaces deteriorated, old bone with new, healthy bone. Bone resorption and bone creation work together during the cycle of bone remodeling to preserve the bone’s volume and microarchitecture. The only bone-resorbing cells in the human body, mononuclear preosteoclasts fuse to form osteoclasts, are multinucleated cells. In numerous animal models or epidemiological studies, vitamin E’s anti-osteoporotic characteristics have been extensively described. This review aims to summarize recent developments in vitamin E’s molecular features as a bone-protective agent. In RANKL/RANK/OPG signaling pathway, vitamin E inhibits synthesis of RANKL, stimulation of c-Fos, and increase level of OPG. Vitamin E also inhibits inflammatory cytokines, such as TNF-α, IL-1, IL-6, IL-27, and MCP-1, negative regulating the JAK–STAT, NF-κB, MAPK signaling pathways. Additionally, vitamin E decreases malondialdehyde and increases superoxide dismutase, GPx and heme oxygenase-1, in suppressing osteoclasts. In this article, we aim to give readers the most recent information on the molecular pathways that vitamin E uses to enhance bone health

PTTG1 Attenuates Drug-Induced Cellular Senescence

As PTTG1 (pituitary tumor transforming gene) abundance correlates with adverse outcomes in cancer treatment, we determined mechanisms underlying this observation by assessing the role of PTTG1 in regulating cell response to anti-neoplastic drugs. HCT116 cells devoid of PTTG1 (PTTG1−/−) exhibited enhanced drug sensitivity as assessed by measuring BrdU incorporation in vitro. Apoptosis, mitosis catastrophe or DNA damage were not detected, but features of senescence were observed using low doses of doxorubicin and TSA. The number of drug-induced PTTG1−/− senescent cells increased ∼4 fold as compared to WT PTTG1-replete cells (p<0.001). p21, an important regulator of cell senescence, was induced ∼3 fold in HCT116 PTTG1−/− cells upon doxorubicin or Trichostatin A treatment. Binding of Sp1, p53 and p300 to the p21 promoter was enhanced in PTTG1−/− cells after treatment, suggesting transcriptional regulation of p21. p21 knock down abrogated the observed senescent effects of these drugs, indicating that PTTG1 likely suppresses p21 to regulate drug-induced senescence. PTTG1 also regulated SW620 colon cancer cells response to doxorubicin and TSA mediated by p21. Subcutaneously xenografted PTTG1−/− HCT116 cells developed smaller tumors and exhibited enhanced responses to doxorubicin. PTTG1−/− tumor tissue derived from excised tumors exhibited increased doxorubicin-induced senescence. As senescence is a determinant of cell responses to anti-neoplastic treatments, these findings suggest PTTG1 as a tumor cell marker to predict anti-neoplastic treatment outcomes

Portsite metastasis of adenocarcinoma after laparoscopic cholecystectomy with an unknown primary tumor: a case report

Port site metastasis of adenocarcinoma after laparoscopic cholecystectomy with an unknown primary tumor is rare. To the best of our knowledge, there are only four such cases reported worldwide. We report a woman in her 70s with cholecystitis who underwent laparoscopic cholecystectomy. Intraoperative laparoscopic exploration did not reveal an abdominal tumor, and postoperative gallbladder pathology did not suggest malignancy. However, 11 months later, she developed an incisional mass in the epigastric port site. In another 6 months, magnetic resonance imaging revealed an abdominal wall tumor. Therefore, she underwent radical resection of the subcutaneous tumor, and postoperative pathology revealed adenocarcinoma. However, no primary tumor was found after systemic imaging examination

Towards Understanding Neural Machine Translation with Attention Heads’ Importance

Although neural machine translation has made great progress, and the Transformer has advanced the state-of-the-art in various language pairs, the decision-making process of the attention mechanism, a crucial component of the Transformer, remains unclear. In this paper, we propose to understand the model’s decisions by the attention heads’ importance. We explore the knowledge acquired by the attention heads, elucidating the decision-making process through the lens of linguistic understanding. Specifically, we quantify the importance of each attention head by assessing its contribution to neural machine translation performance, employing a Masking Attention Heads approach. We evaluate the method and investigate the distribution of attention heads’ importance, as well as its correlation with part-of-speech contribution. To understand the diverse decisions made by attention heads, we concentrate on analyzing multi-granularity linguistic knowledge. Our findings indicate that specialized heads play a crucial role in learning linguistics. By retaining important attention heads and removing the unimportant ones, we can optimize the attention mechanism. This optimization leads to a reduction in the number of model parameters and an increase in the model’s speed. Moreover, by leveraging the connection between attention heads and multi-granular linguistic knowledge, we can enhance the model’s interpretability. Consequently, our research provides valuable insights for the design of improved NMT models

Cervical syphilitic lesions mimicking cervical cancer: a rare case report

A woman presented to the hospital due to postcoital vaginal bleeding. The patient was initially diagnosed with cervical carcinoma by clinicians at a local hospital. However, a biopsy of the cervical lesions revealed chronic inflammation and erosion of the cervical mucosa, and the rapid plasma reagin ratio titer was 1:256. The patient was eventually diagnosed with syphilitic cervicitis and treated with minocycline 0.1 g twice a day. The patient was cured with this treatment

An Ontology-Based Framework for Complex Urban Object Recognition through Integrating Visual Features and Interpretable Semantics

Although previous works have proposed sophisticatedly probabilistic models that has strong capability of extracting features from remote sensing data (e.g., convolutional neural networks, CNN), the efforts that focus on exploring the human’s semantics on the object to be recognized are required more explorations. Moreover, interpretability of feature extraction becomes a major disadvantage of the state-of-the-art CNN. Especially for the complex urban objects, which varies in geometrical shapes, functional structures, environmental contexts, etc, due to the heterogeneity between low-level data features and high-level semantics, the features derived from remote sensing data alone are limited to facilitate an accurate recognition. In this paper, we present an ontology-based methodology framework for enabling object recognition through rules extracted from the high-level semantics, rather than unexplainable features extracted from a CNN. Firstly, we semantically organize the descriptions and definitions of the object as semantics (RDF-triple rules) through our developed domain ontology. Secondly, we exploit semantic web rule language to propose an encoder model for decomposing the RDF-triple rules based on a multilayer strategy. Then, we map the low-level data features, which are defined from optical satellite image and LiDAR height, to the decomposed parts of RDF-triple rules. Eventually, we apply a probabilistic belief network (PBN) to probabilistically represent the relationships between low-level data features and high-level semantics, as well as a modified TanH function is used to optimize the recognition result. The experimental results on lacking of the training process based on data samples show that our proposed approach can reach an accurate recognition with high-level semantics. This work is conducive to the development of complex urban object recognition toward the fields including multilayer learning algorithms and knowledge graph-based relational reinforcement learning