Study on Translations of Five Xing Theory of TCM and Its Core Terminologies

Against the backdrop of the Belt and Road Initiative, the going-out policy of Chinese culture is ensured by a strong language tool. Now the going-out of Traditional Chinese Medicine (TCM) is leading an irreversible trend for TCM is widely expanded as the significant part of traditional Chinese culture. Then, the precise translation of TCM is expected to be a guarantee for its internationalization. To date, mistranslation, omission and inaccuracy have extremely interfered with the cultural exchanges with other countries, affecting the quality of translations. From the aspect of the translations of Five Xing Theory of TCM and its core terminologies, the paper purports to compare WHO International Standard Terminologies on Traditional Medicine in the Western Pacific Region (IST) and International Standard Chinese-English Basic Nomenclature of Chinese Medicine (ISN). And then it can be concluded that when translating Five Xing Theory of TCM and its core terminologies, literal translation and transliteration should be mainly adopted and annotation is supposed to be added in proper situation so as to achieve accurate translation and better the promotion of transmission of TCM

Autonomous Drone Racing: Time-Optimal Spatial Iterative Learning Control within a Virtual Tube

It is often necessary for drones to complete delivery, photography, and rescue in the shortest time to increase efficiency. Many autonomous drone races provide platforms to pursue algorithms to finish races as quickly as possible for the above purpose. Unfortunately, existing methods often fail to keep training and racing time short in drone racing competitions. This motivates us to develop a high-efficient learning method by imitating the training experience of top racing drivers. Unlike traditional iterative learning control methods for accurate tracking, the proposed approach iteratively learns a trajectory online to finish the race as quickly as possible. Simulations and experiments using different models show that the proposed approach is model-free and is able to achieve the optimal result with low computation requirements. Furthermore, this approach surpasses some state-of-the-art methods in racing time on a benchmark drone racing platform. An experiment on a real quadcopter is also performed to demonstrate its effectiveness

Jamming precoding in AF relay-aided PLC systems with multiple eavessdroppers

Enhancing information security has become increasingly significant in the digital age. This paper investigates the concept of physical layer security (PLS) within a relay-aided power line communication (PLC) system operating over a multiple-input multiple-output (MIMO) channel based on MK model. Specifically, we examine the transmission of confidential signals between a source and a distant destination while accounting for the presence of multiple eavesdroppers, both colluding and non-colluding. We propose a two-phase jamming scheme that leverages a full-duplex (FD) amplify-and-forward (AF) relay to address this challenge. Our primary objective is to maximize the secrecy rate, which necessitates the optimization of the jamming precoding and transmitting precoding matrices at both the source and the relay while adhering to transmit power constraints. We present a formulation of this problem and demonstrate that it can be efficiently solved using an effective block coordinate descent (BCD) algorithm. Simulation results are conducted to validate the convergence and performance of the proposed algorithm. These findings confirm the effectiveness of our approach. Furthermore, the numerical analysis reveals that our proposed algorithm surpasses traditional schemes that lack jamming to achieve higher secrecy rates. As a result, the proposed algorithm offers the benefit of guaranteeing secure communications in a realistic channel model, even in scenarios involving colluding eavesdroppers

Ziya-Visual: Bilingual Large Vision-Language Model via Multi-Task Instruction Tuning

Recent advancements enlarge the capabilities of large language models (LLMs) in zero-shot image-to-text generation and understanding by integrating multi-modal inputs. However, such success is typically limited to English scenarios due to the lack of large-scale and high-quality non-English multi-modal resources, making it extremely difficult to establish competitive counterparts in other languages. In this paper, we introduce the Ziya-Visual series, a set of bilingual large-scale vision-language models (LVLMs) designed to incorporate visual semantics into LLM for multi-modal dialogue. Composed of Ziya-Visual-Base and Ziya-Visual-Chat, our models adopt the Querying Transformer from BLIP-2, further exploring the assistance of optimization schemes such as instruction tuning, multi-stage training and low-rank adaptation module for visual-language alignment. In addition, we stimulate the understanding ability of GPT-4 in multi-modal scenarios, translating our gathered English image-text datasets into Chinese and generating instruction-response through the in-context learning method. The experiment results demonstrate that compared to the existing LVLMs, Ziya-Visual achieves competitive performance across a wide range of English-only tasks including zero-shot image-text retrieval, image captioning, and visual question answering. The evaluation leaderboard accessed by GPT-4 also indicates that our models possess satisfactory image-text understanding and generation capabilities in Chinese multi-modal scenario dialogues. Code, demo and models are available at ~\url{https://huggingface.co/IDEA-CCNL/Ziya-BLIP2-14B-Visual-v1}

Analysis of the Efficacy of Autologous Peripheral Blood Stem Cell Transplantation in High-Risk Neuroblastoma

Objective: This study aimed to analyze the efficacy of autologous peripheral blood stem cell transplantation for high-risk neuroblastoma in China. Methods: The data of 90 high-risk neuroblastoma patients treated with the CCCG-NB 2015 regimen were reviewed. The baseline clinicopathological characteristics and prognosis were analyzed and compared. In addition, the prognoses of tandem autologous stem cell transplantation and single autologous stem cell transplantation groups were compared. Results: The results of survival analysis showed that autologous peripheral blood stem cell transplantation based on this pretreatment regimen significantly improved the prognosis of children in the high-risk group. The 3-year event-free survival (EFS) and overall survival (OS) rates for the transplantation group and the nontransplantation group were 65.5% vs. 41.3% (p=0.023) and 77.1% vs. 57.9% (p=0.03), respectively. There was no difference in the distribution of baseline clinical case characteristics between the single transplantation group and the tandem transplantation group (p\u3e0.05), and there was no significant difference in EFS and OS between the two groups (p\u3e0.05). Conclusion: Based on this pretreatment programme, autologous peripheral blood stem cell transplantation is safe and tolerable and significantly improves the prognosis of children in the high-risk group. The value of tandem autologous stem cell transplantation is worthy of further discussion, which should consider various aspects such as the transplantation medication regimen and the patient\u27s state

Discovery and Survey of a New Mandarivirus Associated with Leaf Yellow Mottle Disease of Citrus in Pakistan.

During biological indexing for viruses in citrus trees, in a collection of Symons sweet orange (SSO) (Citrus sinensis L. Osbeck) graft inoculated with bark tissues of citrus trees from the Punjab Province in Pakistan, several SSO trees exhibited leaf symptoms of vein yellowing and mottle. High-throughput sequencing by Illumina of RNA preparation depleted of ribosomal RNAs from one symptomatic tree, followed by BLAST analyses, allowed identification of a novel virus, tentatively named citrus yellow mottle-associated virus (CiYMaV). Genome features of CiYMaV are typical of members of the genus Mandarivirus (family Alphaflexiviridae). Virus particles with elongated flexuous shape and size resembling those of mandariviruses were observed by transmission electron microscopy. The proteins encoded by CiYMaV share high sequence identity, conserved motifs, and phylogenetic relationships with the corresponding proteins encoded by Indian citrus ringspot virus (ICRSV) and citrus yellow vein clearing virus (CYVCV), the two current members of the genus Mandarivirus. Although CYVCV is the virus most closely related to CiYMaV, the two viruses can be serologically and biologically discriminated from each other. A reverse-transcription PCR method designed to specifically detect CiYMaV revealed high prevalence (62%) of this virus in 120 citrus trees from the Punjab Province, Pakistan, where the novel virus was found mainly in mixed infection with CYVCV and citrus tristeza virus. However, a preliminary survey on samples from 200 citrus trees from the Yunnan Province, China failed to detect CiYMaV in this region, suggesting that the molecular, serological, and biological data provided here are timely and can help to prevent the spread of this virus in citrus-producing countries

Hallucination Augmented Contrastive Learning for Multimodal Large Language Model

Multi-modal large language models (MLLMs) have been shown to efficiently integrate natural language with visual information to handle multi-modal tasks. However, MLLMs still face a fundamental limitation of hallucinations, where they tend to generate erroneous or fabricated information. In this paper, we address hallucinations in MLLMs from a novel perspective of representation learning. We first analyzed the representation distribution of textual and visual tokens in MLLM, revealing two important findings: 1) there is a significant gap between textual and visual representations, indicating unsatisfactory cross-modal representation alignment; 2) representations of texts that contain and do not contain hallucinations are entangled, making it challenging to distinguish them. These two observations inspire us with a simple yet effective method to mitigate hallucinations. Specifically, we introduce contrastive learning into MLLMs and use text with hallucination as hard negative examples, naturally bringing representations of non-hallucinative text and visual samples closer while pushing way representations of non-hallucinating and hallucinative text. We evaluate our method quantitatively and qualitatively, showing its effectiveness in reducing hallucination occurrences and improving performance across multiple benchmarks. On the MMhal-Bench benchmark, our method obtains a 34.66% /29.5% improvement over the baseline MiniGPT-4/LLaVA. Our code is available on https://github.com/X-PLUG/mPLUG-HalOwl/tree/main/hacl

Knowledge Graph Link Prediction Fusing Description and Structural Features

Knowledge graph generally has the problem of incomplete knowledge, which makes link prediction an important research content of knowledge graph. Existing models only focus on the embedding representation of triples. On the one hand, in terms of model input, only the embedding representation of entities and relations is randomly initialized, and the description information of entities and relations is not incorporated, which will lack semantic information; on the other hand, in decoding, the influence of the structural features of the triplet itself on the link prediction results is ignored. Aiming at the above problems, this paper proposes a knowledge graph link prediction model BFGAT (graph attention network link prediction based on fusion of description information and structural features) that integrates description information and structural features. The BFGAT model uses the BERT pretraining model to encode the description information of entities and relations, and integrates the description information into the embedding representation of entities and relations to solve the problem of missing semantic information. In the coding process, graph attention mechanism is used to aggregate the information of adjacent nodes to solve the problem that the target node can obtain more information. The embedding representation of triples is spliced into a matrix in the decoding process, using a method based on CNN convolution pooling to solve the problem of triple structural features. The model is subjected to detailed experiments on the public datasets FB15k-237 and WN18RR, and the experiments show that the BFGAT model can effectively improve the effect of knowledge graph link prediction

ABCB5+ mesenchymal stromal cells therapy protects from hypoxia by restoring Ca2+ homeostasis in vitro and in vivo

Background: Hypoxia in ischemic disease impairs Ca2+ homeostasis and may promote angiogenesis. The therapeutic efficacy of mesenchymal stromal cells (MSCs) in peripheral arterial occlusive disease is well established, yet its influence on cellular Ca2+ homeostasis remains to be elucidated. We addressed the influence of ATP-binding cassette subfamily B member 5 positive mesenchymal stromal cells (ABCB5+ MSCs) on Ca2+ homeostasis in hypoxic human umbilical vein endothelial cells (HUVECs) in vitro and in vivo. Methods: Hypoxia was induced in HUVECs by Cobalt (II) chloride (CoCl2) or Deferoxamine (DFO). Dynamic changes in the cytosolic- and endoplasmic reticulum (ER) Ca2+ and changes in reactive oxygen species were assessed by appropriate fluorescence-based sensors. Metabolic activity, cell migration, and tube formation were assessed by standard assays. Acute-on-chronic ischemia in Apolipoprotein E knock-out (ApoE−/−) mice was performed by double ligation of the right femoral artery (DFLA). ABCB5+ MSC cells were injected into the ischemic limb. Functional recovery after DFLA and histology of gastrocnemius and aorta were assessed. Results: Hypoxia-induced impairment of cytosolic and ER Ca2+ were restored by ABCB5+ MSCs or their conditioned medium. Similar was found for changes in intracellular ROS production, metabolic activity, migratory ability and tube formation. The restoration was paralleled by an increased expression of the Ca2+ transporter Sarco-/endoplasmic reticulum ATPase 2a (SERCA2a) and the phosphorylation of Phospholamban (PLN). In acute-on-chronic ischemia, ABCB5+ MSCs treated mice showed a higher microvascular density, increased SERCA2a expression and PLN phosphorylation relative to untreated controls. Conclusions: ABCB5+ MSCs therapy can restore cellular Ca2+ homeostasis, which may beneficially affect the angiogenic function of endothelial cells under hypoxia in vitro and in vivo