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In recent years, hyperthyroid heart disease has become a condition with high incidence rate and high mortality rate. This paper discusses the pathogenesis, treatment, and influencing factors of hyperthyroid heart disease from two different angles – traditional Chinese medicine and western medicine – in hope to provide a reference basis for the treatment of hyperthyroid heart disease.

It has been discovered that the number of hypertensive patients with various types of sleep disorders is on the rise, which significantly increases the morbidity and mortality of cardiovascular diseases. Modern medicine has not reached a consensus on the mechanism and treatment of these diseases but relies on drugs to improve sleep disorders and blood pressure. We regard TCM syndrome differentiation as the breakthrough point, along with comprehensive modern and traditional medical methods based on dialectical thinking as means of holistic and symptomatic treatment of Western medicine integrated with the eight principles of TCM diagnosis, Zang-Fu organs, six meridians, Qi, blood and fluid, as well as other dialectical methods, in order to provide a broader idea for TCM treatment and lay a foundation for further and better development of integrated TCM and Western medicine treatment.

Diffuse optics (DO) is a light-based technique used to study the human brain, but it suffers from low brain specificity. Interferometric diffuse optics (iDO) promises to improve the quantitative accuracy and depth specificity of DO, and particularly, coherent light fluctuations (CLFs) arising from blood flow. iDO techniques have alternatively achieved either time-of-flight (TOF) discrimination or highly parallel detection, but not both at once. Here, we break this barrier with a single iDO instrument. Specifically, we show that rapid tuning of a temporally coherent laser during the sensor integration time increases the effective linewidth seen by a highly parallel interferometer. Using this concept to create a continuously variable and user-specified TOF filter, we demonstrate a solution to the canonical problem of DO, measuring optical properties. Then, with a deep TOF filter, we reduce scalp sensitivity of CLFs by 2.7 times at 1 cm source-collector separation. With this unique combination of desirable features, i.e., TOF-discrimination, spatial localization, and highly parallel CLF detection, we perform multiparametric imaging of light intensities and CLFs via the human forehead.

With the aggravation of an aging population, the prevalence of chronic heart failure is increasing. As a famous traditional Chinese medicine practitioner in Shaanxi Province, Mingjun Zhao is amicable at using traditional Chinese medicine to treat chronic heart failure. He believes that the pathogenesis of the condition is mainly due to the lack of heart Yang, deficiency of heart Qi, as well as the combination of water and blood stasis. Therefore, in treating this condition, it is essential to warm and nourish heart Yang, replenish Qi to benefit water, and promote blood circulation to remove blood stasis. Based on the standardized treatment used in western medicine, traditional Chinese medicine should be reasonably and accurately combined with it as a drug pair into a prescription, thus highlighting the advantages of the combination of traditional Chinese medicine in the treatment of chronic heart failure to a great extent.

Fungal taxonomy is a complex and rapidly changing subject, which makes proper naming of fungi challenging for taxonomists. A registration platform with a standardized and information-integrated database is a powerful tool for efficient research on fungal taxonomy. Fungal Names (FN, https://nmdc.cn/fungalnames/; launched in 2011) is one of the three official fungal nomenclatural repositories authorized by the International Nomenclature Committee for Fungi (NCF). Currently, FN includes >567 000 taxon names from >10 000 related journals and books published since 1596 and covers >147 000 collection records of type specimens/illustrations from >5000 preserving agencies. FN is also a knowledge base that integrates nomenclature information with specimens, culture collections and herbaria/fungaria, publications and taxonomists, and represents a summary of the history and recent advances in fungal taxonomy. Published fungal names are categorized based on well-accepted nomenclature rules and can be readily searched with different keywords and strategies. In combination with a standardized name checking tool and a sequence alignment-based identification package, FN makes the registration and typification of nomenclatural novelties of fungi convenient and accurate.

Wendan Decoction is one of the commonly used prescriptions in clinical practice.it has a wide range of applications. On the basis of traditional Chinese medicine (TCM) syndrome differentiation, as long as it can be added or subdivided flexibly, it can be used to conquer various difficult and miscellaneous diseases seen in daily life.

In diffuse optics, quantitative assessment of the human brain is confounded by the skull and scalp. To better understand these superficial tissues, we advance interferometric near-infrared spectroscopy (iNIRS) to form images of the human superficial forehead blood flow index (BFI). We present a null source–collector (S-C) polarization splitting approach that enables galvanometer scanning and eliminates unwanted backscattered light. Images show an order-of-magnitude heterogeneity in superficial dynamics, implying an order-of-magnitude heterogeneity in brain specificity, depending on forehead location. Along the time-of-flight dimension, autocorrelation decay rates support a three-layer model with increasing BFI from the skull to the scalp to the brain. By accurately characterizing superficial tissues, this approach can help improve specificity for the human brain.

Objective. To investigate the application effect of bladder function training combined with Kangaiping pills on permanent bladder stoma after radical prostatectomy (RP). Methods. The clinical data of 80 patients with a permanent bladder stoma after RP in our hospital from December 2018 to December 2019 were retrospectively analyzed, and they were equally split into the experimental group (EG) and control group (CG) according to the odd and even hospitalization numbers. EG received bladder function training combined with Kangaiping pills while CG received routine nursing for permanent bladder stomas to compare the urodynamic indexes and quality of life (QOL) scores after intervention between the two groups. Results. Compared with CG, EG after intervention achieved an obviously higher number of patients with bladder function grade I ( ∗ ), higher urodynamic indexes ( P < 0.001 ), a higher SF-36 score ( P < 0.001 ), a lower LUTS score ( P < 0.001 ), and a lower total incidence of postoperative adverse reactions ( P < 0.05 ). Conclusion. Bladder function training combined with Kangaiping pills is a reliable method to improve the bladder function of patients with a permanent bladder stoma after RP. This intervention method greatly enhances the QOL of patients and reduces the risk of postoperative adverse reactions, which is recommended for clinical application.

People often endorse the moral principle that all human lives are equally valuable. At the same time, people often privilege high-status individuals over low-status individuals. These two inclinations come into conflict in a scenario involving the potential killing of a high-status person to save the lives of multiple low-status people. In the present study, participants viewed a series of sacrificial dilemmas in which the social status of the victims and beneficiaries was varied. We measured participants' choice (sacrifice vs. don't sacrifice), response time, and electroencephalographic activity, with an emphasis on conflict negativity (CN). Overall, we found no effects of victim/beneficiaries status on choice and response time. However, participants displayed a more pronounced CN effect when contemplating a high-status victim/low-status beneficiaries tradeoff than a low-status-victim/high-status beneficiaries tradeoff. Further analyses revealed that this effect was primarily driven by participants who endorsed deontological principles (e.g., "Some rules must never be broken, no matter the consequences"). In contrast, those who endorsed utilitarian principles displayed equivalent levels of conflict negativity, regardless of the social status of victims and beneficiaries. These findings shed light on the role of conflict in the phenomenology of moral decision making.

Background: Population-based studies have consistently showed an increased incidence of coronary heart disease and cardiac mortality in patients with type 2 diabetes mellitus (T2DM). Tongmai Jiangtang capsules (TJC) are Chinese patent medicines that have been approved in China for the treatment of diabetic vascular complications. However, the evidence supporting the efficacy of Tongmai Jiangtang capsules in type 2 diabetic coronary heart disease (T2DM-CHD) remains unclear. Herein, we designed a randomized, parallel-controlled clinical trial to investigate a new complementary therapy for T2DM-CHD patients.Methods: A total of 360 T2DM-CHD subjects (aged 18–75 years) will be randomly assigned to the TJC group or the placebo group at a 2:1 ratio. On the basis of western medicine therapy, all the participants will receive TJC or placebo, orally, three capsules/treatment, three per day for 12 weeks. The primary outcomes will be assessed according to the Canadian Cardiovascular Society (CCS) classification. All statistical analyses will be performed setting a two-sided 0.05 significance level, using SAS 9.4 statistical software.Discussion: The efficacy of TJC for the treatment of T2DM-CHD patients will be evaluated. The study will provide reliable clinical research evidence for application of TJC in treating T2DM-CHD patients.Clinical Trial Registration:https://www.chictr.org.cn/enIndex.aspx, Chinese Clinical Trial Registry ChiCTR2000037491.

Translation Quality Estimation is critical to reducing post-editing efforts in machine translation and to cross-lingual corpus cleaning. As a research problem, quality estimation (QE) aims to directly estimate the quality of translation in a given pair of source and target sentences, and highlight the words that need corrections, without referencing to golden translations. In this paper, we propose Verdi, a novel framework for word-level and sentence-level post-editing effort estimation for bilingual corpora. Verdi adopts two word predictors to enable diverse features to be extracted from a pair of sentences for subsequent quality estimation, including a transformer-based neural machine translation (NMT) model and a pre-trained cross-lingual language model (XLM). We exploit the symmetric nature of bilingual corpora and apply model-level dual learning in the NMT predictor, which handles a primal task and a dual task simultaneously with weight sharing, leading to stronger context prediction ability than single-direction NMT models. By taking advantage of the dual learning scheme, we further design a novel feature to directly encode the translated target information without relying on the source context. Extensive experiments conducted on WMT20 QE tasks demonstrate that our method beats the winner of the competition and outperforms other baseline methods by a great margin. We further use the sentence-level scores provided by Verdi to clean a parallel corpus and observe benefits on both model performance and training efficiency., Accepted by The Web Conference 2021

Objective: To explore the target and mechanism of Astragalus membranaceus, poria, salvia miltiorrhiza and semen leiocarpa in the treatment of heart failure by network pharmacology. Methods: The active components of traditional Chinese medicine and the target of heart failure were screened by multi-platform, and the standard gene was transformed by Uniprot. CytoCasp 3.6.1 was used to draw the network diagram of traditional Chinese medicine - component - target. Go and KEGG analysis were performed by Metascape. Results: A total of 36 predictive target sites of Radix Astragalus, Fuling poria, Salvia miltiorrhiza and Draba nemorosa were screened for treatment of heart failure, mainly involving nerve and factor pathways: ADRB2, ADRA1B and AChE. Cancer pathway: TP53, TNF; Pathways of inflammation: IL1B, PTSG2, PTSG1; Sex hormone pathway: ESR1, AR, PGR; Others: SCN5A, HIF1A, etc. The results of GO and KEGG enrichment suggested that the treatment of heart failure with the top four drugs involved cancer pathway, calcium signaling pathway, HIF-1 signaling pathway, and involved in blood circulation, cell proliferation and other processes. Conclusion: This study combines the pharmacological studies of Chinese medicine and western medicine to reveal the mechanism of multi-target and multi-channel regulation of body balance in Chinese medicine treatment.

New technology noninvasively monitors cerebral blood flow index and brain function in adult humans., Cerebral blood flow (CBF) is essential for brain function, and CBF-related signals can inform us about brain activity. Yet currently, high-end medical instrumentation is needed to perform a CBF measurement in adult humans. Here, we describe functional interferometric diffusing wave spectroscopy (fiDWS), which introduces and collects near-infrared light via the scalp, using inexpensive detector arrays to rapidly monitor coherent light fluctuations that encode brain blood flow index (BFI), a surrogate for CBF. Compared to other functional optical approaches, fiDWS measures BFI faster and deeper while also providing continuous wave absorption signals. Achieving clear pulsatile BFI waveforms at source-collector separations of 3.5 cm, we confirm that optical BFI, not absorption, shows a graded hypercapnic response consistent with human cerebrovascular physiology, and that BFI has a better contrast-to-noise ratio than absorption during brain activation. By providing high-throughput measurements of optical BFI at low cost, fiDWS will expand access to CBF.

Significance: There is an essential need to develop wearable multimodality technologies that can continuously measure both blood flow and oxygenation in deep tissues to investigate and manage various vascular/cellular diseases. Aim: To develop a wearable dual-wavelength diffuse speckle contrast flow oximetry (DSCFO) for simultaneous measurements of blood flow and oxygenation variations in deep tissues. Approach: A wearable fiber-free DSCFO probe was fabricated using 3D printing to confine two small near-infrared laser diodes and a tiny CMOS camera in positions for DSCFO measurements. The spatial diffuse speckle contrast and light intensity measurements at the two different wavelengths enable quantification of tissue blood flow and oxygenation, respectively. The DSCFO was first calibrated using tissue phantoms and then tested in adult forearms during artery cuff occlusion. Results: Phantom tests determined the largest effective source–detector distance (15 mm) and optimal camera exposure time (10 ms) and verified the accuracy of DSCFO in measuring absorption coefficient variations. The DSCFO detected substantial changes in forearm blood flow and oxygenation resulting from the artery occlusion, which meet physiological expectations and are consistent with previous study results. Conclusions: The wearable DSCFO may be used for continuous and simultaneous monitoring of blood flow and oxygenation variations in freely behaving subjects.

Language model pre-training based on large corpora has achieved tremendous success in terms of constructing enriched contextual representations and has led to significant performance gains on a diverse range of Natural Language Understanding (NLU) tasks. Despite the success, most current pre-trained language models, such as BERT, are trained based on single-grained tokenization, usually with fine-grained characters or sub-words, making it hard for them to learn the precise meaning of coarse-grained words and phrases. In this paper, we propose a simple yet effective pre-training method named LICHEE to efficiently incorporate multi-grained information of input text. Our method can be applied to various pre-trained language models and improve their representation capability. Extensive experiments conducted on CLUE and SuperGLUE demonstrate that our method achieves comprehensive improvements on a wide variety of NLU tasks in both Chinese and English with little extra inference cost incurred, and that our best ensemble model achieves the state-of-the-art performance on CLUE benchmark competition., Comment: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

Employing interferometric Diffusing Wave Spectroscopy at 3.5 cm source-collector separation on the human forehead, we evaluate improvements in data analysis: zero-lag derivative, multilayer models, and absorption compensation, during voluntary apnea.

Cross-domain recommendation can help alleviate the data sparsity issue in traditional sequential recommender systems. In this paper, we propose the RecGURU algorithm framework to generate a Generalized User Representation (GUR) incorporating user information across domains in sequential recommendation, even when there is minimum or no common users in the two domains. We propose a self-attentive autoencoder to derive latent user representations, and a domain discriminator, which aims to predict the origin domain of a generated latent representation. We propose a novel adversarial learning method to train the two modules to unify user embeddings generated from different domains into a single global GUR for each user. The learned GUR captures the overall preferences and characteristics of a user and thus can be used to augment the behavior data and improve recommendations in any single domain in which the user is involved. Extensive experiments have been conducted on two public cross-domain recommendation datasets as well as a large dataset collected from real-world applications. The results demonstrate that RecGURU boosts performance and outperforms various state-of-the-art sequential recommendation and cross-domain recommendation methods. The collected data will be released to facilitate future research., Comment: 11 pages, 2 figures, 4 tables, Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

Cloud computing can handle huge amounts of data in a very short time, so as to build a powerful network service capability. However, the existing cloud computing platform (e.g. LS-DYNA) cannot predict the calculation time and then provide appropriate reference conditions for users. Furthermore, the relationship between calculation time and hardware configuration are not always negative correlation. In this paper, the design idea of time prediction model is first put forward. Specifically, the correlation between the hardware conditions of the relevant historical model and the calculation time is set as the training set. Following this idea, a more accurate time prediction model is built and further improved. In this paper, regression analysis is adopted and four core steps are generally required to establish the regression equation, including curve estimation, automatic linear modeling, regression analysis and Durbin-Watson test. When compared with the original LS-DYNA, experimental results fully demonstrate the effectiveness of the proposed method for its small prediction error.

Query-based document summarization aims to extract or generate a summary of a document which directly answers or is relevant to the search query. It is an important technique that can be beneficial to a variety of applications such as search engines, document-level machine reading comprehension, and chatbots. Currently, datasets designed for query-based summarization are short in numbers and existing datasets are also limited in both scale and quality. Moreover, to the best of our knowledge, there is no publicly available dataset for Chinese query-based document summarization. In this paper, we present QBSUM, a high-quality large-scale dataset consisting of 49,000+ data samples for the task of Chinese query-based document summarization. We also propose multiple unsupervised and supervised solutions to the task and demonstrate their high-speed inference and superior performance via both offline experiments and online A/B tests. The QBSUM dataset is released in order to facilitate future advancement of this research field., accepted by Computer Speech & Language

This paper summarizes our recent progresses in developing and applying an innovative noncontact, multiscale, speckle contrast diffuse correlation tomography (scDCT) for rapid and high-density 3D imaging of tissue blood flow distributions in animals and humans.