We introduce Sta n z a , an open-source Python natural language processing toolkit supporting 66 human languages. Compared to existing widely used toolkits, Sta n z a features a language-agnostic fully neural pipeline for text analysis, including tokenization, multiword token expansion, lemmatization, part-ofspeech and morphological feature tagging, dependency parsing, and named entity recognition. We have trained Sta n z a on a total of 112 datasets, including the Universal Dependencies treebanks and other multilingual corpora, and show that the same neural architecture generalizes well and achieves competitive performance on all languages tested. Additionally, Sta n z a includes a native Python interface to the widely used Java Stanford CoreNLP software, which further extends its functionality to cover other tasks such as coreference resolution and relation extraction. Source code, documentation, and pretrained models for 66 languages are available at https:// stanfordnlp.github.io/stanza/. * Equal contribution. Order decided by a tossed coin.

AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles (e.g., model architectures, training procedures, data, systems, security, evaluation, theory) to their applications (e.g., law, healthcare, education) and societal impact (e.g., inequity, misuse, economic and environmental impact, legal and ethical considerations). Though foundation models are based on standard deep learning and transfer learning, their scale results in new emergent capabilities, and their effectiveness across so many tasks incentivizes homogenization. Homogenization provides powerful leverage but demands caution, as the defects of the foundation model are inherited by all the adapted models downstream. Despite the impending widespread deployment of foundation models, we currently lack a clear understanding of how they work, when they fail, and what they are even capable of due to their emergent properties. To tackle these questions, we believe much of the critical research on foundation models will require deep interdisciplinary collaboration commensurate with their fundamentally sociotechnical nature.

On a background of standard treatment, qili qiangxin capsules further reduced the levels of NT-proBNP. Together, our data suggest that qili qiangxin capsules could be used in combination therapy for CHF.

The article compares the incidence of public healthcare across 11 Asian countries and provinces, testing the dominance of healthcare concentration curves against an equal distribution and Lorenz curves and across countries. The analysis reveals that the distribution of public healthcare is prorich in most developing countries. That distribution is avoidable, but a propoor incidence is easier to realize at higher national incomes. The experiences of Malaysia, Sri Lanka, and Thailand suggest that increasing the incidence of propoor healthcare requires limiting the use of user fees, or protecting the poor effectively from them, and building a wide network of health facilities. Economic growth may not only relax the government budget constraint on propoor policies but also increase propoor incidence indirectly by raising richer individuals' demand for private sector alternatives. JEL Codes: H22, H42, H51.

Increasing evidence suggests that long noncoding RNAs (lncRNAs) play crucial roles in various biological processes. However, little is known about the effects of lncRNAs on autophagy. Here we report that a lncRNA, termed cardiac autophagy inhibitory factor (CAIF), suppresses cardiac autophagy and attenuates myocardial infarction by targeting p53-mediated myocardin transcription. Myocardin expression is upregulated upon H2O2 and ischemia/reperfusion, and knockdown of myocardin inhibits autophagy and attenuates myocardial infarction. p53 regulates cardiomyocytes autophagy and myocardial ischemia/reperfusion injury by regulating myocardin expression. CAIF directly binds to p53 protein and blocks p53-mediated myocardin transcription, which results in the decrease of myocardin expression. Collectively, our data reveal a novel CAIF-p53-myocardin axis as a critical regulator in cardiomyocyte autophagy, which will be potential therapeutic targets in treatment of defective autophagy-associated cardiovascular diseases.

We report here an injectable, self-healing coordinative hydrogel with antibacterial and angiogenic properties for diabetic wound regeneration. The hydrogel was prepared by coordinative cross-linking of multi-arm thiolated polyethylene glycol (SH-PEG) with silver nitrate (AgNO3). Due to the dynamic nature of Ag-S coordination bond and bacteria-killing activity of Ag+, the resultant coordinative hydrogel featured self-healing, injectable and antibacterial properties. In this study, we synchronously loaded an angiogenic drug, desferrioxamine (DFO), in the coordinative hydrogel during cross-linking. We finally obtained a multifunctional hydrogel that is manageable, resistant to mechanical irritation, antibacterial and angiogenic in vitro. Our in vivo studies further demonstrated that the injectable self-healing hydrogel could efficiently repair diabetic skin wounds with low bacteria-infection and enhance angiogenic activity. In short, besides diabetic skin wound repair, such dynamic multifunctional hydrogel scaffolds would show great promise in the regeneration of different types of exposed wounds, in particular, in situations with disturbed physiological functions, high risk of bacterial infections, and external mechanical irritation.

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The coronavirus disease 2019 (COVID‐19) pandemic of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) infection is causing considerable morbidity and mortality worldwide. Multiple reports have suggested that patients with heart failure (HF) are at a higher risk of severe disease and mortality with COVID‐19. Moreover, evaluating and treating HF patients with comorbid COVID‐19 represents aformidable clinical challenge as symptoms of both conditions may overlap and they may potentiate each other. Limited data exist regarding comprehensive management of HF patients with concomitant COVID‐19. Since these issues pose serious new challenges for clinicians worldwide, HF specialists must develop a structured approach to the care of patients with COVID‐19 and be included early in the care of these patients. Therefore, the Heart Failure Association of the European Society of Cardiology and the Chinese Heart Failure Association & National Heart Failure Committee conducted web‐based meetings to discuss these unique clinical challenges and reach a consensus opinion to help providers worldwide deliver better patient care. The main objective of this position paper is to outline the management of HF patients with concomitant COVID‐19 based on the available data and personal experiences of physicians from Asia, Europe and the United States.