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1 Summary The 2015 Nobel Prize in Physiology or Medicine was awarded to Professor Tu YouYou for her key contributions to the discovery of artemisinin. Artemisinin has saved mil- lions of lives and represents one of the significant contribu- tions of China to global health. Many scientists were in- volved in the previously unknown 523 Project, and the No- bel Prize given to a single person has not been without con- troversy. Here we summarize some key events in the 523 Project and present our views on the Award to help the pub- lic better understand the rationale of the Nobel Committee＇ s decision, the significance of the discovery, and current is- sues related to artimisinin in treating malaria.

The coronavirus disease 2019 (COVID-19) public health emergency has had a huge impact worldwide. We analyzed news headlines and keywords from the initial period of COVID-19, and explored the dissemination timeline of news related to the epidemic, and the impact of Internet-based media on the public using lifecycle theory and agenda-setting theory. We aimed to explore the impact of Baidu news headlines on public attention during the first wave of COVID-19, as well as the management mechanism of regulatory departments for social public opinion. We searched Baidu News using the keywords \"Novel Coronavirus\" and \"COVID-19\" from 8 January to 21 February 2020, a total of 45 days, and used Python V3.6 to extract news samples during the first wave of the epidemic. We used text analysis software to structurally process captured news topics and content summaries, applied VOSviewer V6.19 and Ucinet V6.0 to examine key aspects of the data. We analyzed the impact of Baidu News headlines on social opinion during the first wave of COVID-19 in the budding, spread, and outbreak stage of the information lifecycle. From clustering visualization and social network analysis perspectives, we explored the characteristics of Baidu News during the initial stage of the COVID-19. The results indicated that agenda-setting coverage through online media helped to mitigate the negative impact of COVID-19. The findings revealed that news reporting generated a high level of public attention toward a specific emergency event. The public requires accurate and objective information on the progress of COVID-19 through Baidu News headlines to inform their planning for the epidemic. Meanwhile, government can enhance the management mechanism of news dissemination, correct false and inaccurate news, and guide public opinion in a positive direction. In addition, timely official announcements on the progress of the COVID-19 outbreak and responses to matters of public concern can help calm tensions and maintain social stability.

Malaria parasites have coevolved with their human and mammalian hosts. These Plasmodium species invade the iron-rich environment of red blood cells. Zhang et al. found that the iron transporter ferroportin persists on the surface of mature mammalian red blood cells. Red blood cells are at risk of oxidative damage if their hemoglobin releases its iron; ferroportin is thus important to expel this iron. The authors also found that the transporter can deprive malaria parasites of the iron they need for proliferation. The Q248H mutation in the human ferroportin gene enhances ferroportin expression during development and seems to provide protection against malaria. This effect may explain the enrichment of the Q248H mutation among African populations. Science , this issue p. 1520 Ferroportin exports free iron from mature erythrocytes to protect cells from oxidative damage and the malaria parasite. Malaria parasites invade red blood cells (RBCs), consume copious amounts of hemoglobin, and severely disrupt iron regulation in humans. Anemia often accompanies malaria disease; however, iron supplementation therapy inexplicably exacerbates malarial infections. Here we found that the iron exporter ferroportin (FPN) was highly abundant in RBCs, and iron supplementation suppressed its activity. Conditional deletion of the Fpn gene in erythroid cells resulted in accumulation of excess intracellular iron, cellular damage, hemolysis, and increased fatality in malaria-infected mice. In humans, a prevalent FPN mutation, Q248H (glutamine to histidine at position 248), prevented hepcidin-induced degradation of FPN and protected against severe malaria disease. FPN Q248H appears to have been positively selected in African populations in response to the impact of malaria disease. Thus, FPN protects RBCs against oxidative stress and malaria infection.

The existence of clouds is one of the main factors that contributes to missing information in optical remote sensing images, restricting their further applications for Earth observation, so how to reconstruct the missing information caused by clouds is of great concern. Inspired by the image-to-image translation work based on convolutional neural network model and the heterogeneous information fusion thought, we propose a novel cloud removal method in this paper. The approach can be roughly divided into two steps: in the first step, a specially designed convolutional neural network (CNN) translates the synthetic aperture radar (SAR) images into simulated optical images in an object-to-object manner; in the second step, the simulated optical image, together with the SAR image and the optical image corrupted by clouds, is fused to reconstruct the corrupted area by a generative adversarial network (GAN) with a particular loss function. Between the first step and the second step, the contrast and luminance of the simulated optical image are randomly altered to make the model more robust. Two simulation experiments and one real-data experiment are conducted to confirm the effectiveness of the proposed method on Sentinel 1/2, GF 2/3 and airborne SAR/optical data. The results demonstrate that the proposed method outperforms state-of-the-art algorithms that also employ SAR images as auxiliary data.

The potential threat of parasite resistance to current antimalarials begs further research into antimalarial drug discovery to control disease progression. In addition, even when effective drugs are used, severe malaria symptoms still pose an important risk for death and cerebral residual disease in children. Further understanding of the pathophysiology of malaria and the biology of the parasite will open doors to new antimalarial treatments. Plasmodium falciparum malaria, an infectious disease caused by a parasitic protozoan, claims the lives of nearly a million children each year in Africa alone and is a top public health concern. Evidence is accumulating that resistance to artemisinin derivatives, the frontline therapy for the asexual blood stage of the infection, is developing in southeast Asia. Renewed initiatives to eliminate malaria will benefit from an expanded repertoire of antimalarials, including new drugs that kill circulating P. falciparum gametocytes, thereby preventing transmission. Our current understanding of the biology of asexual blood-stage parasites and gametocytes and the ability to culture them in vitro lends optimism that high-throughput screenings of large chemical libraries will produce a new generation of antimalarial drugs. There is also a need for new therapies to reduce the high mortality of severe malaria. An understanding of the pathophysiology of severe disease may identify rational targets for drugs that improve survival.

Ischemic stroke is the major type of cerebrovascular disease usually resulting in death or disability among the aging population globally. Oxidative stress has been closely linked with ischemic stroke. Disequilibrium between excessive production of reactive oxygen species (ROS) and inherent antioxidant capacity leads to subsequent oxidative damage in the pathological progression of ischemic brain injury. Acupuncture has been applied widely in treating cerebrovascular diseases from time immemorial in China. This review mainly lays stress on the evidence to illuminate the possible mechanisms of acupuncture therapy in treating ischemic stroke through regulating oxidative stress. We found that by regulating a battery of molecular signaling pathways involved in redox modulation, acupuncture not only activates the inherent antioxidant enzyme system but also inhibits the excessive generation of ROS. Acupuncture therapy possesses the potential in alleviating oxidative stress caused by cerebral ischemia, which may be linked with the neuroprotective effect of acupuncture.

In the context of ongoing clashes in the Sino-US trade and technology war and the rise of emerging disruptive technologies such as artificial intelligence, technological innovation is not only an important engine for promoting high-quality economic development, but also a crucial approach to overcoming key bottlenecks in core technologies. Xi Jinping's view on technological innovation is a significant theoretical weapon for promoting technological innovation and achieving national self-reliance in science and technology. Perception and request Xi Jinping's view on technological innovation has of course become an significant topic for many intellectual to travel around and interchange. This newspaper analyzes the rational procedure of the establishment of Xi Jinping's view on technological innovation, which highlight the efficient legacy and novel development of the Marxist view on science and technology, as well as its inclusion with the cement fact of China's left-wing feature. It highlights the practical value of Xi Jinping's view on technological innovation in promoting high-quality economic development, breaking through key technological bottlenecks, and breaking the traditional shackles of education. This paper also underscores its theoretical guidance and practical significance, providing answers to the question of how to understand and apply Xi Jinping's view on technological innovation.

Emerging technologies such as blockchain have accelerated the digitization of a variety of industries, improved the operational efficiency of enterprises, and promoted in-depth integration of digital technology with the real economy. Blockchain has characteristics that include distributed storage, peer-to-peer transmission, strong confidentiality, and easy traceability. This article introduces blockchain into an enterprise’s information management system with the aim of breaking the enterprise’s digital barriers by using technologies such as distributed ledgers, smart contracts, and asymmetric encryption, thus improving the security and applicability of the enterprise data assets. This article explores the characteristics and security of three types of blockchain in depth, designs the model framework of the blockchain digital system (BDS) based on industry needs, and analyzes the functions and the operating mechanisms of each level of the system in detail. Finally, based on the characteristics of public blockchain, consortium blockchain, and private blockchain, three typical application scenarios in which the BDS can be used are selected, and the article discusses how E-retail supply chains, virtual power plants, and carbon trading platforms can realize digital management using the BDS, thus providing a practical basis for construction and application of the BDS.

The external institutional environment is the foundation for the survival and development of enterprises. Enterprises can only obtain legitimate and heterogeneous resources through continuous strategic reform and institutional innovation to match their internal strategies with the external environment. The paper selected the manufacturing enterprises that implemented service-oriented transformation strategies in China from 2016–2019 as a sample based on the realistic background of frequent institutional changes and overlapping market changes during China’s economic transformation. The impact of different aspects of the institutional environment on service-oriented transformation and the moderating role of technological innovation capacity were examined empirically. The results showed that (1) improved government governance greatly facilitates the service-oriented transformation of manufacturing enterprises, which was more pronounced within the industry. The regulated development of the market facilitated service-oriented transformation within the industry in the short term, while the impact on cross-border transformation lagged. The improvement of the legalized business environment was conducive to the transformation of enterprises and promoted intra-industry transformation significantly more than cross-industry transformation. (2) Technological innovation capability strengthened the influence of institutional environment on intra-industry transformation, but had no significant impact on the relationship between institutional environment and cross-border transformation. (3) The moderating effect of technological innovation capability was markedly heterogeneous in terms of property rights and regional heterogeneity. The findings of this study provide an important reference for policy makers to break through the current institutional barriers to transformation and upgrading of the manufacturing industry, and to reshape and optimize the institutional environment for the integration and development of the secondary and tertiary industries.

Background Antibiotic resistance has become an increasingly serious problem in the past decades. As an alternative choice, antimicrobial peptides (AMPs) have attracted lots of attention. To identify new AMPs, machine learning methods have been commonly used. More recently, some deep learning methods have also been applied to this problem. Results In this paper, we designed a deep learning model to identify AMP sequences. We employed the embedding layer and the multi-scale convolutional network in our model. The multi-scale convolutional network, which contains multiple convolutional layers of varying filter lengths, could utilize all latent features captured by the multiple convolutional layers. To further improve the performance, we also incorporated additional information into the designed model and proposed a fusion model. Results showed that our model outperforms the state-of-the-art models on two AMP datasets and the Antimicrobial Peptide Database (APD)3 benchmark dataset. The fusion model also outperforms the state-of-the-art model on an anti-inflammatory peptides (AIPs) dataset at the accuracy. Conclusions Multi-scale convolutional network is a novel addition to existing deep neural network (DNN) models. The proposed DNN model and the modified fusion model outperform the state-of-the-art models for new AMP discovery. The source code and data are available at https://github.com/zhanglabNKU/APIN .