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This book figures out that network security and informatization have entered a development stage of greater permeation and deeper integration into all aspects of the economy and society. In particular since the 2008 financial crisis, the world's major economies have placed a strategic focus on network security and informatization in order to seek out new growth points, alleviate energy and ecological pressures, improve living standards, and improve social governance through new technological transformations in fields such as cloud computing, the Internet of Things, mobile internet, big data, smart cities, and a wave of applications. The effects on the economy and society have emerged and will continue to make significant progress. Based on China's stage of new urbanization, industrialization, informatization, and agricultural modernization and major characteristics, as well as the intrinsic need for synchronized development, this book encourages society to accelerate the pace of development, expand the scope of work and promote informatization and the comprehensive, coordinated, effective linking and deep integration of informatization with all areas of the economy and society.-- Provided by publisher.

Taxol®, an antitumor drug with significant activity, is the first microtubule stabilizing agent described in the literature. This short review of the mechanism of action of Taxol® emphasizes the research done in the Horwitz’ laboratory. It discusses the contribution of photoaffinity labeled analogues of Taxol® toward our understanding of the binding site of the drug on the microtubule. The importance of hydrogen/deuterium exchange experiments to further our insights into the stabilization of microtubules by Taxol® is addressed. The development of drug resistance, a major problem that arises in the clinic, is discussed. Studies describing differential drug binding to distinct β-tubulin isotypes are presented. Looking forward, it is suggested that the β-tubulin isotype content of a tumor may influence its responses to Taxol®.

COVID-19 has spread globally. Epidemiological susceptibility to COVID-19 has been reported in patients with cancer. We aimed to systematically characterise clinical features and determine risk factors of COVID-19 disease severity for patients with cancer and COVID-19. In this multicentre, retrospective, cohort study, we included all adult patients (aged ≥18 years) with any type of malignant solid tumours and haematological malignancy who were admitted to nine hospitals in Wuhan, China, with laboratory-confirmed COVID-19 between Jan 13 and March 18, 2020. Enrolled patients were statistically matched (2:1) with patients admitted with COVID-19 who did not have cancer with propensity score on the basis of age, sex, and comorbidities. Demographic characteristics, laboratory examinations, illness severity, and clinical interventions were compared between patients with COVID-19 with or without cancer as well as between patients with cancer with non-severe or severe COVID-19. COVID-19 disease severity was defined on admission on the basis of the WHO guidelines. Univariable and multivariable logistic regression, adjusted for age, sex, comorbidities, cancer type, tumour stage, and antitumour treatments, were used to explore risk factors associated with COVID-19 disease severity. This study was registered in the Chinese Clinical Trial Register, ChiCTR2000030807. Between Jan 13 and March 18, 2020, 13 077 patients with COVID-19 were admitted to the nine hospitals in Wuhan and 232 patients with cancer and 519 statistically matched patients without cancer were enrolled. Median follow-up was 29 days (IQR 22–38) in patients with cancer and 27 days (20–35) in patients without cancer. Patients with cancer were more likely to have severe COVID-19 than patients without cancer (148 [64%] of 232 vs 166 [32%] of 519; odds ratio [OR] 3·61 [95% CI 2·59–5·04]; p<0·0001). Risk factors previously reported in patients without cancer, such as older age; elevated interleukin 6, procalcitonin, and D-dimer; and reduced lymphocytes were validated in patients with cancer. We also identified advanced tumour stage (OR 2·60, 95% CI 1·05–6·43; p=0·039), elevated tumour necrosis factor α (1·22, 1·01–1·47; p=0·037), elevated N-terminal pro-B-type natriuretic peptide (1·65, 1·03–2·78; p=0·032), reduced CD4+ T cells (0·84, 0·71–0·98; p=0·031), and reduced albumin–globulin ratio (0·12, 0·02–0·77; p=0·024) as risk factors of COVID-19 severity in patients with cancer. Patients with cancer and COVID-19 were more likely to deteriorate into severe illness than those without cancer. The risk factors identified here could be helpful for early clinical surveillance of disease progression in patients with cancer who present with COVID-19. China National Natural Science Foundation.

Climate change has the potential to change the distribution of pests globally and their resistance to pesticides, thereby threatening global food security in the 21st century. However, predicting where these changes occur and how they will influence current pest control efforts is a challenge. Using experimentally parameterised and field-tested models, we show that climate change over the past 50 years increased the overwintering range of a global agricultural insect pest, the diamondback moth ( Plutella xylostella ), by ~2.4 million km 2 worldwide. Our analysis of global data sets revealed that pesticide resistance levels are linked to the species’ overwintering range: mean pesticide resistance was 158 times higher in overwintering sites compared to sites with only seasonal occurrence. By facilitating local persistence all year round, climate change can promote and expand pesticide resistance of this destructive species globally. These ecological and evolutionary changes would severely impede effectiveness of current pest control efforts and potentially cause large economic losses. Climate-driven range shifts may affect pesticide resistance. Here, the authors analyse experimentally parameterised and field-tested models to show that a cosmopolitan insect pest, the diamondback moth, is acquiring resistance against local pesticides through expanding overwintering range.

Background COVID-19 is an extremely severe infectious disease. However, few studies have focused on the epidemiological and clinical characteristics of pediatric COVID-19. This study conducted a retrospective review of the epidemiological and clinical features of COVID-19 in children. Methods A retrospective study was conducted on children with a definite diagnosis of COVID-19 in mainland China using the web crawler technique to collect anonymous COVID-19 updates published by local health authorities. Results Three hundred forty-one children aged 4 days to 14 years with a median age of 7 years were included. Sixty-six percent of pediatric patients were infected via family members with COVID-19. The median incubation period was 9 days (interquartile range, 6 to 13). Asymptomatic cases accounted for 5.9%, of which 30% had abnormal chest radiologic findings. A majority of pediatric COVID-19 cases showed mild to moderate clinical features, and only a few developed severe or critical diseases (0.6% and 0.3%, respectively). Fever (77.9%) and cough (32.4%) were the predominant presenting symptoms of pediatric COVID-19. The pediatric patients had fewer underlying diseases and complications than adults. The treatment modalities for pediatric COVID-19 patients were not as complex as those of adult COVID-19 patients. The overall prognosis of pediatric COVID-19 was benign with a decent recovery. The median time from onset to cure was 16 days (interquartile range, 13 to 21). Conclusions Compared to adults, COVID-19 in children has distinct features of epidemiology and clinical manifestations. The findings from this study might help to guide the development of measures to prevent and treat this ongoing global pandemic. Trial registration Chinese Clinical Trial Registry ( chictr.org.cn ) identifier: ChiCTR2000030464.

Distributed network architecture of heterogeneous computing faces with such problems as strict performance constraints of network control, unpredictable mapping relationship between computing data algorithms of different mobile terminals and inconsistency between computing algorithms and link control of data networks. In order to solve the above problems, we begin with software definition network architecture and load balancing algorithm for heterogeneous computing, and gradually improve the real-time and reliability of heterogeneous computing. On the one hand, the heterogeneous computing data of fog node and cloud computing system are distributed. The centralized service of software-defined network combines with distributed computing of mobile edge terminal and its subnet. On the other hand, we define the centralized information and distributed scheduler of the network. In addition, we deploy the optimal assignment of data sharing and heterogeneous computing tasks in real time with ellipse-partitioned area as the object. A series of algorithms for classifying and assigning heterogeneous computing data streams in software-defined networks are designed to achieve the optimal balance among load balancing, minimum classification of large data streams, minimum resource occupation and time constraints. Experimental comparison compared and evaluated the Load Balancing with big data stream (LBBS), Load Balancing with Heterogeneous Computing (LBHC) and the proposed LBBHD. Compared with the other two algorithms, the proposed algorithm improves workload skewness, throughput and load balancing error respectively about 2.1%, 1.96%, 2.9%, 2.2%; 5.57%. 2.51%.

Impacts of small‐scale surface irregularities, or surface roughness, of atmospheric ice crystals on lidar backscattering properties are quantified. Geometric ice crystal models with various degrees of surface roughness and state‐of‐the‐science light‐scattering computational capabilities are utilized to simulate the single‐scattering properties across the entire practical size parameter range. The simulated bulk lidar and depolarization ratios of polydisperse ice crystals at wavelength 532 nm are strongly sensitive to the degree of surface roughness. Comparisons of these quantities between the theoretical simulations and counterparts inferred from spaceborne lidar observations for cold cirrus clouds suggest a typical surface‐roughness‐degree range of 0.03–0.15 in the cases of compact hexagonal ice crystals, which is most consistent with direct measurements of scanning electron microscopic images. To properly interpret lidar backscattering observations of ice clouds, it is necessary to account for the degree of surface roughness in light‐scattering computations involving ice crystals. Plain Language Summary Lidar (Light Detection and Ranging) instruments on satellites use reflected, or backscattered, laser beams to investigate ice clouds in the atmosphere. However, it has long been a challenge to interpret lidar signals, called backscattering properties, to infer ice cloud characteristics accurately. This study uses theoretical simulations to investigate how small‐scale surface irregularities of ice crystals affect the lidar signals associated with ice clouds. These simulations demonstrate the significant impacts of small‐scale surface irregularities of ice crystals on backscattering. Based on comparisons between the theoretical simulations and satellite lidar observations, it is necessary to assume a moderate degree of small‐scale surface irregularities to explain lidar observations of typical ice clouds. Key Points The sensitivity of the backscattering properties to the surface roughness of atmospheric ice crystals is theoretically investigated The depolarization ratio is substantially sensitive to the degree of surface roughness of ice crystals Compact hexagonal ice models with degrees of surface roughness ranging 0.03–0.15 reasonably explain the Cloud‐Aerosol Lidar with Orthogonal Polarization backscattering signals

The photocatalytic activity of catalysts depends on the energy-harvesting ability and the separation or transport of photogenerated carriers. The light absorption capacity of graphitic carbon nitride (g-C 3 N 4 )-based composites can be enhanced by adjusting the surface plasmon resonance (SPR) of noble metal nanoparticles (e.g., Cu, Au, and Pd) in the entire visible region. Adjustments can be carried out by varying the nanocomponents of the materials. The SPR of noble metals can enhance the local electromagnetic field and improve interband transition, and resonant energy transfer occurs from plasmonic dipoles to electron–hole pairs via near-field electromagnetic interactions. Thus, noble metals have emerged as relevant nanocomponents for g-C 3 N 4 used in CO 2 photoreduction and water splitting. Herein, recent key advances in noble metals (either in single atom, cluster, or nanoparticle forms) and composite photocatalysts based on inorganic or organic nanocomponent-incorporated g-C 3 N 4 nanosheets are systematically discussed, including the applications of these photocatalysts, which exhibit improved photoinduced charge mobility in CO 2 photoconversion and H 2 production. Issues related to the different types of multi-nanocomponent heterostructures (involving Schottky junctions, Z-/S-scheme heterostructures, noble metals, and additional semiconductor nanocomponents) and the adjustment of dimensionality of heterostructures (by incorporating noble metal nanoplates on g-C 3 N 4 forming 2D/2D heterostructures) are explored. The current prospects and possible challenges of g-C 3 N 4 composite photocatalysts incorporated with noble metals (e.g., Au, Pt, Pd, and Cu), particularly in water splitting, CO 2 reduction, pollution degradation, and chemical conversion applications, are summarized.

The sudden outbreak of 2019 novel coronavirus (2019-nCoV, later named SARS-CoV-2) in Wuhan, China, which rapidly grew into a global pandemic, marked the third introduction of a virulent coronavirus into the human society, affecting not only the healthcare system, but also the global economy. Although our understanding of coronaviruses has undergone a huge leap after two precedents, the effective approaches to treatment and epidemiological control are still lacking. In this article, we present a succinct overview of the epidemiology, clinical features, and molecular characteristics of SARS-CoV-2. We summarize the current epidemiological and clinical data from the initial Wuhan studies, and emphasize several features of SARS-CoV-2, which differentiate it from SARS-CoV and Middle East respiratory syndrome coronavirus (MERS-CoV), such as high variability of disease presentation. We systematize the current clinical trials that have been rapidly initiated after the outbreak of COVID-19 pandemic. Whereas the trials on SARS-CoV-2 genome-based specific vaccines and therapeutic antibodies are currently being tested, this solution is more long-term, as they require thorough testing of their safety. On the other hand, the repurposing of the existing therapeutic agents previously designed for other virus infections and pathologies happens to be the only practical approach as a rapid response measure to the emergent pandemic, as most of these agents have already been tested for their safety. These agents can be divided into two broad categories, those that can directly target the virus replication cycle, and those based on immunotherapy approaches either aimed to boost innate antiviral immune responses or alleviate damage induced by dysregulated inflammatory responses. The initial clinical studies revealed the promising therapeutic potential of several of such drugs, including favipiravir, a broad-spectrum antiviral drug that interferes with the viral replication, and hydroxychloroquine, the repurposed antimalarial drug that interferes with the virus endosomal entry pathway. We speculate that the current pandemic emergency will be a trigger for more systematic drug repurposing design approaches based on big data analysis.