Research on the evolution of the Chinese urban biomedicine innovation network pattern: An analysis using multispatial scales.

This paper addresses the spatial pattern of urban biomedicine innovation networks by separately using four scales, i.e., the national scale, interregional scale, urban agglomeration scale, and provincial scale, on the basis of Chinese biomedicine patent data from the incoPat global patent database (GPD) (2001-2020) and using the method of social network analysis (SNA). Through the research, it is found that (1) on the national scale, the Chinese biomedicine innovation network becomes denser from west to the east as its complexity continuously increases. Its spatial structure takes the form of a radial network pattern with Beijing and Shanghai as its centers. The COVID-19 pandemic has not had an obvious negative impact on this network at present. (2) On the interregional scale, the strength of interregional network ties is greater than that of intraregional network ties. The eastern, central and western biomedicine innovation networks appear to be heterogeneous networks with regional central cities as the cores. (3) At the urban agglomeration scale, the strength of intraurban-agglomeration network ties is greater than that of interurban-agglomeration network ties. The three major urban agglomerations have formed radial spatial patterns with central cities as the hubs. (4) At the provincial scale, the intraprovincial networks have poor connectivity and low internal ties strength, which manifest as core-periphery structures with the provincial capitals as centers. Our research conclusion helps to clarify the current accumulation of technology and offer guidance for the development of China's biomedicine industry.

The New Historical Divide of Online Education: Dialogues with Key Leaders During the Epidemic

Another point of view from Dr. Hoshi from the Stanford Online High School demonstrated that current situation compels people to learn more about online education than ever before while at the same time arouses a new round of skepticism toward it. [...]this ineffectiveness, in turn, is going to promote the development of online education faster and better with the really good remaining methods and approaches. In the long run, the main problem would transfer to the professional development of teachers and students and the technical capacity building of institutions. "Because people in higher education systems should try and explore numbers of tools, more importantly, to make sense of teaching and learning in support of technologies,” as several participating scholars have echoed. [...]there were many universities already providing online programs for students, and these programs were assumed to have no essential difference between face-to-face teaching and online teaching. [...]some participants suggested that faculties would ideally like to use one comprehensive tool for online teaching instead of combining multiple different technologies.

Facile fabrication of phenylenediamine residue derived N, O co-doped hierarchical hyperporous carbon for high-efficient chloroxylenol removal

The chloroxylenol (PCMX) has shown well virucidal efficacy against COVID-19, but the large-scale utilization of which will undoubtedly pose extra environmental threaten. In the present study, the recycled industrial phenylenediamine residue was used and an integrated strategy of "carbonization-casting-activation" using super low-dose of activator and templates was established to achieve in-situ N/O co-doping and facile synthesis of a kind of hierarchical hyperporous carbons (HHPC). The sample of HHPC-1.25-0.5 obtained with activator and template to residue of 1.25 and 0.5 respectively shows super-high specific surface area of 3602 m2/g and volume of 2.81 cm3/g and demonstrates remarkable adsorption capacity of 1475 mg/g for PCMX in batch and of 1148 mg/g in dynamic column adsorption test. In addition, the HHPC-1.25-0.5 exhibits excellent reusability and tolerance for PCMX adsorption under various ionic backgrounds and real water matrix conditions. The combined physio-chemistry characterization, kinetic study and DFT calculation reveal that the enhanced high performances originate from the hierarchical pore structure and strong electrostatic interaction between PCMX and surface rich pyridinic-N and carbonyl groups.

Research on the evolution of the Chinese urban biomedicine innovation network pattern: An analysis using multispatial scales

This paper addresses the spatial pattern of urban biomedicine innovation networks by separately using four scales, i.e., the national scale, interregional scale, urban agglomeration scale, and provincial scale, on the basis of Chinese biomedicine patent data from the incoPat global patent database (GPD) (2001–2020) and using the method of social network analysis (SNA). Through the research, it is found that (1) on the national scale, the Chinese biomedicine innovation network becomes denser from west to the east as its complexity continuously increases. Its spatial structure takes the form of a radial network pattern with Beijing and Shanghai as its centers. The COVID-19 pandemic has not had an obvious negative impact on this network at present. (2) On the interregional scale, the strength of interregional network ties is greater than that of intraregional network ties. The eastern, central and western biomedicine innovation networks appear to be heterogeneous networks with regional central cities as the cores. (3) At the urban agglomeration scale, the strength of intraurban-agglomeration network ties is greater than that of interurban-agglomeration network ties. The three major urban agglomerations have formed radial spatial patterns with central cities as the hubs. (4) At the provincial scale, the intraprovincial networks have poor connectivity and low internal ties strength, which manifest as core-periphery structures with the provincial capitals as centers. Our research conclusion helps to clarify the current accumulation of technology and offer guidance for the development of China's biomedicine industry.

DeepRF: A deep learning method for predicting metabolic pathways in organisms based on annotated genomes.

The rapid increase of metabolomics has led to an increasing focus on metabolic pathway modeling and reconstruction. In particular, reconstructing an organism's metabolic network based on its genome sequence is a key challenge in systems biology. The method used to address this problem predicts the presence or absence of metabolic pathways from known pathways in a reference database. However, this method is based on manual metabolic pathway construction and cannot be used for large genome sequencing data. To address such problems, we apply a supervised machine learning approach consisting of deep neural networks to learn feature representations of metabolic pathways and feed these representations into random forests to predict metabolic pathways. The supervised learning model, DeepRF, predicts all known and unknown metabolic pathways in an organism. Evaluation of DeepRF on over 318,016 instances shows that the model can predict metabolic pathways with high-performance metrics accuracy (>97%), recall (>95%), and precision (>99%). Comparing DeepRF with other methods in the literature shows that DeepRF produces more reliable results than other methods.

Facile fabrication of phenylenediamine residue derived N, O co-doped hierarchical hyperporous carbon for high-efficient chloroxylenol removal

The chloroxylenol (PCMX) has shown well virucidal efficacy against COVID-19, but the large-scale utilization of which will undoubtedly pose extra environmental threaten. In the present study, the recycled industrial phenylenediamine residue was used and an integrated strategy of “carbonization-casting-activation” using super low-dose of activator and templates was established to achieve in-situ N/O co-doping and facile synthesis of a kind of hierarchical hyperporous carbons (HHPC). The sample of HHPC-1.25-0.5 obtained with activator and template to residue of 1.25 and 0.5 respectively shows super-high specific surface area of 3602 m2/g and volume of 2.81 cm3/g and demonstrates remarkable adsorption capacity of 1475 mg/g for PCMX in batch and of 1148 mg/g in dynamic column adsorption test. In addition, the HHPC-1.25-0.5 exhibits excellent reusability and tolerance for PCMX adsorption under various ionic backgrounds and real water matrix conditions. The combined physio-chemistry characterization, kinetic study and DFT calculation reveal that the enhanced high performances originate from the hierarchical pore structure and strong electrostatic interaction between PCMX and surface rich pyridinic-N and carbonyl groups.

Waste eliminated by waste under COVID-19 pandemic: Mixed plastic waste derived N,O-rich porous carbon nano-coral reefs for chlorophenol pollutants efficient capture

Both increasing emission of chlorophenol chemicals and plastic waste pollution are severe global environmental challenges, which is worth being explored to respond with a "waste eliminated by waste" coupling treatment strategy. Herein, we propose a one-step recyclable nano-MgO template strategy to prepare in-situ N, O-doped novel porous carbon coral reefs (PCNCR) directly derived from the mixture of real waste PET and melamine formaldehyde resin. The template precursor can be conveniently recycled by facile acetic acid pickling, vacuum evaporation and drying, and the whole preparing process does not involve any toxic solvents, massive consumption of pore-forming agents, or discharge of salty wastewater. The obtained PCNCRs have hierarchical porous structures and abundant N, O-doped species, and the textural parameters can be modulation linearly by varying the mass ratio of magnesium acetate to mixed waste plastics. The optimized PCNCR-1.25 exhibited remarkable adsorption capacity for chlorophenol contaminants (777.5mg/g for 4-Chloro-3,5-dimethylphenol, PCMX and 538.74mg/g for 4-Chloro-3-methylphenol, PCMC) in aqueous solution. Adsorption isotherm fitting studies revealed chlorophenol contaminants tended to be rapidly adsorbed on the PCNCR surface in the form of monolayer. And the system reached adsorption equilibrium at about 15min, which was described by linear driving force model. Reusability test verified that PCNCR-1.25 had excellent adsorption stability for chlorophenol in continuous adsorption-regeneration cycles. DFT calculation illustrated the enhancement of chlorophenol adsorption by heteroatom species, especially nitrogen dopants. This work highlighted a low-cost and sustainable approach to defuse the dual environmental threat of chlorophenol emissions and plastic waste pollution simultaneously.

Facing coronavirus disease 2019: What do we know so far? (Review).

Although the World Health Organization declared the outbreak of coronavirus disease 2019 (COVID-19), which originated in China, as a public health emergency of international concern as early as January 30, 2020, the current COVID-19 epidemic is spreading rapidly. As of April 19, 2020, total of 2,392,165 confirmed cases had been reported in 211 countries and regions, with 614,421 (25.68%) cured cases and 164,391 (6.87%) deaths. Scientists and clinicians have made great efforts to learn much about COVID-19 so that it can be controlled as soon as possible. Herein, this review will discuss the epidemiology, pathology, clinical features, diagnosis and treatment of COVID-19 based on the current evidence.

Management of Intractable Pain in Patients With Implanted Spinal Cord Stimulation Devices During the COVID-19 Pandemic Using a Remote and Wireless Programming System.

As COVID-19 rampages throughout the world and has a major impact on the healthcare system, non-emergency medical procedures have nearly come to a halt due to appropriate resource reallocation. However, pain never stops, particularly for patients with chronic intractable pain and implanted spinal cord stimulation (SCS) devices. The isolation required to fight this pandemic makes it impossible for such patients to adjust the parameters or configuration of the device on site. Although telemedicine has shown a great effect in many healthcare scenarios, there have been fewer applications of such technology focusing on the interaction with implanted devices. Here, we introduce the first remote and wireless programming system that enables healthcare providers to perform video-based real-time programming and palliative medicine for pain patients with a SCS implant. During the COVID-19 pandemic from January 23, 2020, the date of lockdown of Wuhan, to April 30, 2020, 34 sessions of remote programming were conducted with 16 patients. Thirteen of the 16 patients required programming for parameter optimization. Improvement was achieved with programming adjustment in 12 of 13 (92.3%) cases. Eleven of the 16 (68.8%) patients reported that the system was user-friendly and met their needs. Five patients complained of an unstable connection resulting from the low network speed initially, and three of these patients solved this problem. In summary, we demonstrated that a remote wireless programming system can deliver safe and effective programming operations of implantable SCS device, thereby providing palliative care of value to the most vulnerable chronic pain patients during a pandemic. CLINICAL TRIAL REGISTRATION: www.clinicaltrials.gov, identifier NCT03858790.

Pharmacological and cardiovascular perspectives on the treatment of COVID-19 with chloroquine derivatives.

The novel severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19) and an ongoing severe pandemic. Curative drugs specific for COVID-19 are currently lacking. Chloroquine phosphate and its derivative hydroxychloroquine, which have been used in the treatment and prevention of malaria and autoimmune diseases for decades, were found to inhibit SARS-CoV-2 infection with high potency in vitro and have shown clinical and virologic benefits in COVID-19 patients. Therefore, chloroquine phosphate was first used in the treatment of COVID-19 in China. Later, under a limited emergency-use authorization from the FDA, hydroxychloroquine in combination with azithromycin was used to treat COVID-19 patients in the USA, although the mechanisms of the anti-COVID-19 effects remain unclear. Preliminary outcomes from clinical trials in several countries have generated controversial results. The desperation to control the pandemic overrode the concerns regarding the serious adverse effects of chloroquine derivatives and combination drugs, including lethal arrhythmias and cardiomyopathy. The risks of these treatments have become more complex as a result of findings that COVID-19 is actually a multisystem disease. While respiratory symptoms are the major clinical manifestations, cardiovascular abnormalities, including arrhythmias, myocarditis, heart failure, and ischemic stroke, have been reported in a significant number of COVID-19 patients. Patients with preexisting cardiovascular conditions (hypertension, arrhythmias, etc.) are at increased risk of severe COVID-19 and death. From pharmacological and cardiovascular perspectives, therefore, the treatment of COVID-19 with chloroquine and its derivatives should be systematically evaluated, and patients should be routinely monitored for cardiovascular conditions to prevent lethal adverse events.

ARIMA modelling and forecasting of irregularly patterned COVID-19 outbreaks using Japanese and South Korean data.

The World Health Organization (WHO) upgraded the status of the coronavirus disease 2019 (COVID-19) outbreak from epidemic to global pandemic on March 11, 2020. Various mathematical and statistical models have been proposed to predict the spread of COVID-2019 [1]. We collated data on daily new confirmed cases of the COVID-19 outbreaks in Japan and South Korea from January 20, 2020 to April 26, 2020. Auto Regressive Integrated Moving Average (ARIMA) model were introduced to analyze two data sets and predict the daily new confirmed cases for the 7-day period from April 27, 2020 to May 3, 2020. Also, the forecasting results and both data sets are provided.

An updated analysis of turning point, duration and attack rate of COVID-19 outbreaks in major Western countries with data of daily new cases.

As coronavirus spreads around the world, the study of its effects is of great practical significance. We collated data on daily new cases of the COVID-19 outbreaks in the six Western countries of the Group of Seven and the dates of governments' interventions. We studied the periods before and after the dates of major governments' interventions integrally based on a segmented Poisson model. The relevant results are published in the paper of "Predicting turning point, duration and attack rate of COVID - 19 outbreaks in major Western countries" [1]. Our method can be used to update prediction daily as COVID-19 outbreaks evolve. In this article, we illustrate an updated analysis with our method to facilitate reproducibility. Both datasets used and updated are provided.

Predicting turning point, duration and attack rate of COVID-19 outbreaks in major Western countries.

In this paper, we employed a segmented Poisson model to analyze the available daily new cases data of the COVID-19 outbreaks in the six Western countries of the Group of Seven, namely, Canada, France, Germany, Italy, UK and USA. We incorporated the governments' interventions (stay-at-home advises/orders, lockdowns, quarantines and social distancing) against COVID-19 into consideration. Our analysis allowed us to make a statistical prediction on the turning point (the time that the daily new cases peak), the duration (the period that the outbreak lasts) and the attack rate (the percentage of the total population that will be infected over the course of the outbreak) for these countries.