Spillover Effects of Airdrops: Evidence from Tokenization Platforms

The emergence of tokenization platforms based on blockchain technology has led to the use of free airdrop to replace traditional expensive financial incentives to enhance user engagement. However, critics argue that such incentives may devalue tokens and prompt nonrecipients to panic sell. To investigate the impact of airdrops, we conducted a quasi-experiment on Axie Infinity. Our findings indicate that airdrops significantly enhance engagement among both recipients and nonrecipients. Mechanism analysis shows that cross group spillover effects stems from expectation of another airdrop program and increased market liquidity. While recipients tend to immediately sell tokens and often sell more tokens than received, we did not find evidence of nonrecipients panic selling tokens. Furthermore, we investigated the heterogeneous effects of airdrops. Our work contributes to the ongoing debate of the effectiveness of airdrops and provide insights into the study of tokenization platforms

Heuristic Algorithms for Energy and Performance Dynamic Optimization in Cloud Computing

Cloud computing becomes increasingly popular for hosting all kinds of applications not only due to their ability to support dynamic provisioning of virtualized resources to handle workload fluctuations but also because of the usage based on pricing. This results in the adoption of data centers which store, process and present the data in a seamless, efficient and easy way. Furthermore, it also consumes an enormous amount of electrical energy, then leads to high using cost and carbon dioxide emission. Therefore, we need a Green computing solution that can not only minimize the using costs and reduce the environment impact but also improve the performance. Dynamic consolidation of Virtual Machines (VMs), using live migration of the VMs and switching idle servers to sleep mode or shutdown, optimizes the energy consumption. We propose an adaptive underloading detection method of hosts, VMs migration selecting method and heuristic algorithm for dynamic consolidation of VMs based on the analysis of the historical data. Through extensive simulation based on random data and real workload data, we show that our method and algorithm observably reduce energy consumption and allow the system to meet the Service Level Agreements (SLAs)

Modified HIV envelope proteins with enhanced binding to neutralizing monoclonal antibodies

AbstractThe target for neutralizing antibodies against human immunodeficiency virus (HIV) is the trimeric Env protein on the native virion. Conserved neutralizing epitopes of receptor binding sites are located in the recessed core of the Env protein, partially masked by glycosylations and variable loops. In this study, we have investigated the effects of modifications of the HIV Env protein by glycosylation site mutations, deletions of variable loops, or combinations of both types of mutations on their protein functions and reactivities with neutralizing antibodies. Modified Env proteins were expressed in insect or mammalian cells, and their reactivity with epitope-specific broadly neutralizing monoclonal antibodies (Mabs) was determined by flow cytometry. A unique mutant designated 3G with mutations in three glycosylation motifs within the V3/C3 domains surrounding the CD4 binding site showed higher levels of binding to most broadly neutralizing Mabs (b12 and 2F5) in both insect and mammalian expression systems. Mutants with a deletion of both V1 and V2 loop domains or with a unique combination of both types of mutations also bound to most neutralizing Mabs at higher levels compared to the wild-type control. Most mutants maintained the ability to bind CD4 and to induce syncytium formation at similar or higher levels as compared to that of the wild-type Env protein, except for a mutant with a combination of variable loop deletions and deglycosylation mutations. Our study suggests that modified HIV Env proteins with reduced glycosylation in domains surrounding the CD4 binding site or variable loop-deleted mutants expose important neutralizing epitopes at higher levels than wild type and may provide novel vaccine immunogens

Mapping and Analyzing Stakeholders in China’s Essential Drug System by Using a Circular Model: Who We Should Deal with Next?

AbstractObjectivesTo predict the prospects of the essential drug system by using the Stakeholder Impact Index (SII) and evaluate the current performance of each main stakeholder and suggested dangerous stakeholders and dormant stakeholders.MethodsA Delphi method was used, involving 36 experts with experience in implementation and evaluation of the essential drug policy, to construct the circular model as well as evaluate the performance of each stakeholder.ResultsThe central government was a dominant stakeholder of the whole essential drug system. The provincial governments were definitive stakeholders, whereas local governments and medical institutions were dependent stakeholders. Furthermore, media and drug stores were dormant stakeholders and pharmaceutical manufacturers and delivery enterprises were dangerous stakeholders. Patients, community residents, and medical insurance programs were discretionary stakeholders. The SII for the essential drug system was positive (SIIproj⁎ = 2.72).ConclusionsThe overall anticipation of the essential drug policy is optimistic. Letting definitive stakeholders (provincial governments) having more autonomy can efficiently accelerate the pace of implementation of the essential drug policy in the current situation. Central government, however, also needs to construct an experience exchange platform with the aim of building versatile methods for running the essential drug system in all provinces. Pharmaceutical manufacturers and delivery enterprises were dangerous stakeholders for the essential drug policy. Because of their potential threat to the implementation of the policy, the central government should motivate them to support the construction of the essential drug system spontaneously. In that case, provincial governments need to construct a fair, balanced, and self-stabilized bidding platform

Evolution and rapid spread of a reassortant A(H3N2) virus that predominated the 2017-2018 influenza season

The 2017-2018 North American influenza season caused more hospitalizations and deaths than any year since the 2009 H1N1 pandemic. The majority of recorded influenza infections were caused by A(H3N2) viruses, with most of the virus's North American diversity falling into the A2 clade. Within A2, we observe a subclade which we call A2/re that rose to comprise almost 70 per cent of A(H3N2) viruses circulating in North America by early 2018. Unlike most fast-growing clades, however, A2/re contains no amino acid substitutions in the hemagglutinin (HA) segment. Moreover, hemagglutination inhibition assays did not suggest substantial antigenic differences between A2/re viruses and viruses sampled during the 2016-2017 season. Rather, we observe that the A2/re clade was the result of a reassortment event that occurred in late 2016 or early 2017 and involved the combination of the HA and PB1 segments of an A2 virus with neuraminidase (NA) and other segmentsa virus from the clade A1b. The success of this clade shows the need for antigenic analysis that targets NA in addition to HA. Our results illustrate the potential for non-HA drivers of viral success and necessitate the need for more thorough tracking of full viral genomes to better understand the dynamics of influenza epidemics

Induction of Escherichia coli Into a VBNC State by Continuous-Flow UVC and Subsequent Changes in Metabolic Activity at the Single-Cell Level

A viable but non-culturable (VBNC) state of bacteria induced by disinfection in water treatment poses serious health risks because of possible resuscitation of VBNC cells during transportation. In this study, a setup using continuous-flow ultraviolet (UVC) irradiation ranging from 0 to 172.2 mJ cm-2 was designed to simulate real-world disinfection in both drinking water (SDW) and reclaimed water (SRW) treatment plants. A systematic investigation of UVC-induced VBNC bacteria, including occurrence, resuscitation, and time-dependent recovery of metabolic activity during post-incubation, was conducted. Different techniques including two new ones of “single cell culture” and D2O-labeled single-cell Raman spectroscopy were employed to gain comprehensive insights into VBNC cells. Heterotrophic plate counts (HPC) and 5-cyano-2,3-ditoyl tetrazolium chloride flow cytometry (CTC-FCM) assay demonstrated that exposure to continuous-flow UVC can induce E. coli into a VBNC state. Membranes integrity and 16S rRNA transcription level of VBNC bacteria were demonstrated to be unaffected by UVC exposure even at a high dose of 172.2 mJ cm-2. Resuscitation of VBNC bacteria was identified in a more accurate way based on “single cell culture.” Finally, time-dependent evolution of metabolic activity of UVC-treated cells during post-incubation was examined by D2O-labeled Raman spectroscopy at a high-resolution of single-cell level. C-D Raman bands resulting from incorporation of D2O-derived D into bacterial biomass were used as a sensitive and quantitative indicator of bacterial metabolic activity. A lower UVC dose, longer post-incubation time, and higher initial number of bacteria were demonstrated to result in a faster recovery of metabolic activity. Heterogeneous metabolic activity and subpopulation with higher metabolic activity were also revealed by single-cell Raman, even for UVC-treated cells losing cultivability. The comprehensive assessment of VBNC bacteria in UVC-disinfected drinking and reclaimed water points out treatment deficiencies of UVC and the necessity to develop more effective strategies to eliminate VBNC cells

Identification and quantification of viable Lacticaseibacillus rhamnosus in probiotics using validated PMA-qPCR method

The identification and quantification of viable bacteria at the species/strain level in compound probiotic products is challenging now. Molecular biology methods, e.g., propidium monoazide (PMA) combination with qPCR, have gained prominence for targeted viable cell counts. This study endeavors to establish a robust PMA-qPCR method for viable Lacticaseibacillus rhamnosus detection and systematically validated key metrics encompassing relative trueness, accuracy, limit of quantification, linear, and range. The inclusivity and exclusivity notably underscored high specificity of the primers for L. rhamnosus, which allowed accurate identification of the target bacteria. Furthermore, the conditions employed for PMA treatment were fully verified by 24 different L. rhamnosus including type strain, commercial strains, etc., confirming its effective discrimination between live and dead bacteria. A standard curve constructed by type strain could apply to commercial strains to convert qPCR Cq values to viable cell numbers. The established PMA-qPCR method was applied to 46 samples including pure cultures, probiotics as food ingredients, and compound probiotic products. Noteworthy is the congruity observed between measured and theoretical values within a 95% confidence interval of the upper and lower limits of agreement, demonstrating the relative trueness of this method. Moreover, accurate results were obtained when viable L. rhamnosus ranging from 103 to 108 CFU/mL. The comprehensive appraisal of PMA-qPCR performances provides potential industrial applications of this new technology in quality control and supervision of probiotic products

Comparison of Nanotrap® Microbiome A Particles, membrane filtration, and skim milk workflows for SARS-CoV-2 concentration in wastewater

IntroductionSevere acute respiratory syndrome coronavirus-2 (SARS-CoV-2) RNA monitoring in wastewater has become an important tool for Coronavirus Disease 2019 (COVID-19) surveillance. Grab (quantitative) and passive samples (qualitative) are two distinct wastewater sampling methods. Although many viral concentration methods such as the usage of membrane filtration and skim milk are reported, these methods generally require large volumes of wastewater, expensive lab equipment, and laborious processes.MethodsThe objectives of this study were to compare two workflows (Nanotrap® Microbiome A Particles coupled with MagMax kit and membrane filtration workflows coupled with RNeasy kit) for SARS-CoV-2 recovery in grab samples and two workflows (Nanotrap® Microbiome A Particles and skim milk workflows coupled with MagMax kit) for SARS-CoV-2 recovery in Moore swab samples. The Nanotrap particle workflow was initially evaluated with and without the addition of the enhancement reagent 1 (ER1) in 10 mL wastewater. RT-qPCR targeting the nucleocapsid protein was used for detecting SARS-CoV-2 RNA.ResultsAdding ER1 to wastewater prior to viral concentration significantly improved viral concentration results (P < 0.0001) in 10 mL grab and swab samples processed by automated or manual Nanotrap workflows. SARS-CoV-2 concentrations in 10 mL grab and Moore swab samples with ER1 processed by the automated workflow as a whole showed significantly higher (P < 0.001) results than 150 mL grab samples using the membrane filtration workflow and 250 mL swab samples using the skim milk workflow, respectively. Spiking known genome copies (GC) of inactivated SARS-CoV-2 into 10 mL wastewater indicated that the limit of detection of the automated Nanotrap workflow was ~11.5 GC/mL using the RT-qPCR and 115 GC/mL using the digital PCR methods.DiscussionThese results suggest that Nanotrap workflows could substitute the traditional membrane filtration and skim milk workflows for viral concentration without compromising the assay sensitivity. The manual workflow can be used in resource-limited areas, and the automated workflow is appropriate for large-scale COVID-19 wastewater-based surveillance

Dynamic Performance Optimization for Cloud Computing Using M/M/m Queueing System

Successful development of cloud computing has attracted more and more people and enterprises to use it. On one hand, using cloud computing reduces the cost; on the other hand, using cloud computing improves the efficiency. As the users are largely concerned about the Quality of Services (QoS), performance optimization of the cloud computing has become critical to its successful application. In order to optimize the performance of multiple requesters and services in cloud computing, by means of queueing theory, we analyze and conduct the equation of each parameter of the services in the data center. Then, through analyzing the performance parameters of the queueing system, we propose the synthesis optimization mode, function, and strategy. Lastly, we set up the simulation based on the synthesis optimization mode; we also compare and analyze the simulation results to the classical optimization methods (short service time first and first in, first out method), which show that the proposed model can optimize the average wait time, average queue length, and the number of customer