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The ongoing transmission of mpox in specific countries and regions necessitates urgent action. It is essential to implement targeted containment strategies that concentrate on high-risk populations and critical locations, such as college campuses, to effectively curb the spread of mpox. This study is dedicated to evaluating the performance of various vaccination and quarantine strategies in curbing the spread of mpox and estimating the outbreak risk. To accomplish this, we constructed a stochastic, agent-based, discrete-time susceptible-latent-infectious-recovered (SLIR) model, to examine mpox transmission on a simulated college campus. Our findings reveal that relying solely on PEP is insufficient in containing mpox effectively. To bolster the population immunity and protect the vulnerable, pre-exposure vaccination among high-risk populations prior to an outbreak is imperative. Our study demonstrates that a pre-exposure vaccination rate of 50% in high-risk populations can led to a remarkable 74.2% reduction of infections. This translated to a mere 1.0% cumulative infection incidence in the overall population. In cases where the desired vaccination coverage is not attainable, enhancing case detection and isolation measures can serve as an effective emergency response to contain mpox outbreaks. For pre-exposure vaccination coverage of 20% or lower, a 40% isolation ratio is necessary to keep the cumulative number of infections in check. However, when the coverage exceeds 30%, a reduced isolation ratio of 20% becomes sufficient to manage the outbreak effectively. These insights underscore the importance of strategic pre-exposure vaccination in conjunction with robust surveillance and isolation protocols to safeguard public health and prevent the escalation of mpox outbreaks.

BackgroundThere has been a global decrease in seasonal influenza activity since the onset of the COVID-19 pandemic. ObjectiveWe aimed to describe influenza activity during the 2021/2022 season and compare it to the trends from 2012 to 2023. We also explored the influence of social and public health prevention measures during the COVID-19 pandemic on influenza activity. MethodsWe obtained influenza data from January 1, 2012, to February 5, 2023, from publicly available platforms for China, the United States, and Australia. Mitigation measures were evaluated per the stringency index, a composite index with 9 measures. A general additive model was used to assess the stringency index and the influenza positivity rate correlation, and the deviance explained was calculated. ResultsWe used over 200,000 influenza surveillance data. Influenza activity remained low in the United States and Australia during the 2021/2022 season. However, it increased in the United States with a positive rate of 26.2% in the 49th week of 2022. During the 2021/2022 season, influenza activity significantly increased compared with the previous year in southern and northern China, with peak positivity rates of 28.1% and 35.1% in the second week of 2022, respectively. After the COVID-19 pandemic, the dominant influenza virus genotype in China was type B/Victoria, during the 2021/2022 season, and accounted for >98% (24,541/24,908 in the South and 20,543/20,634 in the North) of all cases. Influenza virus type B/Yamagata was not detected in all these areas after the COVID-19 pandemic. Several measures individually significantly influence local influenza activity, except for influenza type B in Australia. When combined with all the measures, the deviance explained values for influenza A and B were 87.4% (P

Cyclic adenosine monophosphate (cAMP)-dependent protein kinase A (PKA) is a distinctive member of the serine–threonine protein AGC kinase family and an effective kinase for cAMP signal transduction. In recent years, scuticociliate has caused a lot of losses in domestic fishery farming, therefore, we have carried out morphological and molecular biological studies. In this study, diseased guppies (Poecilia reticulata) were collected from an ornamental fish market, and scuticociliate Philaster apodigitiformis Miao et al., 2009 was isolated. In our prior transcriptome sequencing research, we discovered significant expression of the β-PKA gene in P. apodigitiformis during its infection process, leading us to speculate its involvement in pathogenesis. A complete sequence of the β-PKA gene was cloned, and quantified by quantitative reverse transcription-polymerase chain reaction to analyse or to evaluate the functional characteristics of the β-PKA gene. Morphological identification and phylogenetic analysis based on small subunit rRNA sequence, infection experiments and haematoxylin–eosin staining method were also carried out, in order to study the pathological characteristics and infection mechanism of scuticociliate. The present results showed that: (1) our results revealed that β-PKA is a crucial gene involved in P. apodigitiformis infection in guppies, and the findings provide valuable insights for future studies on scuticociliatosis; (2) we characterized a complete gene, β-PKA, that is generally expressed in parasitic organisms during infection stage and (3) the present study indicates that PKA plays a critical role in scuticociliate when infection occurs by controlling essential steps such as cell growth, development and regulating the activity of the sensory body structures and the irritability system.

Summary: Biological nitrogen fixation catalyzed by nitrogenase contributes greatly to the global nitrogen cycle. Nitrogenase expression is subject to regulation in response to nitrogen availability. However, the mechanism through which the transcriptional activator NifA regulates nitrogenase expression by interacting with PII nitrogen regulatory proteins remains unclear in diazotrophic proteobacteria lacking NifL. Here, we demonstrate that in Rhodopseudomonas palustris grown with ammonium, NifA bound deuridylylated PII proteins to form an inactive NifA-PII complex, thereby inhibiting the expression of nitrogenase. Upon nitrogen limitation, the dissociation of uridylylated PII proteins from NifA resulted in the full restoration of NifA activity, and, simultaneously, uridylylation of the significantly up-regulated PII protein GlnK2 led to the increased expression of NifA in R. palustris. This insight into how NifA interacts with PII proteins and controls nitrogenase expression sets the stage for creating highly efficient diazotrophs, reducing the need for energy-intensive chemical fertilizers and helping to diminish carbon emissions.

The number of coronavirus disease 2019 (COVID-19) cases continues to surge, overwhelming healthcare systems and causing excess mortality in many countries. Testing of infectious populations remains a key strategy to contain the COVID-19 outbreak, delay the exponential spread of the disease, and flatten the epidemic curve. Using the Omicron variant outbreak as a background, this study aimed to evaluate the effectiveness of testing strategies with different test combinations and frequencies, analyze the factors associated with testing effectiveness, and optimize testing strategies based on these influencing factors. We developed a stochastic, agent-based, discrete-time susceptible–latent–infectious–recovered model simulating a community to estimate the association between three levels of testing strategies and COVID-19 transmission. Antigen testing and its combination strategies were more efficient than polymerase chain reaction (PCR)-related strategies. Antigen testing also showed better performance in reducing the demand for hospital beds and intensive care unit beds. The delay in the turnaround time of test results had a more significant impact on the efficiency of the testing strategy compared to the detection limit of viral load and detection-related contacts. The main advantage of antigen testing strategies is the short turnaround time, which is also a critical factor to be optimized to improve PCR strategies. After modifying the turnaround time, the strategies with less frequent testing were comparable to daily testing. The choice of testing strategy requires consideration of containment goals, test efficacy, community prevalence, and economic factors. This study provides evidence for the selection and optimization of testing strategies in the post-pandemic era and provides guidance for optimizing healthcare resources.

Objectives This study aimed to assess the healthcare-seeking behaviour and related factors of people with acute respiratory symptoms in the rural areas of central and western China to estimate the disease burden of influenza more accurately.Design Cross-sectional survey.Settings Fifty-two communities/villages in the Wanzhou District, Chongqing, China, a rural area in southwest China, from May 2022 to July 2022.Participants The participants were those who had been living in Wanzhou District continuously for more than 6 months and consented to participate.Outcome measures A semistructured questionnaire was used to determine the healthcare-seeking behaviour of participants, and the dichotomous response of ‘yes’ or ‘no’ was used to assess whether participants had acute respiratory symptoms and their healthcare-seeking behaviour.Results Only 50.92% (360 of 707) of the patients with acute respiratory infection visited medical and health institutions for treatment, whereas 49.08% (347 of 707) avoided treatment or opted for self-medication. The primary reason for not seeing a doctor was that patients felt their condition was not serious and visiting a medical facility for treatment was unnecessary. Short distance (87.54%) and reasonable charges (49.48%) were ranked as the most important reasons for choosing treatment at primary medical and health facilities (80.27%). The primary reasons for which patients visited secondary and tertiary hospitals (7.78% and 8.61%, respectively) were that doctors in such facilities were better at diagnosis (57.14%) and at treatment (87.10%).Conclusion The findings provided in this study indicated that regular healthcare-seeking behaviour investigations should be conducted. The disease burden of influenza can be calculated more accurately when healthcare-seeking behaviour investigations are combined with surveillance in the hospitals.

ABSTRACTThe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination service system lacks standardized indicators to assess resource allocation. Moreover, data on specific vaccination-promoting measures is limited. This study aimed to evaluate vaccination accessibility and capacity and investigate convenience-related factors in China during the Omicron variant epidemic. We collected information on SARS-CoV-2 vaccination services among vaccination sites in Beijing. Analysis was performed using nearest neighbor, Ripley’s K, hot spot analysis, and generalized estimating equations. Overall, 299 vaccination sites were included. The demand for the SARS-CoV-2 vaccine increased with the increase in daily new cases, and the number of staff administering vaccines should be increased in urban areas at the beginning of the epidemic. Providing vaccination for both children and adults, extending vaccination service hours, and offering a wider range of vaccine categories significantly increased the doses of vaccines administered (all P

ABSTRACTGovernments must decide which vaccine priority to include in their public health programs. Using the modified Delphi and entropy method, we developed an indicator framework for vaccine inclusion at the national, provincial, municipal, and district/county levels, each containing three dimensions. In total, 4 primary indicators, 17 secondary indicators, and 45 tertiary indicators were selected, covering vaccine-preventable diseases, candidate vaccines, and social drivers of the supply and demand sides. From a subjective perspective, there was no significant weighting difference in the primary and secondary indicators at all administrative levels. “Vaccine-preventable diseases” as a primary indicator had the greatest weight in the peer group, of which “Health burden” had the highest weight among the secondary indicators. From the objective perspective, the social drivers on the supply and demand sides of the primary indicators accounted for 65% and higher. Among the secondary indicators, “the characteristics of the candidate vaccine” and “vaccine-related policies on the supply side” held 8% of weights or more at both national and provincial levels. “Demographic characteristics” held the highest weight at the municipal (13.50) and district/county (15.45) level. This study indicates that China needs different considerations when using WHO-recommended vaccines at the national, provincial, municipal, and district/county levels. In addition, this study highlights that behavioral and social drivers are important indicators that need to be considered for decision-making. This framework provides a tool for policymakers to determine the inclusion priority of candidate vaccines.

Objectives: Global trade and travel have facilitated infectious disease transmission. In 2022, over a short time, cross-border Mpox (monkeypox) outbreaks were reported. Since, most countries are at risk of cross-border Mpox transmissions, in this study, we developed a real-time risk assessment model for the cross-border transmission of Mpox. Methods: This model includes priori indicators related to the source area before the Mpox outbreak and posterior indicators derived from the quantitative data evaluation afterward. Based on transportation, this model can also be used to assess the global import risk of Mpox for specific countries and cities. Results: European risk values displayed high levels between May and July 2022 and gradually decreased after July. After September 2022, risk values elevated in most countries and regions in the Americas. As for China, high importation risk cities were highly exposed to the United States and moderately exposed to Australia and Germany. Some cities were exposed to the potential risks from only one source country. Conclusions: Dynamic surveillance of the cross-border spread of infectious diseases is essential. Importation risks vary widely across cities and regions, and developing risk prevention and control strategies specific to the traffic flow, medical care capabilities, and risk levels in the main source countries are essential.

In order to understand the role of microorganisms in litter decomposition and the nutrient cycle in volcanic forest ecosystems, the dominant forest species Larix gmelinii in the volcanic lava plateau of the Wudalianchi volcano was considered as the research object. We analyzed the response of bacterial community structure and diversity to litter decomposition for 1 year, with an in situ decomposition experimental design using litter bags and Illumina MiSeq high-throughput sequencing. The results showed that after 365 days, the litter quality residual rate of Larix gmelinii was 77.57%, and the litter N, P, C:N, C:P, and N:P showed significant differences during the decomposition period (p < 0.05). The phyla Cyanobacteria and the genus unclassified_o_Chloroplast were the most dominant groups in early decomposition (January and April). The phyla Proteobacteria, Actinobacteriota, and Acidobacteriota and the genera Massilia, Pseudomonas, and Sphingomona were higher in July and October. The microbial communities showed extremely significant differences during the decomposition period (p < 0.05), with PCoa, RDA, and litter QRR, C:P, and N as the main factors driving litter bacteria succession. Microbial functional prediction analysis showed that Chloroplasts were the major functional group in January and April. Achemoheterotrophy and aerobic chemoheterotrophy showed a significant decrease as litter decomposition progressed.

Volcanic lava is an excellent model of primary succession, in which basalt-associated microorganisms drive the cycling of different elements such as nitrogen, carbon, and other nutrients. Microbial communities in volcanic soils are of particular interest for study on the emergence and evolution of life within special and extreme conditions. The initial processes of colonization and subsequent rock weathering by microbial communities are still poorly understood. We analyzed the soil bacterial and fungal communities and diversities associated with lava (LBL) and kipuka (BK) sites in Wudalianchi using 16S and ITS rRNA Illumina Miseq sequencing techniques. The results showed that soil physical and chemical properties (pH, MC, TOC, TN, TP, AP, DOC, and DON) significantly differed between LBL and BK. The Shannon, Ace, and Pd indexes of fungi in the two sites showed a significant difference (p < 0.05). The dominant bacterial phyla forming communities at LBL and BK sites were Acidobacteria, Proteobacteria, Actinobacteria, and Basidiomycota, and their differences were driven by Gemmatimonadetes and Verrucomicrobia. The dominant fungal phyla of LBL and BK sites were Ascomycota, Zygomycota, and Rozellomcota, which differed significantly between the two sites. The microbial communities showed extremely significant differences (p < 0.05), with MC, pH, and nitrogen being the main influencing factors according to RDA/CCA and correlation analysis. Microbial functional prediction analysis across the two sites showed that the relative abundance of advantageous functional groups was significantly different (p < 0.05). The combined results drive us to conclude that the volcanic soil differences in the deposits appear to be the main factor shaping the microbial communities in Wudalianchi (WDLC) volcanic ecosystems.

Abstract Tetrahymenosis, caused by about 10 Tetrahymena species, is an emerging problem inflicting a significant economic loss on the aquaculture industry worldwide. However, in the order Tetrahymenida, there are many unresolved evolutionary relationships among taxa. Here we report 21 new sequences, including SSU‐rRNA, ITS1‐5.8S‐ITS2 rRNA and LSU‐rRNA, genes of 10 facultative parasitic Tetrahymena associated with tetrahymenosis, and conduct phylogenetic analyses based on each individual gene and a three‐gene concatenated dataset. The main findings are: (1) All the parasitic and facultative parasitic species cluster in borealis group. (2) With the addition of new sequences, Tetrahymena is still divided into three groups, namely the “borealis group”, the “australis group,” and the “paravorax group.” (3) the cluster pattern of all the newly sequenced facultative parasitic Tetrahymena species shows that members of the “borealis” group may be more susceptible to parasitism. (4) phylogeny based on concatenated genes show that T. pyriformis, T. setosa, and T. leucophrys have close relationship.

Abstract Biological catalysis is an important approach for the production of high-value-added compounds, especially for products with complex structures. Limited by the complex steps of chemical synthesis and low yields, the bioconversion of vitamin D3 (VD3) to calcifediol and calcitriol, which are natural steroid products with high added value and significantly higher biological activity compared to VD3, is probably the most promising strategy for calcifediol and calcitriol production, and can be used as an alternative method for chemical synthesis. The conversion efficiency of VD3 to calcifediol and calcitriol has continued to rise in the past few decades with the help of several different VD3 hydroxylases, mostly cytochrome P450s (CYPs), and newly isolated strains. The production of calcifediol and calcitriol can be systematically increased in different ways. Specific CYPs and steroid C25 dehydrogenase (S25DH), as VD3 hydroxylases, are capable of converting VD3 to calcifediol and calcitriol. Some isolated actinomycetes have also been exploited for fermentative production of calcifediol and calcitriol, although the VD3 hydroxylases of these strains have not been elucidated. With the rapid development of synthetic biology and enzyme engineering, quite a lot of advances in bioproduction of calcifediol and calcitriol has been achieved in recent years. Therefore, here we review the successful strategies of promoting VD3 hydroxylation and provide some perspective on how to further improve the bioconversion of VD3 to calcifediol and calcitriol.

BackgroundSeasonal influenza activity showed a sharp decline in activity at the beginning of the emergence of COVID-19. Whether there is an epidemiological correlation between the dynamic of these 2 respiratory infectious diseases and their future trends needs to be explored. ObjectiveWe aimed to assess the correlation between COVID-19 and influenza activity and estimate later epidemiological trends. MethodsWe retrospectively described the dynamics of COVID-19 and influenza in 6 World Health Organization (WHO) regions from January 2020 to March 2023 and used the long short-term memory machine learning model to learn potential patterns in previously observed activity and predict trends for the following 16 weeks. Finally, we used Spearman correlation coefficients to assess the past and future epidemiological correlation between these 2 respiratory infectious diseases. ResultsWith the emergence of the original strain of SARS-CoV-2 and other variants, influenza activity stayed below 10% for more than 1 year in the 6 WHO regions. Subsequently, it gradually rose as Delta activity dropped, but still peaked below Delta. During the Omicron pandemic and the following period, the activity of each disease increased as the other decreased, alternating in dominance more than once, with each alternation lasting for 3 to 4 months. Correlation analysis showed that COVID-19 and influenza activity presented a predominantly negative correlation, with coefficients above –0.3 in WHO regions, especially during the Omicron pandemic and the following estimated period. The diseases had a transient positive correlation in the European region of the WHO and the Western Pacific region of the WHO when multiple dominant strains created a mixed pandemic. ConclusionsInfluenza activity and past seasonal epidemiological patterns were shaken by the COVID-19 pandemic. The activity of these diseases was moderately or greater than moderately inversely correlated, and they suppressed and competed with each other, showing a seesaw effect. In the postpandemic era, this seesaw trend may be more prominent, suggesting the possibility of using one disease as an early warning signal for the other when making future estimates and conducting optimized annual vaccine campaigns.

Due to growing activities of avian influenza, more attention should be paid to avian influenza virus infections. Global summaries or national reports lack data on epidemiological patterns of avian influenza. A descriptive epidemiological analysis of avian influenza outbreaks from 2018 to 2022 was conducted, particularly fowl infections, human infections, and sequence alterations. The number of fowl infection outbreaks in the first half of 2022 was the highest level in the five-year period. Countries or regions could reliably be classified into three clusters according to fowl infection activity scores, with 60.0% of countries or regions in C1 in Europe. Additionally, two host infection patterns of countries were noted, led by the Taiwan (China) region and Germany. Human infections also increased, with 88.1% of cases being in China with an increasing risk of cases in northern China. Sequences that were furin cleavage motif present spread from Asia to Europe and North America over the five-year period. Continuous changes in the global activities of avian influenza highlight the need for sustained global surveillance, including strengthening monitoring capacity for vulnerable population and dynamically detecting new cases or genetic variations of the avian influenza virus under the One Health framework.

Abstract Named entity recognition (NER) is a widely used natural language processing technique; it plays a key role in information extraction from sentences. To be able to test the correctness of NER systems is important, but it is expensive because an automated test oracle is normally unavailable. To address the oracle problem, this study proposes to apply metamorphic testing (MT). The authors conduct a case study with Litigant, an industrial NER system of the Ant Group, and show that MT can effectively detect real‐life bugs in the absence of an ideal oracle. The authors further investigate the causes for a series of entity recognition failures detected. Outcomes of this research further justify the application of MT to the natural language processing domain as well as provide hints for practitioners to improve the quality process of their NER systems.

Abstract Background Current methods for cervical cancer screening result in an increased number of referrals and unnecessary diagnostic procedures. This study aimed to develop and evaluate a more accurate model for cervical cancer screening. Methods Multiple predictors including age, cytology, high-risk human papillomavirus (hrHPV) DNA/mRNA, E6 oncoprotein, HPV genotyping, and p16/Ki-67 were used for model construction in a cross-sectional population including women with normal cervix (N = 1085), cervical intraepithelial neoplasia (CIN, N = 279), and cervical cancer (N = 551) to predict CIN2+ or CIN3+. A base model using age, cytology, and hrHPV was calculated, and extended versions with additional biomarkers were considered. External validations in two screening cohorts with 3-year follow-up were further conducted (N Cohort-I = 3179, N Cohort-II = 3082). Results The base model increased the area under the curve (AUC, 0.91, 95% confidence interval [CI] = 0.88–0.93) and reduced colposcopy referral rates (42.76%, 95% CI = 38.67–46.92) compared to hrHPV and cytology co-testing in the cross-sectional population (AUC 0.80, 95% CI = 0.79–0.82, referrals rates 61.62, 95% CI = 59.4–63.8) to predict CIN2+. The AUC further improved when HPV genotyping and/or E6 oncoprotein were included in the base model. External validation in two screening cohorts further demonstrated that our models had better clinical performances than routine screening methods, yielded AUCs of 0.92 (95% CI = 0.91–0.93) and 0.94 (95% CI = 0.91–0.97) to predict CIN2+ and referrals rates of 17.55% (95% CI = 16.24–18.92) and 7.40% (95% CI = 6.50–8.38) in screening cohort I and II, respectively. Similar results were observed for CIN3+ prediction. Conclusions Compared to routine screening methods, our model using current cervical screening indicators can improve the clinical performance and reduce referral rates.

Curcumin (CUR) is a natural bioactive compound that has attracted attention as a “golden molecule” due to its therapeutic properties against several types of tumors. Nonetheless, the antitumor application of CUR is hampered due to its extremely low aqueous solubility and chemical instability. Herein, a novel type of CUR-loaded polymeric micelles with intracellular K+-responsive controlled-release properties is designed and developed. The polymeric micelles are self-assembled by poly (N-isopropylacrylamide-co-acryloylamidobenzo-15-crown-5-co-N, N-dimethylacrylamide)-b-DSPE (PNDB-b-DSPE) block copolymers, and CUR. CUR is successfully loaded into the micelles with a CUR loading content of 6.26 wt%. The proposed CUR-PNDB-DSPE polymeric micelles exhibit a significant CUR release in simulated intracellular fluid due to the formation of 2 : 1 ‘‘sandwich’’ host–guest complexes of 15-crown-5 and K+, which lead to the hydrophilic outer shell of micelles to collapse and the drug to rapidly migrate out of the micelles. In vitro, the B16F10 cell experiment indicates that CUR-PNDB-DSPE micelles exhibit a high cellular uptake and excellent intracellular drug release in response to the intracellular K+ concentration. Moreover, CUR-PNDB-DSPE micelles show high cytotoxicity to B16F10 cells compared to free CUR and CUR-PEG-DSPE micelles. The polymeric micelles with intracellular K+-responsive controlled release properties proposed in this study provide a new strategy for designing novel targeted drug delivery systems for CUR delivery for cancer treatment.

Abstract Ectoine and hydroxyectoine as typical representatives of compatible solutes are not only essential for extremophiles to survive in extreme environments, but also widely used in cosmetic and medical industries. Ectoine was traditionally produced by Halomonas elongata through a “bacterial milking” process, of which the marked feature is using a high-salt medium to stimulate ectoine biosynthesis and then excreting ectoine into a low-salt medium by osmotic shock. The optimal hydroxyectoine production was achieved by optimizing the fermentation process of Halomonas salina. However, high-salinity broth exacerbates the corrosion to fermenters, and more importantly, brings a big challenge to the subsequent wastewater treatment. Therefore, increasing attention has been paid to reducing the salinity of the fermentation broth but without a sacrifice of ectoine/hydroxyectoine production. With the fast development of functional genomics and synthetic biology, quite a lot of progress on the bioproduction of ectoine/hydroxyectoine has been achieved in recent years. The importation and expression of an ectoine producing pathway in a non-halophilic chassis has so far achieved the highest titer of ectoine (~ 65 g/L), while rational flux-tuning of halophilic chassis represents a promising strategy for the next-generation of ectoine industrial production. However, efficient conversion of ectoine to hydroxyectoine, which could benefit from a clearer understanding of the ectoine hydroxylase, is still a challenge to date.

Model transformations and model-driven engineering (MDE) have been applied widely in service-oriented architecture based information systems. To support the development of such a service-oriented information system, it is necessary to guarantee the quality of model transformations. With the increasing complexity and scale of model transformations, debugging of model transformations becomes more and more time-consuming and difficult, there is an increasing need to rely on efficient and accurate fault localization approaches to help with debugging. Among the existing fault localization approaches, the spectrum-based fault localization (SBFL), as a dynamic analysis method, mainly used the coverage information and execution results of the rules of model transformation to estimate the probability of each rule may be faulty. However, there are many false-positive and false-negative results in the rule coverage information, the accuracy of the SBFL is not ideal, so we consider mining the impact of covered range in different test models to further improve the effectiveness of fault localization. In this paper, we propose an optimized strategy of fault localization based on the impact of the test model, according to the covered range of test models, weight values are assigned to different test models, and the statistical coverage information of rules are weighted and adjusted accordingly. We compare the proposed approach with the SBFL, the experimental results show that under the same techniques for computing the suspiciousness, our approach can help locate around 26% more faulty rules in the ranking Top-1 of the suspicious list than the SBFL, the effectiveness of fault localization techniques can be improved by 50.42% in the best case and 8.9% in the average case.

Relation extraction (RE) is a fundamental NLP task that aims to identify relations between some entities regarding a given text. RE forms the basis for many advanced NLP tasks, such as question answering and text summarization, and thus its quality is critical to the relevant downstream applications. However, evaluating the quality of RE models is non-trivial. On the one hand, obtaining ground truth labels for individual test inputs is tedious and even difficult. On the other hand, there is an increasing need to understand the characteristics of RE models in terms of various aspects. To mitigate these issues, this study proposes evaluating RE models by applying metamorphic testing (MT). A total of eight metamorphic relations (MRs) are identified based on three categories of transformation operations, namely replacement, swap, and combination. These MRs encode some expected properties of different aspects of RE. We further apply MT to three popular RE models. Our experiments reveal a large number of prediction failures in the subject RE models, confirming that MT is effective for evaluating RE models. Further analysis of the experimental results reveals the advantages and disadvantages of our subject models and also uncovers some typical issues of RE models.

This study aimed to understand the intention and correlation of receiving and recommending influenza vaccine (IV) among healthcare workers (HCWs) in China during the 2022/2023 season using the behavior and social drivers (BeSD) tools. A self-administered electronic survey collected 17,832 participants on a media platform. We investigated the willingness of IV and used multivariate logistic regression analysis to explore its associated factors. The average scores of the 3Cs’ model were compared by multiple comparisons. We also explored the factors that potentially correlated with recommendation willingness by partial regression. The willingness of IV was 74.89% among HCWs, and 82.58% of the participants were likely to recommend it to others during this season. Thinking and feeling was the strongest domain independently associated with willingness. All domains in BeSD were significantly different between the hesitancy and acceptance groups. Central factors in the 3Cs model were significantly different among groups (p < 0.01). HCWs’ willingness to IV recommendation was influenced by their ability to answer related questions (r = 0.187, p < 0.001) after controlling for their IV willingness and perceived risk. HCWs’ attitudes towards IV affect their vaccination and recommendation. The BeSD framework revealed the drivers during the decision-making process. Further study should classify the causes in detail to refine HCWs’ education.

Forecasting emergency calls are of great importance in practice. By forecasting the occurrence of unfortunate events, we can learn from these events and further prevent their occurrence in the future. However, because of the uncertainty of event occurrences, it is hard to guarantee their prediction accuracy. In this paper, a combined model, which consists of two parts, is proposed. The first part is a Poisson neural network model (PNN). It is responsible for basic forecasting, and its initial weights and thresholds are trained by applying a genetic algorithm. The second part consists of multiple linear regression (MLR), autoregressive integrated moving average (ARIMA), and multivariate gray (GM), which are responsible for estimating residual errors. The basic prediction result adjusted by the residual error is used as the final forecasting result. The proposed model fully takes the advantages of PNN, MLR, ARIMA, and GM, and thus improves forecasting performance. Our method has been applied to the emergency calls of Ningbo, China. The experimental results show that the proposed model has advantages over some existing forecasting models, such as a neural network model, Poisson regression, and stochastic configuration networks in terms of mean absolute percentage error.

Summary: Adhesive hydrogels containing catechol moieties have many important applications, but the fabrication of effective long-lasting adhesive hydrogels remains a challenge because of oxidative damage. Inspired by the traditional use of quercetin in ancient China, here, we have developed a novel method, based on quercetin-assisted photoradical chemistry, to fabricate a durable adhesive hydrogel, Q-hydrogel. In the presence of light, quercetin generates quinone/semiquinone radicals, which subsequently interact with ammonium persulfate (APS) to produce a large amount of free radicals and initiate polymerization of the hydrogel. As-prepared Q-hydrogel showed good mechanical and adhesive properties, which were attributed to the inherent structural advantages of quercetin. Because of the resistance of quercetin to oxidation, as-prepared Q-hydrogel also showed good adhesive properties even after treatment with oxidizing agents. Capitalizing on its conductivity and adhesive properties, Q-hydrogel was successfully used to produce wearable sensors capable of detecting human motion.

Summary: Achieving efficient and biocompatible detachment between adhered wet materials (i.e., tissues and hydrogels) is a major challenge. Recently, photodetachable topological adhesion has shown great promise as a strategy for conquering this hurdle. However, this photodetachment was triggered by UV light with poor biocompatibility and penetration capacity. This study describes near-infrared (NIR) light-detached topological adhesion based on polyacrylic acid coated upconverting nanoparticles (UCNP@PAA) and a photodetachable adhesive (termed Cell-Fe). Cell-Fe is a coordinated topological adhesive consisting of carboxymethylcellulose and Fe3+ that can be photodecomposed by UV light. To prepare a substrate for NIR-detached topological adhesion, UCNP@PAA and Cell-Fe were mixed and brushed on the surface of the model adherent. The UCNP@PAA can harvest NIR light and convert it into UV light, triggering the decomposition of the Cell-Fe and inducing the detachment. This NIR-detached topological adhesion is also feasible in deep tissue because of the ability of NIR light to penetrate tissue. : Nanoparticles; Biomaterials; Optical Materials Subject Areas: Nanoparticles, Biomaterials, Optical Materials

The photosynthetic bacterium Rhodopseudomonas palustris converts nitrogen gas (N2) to fertilizer ammonia (NH3) and also produces clean energy hydrogen gas (H2) from protons (H+) when it is grown anaerobically in nitrogen fixing medium with illumination, a condition that promotes the expression of active nitrogenase. Compared with quantitative real-time PCR (qRT-PCR) and the lacZ reporter system, two methods commonly used for in vivo study of nitrogenase regulation in photosynthetic bacteria, the fluorescent protein reporter system has advantages in terms of its simplicity and sensitivity. However, little is known concerning if the fluorescent protein reporter system can be used in bacterial cells that need to grow anaerobically. Here, we developed an RFP-based method to measure the nitrogenase gene expression in photosynthetic bacteria grown anaerobically. This method was able to determine the levels of both the genome-based and the plasmid-based nitrogenase expression under anaerobic conditions, providing a better method for in vivo study of gene expression affected by oxygen. The RFP reporter system developed here will promote a better understanding of the molecular mechanism of nitrogenase regulation and will be used on other genes of interest in a wider range of anaerobic bacteria.

Abstract Background 2-phenylethanol (2-PE) is an important aromatic compound with a lovely rose-like scent. Saccharomyces cerevisiae is a desirable microbe for 2-PE production but its natural yield is not high, and one or two crucial genes’ over-expression in S. cerevisiae did not improve 2-PE greatly. Results A new metabolic module was established here, in which, permease Gap1p for l-phenylalanine transportation, catalytic enzymes Aro8p, Aro10p and Adh2p in Ehrlich pathway respectively responsible for transamination, decarboxylation and reduction were assembled, besides, glutamate dehydrogenase Gdh2p was harbored for re-supplying another substrate 2-oxoglutarate, relieving product glutamate repression and regenerating cofactor NADH. Due to different promoter strengths, GAP1, ARO8, ARO9, ARO10, ADH2 and GDH2 in the new modularized YS58(G1-A8-A10-A2)-GDH strain enhanced 11.6-, 15.4-, 3.6-, 17.7-, 12.4- and 7.5-folds respectively, and crucial enzyme activities of aromatic aminotransferases and phenylpyruvate decarboxylase were 4.8- and 7-folds respectively higher than that of the control. Conclusions Under the optimum medium and cell density, YS58(G1-A8-A10-A2)-GDH presented efficient 2-PE synthesis ability with ~ 6.3 g L−1 of 2-PE titer in 5-L fermenter reaching 95% of conversation ratio. Under fed-batch fermentation, 2-PE productivity at 24 h increased 29% than that of single-batch fermentation. Metabolic modularization with promoter strategy provides a new prospective for efficient 2-PE production.

Test oracles are commonly used in software testing to determine the correctness of the execution results of test cases. However, the testing of many software systems faces the test oracle problem: a test oracle may not always be available, or it may be available but too expensive to apply. One such software system is a system involving abundant business processes. This paper focuses on the testing of business-process-based software systems and proposes a metamorphic testing approach for event sequences, called MTES, to alleviate the oracle problem. We utilized event sequences to represent business processes and then applied the technique of metamorphic testing to test the system without using test oracles. To apply metamorphic testing, we studied the general rules for identifying metamorphic relations for business processes and further demonstrated specific metamorphic relations for individual case studies. Three case studies were conducted to evaluate the effectiveness of our approach. The experimental results show that our approach is feasible and effective in testing the applications with rich business processes. In addition, this paper summarizes the experimental findings and proposes guidelines for selecting good metamorphic relations for business processes.

Question Answering (QA) enables the machine to understand and answer questions posed in natural language, which has emerged as a powerful tool in various domains. However, QA is a challenging task and there is an increasing concern about its quality. In this paper, we propose to apply the technique of metamorphic testing (MT) to evaluate QA systems from the users’ perspectives, in order to help the users to better understand the capabilities of these systems and then to select appropriate QA systems for their specific needs. Two typical categories of QA systems, namely, the textual QA (TQA) and visual QA (VQA), are studied, and a total number of 17 metamorphic relations (MRs) are identified for them. These MRs respectively focus on some characteristics of different aspects of QA. We further apply MT to four QA systems (including two APIs from the AllenNLP platform, one API from the Transformers platform, and one API from CloudCV) by using all of the MRs. Our experimental results demonstrate the capabilities of the four subject QA systems from various aspects, revealing their strengths and weaknesses. These results further suggest that MT can be an effective method for assessing QA systems.

The effectiveness analysis of risk evaluation formulas has become a significant research area in spectrum-based fault localization (SBFL). The risk evaluation formula is designed and widely used to evaluate the likelihood of a program spectrum to be faulty. There are numerous empirical and theoretical studies to investigate and compare the performance between sixty risk evaluation formulas. According to previous research, these sixty risk evaluation formulas together form a partially ordered set. Among them, nine formulas are maximal. These nine formulas can further be grouped into five maximal risk evaluation formula groups so that formulas in the same group have the same performance. Moreover, previous research showed that we cannot theoretically compare formulas across these five maximal formula groups. However, experimental data “suggests” that a maximal formula in one group could outperform another one (from a different group) more frequently, though not always. This inspired us to further investigate the performance between any two maximal formulas in different maximal formula groups. Our approach involves two major steps. First, we propose a new condition to compare between two different maximal formulas. Based on this new condition, we present five different scenarios under which a formula performs better than another. This is different from the condition suggested by the previous theoretical study. We performed an empirical study to compare different maximal formulas using our condition. Our results showed that among two maximal risk evaluation formulas, it is feasible to identify one that can outperform the others more frequently.

In past work, QSAR (quantitative structure-activity relationship) models of cinnamaldehyde analogues and derivatives (CADs) have been used to predict the activities of new chemicals based on their mass concentrations, but these approaches are not without shortcomings. Therefore, molar concentrations were used instead of mass concentrations to determine antifungal activity. New QSAR models of CADs against Aspergillus niger and Penicillium citrinum were established, and the molecular design of new CADs was performed. The antifungal properties of the designed CADs were tested, and the experimental Log AR values were in agreement with the predicted Log AR values. The results indicate that the improved QSAR models are more reliable and can be effectively used for CADs molecular design and prediction of the activity of CADs. These findings provide new insight into the development and utilization of cinnamaldehyde compounds.

Alzheimer’s disease (AD) is a common neurodegenerative disease resulting in progressive dementia, and is a principal cause of dementia among older adults. Folate acts through one-carbon metabolism to support the methylation of multiple substrates. We hypothesized that folic acid supplementation modulates DNA methyltransferase (DNMT) activity and may alter amyloid β-peptide (Aβ) production in AD. Mouse Neuro-2a cells expressing human APP695 were incubated with folic acid (2.8–40 μmol/L), and with or without zebularine (the DNMT inhibitor). DNMT activity, cell viability, Aβ and DNMTs expression were then examined. The results showed that folic acid stimulated DNMT gene and protein expression, and DNMT activity. Furthermore, folic acid decreased Aβ protein production, whereas inhibition of DNMT activity by zebularine increased Aβ production. The results indicate that folic acid induces methylation potential-dependent DNMT enzymes, thereby attenuating Aβ production.

ABSTRACT Hydroxydihydrobovolide (HDB) was for the first time isolated from Portulaca oleracea L. and then its cytotoxicity against SH-SYTY cells was studied. Moreover, a rapid and sensitive ultra-high performance liquid chromatographic (UHPLC) method with bergapten as internal standard (IS) was developed and validated to investigate the pharmacokinetics of HDB in rats after intravenous and oral administrations of extract (POE). The UHPLC analysis was performed on a Diamonsil C18 analytical column, using acetonitrile-water (35:65, v/v) as the mobile phase with UV detection at 220 nm. The calibration curve was linear over the range of 0.2-25 µg/mL in rat plasma. The average extraction recovery was from 90.1 to 98.9%, and the relative standard deviations (RSDs) of the intra- and inter-day precisions were less than 4.7 and 4.1%, respectively. The results showed that 50 µM HDB had significant cytotoxicity on the SH-SY5Y cells, which was rapidly distributed with a Tmax of 11 min after oral administration and presented a low absolute bioavailability, 4.12%.

Cinnamaldehyde amino acid Schiff base (CAAS) is a new class of safe, bioactive compounds which could be developed as potential antifungal agents for fungal infections. To design new cinnamaldehyde amino acid Schiff base compounds with high bioactivity, the quantitative structure–activity relationships (QSARs) for CAAS compounds against Aspergillus niger (A. niger) and Penicillium citrinum (P. citrinum) were analysed. The QSAR models (R2 = 0.9346 for A. niger, R2 = 0.9590 for P. citrinum,) were constructed and validated. The models indicated that the molecular polarity and the Max atomic orbital electronic population had a significant effect on antifungal activity. Based on the best QSAR models, two new compounds were designed and synthesized. Antifungal activity tests proved that both of them have great bioactivity against the selected fungi.

Development of new drugs is one of the solutions to fight against the existing antimicrobial resistance threat. Cinnamaldehyde-amino acid Schiff base compounds, are newly discovered compounds that exhibit good antibacterial activity against gram-positive and gram-negative bacteria. Quantitative structure⁻activity relationship (QSAR) methodology was applied to explore the correlation between antibacterial activity and compound structures. The two best QSAR models showed R2 = 0.9354, F = 57.96, and s2 = 0.0020 against Escherichia coli, and R2 = 0.8946, F = 33.94, and s2 = 0.0043 against Staphylococcus aureus. The model analysis showed that the antibacterial activity of cinnamaldehyde compounds was significantly affected by the polarity parameter/square distance and the minimum atomic state energy for an H atom. According to the best QSAR model, the screening, synthesis, and antibacterial activity of three cinnamaldehyde-amino acid Schiff compounds were reported. The experiment value of antibacterial activity demonstrated that the new compounds possessed excellent antibacterial activity that was comparable to that of ciprofloxacin.