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Seasonal influenza virus A/H3N2 is a major cause of death globally. Vaccination remains the most effective preventative. Rapid mutation of hemagglutinin allows viruses to escape adaptive immunity. This antigenic drift necessitates regular vaccine updates. Effective vaccine strains need to represent H3N2 populations circulating one year after strain selection. Experts select strains based on experimental measurements of antigenic drift and predictions made by models from hemagglutinin sequences. We developed a novel influenza forecasting framework that integrates phenotypic measures of antigenic drift and functional constraint with previously published sequence-only fitness estimates. Forecasts informed by phenotypic measures of antigenic drift consistently outperformed previous sequence-only estimates, while sequence-only estimates of functional constraint surpassed more comprehensive experimentally-informed estimates. Importantly, the best models integrated estimates of both functional constraint and either antigenic drift phenotypes or recent population growth. Vaccination is the best protection against seasonal flu. It teaches the immune system what the flu virus looks like, preparing it to fight off an infection. But the flu virus changes its molecular appearance every year, escaping the immune defences learnt the year before. So, every year, the vaccine needs updating. Since it takes almost a year to design and make a new flu vaccine, researchers need to be able to predict what flu viruses will look like in the future. Currently, this prediction relies on experiments that assess the molecular appearance of flu viruses, a complex and slow approach. One alternative is to examine the virus's genetic code. Mathematical models try to predict which genetic changes might alter the appearance of a flu virus, saving the cost of performing specialised experiments. Recent research has shown that these models can make good predictions, but including experimental measures of the virus’ appearance could improve them even further. This could help the model to work out which genetic changes are likely to be beneficial to the virus, and which are not. To find out whether experimental data improves model predictions, Huddleston et al. designed a new forecasting tool which used 25 years of historical data from past flu seasons. Each forecast predicted what the virus population might look like the next year using the previous year's genetic code, experimental data, or both. Huddleston et al. then compared the predictions with the historical data to find the most useful data types. This showed that the best predictions combined changes from the virus's genetic code with experimental measures of its appearance. This new forecasting tool is open source, allowing teams across the world to start using it to improve their predictions straight away. Seasonal flu infects between 5 and 15% of the world's population every year, causing between quarter of a million and half a million deaths. Better predictions could lead to better flu vaccines and fewer illnesses and deaths.

Influenza viruses continually evolve new antigenic variants, through mutations in epitopes of their major surface proteins, hemagglutinin (HA) and neuraminidase (NA). Antigenic drift potentiates the reinfection of previously infected individuals, but the contribution of this process to variability in annual epidemics is not well understood. Here, we link influenza A(H3N2) virus evolution to regional epidemic dynamics in the United States during 1997—2019. We integrate phenotypic measures of HA antigenic drift and sequence-based measures of HA and NA fitness to infer antigenic and genetic distances between viruses circulating in successive seasons. We estimate the magnitude, severity, timing, transmission rate, age-specific patterns, and subtype dominance of each regional outbreak and find that genetic distance based on broad sets of epitope sites is the strongest evolutionary predictor of A(H3N2) virus epidemiology. Increased HA and NA epitope distance between seasons correlates with larger, more intense epidemics, higher transmission, greater A(H3N2) subtype dominance, and a greater proportion of cases in adults relative to children, consistent with increased population susceptibility. Based on random forest models, A(H1N1) incidence impacts A(H3N2) epidemics to a greater extent than viral evolution, suggesting that subtype interference is a major driver of influenza A virus infection ynamics, presumably via heterosubtypic cross-immunity. Seasonal influenza (flu) viruses cause outbreaks every winter. People infected with influenza typically develop mild respiratory symptoms. But flu infections can cause serious illness in young children, older adults and people with chronic medical conditions. Infected or vaccinated individuals develop some immunity, but the viruses evolve quickly to evade these defenses in a process called antigenic drift. As the viruses change, they can re-infect previously immune people. Scientists update the flu vaccine yearly to keep up with this antigenic drift. The immune system fights flu infections by recognizing two proteins, known as antigens, on the virus’s surface, called hemagglutinin (HA) and neuraminidase (NA). However, mutations in the genes encoding these proteins can make them unrecognizable, letting the virus slip past the immune system. Scientists would like to know how these changes affect the size, severity and timing of annual influenza outbreaks. Perofsky et al. show that tracking genetic changes in HA and NA may help improve flu season predictions. The experiments compared the severity of 22 flu seasons caused by the A(H3N2) subtype in the United States with how much HA and NA had evolved since the previous year. The A(H3N2) subtype experiences the fastest rates of antigenic drift and causes more cases and deaths than other seasonal flu viruses. Genetic changes in HA and NA were a better predictor of A(H3N2) outbreak severity than the blood tests for protective antibodies that epidemiologists traditionally use to track flu evolution. However, the prevalence of another subtype of influenza A circulating in the population, called A(H1N1), was an even better predictor of how severe A(H3N2) outbreaks would be. Perofsky et al. are the first to show that genetic changes in NA contribute to the severity of flu seasons. Previous studies suggested a link between genetic changes in HA and flu season severity, and flu vaccines include the HA protein to help the body recognize new influenza strains. The results suggest that adding the NA protein to flu vaccines may improve their effectiveness. In the future, flu forecasters may want to analyze genetic changes in both NA and HA to make their outbreak predictions. Tracking how much of the A(H1N1) subtype is circulating may also be useful for predicting the severity of A(H3N2) outbreaks.

The degree of heterotypic immunity induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strains is a major determinant of the spread of emerging variants and the success of vaccination campaigns, but remains incompletely understood. We examined the immunogenicity of SARS-CoV-2 variant B.1.1.7 (Alpha) that arose in the United Kingdom and spread globally. We determined titres of spike glycoprotein-binding antibodies and authentic virus neutralising antibodies induced by B.1.1.7 infection to infer homotypic and heterotypic immunity. Antibodies elicited by B.1.1.7 infection exhibited significantly reduced recognition and neutralisation of parental strains or of the South Africa variant B.1.351 (Beta) than of the infecting variant. The drop in cross-reactivity was significantly more pronounced following B.1.1.7 than parental strain infection. The results indicate that heterotypic immunity induced by SARS-CoV-2 variants is asymmetric. This work was supported by the Francis Crick Institute and the Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg.

Influenza viruses continually evolve new antigenic variants, through mutations in epitopes of their major surface proteins, hemagglutinin (HA) and neuraminidase (NA). Antigenic drift potentiates the reinfection of previously infected individuals, but the contribution of this process to variability in annual epidemics is not well understood. Here, we link influenza A(H3N2) virus evolution to regional epidemic dynamics in the United States during 1997—2019. We integrate phenotypic measures of HA antigenic drift and sequence-based measures of HA and NA fitness to infer antigenic and genetic distances between viruses circulating in successive seasons. We estimate the magnitude, severity, timing, transmission rate, age-specific patterns, and subtype dominance of each regional outbreak and find that genetic distance based on broad sets of epitope sites is the strongest evolutionary predictor of A(H3N2) virus epidemiology. Increased HA and NA epitope distance between seasons correlates with larger, more intense epidemics, higher transmission, greater A(H3N2) subtype dominance, and a greater proportion of cases in adults relative to children, consistent with increased population susceptibility. Based on random forest models, A(H1N1) incidence impacts A(H3N2) epidemics to a greater extent than viral evolution, suggesting that subtype interference is a major driver of influenza A virus infection ynamics, presumably via heterosubtypic cross-immunity. Seasonal influenza (flu) viruses cause outbreaks every winter. People infected with influenza typically develop mild respiratory symptoms. But flu infections can cause serious illness in young children, older adults and people with chronic medical conditions. Infected or vaccinated individuals develop some immunity, but the viruses evolve quickly to evade these defenses in a process called antigenic drift. As the viruses change, they can re-infect previously immune people. Scientists update the flu vaccine yearly to keep up with this antigenic drift. The immune system fights flu infections by recognizing two proteins, known as antigens, on the virus’s surface, called hemagglutinin (HA) and neuraminidase (NA). However, mutations in the genes encoding these proteins can make them unrecognizable, letting the virus slip past the immune system. Scientists would like to know how these changes affect the size, severity and timing of annual influenza outbreaks. Perofsky et al. show that tracking genetic changes in HA and NA may help improve flu season predictions. The experiments compared the severity of 22 flu seasons caused by the A(H3N2) subtype in the United States with how much HA and NA had evolved since the previous year. The A(H3N2) subtype experiences the fastest rates of antigenic drift and causes more cases and deaths than other seasonal flu viruses. Genetic changes in HA and NA were a better predictor of A(H3N2) outbreak severity than the blood tests for protective antibodies that epidemiologists traditionally use to track flu evolution. However, the prevalence of another subtype of influenza A circulating in the population, called A(H1N1), was an even better predictor of how severe A(H3N2) outbreaks would be. Perofsky et al. are the first to show that genetic changes in NA contribute to the severity of flu seasons. Previous studies suggested a link between genetic changes in HA and flu season severity, and flu vaccines include the HA protein to help the body recognize new influenza strains. The results suggest that adding the NA protein to flu vaccines may improve their effectiveness. In the future, flu forecasters may want to analyze genetic changes in both NA and HA to make their outbreak predictions. Tracking how much of the A(H1N1) subtype is circulating may also be useful for predicting the severity of A(H3N2) outbreaks.

The Antiretroviral Therapy (ART) Program for Malawi started in 2004 and the key providers in provision of ART services in Malawi include; the public sector, the private sector for-profit and non-profit and Christian Health Association of Malawi (CHAM). Since then, no known studies have been conducted to compare primary ART treatment outcomes by service provider type thus public, private and CHAM. In addition, information on variation of primary ART treatment outcomes by service provider type is not known and probably has not been published The Objective was to examine primary Antiretroviral Therapy outcomes in Blantyre District using ART data from 1st January 2017 to 31st December, 2018 in public, private and Christian Health Association of Malawi ART clinicsThis was a cross-sectional study and utilized both quantitative and qualitative methods. The quantitative method used facility level secondary data from the Malawi National ART Program in the Ministry of Health HIV AIDS Department. The qualitative method used in-depth interviews using an interview guide to key informants. Data was analysed using STATA statistical software package version 15.Analysis of Variance (ANOVA) was used to compare the variations of primary ART outcomes among in public, private and CHAM ART sites. To compare proportions, the researcher used Scheffe's-Test. The qualitative data was analyzed using thematic analysis to explain the relationship between the variables. Overall the findings indicate that Primary Antiretroviral Therapy Outcomes in Public, Private and CHAM ART Clinics are different. According to the results, there are more defaulters in the public ART clinics followed by private and lowest in the CHAM ART sites. This may be attributed lack of privacy and confidentiality, stigma and discrimination and long distance to the health facility which result in high cost expenses. Overall died on ART outcome is higher in private ART clinics compared to public and CHAM ART clinics and no significant differences between public and private ART clinics. The study has also clearly demonstrated that the private ART clinics have more transfer outs than public and CHAM due to change of location for work related issues of the clients. Retention in care (Alive on ART) is high in CHAM ART Clinics followed by public then lastly private. Generally, stop on ART is not a common outcome in all service provider types.Overall the findings indicate that Primary Antiretroviral Therapy Outcomes in Public, Private and CHAM ART Clinics are different. Some of the factors contributing to the primary Art outcomes include; lack of privacy and confidentiality, stigma and discrimination and long distance to the health facility which result in high cost expenses and change of location for work related issues of the clients. Generally, stop on ART is not a common outcome in all service provider types.

Purpose – The purpose of this paper is to investigate the impact of organisational business acumen and social network structure on the earnings and labour precarity experienced by creative industry workers. Design/methodology/approach – Results from a survey that collected data from a random sample of 289 creative workers are analysed using structural equation modelling. Mediating effects of social network structure are explored. Findings – Results support the qualitative findings of Crombie and Hagoort (2010) who claim that organisational business acumen is a significant enabler for creative workers. Further, social network structure has a partial mediating effect in mitigating labour precarity. Research limitations/implications – This exploratory study is novel in its use of a quantitative approach to understand the relationship between labour and social network dynamics of the creative industries. For this reason, developed scales, while robust in exploratory and confirmatory factor analysis, warrant further application and maturity. Practical implications – The organisational business acumen of creative workers is found to mitigate labour precarity and increase perceived earnings. Social implications – The results from this study call for policy and management shifts, to focus attention on developing business proficiency of creative workers, in an effort to curb labour precarity in the creative industries, and enhance positive spillovers into other sectors. Originality/value – The paper fills a gap in knowledge regarding the impact of organisational business acumen and social network structure on the pay and working conditions of people working in a sector that is dominated by self-employed and freelance arrangements.

Inflammation of the lung is characterized by the influx of increased numbers of various leukocytes including polymorphonuclear leukocyte (PMN) neutrophils. In addition to cells, numerous studies have pointed to the role of tumor necrosis factor-alpha in the inflammatory process. This study addresses a previously unrecognized interaction between neutrophil-derived myeloperoxidase (MPO) and resident alveolar macrophages (AMø). Rat AMø exposed to either enzymatically active recombinant MPO or enzymatically inactive MPO (iMPO) exhibited an increased respiratory burst (RB). When iMPO was employed, the enhancement of the RB was greater than that observed with MPO. Although the RB was greater with iMPO, macrophage (Mø)-mediated intracellular candidic activity was equivalent for both MPO and iMPO. It is known that pro- inflammatory cytokines contribute to the inflammatory process. When rat AMø were exposed to both forms of myeloperoxidase, iMPO demonstrated greater upregulation of cytokine genes as well as product. These data suggest that at the site of inflammation, neutrophil-derived MPO and iMPO stimulate AMø, resulting in an increased inflammatory and cytotoxic state, and thereby contributing to the general lung inflammatory response.

Purpose - Integrated supplier management (ISM), new product development (NPD) and knowledge sharing (KS) practices are three primary business activities utilised to enhance manufacturers' business performance (BP). The purpose of this paper is to empirically investigate the relationships between these three business activities (i.e. ISM, NPD, KS) and BP in a Taiwanese electronics manufacturing context.Design methodology approach - A questionnaire survey is first administered to a sample of electronic manufacturing companies operating in Taiwan to elicit the opinions of technical and managerial professionals regarding business activities and BP within their companies. A total of 170 respondents from 83 companies respond to the survey. Factor, correlation and path analysis are undertaken on this quantitative data set to derive the key factors which leverage business outcomes in these companies. Following empirical analysis, six semi-structured interviews are undertaken with manufacturing executives to provide qualitative insights into the underlying reasons why certain business activity factors are the strongest predictors of BP.Findings - The investigation shows that the ISM, NPD and KS constructs all play an important role in the success of company operations and creating business outcomes. Specifically, the key factors within these constructs which influenced BP are: supplier evaluation and selection; design simplification and modular design; information technology infrastructure and systems and open communication. Accordingly, sufficient financial and human resources should be allocated to these important activities to derive accelerated rates of improved BP. These findings are supported by the qualitative interviews with manufacturing executives.Originality value - The paper depicts the pathways to improved manufacturing BP, through targeting efforts into the above-mentioned factors within the ISM, NPD and KS constructs. Based on the empirical path model, and the specific insights derived from the explanatory interviews with manufacturing executives, the paper also provides a number of practical implications for manufacturing companies seeking to enhance their BP through improved operational activities.

Immune imprinting is a driver known to shape the anti-hemagglutinin (HA) antibody landscape of individuals born within the same birth cohort. With the HA and neuraminidase (NA) proteins evolving at different rates under immune selection pressures, anti-HA and anti-NA antibody responses since childhood influenza infections have not been evaluated in parallel at the individual level. This is partly due to the limited knowledge of changes in NA antigenicity, as seasonal influenza vaccines have focused on generating neutralising anti-HA antibodies against HA antigenic variants. Here we systematically characterised the NA antigenic variants of seasonal A(H1N1) viruses from 1977 to 1991 and completed the antigenic profile of N1 NAs from 1977 to 2015. We identified that NA proteins of A/USSR/90/77, A/Singapore/06/86, and A/Texas/36/91 were antigenically distinct and mapped N386K as a key determinant of the NA antigenic change from A/USSR/90/77 to A/Singapore/06/86. With comprehensive panels of HA and NA antigenic variants of A(H1N1) and A(H1N1)pdm09 viruses, we determined hemagglutinin inhibition (HI) and neuraminidase inhibition (NI) antibodies from 130 subjects born between 1950-2015. Age-dependent imprinting was observed for both anti-HA and anti-NA antibodies, with the peak HI and NI titers predominantly detected from subjects at 4-12 years old during the year of initial virus isolation, except the age-independent anti-HA antibody response against A(H1N1)pdm09 viruses. More participants possessed antibodies that reacted to multiple antigenically distinct NA proteins than those with antibodies that reacted to multiple antigenically distinct HA proteins. Our results support the need to include NA proteins in seasonal influenza vaccine preparations.