Adverse Event Assessment of Upadacitinib: A Pharmacovigilance Study Based on the FAERS Database.

PURPOSE: Upadacitinib, a Janus kinase (JAK) inhibitor, has been approved by the FDA to treat various autoimmune conditions. This study assessed its adverse events by analyzing reports from the FDA Adverse Event Reporting System (FAERS). METHODS: FAERS data from Q3 2019 to Q4 2023 were extracted, and disproportionality analyses were conducted using four statistical measures, reporting odds ratio, proportionate reporting ratio, Bayesian confidence propagation neural network, and empirical Bayesian geometric mean. RESULTS: A total of 6 879 398 adverse event reports were collected, with 37 700 reports identifying upadacitinib as the "primary suspected." These reports involved 24 system organ classes and 246 preferred terms that met the criteria across all four algorithms. The distribution of adverse events was assessed separately for female and male patients. Further analysis of the top 25 preferred terms revealed that, although the system organ classes were similar between sexes, the specific adverse events differed. The adverse events were analyzed by gender, showing musculoskeletal and skin disorders were prevalent and severe in male patients, while musculoskeletal issues, infections, and abnormal laboratory tests were common in female patients. Unexpected events like trigger finger, biliary sepsis, and serious events such as oral neoplasm were also identified. CONCLUSION: This study provides real-world evidence for the safety evaluation of upadacitinib and underscores the need to monitor sex-specific adverse events. Future prospective studies are necessary to confirm these pharmacovigilance findings.

Improved deep belief network for estimating mango quality indices and grading: A computer vision-based neutrosophic approach.

This research introduces a revolutionary machinet learning algorithm-based quality estimation and grading system. The suggested work is divided into four main parts: Ppre-processing, neutroscopic model transformation, Feature Extraction, and Grading. The raw images are first pre-processed by following five major stages: read, resize, noise removal, contrast enhancement via CLAHE, and Smoothing via filtering. The pre-processed images are then converted into a neutrosophic domain for more effective mango grading. The image is processed under a new Geometric Mean based neutrosophic approach to transforming it into the neutrosophic domain. Finally, the prediction of TSS for the different chilling conditions is done by Improved Deep Belief Network (IDBN) and based on this; the grading of mango is done automatically as the model is already trained with it. Here, the prediction of TSS is carried out under the consideration of SSC, firmness, and TAC. A comparison between the proposed and traditional methods is carried out to confirm the efficacy of various metrics.

Study on Data Preprocessing for Machine Learning Based on Semiconductor Manufacturing Processes.

Various data types generated in the semiconductor manufacturing process can be used to increase product yield and reduce manufacturing costs. On the other hand, the data generated during the process are collected from various sensors, resulting in diverse units and an imbalanced dataset with a bias towards the majority class. This study evaluated analysis and preprocessing methods for predicting good and defective products using machine learning to increase yield and reduce costs in semiconductor manufacturing processes. The SECOM dataset is used to achieve this, and preprocessing steps are performed, such as missing value handling, dimensionality reduction, resampling to address class imbalances, and scaling. Finally, six machine learning models were evaluated and compared using the geometric mean (GM) and other metrics to assess the combinations of preprocessing methods on imbalanced data. Unlike previous studies, this research proposes methods to reduce the number of features used in machine learning to shorten the training and prediction times. Furthermore, this study prevents data leakage during preprocessing by separating the training and test datasets before analysis and preprocessing. The results showed that applying oversampling methods, excluding KM SMOTE, achieves a more balanced class classification. The combination of SVM, ADASYN, and MaxAbs scaling showed the best performance with an accuracy and GM of 85.14% and 72.95%, respectively, outperforming all other combinations.

Sub-Aperture Synthetic Aperture Radar Imaging of Fixed-Platform Beam-Steering Radar for Blast Furnace Burden Surface Detection.

Due to the scheme of fixed-platform beam-steering radar and the space of the blast furnace being subjected to harsh environmental influences, the traditional detection methods of burden surface are limited by geometric distortion, noncoherent clutter, and noise interference, which leads to an increase in the image entropy value and the equivalent number of views, makes the density distribution of burden surface show a diffuse state, and greatly affects the stability and accuracy. In this paper, a new fixed-platform beam-steering radar synthetic aperture radar imaging method (FPBS-SAR) is proposed in the sensory domain of the blast furnace environment. From the perspective of fixed-platform beam-steering radar motion characteristics, the target range-azimuth coupled distance history model under the sub-aperture is established, the azimuthal Doppler variation characteristics of the fixed-platform beam-steering process are analyzed, and the compensation function of the transform domain for geometric disturbance correction is proposed. For noncoherent noise suppression in blast furnaces, the trimmed geometric mean-order-likelihood CFAR method is proposed to take into account the information of burden surface and clutter suppression. To verify the method, point target simulation and imaging for the industrial field measurement data are carried out. The results indicate that geometric distortion is well eliminated, the image entropy value and the equivalent number of views have decreased, and noncoherent noise in blast furnaces is suppressed.

Hepatitis B antibody levels after different doses of hepatitis B vaccination: a retrospective study based on hospitalized children.

Many studies have investigated the positivity rate of hepatitis B surface antibody (HBsAb) after hepatitis B vaccine (HepB) immunization. However, the antibody level, assessed monthly or at more frequent intervals after each of the three doses, particularly within the first year after birth, has not been previously reported. To elucidate the level of antibody formation at various times after vaccination, the current study used the available detection data of HBsAb in hospitalized children to analyze the HBsAb level after immunization combined with their vaccination history. Both the positivity rate and geometric mean concentration (GMC) increased sequentially with immunization doses, reaching their peaks earlier after the third dose than after the first two doses, and the rate of HBsAb positivity was able to reach 100% between 11 and 90 days after completing the three doses of HepB. Within one year after receiving the three doses, the antibody positivity rate and GMC were maintained above 90% and 100 mIU/mL, respectively, and subsequently steadily declined, reaching the lowest value in the 9th and 10th years. The current findings reveal, in more detail, the level of antibody formation at different times following each dose of HepB in hospitalized children, particularly in the age group up to one year after vaccination. For the subjects of this study, we prefer to believe that the proportion of HBsAb non-response should be less than 5% after full immunization with HepB, provided that the appropriate time for blood collection is chosen.

Concurrent measurement of serum and radiomic biomarkers in the clinical investigation of equine musculoskeletal injuries: A prospective pilot study.

The prevention of musculoskeletal injuries and their related welfare and economic impacts represent an immediate priority for the horse racing industry. This prospective pilot study aimed to evaluate a method to quantitatively analyze scintigraphic features of specific anatomical regions of the horse's appendicular skeleton in combination with secondary measures of musculoskeletal metabolism in blood. Twelve horses referred for scintigraphic assessment of lameness were enrolled. Blood samples were collected immediately prior to the administration of radiotracer. Serum concentrations associated with bone turnover were determined for the following biomarkers: C-terminal telopeptides of type I collagen, proteoglycans and sulfated glycosaminoglycans, collagen type II, osteocalcin, and procollagen II C-terminal propeptide. Scintigraphic images underwent radiomic analysis of discrete regions of the distal limbs and these data were correlated to bone turnover markers. Three lame horses demonstrated asymmetrical radiomic abnormalities. The concentration of osteocalcin in the lame horses was significantly higher when compared to the control group, while no significant changes were observed for the other screened serum biomarkers. Findings from the current study provided evidence that radiomic analysis of equine scintigraphy is feasible. This method has the potential to interrogate which serum markers are associated with musculoskeletal injuries.

Evaluating heavy metal pollution risks and enzyme activity in soils with intensive hazelnut cultivation under humid ecological conditions.

In order to promote sustainable agriculture and ensure food security, it has become more vital to identify the causes of soil pollution in agricultural areas. This study was carried out in order to determine the danger of heavy metal contamination in hazelnut production areas and to take the appropriate actions in accordance with the study's findings. In this context, the main objectives of this study were to (i) determine some physical, chemical, and biological properties and heavy metal concentrations of different soils in intensive hazelnut cultivation areas under humid ecological conditions; (ii) reveal the heavy metal pollution risks of these areas by their enrichment factor, contamination factor, geo-accumulation index, degree of contamination, pollution load index, and potential ecological risk index; (iii) analyze the quality of soils contaminated with heavy metals by their total enzyme activity index and the geometric mean of enzymatic activities; and (iv) explore the correlation between heavy metals and soil enzyme activity indices. According to our results, the average concentrations of heavy metals in the study area ranked as Fe > Mn > Zn > Cr > Ni > Cu > Co > Pb > Cd. Based on EF, the area was evaluated as between deficiency to low enrichment and moderate enrichment for all elements except for Cd. When the parameters used to assess the risk of heavy metal contamination were evaluated, it was determined that the risk of contamination of other elements in the study area, except Cd, was low. Finally, analyzing the heavy metals and soil enzyme activity indices shows that there is a negative correlation between Ni and GMea and TEI.

Effect of catalyzed diesel particulate filter and its catalyst loading on emission characteristics of a non-road diesel engine.

In this study, the effects of a diesel oxidation catalyst (DOC) coupled with a catalyzed diesel particulate filter (CDPF) with different catalyst loadings on the power, fuel consumption, gaseous and particulate emissions from a non-road diesel engine were investigated. Results showed that the after-treatment had a negligible effect on the power and fuel consumption. The reduction effect of the DOC on the CO and hydrocarbon (HC) increased with the engine load. Further reductions occurred coupling with the CDPF. Increasing the catalyst loading resulted in a more significant reduction in the HC emissions than CO emissions. The DOC could increase the NO2 proportion to 37.9%, and more NO2 was produced when coupled with the CDPF below 250°C; above 250°C, more NO2 was consumed. The after-treatment could reduce more than 99% of the particle number (PN) and 98% of the particle mass (PM). Further reductions in the PN and PM occurred with a higher CDPF catalyst loading. The DOC had a better reduction effect on the nucleation particles than the accumulation ones, but the trend reversed with the CDPF. The DOC shifted the particle size distribution (PSD) to larger particles with an accumulation particle proportion increasing from 13% to 20%, and the geometric mean diameter (GMD) increased from 18.2 to 26.0 nm. The trend reversed with the CDPF and the accumulation particle proportion declined to less than 10%. A lower catalyst loading on the CDPF led to a higher proportion of nucleation particles and a smaller GMD.

Finding the adaptive needles in a population-structured haystack: A case study in a New Zealand mollusc.

Genetic adaptation to future environmental conditions is crucial to help species persist as the climate changes. Genome scans are powerful tools to understand adaptive landscapes, enabling us to correlate genetic diversity with environmental gradients while disentangling neutral from adaptive variation. However, low gene flow can lead to both local adaptation and highly structured populations, and is a major confounding factor for genome scans, resulting in an inflated number of candidate loci. Here, we compared candidate locus detection in a marine mollusc (Onithochiton neglectus), taking advantage of a natural geographical contrast in the levels of genetic structure between its populations. O. neglectus is endemic to New Zealand and distributed throughout an environmental gradient from the subtropical north to the subantarctic south. Due to a brooding developmental mode, populations tend to be locally isolated. However, adult hitchhiking on rafting kelp increases connectivity among southern populations. We applied two genome scans for outliers (Bayescan and PCAdapt) and two genotype-environment association (GEA) tests (BayeScEnv and RDA). To limit issues with false positives, we combined results using the geometric mean of q-values and performed association tests with random environmental variables. This novel approach is a compromise between stringent and relaxed approaches widely used before, and allowed us to classify candidate loci as low confidence or high confidence. Genome scans for outliers detected a large number of significant outliers in strong and moderately structured populations. No high-confidence GEA loci were detected in the context of strong population structure. However, 86 high-confidence loci were associated predominantly with latitudinally varying abiotic factors in the less structured southern populations. This suggests that the degree of connectivity driven by kelp rafting over the southern scale may be insufficient to counteract local adaptation in this species. Our study supports the expectation that genome scans may be prone to errors in highly structured populations. Nonetheless, it also empirically demonstrates that careful statistical controls enable the identification of candidate loci that invite more detailed investigations. Ultimately, genome scans are valuable tools to help guide further research aiming to determine the potential of non-model species to adapt to future environments.

Impact of irrigation water type and sampling frequency on Microbial Water Quality Profiles required for compliance with U.S. Food Safety Modernization Act Produce Safety Rule standards.

The U.S. Food Safety Modernization Act (FSMA) Produce Safety Rule (PSR) requires that farmers generate a Microbial Water Quality Profile (MWQP) from 20 samples per agricultural water source, taken over 2-4 years and five annual samples thereafter. Farmers must use the MWQP to ascertain a geometric mean (GM) of ≤126 CFU/100 mL and statistical threshold value (STV) of ≤410 CFU/100 mL of generic Escherichia coli. Farmers are responsible for collecting samples and paying for testing, incurring a financial and time burden. To determine if testing frequency can be reduced without compromising accuracy, water samples (n = 279) were collected from twelve sites in the U.S. Mid-Atlantic region from 2016 to 2018 comprising tidal brackish river, non-tidal fresh river, pond, vegetable processing, and reclaimed water. The GM and STV were calculated for all sites and water types using all samples, and for multiple sub-samples of <20 from each site and water type. A Monte Carlo simulation was used to determine the proportion of sub-sample sizes that yielded the same determination as the entire sample size of PSR standard compliance. Four sites, two pond and two reclaimed water sites, complied with PSR GM and STV requirements when using the entire sample set. When a water source's calculated GM and STV using the entire sample set hovered close to the PSR thresholds, sub-sample sizes approached the recommended 20 samples to reach a congruent compliance determination. However, 99% agreement was obtained with a sub-sample of five when the absolute difference between the GM and STV from total samples and the PSR thresholds was ≥2.6 and 4.5 log CFU/100 mL E. coli, respectively. These findings suggest that under certain conditions the MWQP may be generated with well below 20 samples, reducing the economic burden on farmers while still maintaining a representative MWQP.

A Cloud Computing-Based Modified Symbiotic Organisms Search Algorithm (AI) for Optimal Task Scheduling.

The search algorithm based on symbiotic organisms' interactions is a relatively recent bio-inspired algorithm of the swarm intelligence field for solving numerical optimization problems. It is meant to optimize applications based on the simulation of the symbiotic relationship among the distinct species in the ecosystem. The task scheduling problem is NP complete, which makes it hard to obtain a correct solution, especially for large-scale tasks. This paper proposes a modified symbiotic organisms search-based scheduling algorithm for the efficient mapping of heterogeneous tasks to access cloud resources of different capacities. The significant contribution of this technique is the simplified representation of the algorithm's mutualism process, which uses equity as a measure of relationship characteristics or efficiency of species in the current ecosystem to move to the next generation. These relational characteristics are achieved by replacing the original mutual vector, which uses an arithmetic mean to measure the mutual characteristics with a geometric mean that enhances the survival advantage of two distinct species. The modified symbiotic organisms search algorithm (G_SOS) aims to minimize the task execution time (makespan), cost, response time, and degree of imbalance, and improve the convergence speed for an optimal solution in an IaaS cloud. The performance of the proposed technique was evaluated using a CloudSim toolkit simulator, and the percentage of improvement of the proposed G_SOS over classical SOS and PSO-SA in terms of makespan minimization ranges between 0.61-20.08% and 1.92-25.68% over a large-scale task that spans between 100 to 1000 Million Instructions (MI). The solutions are found to be better than the existing standard (SOS) technique and PSO.

Individual reversible plasticity as a genotype-level bet-hedging strategy.

Reversible plasticity in phenotypic traits allows organisms to cope with environmental variation within lifetimes, but costs of plasticity may limit just how well the phenotype matches the environmental optimum. An additional adaptive advantage of plasticity might be to reduce fitness variance, in other words: bet-hedging to maximize geometric (rather than simply arithmetic) mean fitness. Here, we model the evolution of plasticity in the form of reaction norm slopes, with increasing costs as the slope or degree of plasticity increases. We find that greater investment in plasticity (i.e. a steeper reaction norm slope) is favoured in scenarios promoting bet-hedging as a response to multiplicative fitness accumulation (i.e. coarser environmental grains and fewer time steps prior to reproduction), because plasticity lowers fitness variance across environmental conditions. In contrast, in scenarios with finer environmental grain and many time steps prior to reproduction, bet-hedging plays less of a role and individual-level optimization favours evolution of shallower reaction norm slopes. However, the opposite pattern holds if plasticity costs themselves result in increased fitness variation, as might be the case for production costs of plasticity that depend on how much change is made to the phenotype each time step. We discuss these contrasting predictions from this partitioning of adaptive plasticity into short-term individual benefits versus long-term genotypic (bet-hedging) benefits, and how this approach enhances our understanding of the evolution of optimum levels of plasticity in examples from thermal physiology to advances in avian lay dates.

Measuring association among censored antibody titer data.

Censoring due to a limit of detection or limit of quantification happens quite often in many medical studies. Conventional approaches to deal with censoring when analyzing these data include, for example, the substitution method and the complete case (CC) analysis. More recently, maximum likelihood estimation (MLE) has been increasingly used. While the CC analysis and the substitution method usually lead to biased estimates, the MLE approach appears to perform well in many situations. This article proposes an MLE approach to estimate the association between two measurements in the presence of censoring in one or both quantities. The central idea is to use a copula function to join the marginal distributions of the two measurements. In various simulation studies, we show that our approach outperforms existing conventional methods (CC and substitution analyses). In addition, rank-based measures of global association such as Kendall's tau or Spearman's rho can be studied, hence, attention is not only confined to Pearson's product-moment correlation coefficient capturing solely linear association. We have shown in our simulations that our approach is robust to misspecification of the copula function or marginal distributions given a small association. Furthermore, we propose a straightforward MLE method to fit a (multiple) linear regression model in the presence of censoring in a covariate or both the covariate and the response. Given the marginal distribution of the censored covariate, our method outperforms conventional approaches. We also compare and discuss the performance of our method with multiple imputation and missing indicator model approaches.

The geometric mean is a superior frequency response averaging method for human body vibration.

The frequency response data of human body vibration are often used for standardisation, design of transport vehicles and occupational health and safety measures. This article shows that the commonly used methods of averaging frequency response spectra, such as arithmetic averaging in the complex or magnitude domain and median averaging, are not as suitable as the less commonly used geometric averaging in the complex domain. This is because it is necessary to minimise the deviation of the measured values about the mean value and to minimise the bias from the true mean value due to noise, distortion and nonlinearity. Practitioner summary: For averaging frequency response spectra, it is necessary to minimise the bias from the true mean value. This research shows that the commonly used averaging methods, such as arithmetic averaging in the complex or magnitude domain and the median, are not as suitable as geometric averaging in the complex domain. Abbreviations: H1 Estimator: frequency response function estimation method using the cross-spectrum of the output with the input divided by the auto-spectrum of the input; ISO: International Organization for Standardization; NHK: Nippon Hatsujo Kabushiki Kaisha; PCB: PCB Group ("PCB" is abbreviation for "PicoCoulomB"); RMIT: Royal Melbourne Institute of Technology; r.m.s.: root mean square.

A calibrated piecewise-linear FGM approach for travel destination recommendation during the COVID-19 pandemic.

After months of lockdown due to the COVID-19 pandemic, more people are planning regional trips because overseas travel is still not feasible. However, choosing a suitable travel destination during the COVID-19 pandemic is challenging because the factors critical to the selection process are very different from those usually considered. Furthermore, without sufficient literature or data for reference, existing methods based on psychological analyses or mining past experiences may not be applicable. Consequently, a fuzzy multi-criteria decision-making method - the calibrated piecewise-linear fuzzy geometric mean (FGM) approach - is proposed in this study for travel destination recommendation during the COVID-19 pandemic. The contribution of this research is twofold. First, the critical factors that affect the selection of a suitable travel destination during the COVID-19 pandemic are discussed. Second, the accuracy and efficiency using existing fuzzy analytic hierarchy process (FAHP) methods have been enhanced. The calibrated piecewise-linear FGM approach has been successfully applied to recommend suitable travel destinations to fifteen travelers for regional trips in Taiwan during the COVID-19 pandemic.

Dirichlet Polynomials and Entropy.

A Dirichlet polynomial d in one variable y is a function of the form d(y)=anny+⋯+a22y+a11y+a00y for some n,a0,…,an∈N. We will show how to think of a Dirichlet polynomial as a set-theoretic bundle, and thus as an empirical distribution. We can then consider the Shannon entropy H(d) of the corresponding probability distribution, and we define its length (or, classically, its perplexity) by L(d)=2H(d). On the other hand, we will define a rig homomorphism h:Dir→Rect from the rig of Dirichlet polynomials to the so-called rectangle rig, whose underlying set is R⩾0×R⩾0 and whose additive structure involves the weighted geometric mean; we write h(d)=(A(d),W(d)), and call the two components area and width (respectively). The main result of this paper is the following: the rectangle-area formula A(d)=L(d)W(d) holds for any Dirichlet polynomial d. In other words, the entropy of an empirical distribution can be calculated entirely in terms of the homomorphism h applied to its corresponding Dirichlet polynomial. We also show that similar results hold for the cross entropy.

Refined Young Inequality and Its Application to Divergences.

We give bounds on the difference between the weighted arithmetic mean and the weighted geometric mean. These imply refined Young inequalities and the reverses of the Young inequality. We also studied some properties on the difference between the weighted arithmetic mean and the weighted geometric mean. Applying the newly obtained inequalities, we show some results on the Tsallis divergence, the Rényi divergence, the Jeffreys-Tsallis divergence and the Jensen-Shannon-Tsallis divergence.

Impact of a care delivery redesign initiative for vascular surgery.

OBJECTIVE: A hospital-wide quality improvement process through a care delivery redesign (CDR) was initiated to improve patient care efficiency, clinical documentation, and length of stay (LOS). The impact of CDR was assessed through LOS, unplanned readmission rates, and hospital financial metrics. METHODS: The CDR team consisted of the Chief of Vascular Surgery, inpatient nurse practitioner, dedicated case manager, clinical documentation improvement specialist, and vascular surgery residents and faculty. The nurse practitioner facilitated patient care coordination, resident system-based education, and multidisciplinary collaboration. Tools created to track performance and to ensure sustainability included daily discussions of patient care barriers and solutions; standardized order sets; a mobile app for residents containing resident service expectations, disease-specific resources, and vascular surgery journal links; and a weekly inpatient tracker showing real-time patient care data. Outcome measures included LOS, case mix index, contribution margin, and unplanned readmissions. Each outcome was determined for all inpatient admissions the year before and the 12 months after CDR was initiated. Outcomes were compared between the two groups. RESULTS: Implementation of CDR resulted in a 23% decrease in LOS (P = .003), reducing the gap to the Centers for Medicare and Medicaid Services geometric mean LOS from 2.1 days to 0.5 day (P < .001). Clinical documentation resulted in an increase in case mix index of 10% (P = .011). The 30-day unplanned readmission rates did not change in the 12 months after CDR was initiated compared with the year before (P = .92). Financial data demonstrated decreased variable cost and increased revenue resulting in a $1.89 million increase in contribution margin. CONCLUSIONS: A CDR predicated on a dedicated service line advanced practitioner, clinical documentation education, weekly service tracker review, and real-time management of system-related barriers to patient care is described. Implementation of the CDR reduced hospital LOS with no change in unplanned readmissions and provided significant financial benefit to the hospital by increasing revenue and decreasing variable cost.

Antibodies of influenza A(H1N1)pdm09 virus in pigs' sera cross-react with other influenza A virus subtypes. A retrospective epidemiological interpretation of Norway's serosurveillance data from 2009-2017.

Since the incursion of influenza A(H1N1)pdm09 virus in 2009, serosurveillance every year of the Norwegian pig population revealed the herd prevalence for influenza A(H1N1)pdm09 (HIN1pdm09) has stabilised between 40% and 50%. Between 30 September 2009 and 14 September 2017, the Norwegian Veterinary Institute and Norwegian Food Safety Authority screened 35,551 pigs for antibodies to influenza A viruses (IAVs) from 8,636 herds and found 26% or 8,819 pigs' sera ELISA positive (titre ≥40). Subtyping these IAV antibodies from 8,214 pigs in 3,629 herds, by a routine haemagglutination inhibition test (HAIT) against four standard antigens produced 13,771 positive results (HAIT titre ≥40) of binding antibodies. The four antigen subtypes eliciting positive HAIT titre in descending frequencies were immunogen H1N1pdm09 (n = 8,200 or 99.8%), swine influenza A virus (SIVs) subtypes swH1N1 (n = 5,164 or 62%), swH1N2 (n = 395 or 5%) and swH3N2 (n = 12 or 0.1%). Of these 8,214 pig pigs sera, 3,039 produced homologous HAIT subtyping, almost exclusively immunogen H1N1pdm09 (n = 3,026 or 99.6%). Using HAIT titre of pig and herd geometric mean titre (GMT) as two continuous outcome variables, and with the data already structured hierarchically, we used mixed effects linear regression analysis to investigate the impact of predictors of interests had on the outcomes. For the full data, the predictors in the regression model include categorical predictors antigen subtype (H1N1pdm09, swH1N1, swH1N2 & swH3N2), and production type (sow herd or fattening herd), ordinal predictors year (longitudinally from 2009 to 2017) and number of antigens in heterologous reactions (1, 2, 3, 4) in the same pig serum. The last predictor, the proportion of HAIT positive (antigen specific) in tested pigs within the herd, was a continuous predictor, which served as a proxy for days post-infection (dpi) or humoral response time in the pig or herd. Regression analysis on individual pig HAIT titres showed that antigen as a predictor, the coefficient for immunogen H1N1pdm09 was at least fourfold higher (P < 0.001) than the three SIVs antigen subtypes, whose much lower coefficients were statistically no different between the three SIVs antigen subtypes. Correspondingly, for herd GMT, immunogen H1N1pdm09 was 28-40-fold higher than the three SIVs antigen subtypes. Excluding the HAIT data of the three SIVs antigen subtypes, regression analysis focusing only on immunogen H1N1pdm09 increased greatly the coefficients of the predictors in the models. Homologous reactions (99.6% H1N1pdm09) have lower HAIT titres while the likelihood of the number of antigens involved in HAIT heterologous reactions in a single pig serum increased with higher HAIT titres of immunogen H1N1pdm09. For predictor 'production', sows and sow herds had higher HAIT titres and GMT compared to fattening pigs and fattening herds respectively. Herds with 'higher proportion of pigs tested positive' also had higher HAIT titre in the pig and herd GMT.

Bet-hedging across generations can affect the evolution of variance-sensitive strategies within generations.

In order to understand how organisms cope with ongoing changes in environmental variability, it is necessary to consider multiple adaptations to environmental uncertainty on different time scales. Conservative bet-hedging (CBH) represents a long-term genotype-level strategy maximizing lineage geometric mean fitness in stochastic environments by decreasing individual fitness variance, despite also lowering arithmetic mean fitness. Meanwhile, variance-prone (aka risk-prone) strategies produce greater variance in short-term payoffs, because this increases expected arithmetic mean fitness if the relationship between payoffs and fitness is accelerating. Using evolutionary simulation models, we investigate whether selection for such variance-prone strategies is counteracted by selection for bet-hedging that works to adaptively reduce fitness variance. In our model, variance proneness evolves in fine-grained environments (lower correlations among individuals in energetic state and/or payoffs), and with larger numbers of independent decision events over which resources accumulate prior to selection. Conversely, multiplicative fitness accumulation, caused by coarser environmental grain and fewer decision events selection, favours CBH via greater variance aversion. We discuss examples of variance-sensitive strategies in optimal foraging, migration, life histories and cooperative breeding using this bet-hedging perspective. By linking disparate fields of research studying adaptations to variable environments, we should be better able to understand effects of human-induced rapid environmental change.