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Kirill Zhudenkov,1 Robert Palmér,2 Alexandra Jauhiainen,3 Gabriel Helmlinger,4,5 Oleg Stepanov,1 Kirill Peskov,1,6 Ulf G Eriksson,2 Ulrika Wählby Hamrén2 1M&S Decisions LLC, Moscow, Russia; 2Clinical Pharmacology & Quantitative Pharmacology, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Gothenburg, Sweden; 3BioPharma Early Biometrics and Statistical Innovation, Data Science & AI, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden; 4Clinical Pharmacology & Quantitative Pharmacology, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Boston, MA, USA; 5Clinical Pharmacology, Toxicology, Quantitative Sciences, Obsidian Therapeutics, Cambridge, MA, USA; 6I.M. Sechenov First Moscow State Medical University of the Russian Ministry of Health, Moscow, RussiaCorrespondence: Ulrika Wählby HamrénR&D, AstraZeneca, Pepparedsleden 1, Mölndal SE-431 83, SwedenTel +46 31 776 2295Email Ulrika.wahlby-hamren@astrazeneca.comBackground: Lung function, measured as forced expiratory volume in one second (FEV1), and exacerbations are two endpoints evaluated in chronic obstructive pulmonary disease (COPD) clinical trials. Joint analysis of these endpoints could potentially increase statistical power and enable assessment of efficacy in shorter and smaller clinical trials.Objective: To evaluate joint modelling as a tool for analyzing treatment effects in COPD clinical trials by quantifying the association between longitudinal improvements in FEV1 and exacerbation risk reduction.Methods: A joint model of longitudinal FEV1 and exacerbation risk was developed based on patient-level data from a Phase III clinical study in moderate-to-severe COPD (1740 patients), evaluating efficacy of fixed-dose combinations of a long-acting bronchodilator, formoterol, and an inhaled corticosteroid, budesonide. Two additional studies (1604 and 1042 patients) were used for external model validation and parameter re-estimation.Results: A significant (p< 0.0001) association between FEV1 and exacerbation risk was estimated, with an approximate 10% reduction in exacerbation risk per 100 mL improvement in FEV1, consistent across trials and treatment arms. The risk reduction associated with improvements in FEV1 was relatively small compared to the overall exacerbation risk reduction for treatment arms including budesonide (10– 15% per 160 μg budesonide). High baseline breathlessness score and previous history of exacerbations also influenced the risk of exacerbation.Conclusion: Joint modelling can be used to co-analyze longitudinal FEV1 and exacerbation data in COPD clinical trials. The association between the endpoints was consistent and appeared unrelated to treatment mechanism, suggesting that improved lung function is indicative of an exacerbation risk reduction. The risk reduction associated with improved FEV1 was, however, generally small and no major impact on exacerbation trial design can be expected based on FEV1 alone. Further exploration with other longitudinal endpoints should be considered to further evaluate the use of joint modelling in analyzing COPD clinical trials.Keywords: lung function, bronchodilator, anti-inflammatory

Today, the Metaverse consists of various platforms, including digital twins of the physical world as well as virtual and blended digital-material environments that offer immersive experiences for individual users. By going beyond solely physical or virtual realms, these platforms unlock new possibilities for exploration, experimentation, and interaction. This makes it possible to transcend the limitations of innovation processes confined to physical locations, so the Metaverse is thus poised to drive groundbreaking innovations. This article explores the Metaverse as an innovation platform, its opportunities and challenges, including the role of generative AI in it. It discusses how the Metaverse, as a collaboration, creativity, and technological platform, supports innovation potential. By embracing the possibilities and challenges offered by the Metaverse and leveraging the capabilities of generative AI within it, a future in which individuals can truly explore novel synergies between the physical and digital realms, thriving various kinds of innovations. It is crucial to achieve holistic sustainability impacts both within the Metaverse innovation platform and as its outputs.

A successful crop rotation choice is key to the profitability and sustainability of farm management and may simultaneously have an impact on soil organic carbon (SOC) content. In this study, we estimated how changes in crop rotations affected SOC balance in Finland between 2009 and 2018, using geospatial data and Bayesian modeling. The area designated for perennial-dominated and diverse cereal rotations increased over the study period. Perennial grassland rotation was found to have a positive impact on SOC balance, while rotations dominated by annual crops did not differ in their impacts on SOC content. At the national scale, changes in Finnish crop rotations resulted in an estimated annual mitigation of the loss of SOC content by 1336 Mg C year−1 in mineral soils and reduced the carbon dioxide emissions of organic soils by 10,475 Mg C year−1. The combined effect of these two contributions is 11,811 Mg C year−1, with an 80% probability interval of (−6600; 30,300) Mg C year−1. While the overall impact of changes in crop rotations on SOC is relatively small, a continued change to more diverse and perennial-dominated crop rotations may have other agronomic and environmental benefits, e.g. on resilience and biodiversity.

Background The etiology behind different types of chronic sialadenitis (CS), some of which exhibit IgG4 overexpression, is unknown. Further, IgG4-related disease (IgG4-RD) commonly affects the submandibular gland, but its relationship to IgG4-overexpressing CS, and the antigen triggering IgG4 overexpression, remain unknown.Materials and Methods By qPCR, we assessed the presence of 21 DNA-viruses causing IgG4 overexpression in submandibular gland tissue from patients with IgG4-positive and IgG4-negative CS. Healthy submandibular glands and glands with sialolithiasis without CS were used as controls. We examined the distribution of HHV-7, HHV-6B and B19V DNA, within virus PCR-positive tissues with RNAscope in-situ hybridization (RISH).Results We detected DNA from seven viruses in 48/61 samples. EBV DNA was more prevalent within the IgG4-positive samples (6/29; 21%) than the IgG4-negative ones (1/19; 5.3%). B19V DNA was more prevalent within the IgG4-negative samples (5/19; 26%) than the IgG4-positive ones (4/29; 14%). The differences in virus prevalence were not statistically significant. Of the IgG4-RD samples (n = 3) one contained HHV-6B DNA. RISH only showed signals of HHV-7.Conclusions None of the studied viruses are implicated as triggering IgG4-overexpression in CS. Although our results do not confirm viral etiology in the examined conditions, they provide valuable information on the prevalence of viruses in both diseased and healthy submandibular gland tissue.

This study aims to expand and diversify the analysis of crop yields in organic production by utilising the long-term statistical data reported by farmers. We examined the variation of the harvested yields of organic arable crops (season’s ad hoc harvest) on Finnish organic farms and determined the yield potential for different organic crops and in comparison, with conventional counterparts. When comparing median yields per ha, organic yields were on average 65% of conventional yields. The mean yield difference significantly differed between plant species. Among the analysed crops, faba bean performed best, gaining on average 84% of conventional yields, while organic cereals attained an average of 54–68% of conventional yields. The ratio of organic and conventional median yields was compared between geographical regions, and significant regional differences were found. The performance of organic cereals remained stable over the years, but the relative performance of the organic grain legumes, pea and faba bean, depended more on growing conditions. An average yield gap between the best-performing and median farms was 38% for organic farms and 28% for conventional farms. This indicates that Finnish organic farms have greater potential to improve the yield level than conventional farms.

We compiled published peer-reviewed CO2, CH4, and N2O data on managed drained organic forest soils in boreal and temperate zones to revisit the current Tier 1 default emission factors (EFs) provided in the IPCC (2014) Wetlands Supplement: to see whether their uncertainty may be reduced; to evaluate possibilities for breaking the broad categories used for the IPCC EFs into more site-type-specific ones; and to inspect the potential relevance of a number of environmental variables for predicting the annual soil greenhouse gas (GHG) balances, on which the EFs are based. Despite a considerable number of publications applicable for compiling EFs being added, only modest changes were found compared to the Tier 1 default EFs. However, the more specific site type categories generated in this study showed narrower confidence intervals compared to the default categories. Overall, the highest CO2 EFs were found for temperate afforested agricultural lands and boreal forestry-drained sites with very low tree stand productivity. The highest CH4 EFs in turn prevailed in boreal nutrient-poor forests with very low tree stand productivity and temperate forests irrespective of nutrient status, while the EFs for afforested sites were low or showed a sink function. The highest N2O EFs were found for afforested agricultural lands and forestry-drained nutrient-rich sites. The occasional wide confidence intervals could be mainly explained by single or a few highly deviating estimates rather than the broadness of the categories applied. Our EFs for the novel categories were further supported by the statistical models connecting the annual soil GHG balances to site-specific soil nutrient status indicators, tree stand characteristics, and temperature-associated weather and climate variables. The results of this synthesis have important implications for EF revisions and national emission reporting, e.g. by the use of different categories for afforested sites and forestry-drained sites, and more specific site productivity categories based on timber production potential.

Predictive information on plant diseases could help to reduce and optimize the usage of pesticides in agriculture. This research presents classification procedures with linear discriminant analysis to predict three possible severity levels of net blotch in spring barley in Finland. The weather data utilized for classification included mathematical transformations, namely features of outdoor temperature and air humidity with calculated dew point temperature values. Historical field observations of net blotch density were utilized as a target class for the identification of classifiers. The performance of classifiers was analyzed in sliding data windows of two weeks with selected, cumulative, summed feature values. According to classification results from 36 yearly data sets, the prediction of net blotch occurrence in spring barley in Finland can be considered as a linearly separable classification task. Furthermore, this can be achieved with linear discriminant analysis by combining the output probabilities of separate binary classifiers identified for each severity level of net blotch disease. In this case, perfect classification with a resolution of three different net blotch severity levels was achieved during the first 50 days from the beginning of the growing season. This strongly suggests that real-time classification based on a few weather variables measured on a daily basis can be applied to estimate the severity of net blotch in advance. This allows application of the principles of integrated pest management (IPM) and usage of pesticides only when there is a proven need.

False news refers to false, fake, or misleading information presented as real news. In recent years, there has been a noticeable increase in false news on the Internet. The goal of this paper was to study the automatic detection of such false news using machine learning and natural language processing techniques and to determine which techniques work the most effectively. This article first studies what constitutes false news and how it differs from other types of misleading information. We also study the results achieved by other researchers on the same topic. After building a foundation to understand false news and the various ways of automatically detecting it, this article provides its own experiments. These experiments were carried out on four different datasets, one that was made just for this article, using 10 different machine learning methods. The results of this article were satisfactory and provided answers to the original research questions set up at the beginning of this article. This article could determine from the experiments that passive aggressive algorithms, support vector machines, and random forests are the most efficient methods for automatic false news detection. This article also concluded that more complex experiments, such as using multiple levels of identifying false news or detecting computer-generated false news, require more complex machine learning models.

This article contributes to the current discussions on academic teacher identity in universities. In the managerial university and in the context of expanding HE, teaching has become a more complex, more problematised and more managed activity. This article approaches identity as identification on different levels of academic work and asks: What values and conceptions of teaching work do academics identify with? What kind of elements strengthen or constrain the teacher identity of academics? Research data consist of 14 in-depth interviews with eleven academics at one Finnish multidisciplinary university. Academics recognised meaningful and rewarding elements of teaching work and also pointed out problems and tensions associated with being a teacher. Those elements informing academic teacher identity often appeared to be empowering and constraining at the same time. Furthermore, the perceptions and experiences of interviewees were strongly contextualised. Obviously, academic teacher identity is more a local question than that of researcher identity.