Lingonberry (Vaccinium vitis-idaea L.) grows in a range of nature types in the boreal zone, and understanding factors affecting the abundance of the plant, as well as mapping its spatial distribution, is important. The abundance of the species can be an indicator of ecosystem changes, and lingonberry can also be a source for commercial utilisation of berry resources. Using country-wide data from 6404 field plots of the Norwegian national forest inventory (NFI), we modelled the relationship between lingonberry cover and airborne laser scanning (ALS) and satellite metrics and bioclimatic variables describing the forest structure, terrain, soil properties and climate using a generalised mixed-effects model with a quasipoisson distribution. The validation carried out with an independent set of 2124 NFI plots indicated no obvious bias in predictions. The most important predictors were found to be interactions between dominant tree species, stand basal area and latitude, as well as the reflectance in the near-infrared band from Sentinel-2 satellite imagery, the dominant height based on the ALS variable and the long-term mean summer (June–August) temperature. The results provide an indicator of the effects of global warming, as well as the possibility of giving forest management prescriptions that favour lingonberry and locating the most abundant lingonberry sites in Norwegian forests.

The IDA Indoor Climate and Energy (IDA ICE) simulation tool is used to model a research greenhouse in Bucharest, Romania, equipped with a recently implemented energy system that includes an integrated heat pump system, Air Handling Units (AHUs), a dry cooler, and boreholes for thermal energy storage. The integrated heat pump system is designed to provide year-round heating and cooling of the greenhouse. The performance of the system is assessed through simulations conducted for one week in summer (July 2023) and one week in winter (December 2023), using recorded weather data for each respective period. Results from the IDA ICE model are compared with values derived from onsite sensor measurements. During the summer week, the model generally aligns with measured air temperatures in the greenhouse compartment. The total cooling simulated in the model is 26.5 kWh/m2, compared to 27.2 kWh/m2 calculated from onsite measurements. Hourly discrepancies were observed, potentially due to the heat pump system operating at reduced capacity during this summer. In the winter week, the measured and simulated temperatures fall within a comparable range. The calculated and simulated cumulative heating show excellent agreement, both at 15.1 kWh/m2.

In the present work, we consider area minimizing currents in the general setting of arbitrary codimension and arbitrary boundary multiplicity. We study the boundary regularity of 2d area minimizing currents, beyond that, several results are stated in the more general context of (C0,α0,r0)-almost area minimizing currents of arbitrary dimension m and arbitrary codimension taking the boundary with arbitrary multiplicity. Furthermore, we do not consider any type of convex barrier assumption on the boundary, in our main regularity result which states that the regular set, which includes one-sided and two-sided points, of any 2d area minimizing current T is an open dense set in the boundary.

Soil moisture content (SMC), as a pivotal component in the energy and matter exchange processes within the soil-plant-atmosphere continuum, plays a crucial role in surface water dynamics, energy fluxes, and carbon cycling within ecosystems. The development of remote sensing technology has offered new perspectives for monitoring soil moisture at regional scales. Unmanned aerial vehicles (UAV) equip with multispectral remote sensing technology have distinct advantages for vegetation monitoring at the field scale, including rapidity and cost-effectiveness, which has superior applicability and practicality at the field scale. Therefore, in a 5a "Daya" late-maturing citrus orchard, the vegetation index (VI) and texture feature (TF) information of citrus canopy based on UAV multi-spectral images as well as soil and plant analyzer development (SPAD) of citrus physiological parameter were extracted. These different data sources were integrated into the framework of the random forest algorithm (RF) and genetic algorithm-optimized random forest (GA-RF) to evaluate the accuracy of surface SMC (SSMC) estimation in citrus orchard. The Biswas model was utilized to simulate the root zone SMC (RSMC) in citrus orchard. The spatiotemporal variations of soil moisture in citrus orchards were analyzed, and the potential of low-cost sensor-equipped drones in rapidly acquiring spatial and temporal distribution information of soil moisture at a large regional scale was explored. The results indicated that the GA-RF model outperformed the RF model in estimating citrus orchard SMC (with R2 ranging from 0.502 to 0.949 and RMSE ranging from 0.552 to 3.166% for GA-RF, compared to R2 ranging from 0.430 to 0.936 and RMSE ranging from 0.587 to 3.449% for the RF). The GA-RF model using VI+SPAD as inputs exhibited the best performance for SMC at depths of 5cm, 10cm, 20cm and 40cm (SMC5, SMC10, SMC20 and SMC40) across four citrus growth stages (R2 ranging from 0.793 to 0.949 at 5cm, R2 ranging from 0.702 to 0.938 at 10cm, R2 ranging from 0.714 to 0.927 at 20cm). In bud bust to flowering, young fruit and fruit maturation stages (stage Ⅰ, ⅠⅠ and ⅠⅤ), all models demonstrated good accuracy in estimating SMC at depth of 10cm (R2 ranging from 0.567 to 0.908 in stage Ⅰ, with R2 ranging from 0.681 to 0.916 in stage ⅠⅠ and R2 ranging from 0.579 to 0.938 in stage ⅠⅤ). In fruit expansion stage (stage III), the models performed best in predicting SMC5 (R2 ranging from 0.698 to 0.861). The Biswas model was constructed to simulate SMC40 by utilizing the inverted SMC10 and SMC20, thereby generating spatiotemporal distribution maps of SMC at different depths in citrus orchard. The SSMC was susceptible to environmental factors, exhibiting significant spatiotemporal heterogeneity. In summary, this study illustrated that the integration of multiple data sources into GA-RF model enhanced the estimation performance of SMC at different growth stages of late-maturing citrus orchards in the Southwest China. Additionally, it enabled the rapid and efficient monitoring of spatiotemporal variations in SMC, providing an effective method and practical foundation for precision irrigation and improved water use efficiency in agricultural fields.

Abstract The importance of extreme event attribution rises as climate change causes severe damage to populations resulting from unprecedented events. In February 2019, a planetary wave shifted along the U.S.–Canadian border, simultaneously leading to troughing with anomalous cold events and ridging over Alaska and northern Canada with abnormal warm events. Also, a dry-stabilized anticyclonic circulation over low latitudes induced warm extreme events over Mexico and Florida. Most attribution studies compare the climate model simulations under natural or actual forcing conditions and assess probabilistically from a climatological point of view. However, in this study, we use multiple ensembles from an operational forecast model, promising statistical as well as dynamically constrained attribution assessment, often referred to as the storyline approach to extreme event attribution. In the globally averaged results, increasing CO2 concentrations lead to distinct warming signals at the surface, resulting mainly from diabatic heating. Our study finds that CO2-induced warming eventually affects the possibility of extreme events in North America, quantifying the impact of anthropogenic forcing over less than a week’s forecast simulation. Our study assesses the validity of the storyline approach conditional on the forecast lead times, which is hindered by rising noise in CO2 signals and the declining performance of the forecast model. The forecast-based storyline approach is valid for at least half of the land area within a 6-day lead time before the target extreme occurrence. Our attribution results highlight the importance of achieving net-zero emissions ahead of schedule to reduce the occurrence of severe heatwaves.

Cyclosporin A (CsA), an immunosuppressive drug used in transplant recipients, inhibits graft rejection by binding to cyclophilins and competitively inhibiting calcineurin. While concerns about respiratory infections in immunosuppressed patients exist, contradictory data emerged during the COVID-19 pandemic, prompting investigations into CsA's impact on viral infections. This study explores CsA's antiviral effects on SARS-CoV-2 Omicron BA.1, Delta variants, and human parainfluenza virus 3 (HPIV3) using an exvivo model of human airway epithelium (HAE). CsA exhibited a dose-dependent antiviral effect against the SARS-CoV-2 Delta variant, reducing viral load over 10 days. However, no significant impact was observed against SARS-CoV-2 Omicron or HPIV3, indicating a virus-specific effect. At high concentrations, CsA was associated with an increase of IL-8 and a decrease of IFNλ expression in infected and noninfected HAE. This study highlights the complexity of CsA's antiviral mechanisms, more likely involving intricate inflammatory pathways and interactions with specific viral proteins. The research provides novel insights into CsA's effects on respiratory viruses, emphasizing the need for understanding drug-virus interactions in optimizing therapeutic approaches for transplant recipients and advancing knowledge on immunosuppressive treatments' implications on respiratory viral infections. Limitations include the model's inability to assess T lymphocyte activation, suggesting the necessity for further comprehensive studies to decipher the intricate dynamics of immunosuppressive treatments on respiratory viral infections.

BackgroundCardiovascular disease originates in early life. We aimed to investigate the association between breastfeeding in infancy and cardiovascular disease in adult life. MethodsWe followed 364,240 participants from UK Biobank aged 40–73 years from 2006 – 2010 to 2021. Information on breastfeeding in infancy was self-reported by questionnaire. Cox proportional hazard regression models were used to estimate the hazard ratios (HR) and 95% confidence intervals (CI) for the association between breastfeeding and cardiovascular disease in middle-aged and older adulthood. The multivariable Cox models were used by adjusting for the age (used as the time scale), sex, ethnicity, assessment centre, birth weight, multiple birth status, maternal smoking during pregnancy, Townsend deprivation index, smoking status, alcohol drinker status, physical activity, and menopausal status for women. Binary and multinomial multivariable logistic regression models were used to explore the associations of breastfeeding in infancy with cardiovascular disease risk factors including obesity, body composition, metabolic and inflammatory disorders. ResultsDuring a median of 12.6 years of follow-up, we documented 29,796 new cases of cardiovascular disease, including 24,797 coronary heart disease and 6229 stroke. The multivariable adjusted HRs for breastfed versus non-breastfed were 0.94 (95% CI: 0.91, 0.96) for cardiovascular disease, 0.94 (95% CI: 0.91, 0.96) for coronary heart disease, and 0.95 (95% CI: 0.89, 1.01) for stroke. Furthermore, the strength of observed association between breastfeeding and cardiovascular disease seems to decrease with age (P for interaction <0.001), and increase with polygenic risk for cardiovascular disease (P for interaction <0.001). Consistently, breastfeeding in infancy was associated with cardiovascular disease risk factors including lower body mass index 0.92 (95% CI: 0.89, 0.95), body fat percentage 0.85 (95% CI: 0.83, 0.87), android to gynoid fat ratio 0.89 (95% CI: 0.83, 0.96), visceral adipose tissue 0.92 (95% CI: 0.84, 1.01), as well as lower C-reactive protein level 0.95 (95% CI: 0.94, 0.97) and a lower risk of metabolic syndrome 0.89 (95% CI: 0.85, 0.92). ConclusionsBreastfeeding in infancy was associated with a lower risk of cardiovascular disease in middle-aged and older adulthood. Promoting breastfeeding is vital not only for promoting child health, but also for halting the increasing trend of cardiovascular disease in adults.