Expansins facilitate cell expansion by mediating pH-dependent cell wall (CW) loosening. However, the role of expansins in controlling CW biomechanical properties in specific tissues and organs remains elusive. We monitored hormonal responsiveness and spatial specificity of expression and localization of expansins predicted to be the direct targets of cytokinin signaling in Arabidopsis (Arabidopsis thaliana). We found EXPANSIN1 (EXPA1) homogenously distributed throughout the CW of columella/lateral root cap, while EXPA10 and EXPA14 localized predominantly at 3-cell boundaries in the epidermis/cortex in various root zones. EXPA15 revealed cell-type-specific combination of homogenous vs. 3-cell boundaries localization. By comparing Brillouin frequency shift and AFM-measured Young's modulus, we demonstrated Brillouin light scattering (BLS) as a tool suitable for non-invasive in vivo quantitative assessment of CW viscoelasticity. Using both BLS and AFM, we showed that EXPA1 overexpression upregulated CW stiffness in the root transition zone (TZ). The dexamethasone-controlled EXPA1 overexpression induced fast changes in the transcription of numerous CW-associated genes, including several EXPAs and XYLOGLUCAN:XYLOGLUCOSYL TRANSFERASEs (XTHs), and associated with rapid pectin methylesterification determined by in situ Fourier-transform infrared spectroscopy in the root TZ. The EXPA1-induced CW remodeling is associated with the shortening of the root apical meristem, leading to root growth arrest. Based on our results, we propose that expansins control root growth by a delicate orchestration of CW biomechanical properties, possibly regulating both CW loosening and CW remodeling.

As drought and heat stress are major challenges for crop productivity under future climate changes, tolerant cultivars are highly in demand. This study investigated the potential of existing Nordic wheat genotypes to resist unfavorable conditions. Four genotypes were selected based on their heat sensitivity (heat-sensitive: LM19, SF1; heat-tolerant: LM62, NS3). At the tillering stage, the plants were subjected to four treatments under controlled conditions: control, drought, heat and combined drought and heat stress. The morpho-physiological performance was quantified during the early and late phase of stress, as well as the recovery phase. We applied an integrative image-based phenotyping approach monitoring plant growth dynamics by structural Red Green Blue (RGB) imaging, photosynthetic performance by chlorophyll fluorescence imaging and transpiration efficiency by thermal infrared imaging. The results demonstrated that the selected genotypes were moderately affected in their photosynthetic efficiency and growth under drought stress, whereas heat and combined stress caused rapid reductions in photosynthesis and growth. Furthermore, drought stress had a major impact on canopy temperature. The NS3 genotype was the most robust genotype, as indicated by its improved response under all stress treatments due to its relatively small biomass. However, the genotypes showed different tolerance to individual and combined stress.

The impact of drought on wheat (Triticum aestivum L.) production is shown, using an example data set of almost 60 yr from six climate‐specific regions in Poland. Drought was measured using the standardized precipitation index (SPI) and the hydro‐thermal coefficient of Selyaninov (HTC). Yield trends were estimated by Bayesian linear regression over two periods, 1961–1991 and 1992–2019, identified by a changepoint detection method. Bayesian inference is used as it allows the estimation of a credible interval of regression coefficients instead of point estimates and asymptotic confidence intervals, thus comparisons between regression coefficients are more meaningful. We detected an increase in yield in both time periods and in all regions. The average winter wheat yield increased by 97% in the first period and by 35% in the second (19.8–39.1 dt ha⁻¹ and 32.9–44.5 dt ha⁻¹, respectively). Spring wheat yield increased by 96% in the first period and by 42% in the second (16.8–37.9 and 22.9–32.5 dt ha⁻¹, respectively). Yield losses in drought years were estimated using the paired t test to compare mean difference between real yields and yields estimated from regression lines for nondrought years. The highest yield losses due to drought were in regions I (–19.3% spring wheats, –6.3% winter ones) and III (–16.1% spring and –8.3% winter wheats) over the 1992–2019 period.

The achievement of the optimal control of the disease is of cardinal importance in asthma treatment. As the control of the disease is sustained the medication should be gradually reduced and then stopped. Nevertheless, the discontinuation of asthma medication may lead to loss of disease control and eventually to an exacerbation of the disease. The goal of this paper is to examine the performance of Bayesian network classifiers in predicting asthma exacerbation based on several patient's parameters such as objective measurements and medical history data. In this study several Bayesian network classifiers are presented and evaluated. It is shown that the proposed semi-naive network classifier with the use of Backward Sequential Elimination and Joining algorithm is able to predict if a patient will have an exacerbation of the disease after his last assessment with 93.84% accuracy and 90.9% sensitivity. In addition, the resulting structure and the conditional probability tables give a clear view of the probabilistic relationships between the used factors. This network may help the clinicians to identify the patients who are at high risk of having an exacerbation after stopping the medication and to confirm which factors are the most important.

Plants adapt to continual changes in environmental conditions throughout their life spans. High-throughput phenotyping methods have been developed to noninvasively monitor the physiological responses to abiotic/biotic stresses on a scale spanning a long time, covering most of the vegetative and reproductive stages. However, some of the physiological events comprise almost immediate and very fast responses towards the changing environment which might be overlooked in long-term observations. Additionally, there are certain technical difficulties and restrictions in analyzing phenotyping data, especially when dealing with repeated measurements. In this study, a method for comparing means at different time points using generalized linear mixed models combined with classical time series models is presented. As an example, we use multiple chlorophyll time series measurements from different genotypes. The use of additional time series models as random effects is essential as the residuals of the initial mixed model may contain autocorrelations that bias the result. The nature of mixed models offers a viable solution as these can incorporate time series models for residuals as random effects. The results from analyzing chlorophyll content time series show that the autocorrelation is successfully eliminated from the residuals and incorporated into the final model. This allows the use of statistical inference.