Those results highlight that the soil O2 dynamic was the key variable triggering the N2O and CH4 productions. Therefore, detailed information of soil O2 availability could be highly beneficial for optimizing the strategies of organic amendments incorporation in the BSD technique.Pandemics, like the 1918 Spanish Influenza and COVID-19, spread through regions of the World in subsequent waves. Here we propose a consistent picture of the wave pattern based on the epidemic Renormalisation Group (eRG) framework, which is guided by the global symmetries of the system under time rescaling. We show that the rate of spreading of the disease can be interpreted as a time-dilation symmetry, while the final stage of an epidemic episode corresponds to reaching a time scale-invariant state. We find that the endemic period between two waves is a sign of instability in the system, associated to near-breaking of the time scale-invariance. This phenomenon can be described in terms of an eRG model featuring complex fixed points. Our results demonstrate that the key to control the arrival of the next wave of a pandemic is in the strolling period in between waves, i.e. when the number of infections grows linearly. Thus, limiting the virus diffusion in this period is the most effective way to prevent or delay the arrival of the next wave. In this work we establish a new guiding principle for the formulation of mid-term governmental strategies to curb pandemics and avoid recurrent waves of infections, deleterious in terms of human life loss and economic damage.The goal of this study was to develop a deep learning-based algorithm to predict temporomandibular joint (TMJ) disc perforation based on the findings of magnetic resonance imaging (MRI) and to validate its performance through comparison with previously reported results. The study objects were obtained by reviewing medical records from January 2005 to June 2018. 299 joints from 289 patients were divided into perforated and non-perforated groups based on the existence of disc perforation confirmed during surgery. Experienced observers interpreted the TMJ MRI images to extract features. Data containing those features were applied to build and validate prediction models using random forest and multilayer perceptron (MLP) techniques, the latter using the Keras framework, a recent deep learning architecture. The area under the receiver operating characteristic (ROC) curve (AUC) was used to compare the performances of the models. MLP produced the best performance (AUC 0.940), followed by random forest (AUC 0.918) and disc shape alone (AUC 0.791). The MLP and random forest were also superior to previously reported results using MRI (AUC 0.808) and MRI-based nomogram (AUC 0.889). Implementing deep learning showed superior performance in predicting disc perforation in TMJ compared to conventional methods and previous reports.Impaired glucose tolerance (IGT) increases cardiovascular risk and can enlarge myocardial infarction (MI) incidence and severity. There is lack of information about cardioprotective potential of glucose-lowering drugs in IGT. We aimed to evaluate the sustainability of myocardium to ischemia-reperfusion injury in diabetic and IGT rats and to study cardioprotective action of metformin and liraglutide. Type 2 diabetes mellitus (DM) and IGT were modelled in Wistar rats by high-fat diet and streptozotocin + nicotinamide. 4 weeks after rats were divided into 4 groups DM (without treatment) (n = 4), IGT (without treatment) (n = 4), IGT + MET (metformin 200 mg/kg per os once daily 8 weeks) (n = 4), IGT + LIRA (liraglutide 0.06 mg/kg s.c. once daily for 8 weeks) (n = 4). Control (n = 6) and high-fat diet (n = 8) groups were made for comparison. After 8 weeks ischemia-reperfusion injury in isolated hearts was performed. Hemodynamic parameters were evaluated and MI size was measured by staining of myocardium slices in tI size. Both MET and LIRA have infarct-limiting effect independent on their influence on glucose level. LIRA, but not MET, diminishes ischemic contracture in IGT rats.To quantitatively analyze changes in the inner components of the human crystalline lens during accommodation in adults. Eyes of 23 subjects were sequentially examined using CASIA2 Optical Coherence Tomography under 0D, – 3D and – 6D accommodation states. The anterior chamber depth (ACD), anterior and posterior crystalline lens radius of the curvature (ALRC and PLRC) were obtained using built-in software. The lens thickness (LT), lenticular nucleus thickness (NT), anterior cortex thickness (ACT), posterior cortex thickness (PCT), anterior and posterior lenticular nucleus radius of the curvature (ANRC and PNRC), anterior and posterior lenticular nucleus vertex (ANV and PNV) were quantified manually with the Image-pro plus software. During accommodation, the ACD became significantly shallower and LT significantly increased. For changes in the lens, the ALRC decreased by an average magnitude (related to accommodative stimuli) 0.44 mm/D, and PLRC decreased 0.09 mm/D. JAK inhibitor There was no difference for the ACT and PCT in different accommodation states. For lenticular nucleus response, NT increased on average by 30 μm/D. Both the ANRC and PNRC decreased on average by 212 μm/D and 115 μm/D respectively. The ANV moved forward on average by 0.07 mm under – 3D accommodative stimuli and 0.16 mm for – 6D. However, there was no statistically significant difference between different accommodation states in the PNV movement. Under accommodation stimulation, lens thickness changed mainly due to the lenticular nucleus, but not the cortex. For the lenticular nucleus, both the ANRC and PNRC decreased and ANRC changed the most. The anterior surface of the nucleus moved forward while the posterior surface of the nucleus moved backward but only slightly.Insects are essential for the reproduction of pollinator-dependent crops and contribute to the pollination of 87% of wild plants and 75% of the world’s food crops. Understanding pollen flow dynamics between plants and pollinators is thus essential to manage and conserve wild plants and ensure yields are maximized in food crops. However, the determination of pollen transfer in the field is complex and laborious. We developed a field experiment in a pollinator-dependent crop and used high throughput RNA sequencing (RNA-seq) to quantify pollen flow by measuring changes in gene expression between pollination treatments across different apple (Malus domestica Borkh.) cultivars. We tested three potential molecular indicators of successful pollination and validated these results with field data by observing single and multiple visits by honey bees (Apis mellifera) to apple flowers and measured fruit set in a commercial apple orchard. The first indicator of successful outcrossing was revealed via differential gene expression in the cross-pollination treatments after 6 h.