Design of AsLOV2 domain as a carrier of light-induced dissociable FMN photosensitizer

Flavin mononucleotide (FMN) is a highly efficient photosensitizer (PS) yielding singlet oxygen (1 O2 ). However, its 1 O2 production efficiency significantly decreases upon isoalloxazine ring encapsulation into the protein matrix in genetically encoded photosensitizers (GEPS). Reducing isoalloxazine ring interactions with surrounding amino acids by protein engineering may increase 1 O2 production efficiency GEPS, but at the same time weakened native FMN-protein interactions may cause undesirable FMN dissociation. Here, in contrast, we intentionally induce the FMN release by light-triggered sulfur oxidation of strategically placed cysteines (oxidation-prone amino acids) in the isoalloxazine-binding site due to significantly increased volume of the cysteinyl side residue(s). As a proof of concept, in three variants of the LOV2 domain of Avena sativa (AsLOV2), namely V416C, T418C, and V416C/T418C, the effective 1 O2 production strongly correlated with the efficiency of irradiation-induced FMN dissociation (wild type (WT) < V416C < T418C < V416C/T418C). This alternative approach enables us: (i) to overcome the low 1 O2 production efficiency of flavin-based GEPSs without affecting native isoalloxazine ring-protein interactions and (ii) to utilize AsLOV2, due to its inherent binding propensity to FMN, as a PS vehicle, which is released at a target by light irradiation.

There must be a way out: The consensual qualitative analysis of best coping practices during the COVID-19 pandemic

Despite the continuous efforts to understand coping processes, very little is known about the utilization of best coping strategies during the COVID-19 pandemic. In this study, we aimed to analyze the coping strategies of individuals who scored high on an adaptive coping questionnaire in order to understand the most adaptive coping strategies during the COVID-19 pandemic. We used consensual qualitative analysis in a team of four researchers and one auditor. The convenience sample from which we identified the high scorers comprised 1,683 participants (67% women, 32.35% men, and 0.65% did not report their gender) with a mean age of 31.02 years (SD = 11.99) ranging between 18 and 77 years old. Based on their scoring in the COPE Inventory, nine participants were selected from the sample with the highest scores in coping skills in at least two out of its 15 subscales. In-depth repeated interviews with six participants for the main analysis were conducted, and three were added to check the data saturation. The results showed that the most adaptive coping strategies used during the COVID-19 pandemic could be categorized into four main domains: self-compassion, compassion to others, compassion from others, and mutual compassion. The most frequently mentioned and the most elaborated upon by our respondents was the domain of self-compassion. The most interesting finding was the emergence of the fourth type of compassion, labeled mutual compassion, which referred to deliberate attempts to take care of oneself and others while suffering together in order to elevate the suffering for both. This kind of compassion might arise in the situations of collective suffering, such as a catastrophe or a pandemic and might have the additional benefit of bringing people closer to each other in difficult times.

Posttraumatic Growth and Its Measurement: A Closer Look at the PTGI’s Psychometric Properties and Structure

Despite negative connotations, surviving trauma can result in improvements in some domains of a person’s life. This phenomenon is known as posttraumatic growth (PTG), and it is typically measured using the Posttraumatic Growth Inventory (PTGI). Given the ambiguous results of the existing validation studies, the present study aimed to verify the psychometric properties of the Slovak version of the PTGI in a representative sample of Slovak citizens. Although the results suggest that a modified one-factor structure fit the data best, other issues, such as extremely high correlations between the latent factors related to the PTGI’s factor structure, were observed. It is likely that the application of the latent variable model does not represent the essence of PTG adequately and the network approach thus appears to be a far more suitable conceptualization of PTG. More detailed information on between-person differences and within-person changes in PTG could help to tailor more effective interventions or preventive programs.

Mechanochemistry for Energy Materials: Impact of High-Energy Milling on Chemical, Electric and Thermal Transport Properties of Chalcopyrite CuFeS2 Nanoparticles

Chalcopyrite CuFeS2 , a semiconductor with applications in chemical sector and energy conversion engineering, was synthetized in a planetary mill from elemental precursors. The synthesis is environmentally friendly, waste-free and inexpensive. The synthesized nano-powders were characterized by XRD, SEM, EDX, BET and UV/Vis techniques, tests of chemical reactivity and, namely, thermoelectric performance of sintered ceramics followed. The crystallite size of ∼13 nm and the strain of ∼17 were calculated for CuFeS2 powders milled for 60, 120, 180 and 240 min, respectively. The evolution of characteristic band gaps, Eg, and the rate constant of leaching, k, of nano-powders are corroborated by the universal evolution of the parameter SBET /X (SBET -specific surface area, X-crystallinity) introduced for complex characterization of mechanochemically activated solids in various fields such as chemical engineering and/or energy conversion. The focus on non-doped semiconducting CuFeS2 enabled to assess the role of impurities, which critically and often negatively influence the thermoelectric properties.

Tree mortality submodels drive simulated long-term forest dynamics: assessing 15 models from the stand to global scale

Models are pivotal for assessing future forest dynamics under the impacts of changing climate and management practices, incorporating representations of tree growth, mortality, and regeneration. Quantitative studies on the importance of mortality submodels are scarce. We evaluated 15 dynamic vegetation models (DVMs) regarding their sensitivity to different formulations of tree mortality under different degrees of climate change. The set of models comprised eight DVMs at the stand scale, three at the landscape scale, and four typically applied at the continental to global scale. Some incorporate empirically derived mortality models, and others are based on experimental data, whereas still others are based on theoretical reasoning. Each DVM was run with at least two alternative mortality submodels. Model behavior was evaluated against empirical time series data, and then, the models were subjected to different scenarios of climate change. Most DVMs matched empirical data quite well, irrespective of the mortality submodel that was used. However, mortality submodels that performed in a very similar manner against past data often led to sharply different trajectories of forest dynamics under future climate change. Most DVMs featured high sensitivity to the mortality submodel, with deviations of basal area and stem numbers on the order of 10-40% per century under current climate and 20-170% under climate change. The sensitivity of a given DVM to scenarios of climate change, however, was typically lower by a factor of two to three. We conclude that (1) mortality is one of the most uncertain processes when it comes to assessing forest response to climate change, and (2) more data and a better process understanding of tree mortality are needed to improve the robustness of simulated future forest dynamics. Our study highlights that comparing several alternative mortality formulations in DVMs provides valuable insights into the effects of process uncertainties on simulated future forest dynamics.