Anisotropic relaxation in NADH excited states studied by polarization-modulation pump-probe transient spectroscopy

We present the results of experimental and theoretical studies of fast anisotropic relaxation and rotational diffusion in the first electron excited state of biological coenzyme NADH in water-ethanol solutions. The experiments have been carried out by means of a novel polarization-modulation transient method and fluorescence polarization spectroscopy. For interpretation of the experimental results a model of the anisotropic relaxation in terms of scalar and vector properties of transition dipole moments and based on the Born-Oppenheimer approximation has been developed. The results obtained suggest that the dynamics of anisotropic rovibronic relaxation in NADH under excitation with 100~fs pump laser pulses can be characterised by a single vibration relaxation time $\tau_v$ laying in the range 2--15~ps and a single rotation diffusion time $\tau_r$ laying in the range 100--450~ps a subject of ethanol concentration. The dependence of the times $\tau_v$ and $\tau_r$ on the solution polarity (static permittivity) and viscosity has been determined and analyzed. Limiting values of an important parameter $\langle P_2(\cos\theta(t))\rangle$ describing the rotation of the transition dipole moment in the course of vibrational relaxation has been determined from experiment as function of the ethanol concentration and analyzed.Comment: 14 pages, 13 figure

FLORA: a novel method to predict protein function from structure in diverse superfamilies

Predicting protein function from structure remains an active area of interest, particularly for the structural genomics initiatives where a substantial number of structures are initially solved with little or no functional characterisation. Although global structure comparison methods can be used to transfer functional annotations, the relationship between fold and function is complex, particularly in functionally diverse superfamilies that have evolved through different secondary structure embellishments to a common structural core. The majority of prediction algorithms employ local templates built on known or predicted functional residues. Here, we present a novel method (FLORA) that automatically generates structural motifs associated with different functional sub-families (FSGs) within functionally diverse domain superfamilies. Templates are created purely on the basis of their specificity for a given FSG, and the method makes no prior prediction of functional sites, nor assumes specific physico-chemical properties of residues. FLORA is able to accurately discriminate between homologous domains with different functions and substantially outperforms (a 2–3 fold increase in coverage at low error rates) popular structure comparison methods and a leading function prediction method. We benchmark FLORA on a large data set of enzyme superfamilies from all three major protein classes (α, β, αβ) and demonstrate the functional relevance of the motifs it identifies. We also provide novel predictions of enzymatic activity for a large number of structures solved by the Protein Structure Initiative. Overall, we show that FLORA is able to effectively detect functionally similar protein domain structures by purely using patterns of structural conservation of all residues

Pandemic Boredom: Little Evidence That Lockdown-Related Boredom Affects Risky Public Health Behaviors Across 116 Countries

Some public officials have expressed concern that policies mandating collective public health behaviors (e.g., national/regional "lockdown ") may result in behavioral fatigue that ultimately renders such policies ineffective. Boredom, specifically, has been singled out as one potential risk factor for noncompliance. We examined whether there was empirical evidence to support this concern during the COVID-19 pandemic in a large cross-national sample of 63,336 community respondents from 116 countries. Although boredom was higher in countries with more COVID-19 cases and in countries that instituted more stringent lockdowns, such boredom did not predict longitudinal within-person decreases in social distancing behavior (or vice versa; n = 8,031) in early spring and summer of 2020. Overall, we found little evidence that changes in boredom predict individual public health behaviors (handwashing, staying home, self-quarantining, and avoiding crowds) over time, or that such behaviors had any reliable longitudinal effects on boredom itself. In summary, contrary to concerns, we found little evidence that boredom posed a public health risk during lockdown and quarantine

Domain architecture evolution of pattern-recognition receptors

In animals, the innate immune system is the first line of defense against invading microorganisms, and the pattern-recognition receptors (PRRs) are the key components of this system, detecting microbial invasion and initiating innate immune defenses. Two families of PRRs, the intracellular NOD-like receptors (NLRs) and the transmembrane Toll-like receptors (TLRs), are of particular interest because of their roles in a number of diseases. Understanding the evolutionary history of these families and their pattern of evolutionary changes may lead to new insights into the functioning of this critical system. We found that the evolution of both NLR and TLR families included massive species-specific expansions and domain shuffling in various lineages, which resulted in the same domain architectures evolving independently within different lineages in a process that fits the definition of parallel evolution. This observation illustrates both the dynamics of the innate immune system and the effects of “combinatorially constrained” evolution, where existence of the limited numbers of functionally relevant domains constrains the choices of domain architectures for new members in the family, resulting in the emergence of independently evolved proteins with identical domain architectures, often mistaken for orthologs

Lives versus Livelihoods? Perceived economic risk has a stronger association with support for COVID-19 preventive measures than perceived health risk

This paper examines whether compliance with COVID-19 mitigation measures is motivated by wanting to save lives or save the economy (or both), and which implications this carries to fight the pandemic. National representative samples were collected from 24 countries (N = 25,435). The main predictors were (1) perceived risk to contract coronavirus, (2) perceived risk to suffer economic losses due to coronavirus, and (3) their interaction effect. Individual and country-level variables were added as covariates in multilevel regression models. We examined compliance with various preventive health behaviors and support for strict containment policies. Results show that perceived economic risk consistently predicted mitigation behavior and policy support—and its effects were positive. Perceived health risk had mixed effects. Only two significant interactions between health and economic risk were identified—both positive

Identifying important individual‐ and country‐level predictors of conspiracy theorizing: a machine learning analysis

Psychological research on the predictors of conspiracy theorizing—explaining important social and political events or circumstances as secret plots by malevolent groups—has flourished in recent years. However, research has typically examined only a small number of predictors in one, or a small number of, national contexts. Such approaches make it difficult to examine the relative importance of predictors, and risk overlooking some potentially relevant variables altogether. To overcome this limitation, the present study used machine learning to rank-order the importance of 115 individual- and country-level variables in predicting conspiracy theorizing. Data were collected from 56,072 respondents across 28 countries during the early weeks of the COVID-19 pandemic. Echoing previous findings, important predictors at the individual level included societal discontent, paranoia, and personal struggle. Contrary to prior research, important country-level predictors included indicators of political stability and effective government COVID response, which suggests that conspiracy theorizing may thrive in relatively well-functioning democracies

Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic

Before vaccines for coronavirus disease 2019 (COVID-19) became available, a set of infection-prevention behaviors constituted the primary means to mitigate the virus spread. Our study aimed to identify important predictors of this set of behaviors. Whereas social and health psychological theories suggest a limited set of predictors, machine-learning analyses can identify correlates from a larger pool of candidate predictors. We used random forests to rank 115 candidate correlates of infection-prevention behavior in 56,072 participants across 28 countries, administered in March to May 2020. The machine-learning model predicted 52% of the variance in infection-prevention behavior in a separate test sample—exceeding the performance of psychological models of health behavior. Results indicated the two most important predictors related to individual-level injunctive norms. Illustrating how data-driven methods can complement theory, some of the most important predictors were not derived from theories of health behavior—and some theoretically derived predictors were relatively unimportant