Are there non-linear relationships between alcohol consumption and long-term health?: a systematic review of observational studies employing approaches to improve causal inference

Background Research has long found ‘J-shaped’ relationships between alcohol consumption and certain health outcomes, indicating a protective effect of moderate consumption. However, methodological limitations in most studies hinder causal inference. This review aimed to identify all observational studies employing improved approaches to mitigate confounding in characterizing alcohol–long-term health relationships, and to qualitatively synthesize their findings. Methods Eligible studies met the above description, were longitudinal (with pre-defined exceptions), discretized alcohol consumption, and were conducted with human populations. MEDLINE, PsycINFO, Embase and SCOPUS were searched in May 2020, yielding 16 published manuscripts reporting on cancer, diabetes, dementia, mental health, cardiovascular health, mortality, HIV seroconversion, and musculoskeletal health. Risk of bias of cohort studies was evaluated using the Newcastle-Ottawa Scale, and a recently developed tool was used for Mendelian Randomization studies. Results A variety of functional forms were found, including reverse J/J-shaped relationships for prostate cancer and related mortality, dementia risk, mental health, and certain lipids. However, most outcomes were only evaluated by a single study, and few studies provided information on the role of alcohol consumption pattern. Conclusions More research employing enhanced causal inference methods is urgently required to accurately characterize alcohol–long-term health relationships. Those studies that have been conducted find a variety of linear and non-linear functional forms, with results tending to be discrepant even within specific health outcomes. Trial registration PROSPERO registration number CRD42020185861.

Optimal control methods for nonlinear parameter estimation in biophysical neuron models

Functional forms of biophysically-realistic neuron models are constrained by neurobiological and anatomical considerations, such as cell morphologies and the presence of known ion channels. Despite these constraints, neurons models still contain unknown static parameters which must be inferred from experiment. This inference task is most readily cast into the framework of state-space models, which systematically takes into account partial observability and measurement noise. Inferring only dynamical state variables such as membrane voltages is a well-studied problem, and has been approached with a wide range of techniques beginning with the well-known Kalman filter. Inferring both states and fixed parameters, on the other hand, is less straightforward. Here, we develop a method for joint parameter and state inference that combines traditional state space modeling with chaotic synchronization and optimal control. Our methods are tailored particularly to situations with considerable measurement noise, sparse observability, very nonlinear or chaotic dynamics, and highly uninformed priors. We illustrate our approach both in a canonical chaotic model and in a phenomenological neuron model, showing that many unknown parameters can be uncovered reliably and accurately from short and noisy observed time traces. Our method holds promise for estimation in larger-scale systems, given ongoing improvements in calcium reporters and genetically-encoded voltage indicators.

Mosaics of combinatorial designs for information-theoretic security

We study security functions which can serve to establish semantic security for the two central problems of information-theoretic security: the wiretap channel, and privacy amplification for secret key generation. The security functions are functional forms of mosaics of combinatorial designs, more precisely, of group divisible designs and balanced incomplete block designs. Every member of a mosaic is associated with a unique color, and each color corresponds to a unique message or key value. Every block index of the mosaic corresponds to a public seed shared between the two trusted communicating parties. The seed set should be as small as possible. We give explicit examples which have an optimal or nearly optimal trade-off of seed length versus color (i.e., message or key) rate. We also derive bounds for the security performance of security functions given by functional forms of mosaics of designs.

SPIN-CROSSOVER IRON(II) COORDINATION COM­POUNDS: FABRICATION OF FUNCTIONAL MATERIALS AND THEIR INTEGRATION INTO MICRO- AND NANOCONSTRUCTIONS

Development of micro- and nanosized spin-crossover (SCO) materials has become an important research direction within the past decade. Such an interest is associated with high perceptive of practical application of these materials in nanoelectronic devices. Therefore, researches working in the field of SCO put considerable efforts to obtain SCO complexes in various functional forms, such as nanoparticles, thin films, etc. Fabrication of these materials is realized through different chemical and/or lithographical approaches, which allow to adjust size, shape and even organization of nanoobjects. In this review theoretical background of SCO phenomenon is described, additionally different classes of coordination compounds exhibiting spin crossover are covered. It is demonstrated that electric field, temperature and light irradiation can be effectively used for switching and control of spin state in nanosized SCO systems. Cooperative SCO with transition close to room temperature, wide hysteresis loop and distinct thermochromic effect is most often observed for Fe(II) coordination complexes. Therefore, Fe(II) SCO compounds form one of the most perspective classes of compounds for obtaining functional materials. It is shown that integration of Fe(II) compounds into micro- and nanohybrid devi­ces allows to combine unique functional pro­perties in one material due to synergy between SCO and physical properties (luminescent, electrical, etc.) of the other component. As a result, SCO compounds are interesting not only from the fundamental point of view, but also from practical, thanks to the possibility of integration of SCO Fe(II) complexes as active materials in devices of different configurations. It is expected that obtaining of new Fe(II) coordination polymers with unique SCO cha­racteristics will favor the development of new functional materials and devices on their basis in the nearest future.

(Non-)Parametric Recoverability of Preferences and Choice Prediction

Simple functional forms for utility require restrictive structural assumptions that are often contrary to observed behavior. Even so, they are widely used in applied economic research. I address this issue using a two-part adaptive experimental design to compare the predictions of a popular parametric model of decision making under risk to those of non-parametric bounds on indifference curves. Interpreting the latter as an approximate upper bound, I find the parametric model sacrifices very little in terms of predictive success. This suggests that, despite their restrictiveness, simple functional forms may nevertheless be useful representations of preferences over risky alternatives.

Spectral density of individual trajectories of an active Brownian particle

We study analytically the single-trajectory spectral density (STSD) of an active Brownian motion as exhibited, for example, by the dynamics of a chemically-active Janus colloid. We evaluate the standardly-defined spectral density, {\it i.e.} the STSD averaged over a statistical ensemble of trajectories in the limit of an infinitely long observation time $T$, and also go beyond the standard analysis by considering the coefficient of variation $\gamma$ of the distribution of the STSD. Moreover, we analyse the finite-$T$ behaviour of the STSD and $\gamma$, determine the cross-correlations between spatial components of the STSD, and address the effects of translational diffusion on the functional forms of spectral densities. The exact expressions that we obtain unveil many distinctive features of active Brownian motion compared to its passive counterpart, which allow to distinguish between these two classes based solely on the spectral content of individual trajectories.

Time Wasters?

The Dark Triad personality traits have previously been linked with dysfunctional types of procrastination (i.e., delaying certain tasks). From an evolutionary perspective, procrastination is recognized for facilitating a fast life history strategy. The present study investigated links between active and passive procrastination and the extended Dark Tetrad personality traits (psychopathy, Machiavellianism, narcissism, sadism). Participants ( N = 357) were invited via Prolific data collection platform and Survey Circle research sites to participate in an online survey exploring personality and procrastination. Path analyses revealed that all Dark Tetrad traits positively predicted several aspects of active procrastination only. Narcissism emerged as the only negative predictor of passive procrastination. Rather than linking these traits with dysfunctional procrastination types only, our results highlight the importance of considering the Dark Tetrad about functional forms of procrastination, which may be more beneficial for facilitating a fast life history strategy.