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Experimental studies have shown that just 1 night of sleep loss impairs next-day performance, mood, and energy. Yet, little is known about the effects of consecutive sleep loss on daily well-being in participants’ own settings.

This study examined whether and how naturally occurring consecutive sleep loss is associated with day-to-day trajectories of affective and physical well-being.

Participants were adults (N = 1,958) from the Midlife in the United States Study who provided daily diary data for eight consecutive days. Consecutive sleep loss was operationalized as the within-person number of consecutive nights with <6 hr of sleep. Multilevel models evaluated the linear, quadratic, and cubic effects of consecutive sleep loss on daily well-being, after controlling for sociodemographic, health, and daily covariates.

Daily negative affect increased and positive affect decreased in curvilinear fashion as the number of consecutive sleep loss increased. For example, daily negative affect increased (linear), but the rate of increase decelerated as the number of consecutive sleep loss increased (quadratic). Results were consistent for the number and severity of physical symptoms. For negative affect and the severity of physical symptoms, cubic effect was also significant such that the rate of increase accelerated again in the days most distal to baseline (no sleep loss).

Consecutive sleep loss was associated with degraded trajectories of daily affective and physical well-being. Making efforts to break the vicious cycle of sleep loss may protect daily well-being in adults whose sleep time is often compromised.

Consecutive sleep loss was associated with degraded trajectories of daily affective and physical well-being. Making efforts to break the vicious cycle of sleep loss may protect daily well-being in adults whose sleep time is often compromised.

Is anogenital distance (AGD) shorter in testicular cancer (TC) survivors than in men from the general population, and is AGD affected by testosterone replacement therapy in adulthood?

AGD, measured as distance from anus to scrotum (AGDas), is shorter in TC survivors and does not change as a result of testosterone replacement therapy.

Animal studies have shown that AGD is a postnatal ‘read-out’ of foetal androgen action, and short AGD in male offspring is considered a sign of feminization caused by in utero disruption of the reproductive system. Likewise, measurement of AGD in human studies has suggested AGD to be part of the testicular dysgenesis syndrome hypothesis, which proposes that male reproductive disorders, such as hypospadias, cryptorchidism, some cases of impaired semen quality and TC, all share a common foetal origin.

The aim was to assess AGD in men with a history of TC and controls, and furthermore to examine AGD during testosterone replacement therapy in adulthood. Study participants wer209, 25 November 2016.A challenge in designing self-assembling building blocks is to ensure the target state is both thermodynamically stable and kinetically accessible. These two objectives are known to be typically in competition, but it is not known how to simultaneously optimize them. We consider this problem through the lens of multi-objective optimization theory we develop a genetic algorithm to compute the Pareto fronts characterizing the tradeoff between equilibrium probability and folding rate, for a model system of small polymers of colloids with tunable short-ranged interaction energies. We use a coarse-grained model for the particles’ dynamics that allows us to efficiently search over parameters, for systems small enough to be enumerated. For most target states there is a tradeoff when the number of types of particles is small, with medium-weak bonds favouring fast folding, and strong bonds favouring high equilibrium probability. The tradeoff disappears when the number of particle types reaches a value m*, that is usually much less than the total number of particles. This general approach of computing Pareto fronts allows one to identify the minimum number of design parameters to avoid a thermodynamic-kinetic tradeoff. However, we argue, by contrasting our coarse-grained model’s predictions with those of Brownian dynamics simulations, that particles with short-ranged isotropic interactions should generically have a tradeoff, and avoiding it in larger systems will require orientation-dependent interactions.In this work, based on the framework of preferential adsorption concept and analytical self-consistent field (SCF) theory, a model is proposed to investigate the reentrant transition experimentally observed from the thermoresponsive spherical brush in a series of aqueous alcohol solutions. The interaction between monomers is incorporated into the model. Conformational transitions of the spherical brush are quantitatively correlated to the physical parameters, including the number of adsorbed cosolvents which facilitates the solvent quality, the number of cosolvent bridges which drives the brush collapse, as well as their partition coefficients between the brush and the bulk solution. An analytical formula for the number of Kuhn segments per tethered chain is obtained based on the analytical SCF theory, which elucidates the flexibility of tethered chains in the intricate system of multicomponents involving the conformational transitions. Under the experimental conditions associated with the cosolvent-brush interaction, the variation of the monomer chemical potential with the monomer concentration indicates that the monomer distribution of the spherical brush remains continuous. FIIN-2 clinical trial The analysis based on the SFC theory also reveals that the distribution of adsorbed cosolvents is a positive parabola while the distribution of cosolvent bridges appears to be an exponential decay function, implying that the intervening space between tethered chains, rather than the number of adsorbed cosolvents, plays a crucial role in forming the cosolvent bridge. We demonstrate that the model formulated for the reentrant transition under weaker cosolvent-brush interactions provides guidelines for the one under stronger nanoparticle-brush interactions.The linear μ-carbido complex [Rh2(μ-C)Cl2(dppm)2] (dppm = bis(diphenylphosphino)methane) reacts with a benzyne equivalent (Me3SiC6H4OTf-2/F-) to afford [Rh2(μ-CC6H4)(μ-Cl)(C6H5)Cl2(μ-dppm)2], in which the benzyne moiety adds across one of the two metal-carbon double bonds.Given the highly possible existence of nickel and silicon in the Earth’s core, the study of the reaction between Ni and Si and the resulting structures at the pressure corresponding to that of the Earth’s core is highly required. Therefore, we have investigated the crystal structures of Ni-Si compounds at pressures of 0-350 GPa by adopting a crystal structure search algorithm in conjunction with first-principles calculations. We uncover two high Ni-content Ni5Si and Ni6Si compounds with 12-coordination Si bonded with Ni, with both showing strong chemical stability in the Earth’s core. Bonding analysis reveals that the Ni atoms in these Ni-Si compounds present oxidant features and act as electron acceptors. This distinctive anomaly is the natural result of the energy shifts of the Ni 3d and Si 3p bands, resulting in charge transfer from Si to Ni. By examining the key properties (e.g., density and sound velocities) of the Ni5Si and Ni6Si compounds, the obtained density lies within the range of the Earth’s inner core, and the estimated sound velocities are found to be consistent with seismic data. These results indicate that these two compounds could be considered as possible core constituents. Our findings provide valuable insights into the enigmatic Earth’s core as well as geophysical and geochemical processes.We demonstrate that deep learning techniques can be used to predict motility-induced phase separation (MIPS) in suspensions of active Brownian particles (ABPs) by creating a notion of phase at the particle level. Using a fully connected network in conjunction with a graph neural network we use individual particle features to predict to which phase a particle belongs. From this, we are able to compute the fraction of dilute particles to determine if the system is in the homogeneous dilute, dense, or coexistence region. Our predictions are compared against the MIPS binodal computed from simulation. The strong agreement between the two suggests that machine learning provides an effective way to determine the phase behavior of ABPs and could prove useful for determining more complex phase diagrams.Cynomorium songaricum is a medicinal, edible, and endangered plant species. Since inflorescences are not considered medicinal parts, their discard causes a waste of resources. To expand the medicinal uses of C. songaricum, we evaluated their chemistry and pharmacology by applying widely targeted metabolomics, network pharmacology, and molecular docking. Widely targeted metabolomics results indicated chemical diversity in C. songaricum with 599 compounds. Among them, 280 compounds were different between the succulent stem and inflorescence. With 218 upregulated compounds, inflorescence has more abundant compounds than the succulent stem, especially pigment compounds such as flavonols, flavones, and flavanones. Moreover, anthocyanin and proanthocyanidin were unique compounds in the inflorescence and succulent stem, respectively. Sixty-five compounds in inflorescence and 18 compounds in succulent stems were found to be associated with atherosclerosis in the network pharmacology analysis. Tests revealed that inflorescence had a stronger anti-atherosclerotic effect than succulent stems. Molecular docking analysis revealed that 30 compounds (29 pigment compounds) in inflorescence and 6 compounds (4 pigment compounds) in succulent stem showed strong binding affinities with three target proteins, namely ALB, MPO, and NOS2, especially amentoflavone, quercetin 7-O-rutinoside, and luteolin 7-O-glucoside (cynaroside). Results demonstrated that the inflorescence is rich in pigment compounds and has a potential anti-atherosclerosis effect. This study provides novel methods and ideas for the sustainable development of endangered medicinal plants.Using Brownian dynamics (BD) simulations we investigate the self-organization of a monolayer of chiral active particles with dipolar interactions. Each particle is driven by both, translational and rotational self-propulsion, and carries a permanent point dipole moment at its center. The direction of the translational propulsion for each particle is chosen to be parallel to its dipole moment. Simulations are performed at high dipolar coupling strength and a density below that related to motility-induced phase separation in simple active Brownian particles. Despite this restriction, we observe a wealth of phenomena including formation of two types of vortices, phase separation, and flocking transitions. To understand the appearance and disappearance of vortices in the many-particle system, we further investigate the dynamics of simple ring structures under the impact of self-propulsion.Knowledge of the microscopic structure of fluids and changes thereof with pressure and temperature is important for the understanding of chemistry and geochemical processes. In this work we investigate the influence of sodium chloride on the hydrogen-bond network in aqueous solution up to supercritical conditions. A combination of in situ X-ray Raman scattering and ab initio molecular dynamics simulations is used to probe the oxygen K-edge of the alkali halide aqueous solution in order to obtain unique information about the oxygen’s local coordination around the ions, e.g. solvation-shell structure and the influence of ion pairing. The measured spectra exhibit systematic temperature dependent changes, which are entirely reproduced by calculations on the basis of structural snapshots obtained via ab initio molecular dynamics simulations. Analysis of the simulated trajectories allowed us to extract detailed structural information. This combined analysis reveals a net destabilizing effect of the dissolved ions which is reduced with rising temperature.