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Cartilage matrix is a composite of discrete, but interacting suprastructures, i.e. cartilage fibers with microfibrillar or network-like aggregates and penetrating extrafibrillar proteoglycan matrix. The biomechanical function of the proteoglycan matrix and the collagen fibers are to absorb compressive and tensional loads, respectively. Here, we are focusing on the suprastructural organization of collagen fibrils and the degradation process of their hierarchical organized fiber architecture studied at high resolution at the authentic location within cartilage. We present electron micrographs of the collagenous cores of such fibers obtained by an improved protocol for scanning electron microscopy (SEM). Articular cartilages are permeated by small prototypic fibrils with a homogeneous diameter of 18 ± 5 nm that can align in their D-periodic pattern and merge into larger fibers by lateral association. Interestingly, these fibers have tissue-specific organizations in cartilage. They are twisted ropes in superficial regions of knee joints or assemble into parallel aligned cable-like structures in deeper regions of knee joint- or throughout hip joints articular cartilage. These novel observations contribute to an improved understanding of collagen fiber biogenesis, function, and homeostasis in hyaline cartilage.

Several metal surfaces, such as titanium, aluminum and copper, were exposed to highfluxes (in the range of 10 23 m −2 s −1 ) of low energy(<100 eV) Helium (He) ions. The surfaces were analyzed by scanningelectron microscopy and to get a better understanding on morphology changes both topview and cross sectional images were taken. Different surface modifications, such asvoids and nano pillars, are observed on these metals. The differences andsimilarities in the development of surface morphologies are discussed in terms ofthe material properties and compared with the results of similar experimentalstudies. The results show that He ions induced void growth and physical sputteringplay a significant role in surface modification using high fluxes of low energy Heions.

This paper outlines a hierarchical Bayesian framework for interoception, homeostatic/allostatic control, and meta-cognition that connects fatigue and depression to the experience of chronic dyshomeostasis. Specifically, viewing interoception as the inversion of a generative model of viscerosensory inputs allows for a formal definition of dyshomeostasis (as chronically enhanced surprise about bodily signals, or, equivalently, low evidence for the brain's model of bodily states) and allostasis (as a change in prior beliefs or predictions which define setpoints for homeostatic reflex arcs). Critically, we propose that the performance of interoceptive-allostatic circuitry is monitored by a metacognitive layer that updates beliefs about the brain's capacity to successfully regulate bodily states (allostatic self-efficacy). In this framework, fatigue and depression can be understood as sequential responses to the interoceptive experience of dyshomeostasis and the ensuing metacognitive diagnosis of low allostatic self-efficacy. While fatigue might represent an early response with adaptive value (cf. sickness behavior), the experience of chronic dyshomeostasis may trigger a generalized belief of low self-efficacy and lack of control (cf. learned helplessness), resulting in depression. This perspective implies alternative pathophysiological mechanisms that are reflected by differential abnormalities in the effective connectivity of circuits for interoception and allostasis. We discuss suitably extended models of effective connectivity that could distinguish these connectivity patterns in individual patients and may help inform differential diagnosis of fatigue and depression in the future.

In its full sense, perception rests on an agent's model of how its sensory input comes about and the inferences it draws based on this model. These inferences are necessarily uncertain. Here, we illustrate how the Hierarchical Gaussian Filter (HGF) offers a principled and generic way to deal with the several forms that uncertainty in perception takes. The HGF is a recent derivation of one-step update equations from Bayesian principles that rests on a hierarchical generative model of the environment and its (in)stability. It is computationally highly efficient, allows for online estimates of hidden states, and has found numerous applications to experimental data from human subjects. In this paper, we generalize previous descriptions of the HGF and its account of perceptual uncertainty. First, we explicitly formulate the extension of the HGF's hierarchy to any number of levels; second, we discuss how various forms of uncertainty are accommodated by the minimization of variational free energy as encoded in the update equations; third, we combine the HGF with decision models and demonstrate the inversion of this combination; finally, we report a simulation study that compared four optimization methods for inverting the HGF/decision model combination at different noise levels. These four methods (Nelder-Mead simplex algorithm, Gaussian process-based global optimization, variational Bayes and Markov chain Monte Carlo sampling) all performed well even under considerable noise, with variational Bayes offering the best combination of efficiency and informativeness of inference. Our results demonstrate that the HGF provides a principled, flexible, and efficient-but at the same time intuitive-framework for the resolution of perceptual uncertainty in behaving agents.

A bstract Massless spinning correlators in cosmology are extremely complicated. In contrast, the scattering amplitudes of massless particles with spin are very simple. We propose that the reason for the unreasonable complexity of these correlators lies in the use of inconvenient kinematic variables. For example, in de Sitter space, consistency with unitarity and the background isometries imply that the correlators must be conformally covariant and also conserved. However, the commonly used kinematic variables for correlators do not make all of these properties manifest. In this paper, we introduce twistor space as a powerful way to satisfy all kinematic constraints. We show that conformal correlators of conserved currents can be written as twistor integrals, where the conservation condition translates into holomorphicity of the integrand. The functional form of the twistor-space correlators is very simple and easily bootstrapped. For the case of two- and three-point functions, we verify explicitly that this reproduces known results in embedding space. We also perform a half-Fourier transform of the twistor-space correlators to obtain their counterparts in momentum space. We conclude that twistors provide a promising new avenue to study conformal correlation functions that exposes their hidden simplicity.

The amount of CO 2 embodied in trade has substantially increased over the last decades. We contribute to understanding the reasons for this evolution by studying the trends and some drivers of the carbon intensity of trade over the period 1995–2009 in 41 countries and 35 sectors. Our empirical analysis relies on the World Input-Output Database (WIOD) to compute embodied carbon emissions. Our main findings are the following. First, average emission intensity of traded goods is higher than average emission intensity of final demand. Second, relatively “dirty” countries tend to specialize in emission-intensive sectors. Third, the share of goods produced in emission-intensive countries is rising. Finally, we find that coal abundance (measured as fuel rent and controlling for reverse causality) leads both to a specialization in “dirty” sectors and to an increase in emissions per output when controlling for sector structure, which amounts to a fossil fuel endowment effect. These findings suggest trade liberalization may increase global emissions and therefore highlight the importance of considering trade when designing CO 2 reduction strategies.

Background Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by impairments of social interaction and communication as well as repetitive, stereotyped behaviour. Previous research indicates that ASD without intellectual impairment is associated with underactivity and reduced functional connectivity of the brain’s mentalizing pathway, to which the right temporo-parietal junction (rTPJ) serves as an important entry point and hub. In this study, we aim to utilize functional magnetic resonance imaging (fMRI) to localize activation maxima in the rTPJ and other regions involved in social cognition to generate individualized targets for neuro-navigated, intermittent theta burst stimulation (iTBS) in order to modulate brain activity in a region centrally engaged in social information processing. Methods In this single-blind, randomized, between-subject neuroimaging-guided brain stimulation study we plan to recruit 52 participants with prediagnosed ASD and 52 controls without ASD aged between 18 and 65 years. Participants will be classified into two groups and will randomly receive one session of either verum- or sham-iTBS. Effects will be assessed by using well-established experimental tasks that interrogate social behaviour, but also use computational modelling to investigate brain stimulation effects at this level. Discussion This study aims to use personalized, non-invasive brain stimulation to alter social information processing in adults with and without high-functioning ASD, which has not been studied before with a similar protocol or a sample size of this magnitude. By doing so in combination with behavioural and computational tasks, this study has the potential to provide new mechanistic insights into the workings of the social brain. Trial registration German Clinical Trial Register, DRKS-ID: DRKS00028819. Registered 14 June 2022.