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The success of any physical model critically depends upon adopting an appropriate representation for the phenomenon of interest. Unfortunately, it remains generally challenging to identify the essential degrees of freedom or, equivalently, the proper order parameters for describing complex phenomena. Here we develop a statistical physics framework for exploring and quantitatively characterizing the space of order parameters for representing physical systems. Specifically, we examine the space of low-resolution representations that correspond to particle-based coarse-grained (CG) models for a simple microscopic model of protein fluctuations. We employ Monte Carlo (MC) methods to sample this space and determine the density of states for CG representations as a function of their ability to preserve the configurational information, I, and large-scale fluctuations, 𝓠, of the microscopic model. These two metrics are uncorrelated in high-resolution representations but become anticorrelated at lower resolutions. Moreover, our MC simulations suggest an emergent length scale for coarse-graining proteins, as well as a qualitative distinction between good and bad representations of proteins. Finally, we relate our work to recent approaches for clustering graphs and detecting communities in networks.

Regular mindfulness practice has been demonstrated to be beneficial for mental health, but mindfulness can be challenging to adopt, with environmental and personal distractors often cited as challenges. Virtual reality (VR) may address these challenges by providing an immersive environment for practicing mindfulness and by supporting the user to orient attention to the present moment within a tailored virtual setting. However, there is currently a limited understanding of the ways in which VR can support or hinder mindfulness practice. Such an understanding is required to design effective VR apps while ensuring that VR-supported mindfulness is acceptable to end users. This study aimed to explore how VR can support mindfulness practice and to understand user experience issues that may affect the acceptability and efficacy of VR mindfulness for users in the general population. A sample of 37 participants from the general population trialed a VR mindfulness app in a controlled laboratory setting. The VR app presented users with an omnidirectional video of a peaceful forest environment with a guided mindfulness voiceover that was delivered by a male narrator. Scores on the State Mindfulness Scale, Simulator Sickness Questionnaire, and single-item measures of positive and negative emotion and arousal were measured pre- and post-VR for all participants. Qualitative feedback was collected through interviews with a subset of 19 participants. The interviews sought to understand the user experience of mindfulness practice in VR. State mindfulness (P<.001; Cohen d=1.80) and positive affect (P=.006; r=.45) significantly increased after using the VR mindfulness app. No notable changes in negative emotion, subjective arousal, or symptoms of simulator sickness were observed across the sample. Participants described the user experience as relaxing, calming, and peaceful. Participants suggested that the use of VR helped them to focus on the present moment by using visual and auditory elements of VR as attentional anchors. The sense of presence in the virtual environment (VE) was identified by participants as being helpful to practicing mindfulness. Interruptions to presence acted as distractors. Some uncomfortable experiences were discussed, primarily in relation to video fidelity and the weight of the VR headset, although these were infrequent and minor. This study suggests that an appropriately designed VR app can support mindfulness practice by enhancing state mindfulness and inducing positive affect. VR may help address the challenges of practicing mindfulness by creating a sense of presence in a tailored VE; by allowing users to attend to visual and auditory anchors of their choice; and by reducing the scope of the content in users' mind-wandering. VR has the unique capability to combine guided mindfulness practice with tailored VEs that lend themselves to support individuals to focus attention on the present moment.

Pneumonia remains a leading cause of hospitalization and death among young children worldwide, and the diagnostic challenge of differentiating bacterial from non-bacterial pneumonia is the main driver of antibiotic use for treating pneumonia in children. Causal Bayesian networks (BNs) serve as powerful tools for this problem as they provide clear maps of probabilistic relationships between variables and produce results in an explainable way by incorporating both domain expert knowledge and numerical data. We used domain expert knowledge and data in combination and iteratively, to construct, parameterise and validate a causal BN to predict causative pathogens for childhood pneumonia. Expert knowledge elicitation occurred through a series of group workshops, surveys and one-on-one meetings involving 6-8 experts from diverse domain areas. The model performance was evaluated based on both quantitative metrics and qualitative expert validation. Sensitivity analyses were conducted to investigate how the target output is influenced by varying key assumptions of a particularly high degree of uncertainty around data or domain expert knowledge. Designed to apply to a cohort of children with X-ray confirmed pneumonia who presented to a tertiary paediatric hospital in Australia, the resulting BN offers explainable and quantitative predictions on a range of variables of interest, including the diagnosis of bacterial pneumonia, detection of respiratory pathogens in the nasopharynx, and the clinical phenotype of a pneumonia episode. Satisfactory numeric performance has been achieved including an area under the receiver operating characteristic curve of 0.8 in predicting clinically-confirmed bacterial pneumonia with sensitivity 88% and specificity 66% given certain input scenarios (i.e., information that is available and entered into the model) and trade-off preferences (i.e., relative weightings of the consequences of false positive versus false negative predictions). We specifically highlight that a desirable model output threshold for practical use is very dependent upon different input scenarios and trade-off preferences. Three commonly encountered scenarios were presented to demonstrate the potential usefulness of the BN outputs in various clinical pictures. To our knowledge, this is the first causal model developed to help determine the causative pathogen for paediatric pneumonia. We have shown how the method works and how it would help decision making on the use of antibiotics, providing insight into how computational model predictions may be translated to actionable decisions in practice. We discussed key next steps including external validation, adaptation and implementation. Our model framework and the methodological approach can be adapted beyond our context to broad respiratory infections and geographical and healthcare settings.

In a phase 1 study involving children with giant axonal neuropathy, intrathecal administration of an adeno-associated virus containing a GAN transgene resulted in some improvement in motor function scores.

Astrocytes intimately interact with synapses, both morphologically and, as evidenced in the past 20 years, at the functional level. Ultrathin astrocytic processes contact and sometimes enwrap the synaptic elements, sense synaptic transmission and shape or alter the synaptic signal by releasing signalling molecules. Yet, the consequences of such interactions in terms of information processing in the brain remain very elusive. This is largely due to two major constraints: (i) the exquisitely complex, dynamic and ultrathin nature of distal astrocytic processes that renders their investigation highly challenging and (ii) our lack of understanding of how information is encoded by local and global fluctuations of intracellular calcium concentrations in astrocytes. Here, we will review the existing anatomical and functional evidence of local interactions between astrocytes and synapses, and how it underlies a role for astrocytes in the computation of synaptic information. This article is part of the themed issue ‘Integrating Hebbian and homeostatic plasticity’.

A massive star's exit Stars like the Sun end their stellar lives as white dwarfs. Theory predicts a different fate for stars with masses over 140 times that of the Sun (if they exist, which they don't in the Milky Way). When they have evolved to the stage of having oxygen cores the pressure-supporting photons turn into electron–positron pairs, absorbing energy and letting the core collapse to produce a 'pair instability' supernova. Analysis of the spectrum and light curve of supernova 2007bi, a luminous event in a nearby dwarf galaxy, provides evidence of such an explosion. The SN 2007bi progenitor is estimated to have had a core of greater than 100 solar masses. Calculations point to an explosion producing more than three solar masses worth of radioactive nickel-56, in line with what would be expected from a massive oxygen core. The implication is that there are extremely massive stars in the local Universe that could provide astronomers with a close-up of the type of star that may have dominated the early Universe. Extremely massive stars with initial masses of more than 140 solar masses end their lives when pressure-supporting photons turn into electron–positron pairs, leading to a violent contraction that triggers a nuclear explosion, unbinding the star in a pair-instability supernova. Here, the mass of the exploding core of supernova SN 2007bi is estimated at around 100 solar masses, in which case theory unambiguously predicts a pair-instability supernova. Further observations are well fitted by models of pair-instability supernovae. Stars with initial masses such that 10  ≤  M initial  ≤ 100 , where is the solar mass, fuse progressively heavier elements in their centres, until the core is inert iron. The core then gravitationally collapses to a neutron star or a black hole, leading to an explosion—an iron-core-collapse supernova 1 , 2 . By contrast, extremely massive stars with M initial  ≥ 140 (if such exist) develop oxygen cores with masses, M core , that exceed 50 , where high temperatures are reached at relatively low densities. Conversion of energetic, pressure-supporting photons into electron–positron pairs occurs before oxygen ignition and leads to a violent contraction which triggers a nuclear explosion 3 , 4 , 5 that unbinds the star in a pair-instability supernova. Transitional objects with 100  <  M initial  < 140 may end up as iron-core-collapse supernovae following violent mass ejections, perhaps as a result of brief episodes of pair instability, and may already have been identified 6 , 7 , 8 . Here we report observations of supernova SN 2007bi, a luminous, slowly evolving object located within a dwarf galaxy. We estimate the exploding core mass to be M core  ≈ 100 , in which case theory unambiguously predicts a pair-instability supernova. We show that >3 of radioactive 56 Ni was synthesized during the explosion and that our observations are well fitted by models of pair-instability supernovae 9 , 10 . This indicates that nearby dwarf galaxies probably host extremely massive stars, above the apparent Galactic stellar mass limit 11 , which perhaps result from processes similar to those that created the first stars in the Universe.

The symptoms of Parkinson’s disease (PD) reflect disruptions of a number of brain neurotransmitter systems of varying type and degree. Pharmacological agents with multiple neurochemical mechanisms of action are therefore promising candidates for countering these problems and providing comprehensive symptomatic relief for patients. The pharmacological profile of safinamide includes reversible monoamine oxidase B inhibition, blockage of voltage-dependent Na + channels, modulation of Ca 2+ channels, and inhibition of glutamate release. Safinamide is administered once daily at oral doses of 50–100 mg; it is well-tolerated and safe. Clinical trials have found that it ameliorates motor symptoms when added to established levodopa or single dopamine receptor agonist therapy. The future role of safinamide in PD may be that it enables a reduction in the dosage of dopamine replacement therapies, thereby reducing the adverse effects associated with these treatments. The clinical convenience (once-daily administration), safety, and tolerability of safinamide are better than those of dopamine receptor agonists. The introduction of safinamide reflects a change of approach to drug development for anti-parkinsonian agents in that its broad spectrum of action corresponds to the multiple heterogeneous alterations of brain neurochemistry in PD, rather than being targeted at a single receptor type or neurochemical process. Safinamide is a promising new instrument for the effective symptomatic therapy of PD.

The final fate of massive stars, and the nature of the compact remnants they leave behind (black holes and neutron stars), are open questions in astrophysics. Many massive stars are stripped of their outer hydrogen envelopes as they evolve. Such Wolf–Rayet stars 1 emit strong and rapidly expanding winds with speeds greater than 1,000 kilometres per second. A fraction of this population is also helium-depleted, with spectra dominated by highly ionized emission lines of carbon and oxygen (types WC/WO). Evidence indicates that the most commonly observed supernova explosions that lack hydrogen and helium (types Ib/Ic) cannot result from massive WC/WO stars 2 , 3 , leading some to suggest that most such stars collapse directly into black holes without a visible supernova explosion 4 . Here we report observations of SN 2019hgp, beginning about a day after the explosion. Its short rise time and rapid decline place it among an emerging population of rapidly evolving transients 5 – 8 . Spectroscopy reveals a rich set of emission lines indicating that the explosion occurred within a nebula composed of carbon, oxygen and neon. Narrow absorption features show that this material is expanding at high velocities (greater than 1,500 kilometres per second), requiring a compact progenitor. Our observations are consistent with an explosion of a massive WC/WO star, and suggest that massive Wolf–Rayet stars may be the progenitors of some rapidly evolving transients. Observations of the supernova SN 2019hgp, identified about a day after its explosion, show that it occurred within a nebula of carbon, oxygen and neon, and was probably the explosion of a massive WC/WO star.

Left ventricular (LV) wall thickness is a prognostic marker in hypertrophic cardiomyopathy (HC). LV wall thickness ≥30 mm (massive hypertrophy) is independently associated with sudden cardiac death. Presence of massive hypertrophy is used to guide decision making for cardiac defibrillator implantation. We sought to determine whether measurements of maximal LV wall thickness differ between cardiac magnetic resonance imaging (MRI) and transthoracic echocardiography (TTE). Consecutive patients were studied who had HC without previous septal ablation or myectomy and underwent both cardiac MRI and TTE at a single tertiary referral center. Reported maximal LV wall thickness was compared between the imaging techniques. Patients with ≥1 technique reporting massive hypertrophy received subset analysis. In total, 618 patients were evaluated from January 1, 2003, to December 21, 2012 (mean [SD] age, 53 [15] years; 381 men [62%]). In 75 patients (12%), reported maximal LV wall thickness was identical between MRI and TTE. Median difference in reported maximal LV wall thickness between the techniques was 3 mm (maximum difference, 17 mm). Of the 63 patients with ≥1 technique measuring maximal LV wall thickness ≥30 mm, 44 patients (70%) had discrepant classification regarding massive hypertrophy. MRI identified 52 patients (83%) with massive hypertrophy; TTE, 30 patients (48%). Although guidelines recommend MRI or TTE imaging to assess cardiac anatomy in HC, this study shows discrepancy between the techniques for maximal reported LV wall thickness assessment. In conclusion, because this measure clinically affects prognosis and therapeutic decision making, efforts to resolve these discrepancies are critical.

We re-examine the classifications of supernovae (SNe) presented in the Lick Observatory Supernova Search (LOSS) volume-limited sample with a focus on the stripped-envelope SNe. The LOSS volume-limited sample, presented by Leaman et al. and Li et al., was calibrated to provide meaningful measurements of SN rates in the local universe; the results presented therein continue to be used for comparisons to theoretical and modeling efforts. Many of the objects from the LOSS sample were originally classified based upon only a small subset of the data now available, however, and recent studies have both updated some subtype distinctions and improved our ability to perform robust classifications, especially for stripped-envelope SNe. We re-examine the spectroscopic classifications of all events in the LOSS volume-limited sample (180 SNe and SN impostors) and update them if necessary. We discuss the populations of rare objects in our sample including broad-lined SNe Ic, Ca-rich SNe, SN 1987A-like events (we identify SN 2005io as SN 1987A-like here for the first time), and peculiar subtypes. The relative fractions of SNe Ia, SNe II, and stripped-envelope SNe in the local universe are not affected, but those of some subtypes are. Most significantly, after discussing the often unclear boundary between SNe Ib and Ic when only noisy spectra are available, we find a higher SN Ib fraction and a lower SN Ic fraction than calculated by Li et al.: spectroscopically normal SNe Ib occur in the local universe 1.7 0.9 times more often than do normal SNe Ic.