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About half of the satellites in the Andromeda galaxy (M 31), all with the same sense of rotation about their host, form a planar subgroup that is extremely wide but also very thin. The Andromeda galaxy's orbiting companions Giant spiral galaxies are assembled from smaller systems through a process known as hierarchical clustering. In orbit around these giants are dwarf galaxies, which are presumably remnants of the galactic progenitors. Recent studies of the dwarf galaxies of the Milky Way have led some astronomers to suspect that their orbits are not randomly distributed. This suspicion, which challenges current theories of galaxy formation, is now bolstered by the discovery of a plane of dwarf galaxies corotating as a coherent pancake-like structure around the Andromeda galaxy, the Milky Way's close neighbour and in many respects its 'twin'. The structure is extremely thin yet contains about half of the dwarf galaxies in the Andromeda system. The authors report that 13 of the 15 satellites in the plane share the same sense of rotation. Dwarf satellite galaxies are thought to be the remnants of the population of primordial structures that coalesced to form giant galaxies like the Milky Way 1 . It has previously been suspected 2 that dwarf galaxies may not be isotropically distributed around our Galaxy, because several are correlated with streams of H  i emission, and may form coplanar groups 3 . These suspicions are supported by recent analyses 4 , 5 , 6 , 7 . It has been claimed 7 that the apparently planar distribution of satellites is not predicted within standard cosmology 8 , and cannot simply represent a memory of past coherent accretion. However, other studies dispute this conclusion 9 , 10 , 11 . Here we report the existence of a planar subgroup of satellites in the Andromeda galaxy (M 31), comprising about half of the population. The structure is at least 400 kiloparsecs in diameter, but also extremely thin, with a perpendicular scatter of less than 14.1 kiloparsecs. Radial velocity measurements 12 , 13 , 14 , 15 reveal that the satellites in this structure have the same sense of rotation about their host. This shows conclusively that substantial numbers of dwarf satellite galaxies share the same dynamical orbital properties and direction of angular momentum. Intriguingly, the plane we identify is approximately aligned with the pole of the Milky Way’s disk and with the vector between the Milky Way and Andromeda.

We discuss some of the key open questions regarding the formation and evolution of globular clusters (GCs) during galaxy formation and assembly within a cosmological framework. The current state of the art for both observations and simulations is described, and we briefly mention directions for future research. The oldest GCs have ages greater than or equal to 12.5 Gyr and formed around the time of reionization. Resolved colour-magnitude diagrams of Milky Way GCs and direct imaging of lensed proto-GCs at z∼6 with the James Webb Space Telescope (JWST) promise further insight. GCs are known to host multiple populations of stars with variations in their chemical abundances. Recently, such multiple populations have been detected in ∼2 Gyr old compact, massive star clusters. This suggests a common, single pathway for the formation of GCs at high and low redshift. The shape of the initial mass function for GCs remains unknown; however, for massive galaxies a power-law mass function is favoured. Significant progress has been made recently modelling GC formation in the context of galaxy formation, with success in reproducing many of the observed GC-galaxy scaling relations.

Clinically, the Berlin ARDS definition describes acute respiratory distress syndrome (ARDS) as acute hypoxaemic respiratory failure that is not fully explained by cardiac failure or fluid overload, that develops within 7 days of clinical recognition of a known risk factor, with bilateral radiographic opacities that are not fully explained by effusions, lobar/lung collapse, or nodules. Tree risk strata were defined on the basis of the severity of hypoxaemia represented by the ratio of partial pressure of oxygen in arterial blood to inspired oxygen concentration (PaO2/FiO2 ratio), assessed at a minimum positive end-expiratory pressure (PEEP) of 5 cmH2O [1]. Hospital mortality worsens with severity of hypoxaemia and thus grade of ARDS (from 35% in mild ARDS to 46% in severe ARDS) [1, 2].

Glioblastomas contains stem-like tumor cells that display differential metabolic profiles. Here the authors show that brain tumor initiating cells contain fragmented mitochondria owing to activation of the key mediator of mitochondrial fission, DRP1, controlled by a competitive CDK5–CAMK2 axis. Targeting DRP1 activity attenuates growth of stem-like tumor cells, and activated DRP1 informs poor patient prognosis. Brain tumor initiating cells (BTICs) co-opt the neuronal high affinity glucose transporter, GLUT3, to withstand metabolic stress. We investigated another mechanism critical to brain metabolism, mitochondrial morphology, in BTICs. BTIC mitochondria were fragmented relative to non-BTIC tumor cell mitochondria, suggesting that BTICs increase mitochondrial fission. The essential mediator of mitochondrial fission, dynamin-related protein 1 (DRP1), showed activating phosphorylation in BTICs and inhibitory phosphorylation in non-BTIC tumor cells. Targeting DRP1 using RNA interference or pharmacologic inhibition induced BTIC apoptosis and inhibited tumor growth. Downstream, DRP1 activity regulated the essential metabolic stress sensor, AMP-activated protein kinase (AMPK), and targeting AMPK rescued the effects of DRP1 disruption. Cyclin-dependent kinase 5 (CDK5) phosphorylated DRP1 to increase its activity in BTICs, whereas Ca 2+ -calmodulin-dependent protein kinase 2 (CAMK2) inhibited DRP1 in non-BTIC tumor cells, suggesting that tumor cell differentiation induces a regulatory switch in mitochondrial morphology. DRP1 activation correlated with poor prognosis in glioblastoma, suggesting that mitochondrial dynamics may represent a therapeutic target for BTICs.

Joint uncertainty is a subject of much interest in structural dynamics’ research, as joint behaviour potentially becomes more significant at higher frequencies. Therefore, an accurate determination of the scattering properties of uncertain joint elements for wave-based methods becomes more important on the subsequent prediction sensitivity. For this reason, this study examines the resulting scattering properties (reflection and transmission efficiencies) due to uncertain joints using a hybrid methodology. The scattering is calculated for a beam-to-beam joint via a combined hybrid Wave Finite Element and Finite Element (abbreviated as hybrid WFE) model, while Polynomial Chaos Expansion is utilized for the uncertainty modelling. It is assumed that the joint has a uniformly distributed uncertain thickness and loss factor. The results are compared to analytical transmission efficiencies and Monte Carlo simulations. The results show that the uncertainty in the joint does not become more evident as the frequency increases as expected, and the proposed methodology successfully models the joint uncertainty problem.

Dengue pathogenesis is extremely complex. Dengue infections are thought to induce life-long immunity from homologous challenges as well as a multi-factorial heterologous risk enhancement. Here, we use the data collected from a prospective cohort study of dengue infections in schoolchildren in Vietnam to disentangle how serotype interactions modulate clinical disease risk in the year following serum collection. We use multinomial logistic regression to correlate the yearly neutralizing antibody measurements obtained with each infecting serotype in all dengue clinical cases collected over the course of 6 years (2004–2009). This allowed us to extrapolate a fully discretised matrix of serotype interactions, revealing clear signals of increased risk of clinical illness in individuals primed with a previous dengue infection. The sequences of infections which produced a higher risk of dengue fever upon secondary infection are: DEN1 followed by DEN2; DEN1 followed by DEN4; DEN2 followed by DEN3; and DEN4 followed by DEN3. We also used this longitudinal data to train a machine learning algorithm on antibody titre differences between consecutive years to unveil asymptomatic dengue infections and estimate asymptomatic infection to clinical case ratios over time, allowing for a better characterisation of the population’s past exposure to different serotypes.

The Omicron variant of SARS-CoV-2 became the globally dominant variant in early 2022. A sub-lineage of the Omicron variant (BA.2) was identified in England in January 2022. Here, we investigated hospitalisation and mortality risks of COVID-19 cases with the Omicron sub-lineage BA.2 ( n  = 258,875) compared to BA.1 ( n  = 984,337) in a large cohort study in England. We estimated the risk of hospital attendance, hospital admission or death using multivariable stratified proportional hazards regression models. After adjustment for confounders, BA.2 cases had lower or similar risks of death (HR = 0.80, 95% CI 0.71–0.90), hospital admission (HR = 0.88, 95% CI 0.83–0.94) and any hospital attendance (HR = 0.98, 95% CI 0.95–1.01). These findings that the risk of severe outcomes following infection with BA.2 SARS-CoV-2 was slightly lower or equivalent to the BA.1 sub-lineage can inform public health strategies in countries where BA.2 is spreading. In this cohort study, the authors investigate the risk of severe outcomes following infection from Omicron BA.1 and BA.2 sub-lineages. Using whole genome sequencing and electronic health record data for ~980,000 BA.1 and ~250,000 BA.2 cases in England, they find a slightly lower risk of death and hospitalisation associated with BA.2.

Galactic detritus around M31 A panoramic survey of the region around our nearest galactic neighbour, the well known Andromeda galaxy M31, has detected stars and coherent structures that are almost certainly remnants of dwarf galaxies destroyed by M31's tidal field. The brightest companion, the Triangulum galaxy (M33), is surrounded by a previously unknown prominent stellar structure that provides evidence for a recent encounter with M31. This new view of galactic structures is consistent with hierarchical cosmological models in which galaxies grow in mass by the accretion of smaller ones. In hierarchical cosmological models, galaxies grow in mass through the continual accretion of smaller ones. The tidal disruption of these systems is expected to result in loosely bound and distant stars surrounding the galaxy. A panoramic survey of the Andromeda galaxy (M31) now reveals stars and coherent structures that are almost certainly remnants of dwarf galaxies destroyed by the tidal field of M31. In hierarchical cosmological models 1 , galaxies grow in mass through the continual accretion of smaller ones. The tidal disruption of these systems is expected to result in loosely bound stars surrounding the galaxy, at distances that reach 10–100 times the radius of the central disk 2 , 3 . The number, luminosity and morphology of the relics of this process provide significant clues to galaxy formation history 4 , but obtaining a comprehensive survey of these components is difficult because of their intrinsic faintness and vast extent. Here we report a panoramic survey of the Andromeda galaxy (M31). We detect stars and coherent structures that are almost certainly remnants of dwarf galaxies destroyed by the tidal field of M31. An improved census of their surviving counterparts implies that three-quarters of M31’s satellites brighter than M v = -6 await discovery. The brightest companion, Triangulum (M33), is surrounded by a stellar structure that provides persuasive evidence for a recent encounter with M31. This panorama of galaxy structure directly confirms the basic tenets of the hierarchical galaxy formation model and reveals the shared history of M31 and M33 in the unceasing build-up of galaxies.

Mechanistic models (MMs) have served as causal pathway analysis and ‘decision-support’ tools within animal production systems for decades. Such models quantitatively define how a biological system works based on causal relationships and use that cumulative biological knowledge to generate predictions and recommendations (in practice) and generate/evaluate hypotheses (in research). Their limitations revolve around obtaining sufficiently accurate inputs, user training and accuracy/precision of predictions on-farm. The new wave in digitalization technologies may negate some of these challenges. New data-driven (DD) modelling methods such as machine learning (ML) and deep learning (DL) examine patterns in data to produce accurate predictions (forecasting, classification of animals, etc.). The deluge of sensor data and new self-learning modelling techniques may address some of the limitations of traditional MM approaches – access to input data (e.g. sensors) and on-farm calibration. However, most of these new methods lack transparency in the reasoning behind predictions, in contrast to MM that have historically been used to translate knowledge into wisdom. The objective of this paper is to propose means to hybridize these two seemingly divergent methodologies to advance the models we use in animal production systems and support movement towards truly knowledge-based precision agriculture. In order to identify potential niches for models in animal production of the future, a cross-species (dairy, swine and poultry) examination of the current state of the art in MM and new DD methodologies (ML, DL analytics) is undertaken. We hypothesize that there are several ways via which synergy may be achieved to advance both our predictive capabilities and system understanding, being: (1) building and utilizing data streams (e.g. intake, rumination behaviour, rumen sensors, activity sensors, environmental sensors, cameras and near IR) to apply MM in real-time and/or with new resolution and capabilities; (2) hybridization of MM and DD approaches where, for example, a ML framework is augmented by MM-generated parameters or predicted outcomes and (3) hybridization of the MM and DD approaches, where biological bounds are placed on parameters within a MM framework, and the DD system parameterizes the MM for individual animals, farms or other such clusters of data. As animal systems modellers, we should expand our toolbox to explore new DD approaches and big data to find opportunities to increase understanding of biological systems, find new patterns in data and move the field towards intelligent, knowledge-based precision agriculture systems.

The objective of this study was to develop a novel methodology that enables pig diets to be formulated explicitly for environmental impact objectives using a Life Cycle Assessment (LCA) approach. To achieve this, the following methodological issues had to be addressed: (1) account for environmental impacts caused by both ingredient choice and nutrient excretion, (2) formulate diets for multiple environmental impact objectives and (3) allow flexibility to identify the optimal nutritional composition for each environmental impact objective. An LCA model based on Canadian pig farms was integrated into a diet formulation tool to compare the use of different ingredients in Eastern and Western Canada. By allowing the feed energy content to vary, it was possible to identify the optimum energy density for different environmental impact objectives, while accounting for the expected effect of energy density on feed intake. A least-cost diet was compared with diets formulated to minimise the following objectives: non-renewable resource use, acidification potential, eutrophication potential, global warming potential and a combined environmental impact score (using these four categories). The resulting environmental impacts were compared using parallel Monte Carlo simulations to account for shared uncertainty. When optimising diets to minimise a single environmental impact category, reductions in the said category were observed in all cases. However, this was at the expense of increasing the impact in other categories and higher dietary costs. The methodology can identify nutritional strategies to minimise environmental impacts, such as increasing the nutritional density of the diets, compared with the least-cost formulation.