Most of what we know about adaptive immunity has come from inbred mouse studies, using methods that are often difficult or impossible to confirm in humans. In addition, vaccine responses in mice are ...often poorly predictive of responses to those same vaccines in humans. Here we use human tonsils, readily available lymphoid organs, to develop a functional organotypic system that recapitulates key germinal center features in vitro, including the production of antigen-specific antibodies, somatic hypermutation and affinity maturation, plasmablast differentiation and class-switch recombination. We use this system to define the essential cellular components necessary to produce an influenza vaccine response. We also show that it can be used to evaluate humoral immune responses to two priming antigens, rabies vaccine and an adenovirus-based severe acute respiratory syndrome coronavirus 2 vaccine, and to assess the effects of different adjuvants. This system should prove useful for studying critical mechanisms underlying adaptive immunity in much greater depth than previously possible and to rapidly test vaccine candidates and adjuvants in an entirely human system.
The Markov state model (MSM) is a popular theoretical tool for describing the hierarchy of time scales involved in the function of many proteins especially ion channel gating. An MSM is a particular ...case of the general non-Markovian model, where the rate of transition from one state to another does not depend on the history of state occupancy within the system, i.e. it only includes reversible, non-dissipative processes. However, an MSM requires knowledge of the precise conformational state of the protein and is not predictive when those details are not known. In the case of ion channels, this simple description fails in real (non-equilibrium) situations, for example when local temperature changes, or when energy losses occur during channel gating. Here, we show it is possible to use non-Markovian equations (i.e. offer a general description that includes the MSM as a particular case) to develop a relatively simple analytical model that describes the non-equilibrium behaviour of the temperature-sensitive transient receptor potential (TRP) ion channels, TRPV1 and TRPM8. This model accurately predicts asymmetrical opening and closing rates, infinite processes and the creation of new states, as well as the effect of temperature changes throughout the process. This approach therefore overcomes the limitations of the MSM and allows us to go beyond a mere phenomenological description of the dynamics of ion channel gating towards a better understanding of the physics underlying these processes.
•A total of 374 multi-detector CT scans are made available to the research community, the biggest such dataset on spine until date (VerSe’19: https://osf.io/nqjyw/; VerSe’20: ...https://osf.io/t98fz/).•The VerSe benchmark includes annotations for two fundamental processing tasks, namely vertebrae labelling and segmentation.•Twenty-six fully-automated algorithms (eleven for VerSe’19, thirteen for VerSe’20, and one baseline) are benchmarked on this dataset, with the top performing algorithm achieving a mean vertebrae identification rate of 96.6% and a Dice coefficient of 91.7% in VerSe’20.•Further insights into these algorithms are provided by examining them at various levels of granularity ranging from dataset-level experiments to vertebrae-level performances to a field-of-view-related analysis.
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Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision support systems for diagnosis, surgery planning, and population-based analysis of spine and bone health. However, designing automated algorithms for spine processing is challenging predominantly due to considerable variations in anatomy and acquisition protocols and due to a severe shortage of publicly available data. Addressing these limitations, the Large Scale Vertebrae Segmentation Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2019 and 2020, with a call for algorithms tackling the labelling and segmentation of vertebrae. Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel level by a human-machine hybrid algorithm (https://osf.io/nqjyw/, https://osf.io/t98fz/). A total of 25 algorithms were benchmarked on these datasets. In this work, we present the results of this evaluation and further investigate the performance variation at the vertebra level, scan level, and different fields of view. We also evaluate the generalisability of the approaches to an implicit domain shift in data by evaluating the top-performing algorithms of one challenge iteration on data from the other iteration. The principal takeaway from VerSe: the performance of an algorithm in labelling and segmenting a spine scan hinges on its ability to correctly identify vertebrae in cases of rare anatomical variations. The VerSe content and code can be accessed at: https://github.com/anjany/verse.
Concussion is the most common match injury in professional Rugby Union, accounting for 25% of match injuries. The primary prevention of head injuries requires that the injury mechanism be known so ...that interventions can be targeted to specifically overall incidence by focusing on characteristics with the greatest propensity to cause a head injury.
611 head injury assessment (HIA) events in professional Rugby Union over a 3-year period were analysed, with specific reference to match events, position, time and nature of head contact.
464 (76%) of HIA events occur during tackles, with the tackler experiencing a significantly greater propensity for an HIA than the ball carrier (1.40 HIAs/1000 tackles for the tackler vs 0.54 HIAs/1000 tackles for the ball carrier, incidence rate ratio (IRR) 2.59). Propensity was significantly greater for backline players than forwards (IRR 1.54, 95% CI 1.28 to 1.84), but did not increase over the course of the match. Head to head contact accounted for the most tackler HIAs, with the greatest propensity.
By virtue of its high propensity and frequency, the tackle should be the focus for interventions that may include law change and technique education. A specific investigation of the characteristics of the tackle is warranted to refine the approach to preventative strategies.
Though the temporal precision of neural computation has been studied intensively, a data-driven determination of this precision remains a fundamental challenge. Reproducible spike patterns may be ...obscured on single trials by uncontrolled temporal variability in behavior and cognition and may not be time locked to measurable signatures in behavior or local field potentials (LFP). To overcome these challenges, we describe a general-purpose time warping framework that reveals precise spike-time patterns in an unsupervised manner, even when these patterns are decoupled from behavior or are temporally stretched across single trials. We demonstrate this method across diverse systems: cued reaching in nonhuman primates, motor sequence production in rats, and olfaction in mice. This approach flexibly uncovers diverse dynamical firing patterns, including pulsatile responses to behavioral events, LFP-aligned oscillatory spiking, and even unanticipated patterns, such as 7 Hz oscillations in rat motor cortex that are not time locked to measured behaviors or LFP.
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•Trial-to-trial variability in spike timing is correlated across co-recorded neurons•Unsupervised time warping reveals precise spike patterns from neural data alone•Simple and interpretable warping functions (piecewise linear) are often sufficient•Spike time precision may be systematically underestimated without warping
The timing of neural dynamics can be highly variable across trials due to uncontrolled behavioral variability or unobserved cognitive states. Williams et al. describe an interpretable statistical model to control for these misalignments and use this approach to uncover fine-scale temporal structure that is imperceptible in raw data.
ObjectivesThis study analysed the overall sentiment of attitudes, opinions, views and emotions expressed in posts on X related to red-carded and yellow-carded tackles during the 2019 Rugby World Cup ...(RWC).MethodsSentiment analysis was conducted on posts on X about red or yellow cards issued at the 2019 RWC. Posts were classified as ‘agree’, ‘disagree’ and ‘neutral’. The frequency of posts, red cards, yellow cards, all injuries, tackle injuries and total number of tackles per match were also synced to the 45-match playing schedule.ResultsFive tackle-related red cards were issued during the 2019 RWC, and 15 tackle-related yellow cards, with 337 and 302 posts identified for each card decision, respectively. For red cards, 42% of posts (n=158/377) agreed with the referee’s decision, 19% (n=71/377) disagreed and 40% were neutral. For yellow cards, 24% (n=73/302) agreed with the referee’s decision, 33% (n=99/302) disagreed and 43% were neutral.ConclusionsFor red cards, posts were 2.2 times more likely to agree with the referee’s decision than disagree. Posts that agreed with a red card decision were also more likely to be shared (reposted) than posts that disagreed with a red card decision. In contrast, sentiments expressed for yellow card decisions were mixed. This may be related to interpreting the degree of danger and whether mitigation is applied. Within the ecosystem of rugby, sharing sentiments on social media plays a powerful role in creating a positive player welfare narrative.
We sought to undertake a systematic review to assess the current research and to provide a platform for future research on the psychological health impact of chronic environmental contamination ...(CEC). CEC is the experience of living in an area where hazardous substances are known or perceived to be present in air, water, or soil at elevated levels for a prolonged and unknown period of time. We employed a systematic review approach to assess the psychological health impact of CEC in literature from 1995 to 2019, and conducted a meta-analysis of available findings (k = 60, N = 25,858) on the impact of CEC on anxiety, general stress, depression, and PTSD. We also present a narrative synthesis of findings that suggest risk factors for the experience of psychological health impacts in the wake of CEC. Likely factors increasing risk for elevated psychological health impact from CEC experience are institutional delegitimization of community concerns and the real or perceived presence of health effects from CEC. The meta-analyses observed small-to-medium effects of experiencing CEC on anxiety, general stress, depression, and PTSD. However, there was also evident risk of bias in the data. Our review suggests that psychological health in the context of CEC is an important potential public health burden and a key area for future improved research.
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•Psychological health impact of chronic environmental contamination is understudied.•Contamination was associated with anxiety, general stress, depression, and PTSD.•Institutional delegitimization and physical health impacts may be risk factors.
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