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Towards chemistry at absolute zero
No full textThe prospect of cooling matter down to temperatures that are close to absolute zero raises intriguing questions about how chemical reactivity changes under these extreme conditions. Although some types of chemical reaction still occur at 1 μK, they can no longer adhere to the conventional picture of reactants passing over an activation energy barrier to become products. Indeed, at ultracold temperatures, the system enters a fully quantum regime, and quantum mechanics replaces the classical picture of colliding particles. In this Review, we discuss recent experimental and theoretical developments that allow us to explore chemical reactions at temperatures that range from 100 K to 500 nK. Although the field is still in its infancy, exceptional control has already been demonstrated over reactivity at low temperatures
Scent in motion: On the multiple uses of ambient scent in the context of passenger transport
No full textThere is undoubtedly growing interest in the role of scent in the design of multisensory experiences. However, to date, the majority of the research has focused on its use in the (static) built environment. As highlighted by this narrative review, somewhat different challenges and opportunities arise just as soon as one starts to consider olfaction in the case of transportation–what might be called “scent in motion.” For instance, levels of anxiety/stress while traveling are often higher (especially in the case of air travel), while, at the same time, the passenger's personal space is frequently compromised. Four key functional roles for scent in the context of passenger transportation are outlined. They include the masking of malodour, the introduction of branded signature scents, short-term olfactory marketing interventions, and the functional use of scent to enhance the experience of travel. In the latter case, one might consider the use of scent to help reduce the stress/anxiety amongst airplane passengers or to give the impression of cleanliness. Meanwhile, in the case of driving, scents have been suggested as an inoffensive means of alerting/relaxing the driver and may also help tackle the problem of motion sickness. The specific challenges associated with scent in motion are reviewed and a number of future opportunities highlighted
The role of orthographic neighbourhood effects in lateralized lexical decision: a replication study and meta-analysis
No full textThe effect of orthographic neighbourhood size (N) on lexical decision reaction time differs when words are presented in the left or right visual fields. Evidence suggests a facilitatory N effect (i.e., faster reaction times for words with larger neighbourhoods) in the left visual field. However, the N effect in the right visual field remains controversial: it may have a weaker facilitative role or it may even be inhibitory. In a pre-registered online experiment, we replicated the interaction between N and visual field and provided support for an inhibitory N effect in the right visual field. We subsequently conducted a pre-registered systematic review and meta-analysis to synthesise the available evidence and determine the direction of N effects across visual fields. Based on the evidence, it would seem the effect is inhibitory in the right visual field. Furthermore, the size of the N effect is considerably smaller in the right visual field. Both studies revealed considerable heterogeneity between participants and studies, and we consider the implications of this for future work
Voxel-wise and spatial modelling of binary lesion masks: comparison of methods with a realistic simulation framework
No full textObjectives. White matter lesions are a very common finding on MRI in older adults and their presence increases the risk of stroke and dementia. Accurate and computationally efficient modelling methods are necessary to map the association of lesion incidence with risk factors, such as hypertension. However, there is no consensus in the brain mapping literature whether a voxel-wise modelling approach is better for binary lesion data than a more computationally intensive spatial modelling approach that accounts for voxel dependence.
Methods. We review three regression approaches for modelling binary lesion masks including mass-univariate probit regression modelling with either maximum likelihood estimates, or mean bias-reduced estimates, and spatial Bayesian modelling, where the regression coefficients have a conditional autoregressive model prior to account for local spatial dependence. We design a novel simulation framework of artificial lesion maps to compare the three alternative lesion mapping methods. The age effect on lesion probability estimated from a reference data set (13,680 individuals from the UK Biobank) is used to simulate a realistic voxel-wise distribution of lesions across age. To mimic the real features of lesion masks, we propose matching brain lesion summaries (total lesion volume, average lesion size and lesion count) across the reference data set and the simulated data sets. Thus, we allow for a fair comparison between the modelling approaches, under a realistic simulation setting.
Results. Our findings suggest that bias-reduced estimates for voxel-wise binary-response generalized linear models (GLMs) overcome the drawbacks of infinite and biased maximum likelihood estimates and scale well for large data sets because voxel-wise estimation can be performed in parallel across voxels. Contrary to the assumption of spatial dependence being key in lesion mapping, our results show that voxel-wise bias-reduction and spatial modelling result in largely similar estimates.
Conclusion. Bias-reduced estimates for voxel-wise GLMs are not only accurate but also computationally efficient, which will become increasingly important as more biobank-scale neuroimaging data sets become available
Robust test statistics for data sets with missing correlation information
No full textNot all experiments publish their results with a description of the correlations between the data points. This makes it difficult to do hypothesis tests or model fits with that data, since just assuming no correlation can lead to an overestimation or underestimation of the resulting uncertainties. This work presents robust test statistics that can be used with datasets with missing correlation information. They are exact in the case of no correlation and either guaranteed to be conservative—i.e., the uncertainty is never underestimated—in the presence of correlations, or they are also exact in the degenerate case of perfect correlation between the data points
Bayesian uncertainty quantification for data-driven equation learning
No full textEquation learning aims to infer differential equation models from data. While
a number of studies have shown that differential equation models can be
successfully identified when the data are sufficiently detailed and corrupted
with relatively small amounts of noise, the relationship between observation
noise and uncertainty in the learned differential equation models remains
unexplored. We demonstrate that for noisy data sets there exists great
variation in both the structure of the learned differential equation models as
well as the parameter values. We explore how to combine data sets to quantify
uncertainty in the learned models, and at the same time draw mechanistic
conclusions about the target differential equations. We generate noisy data
using a stochastic agent-based model and combine equation learning methods with
approximate Bayesian computation (ABC) to show that the correct differential
equation model can be successfully learned from data, while a quantification of
uncertainty is given by a posterior distribution in parameter space
Time-encoded golden angle radial arterial spin labeling: simultaneous acquisition of angiography and perfusion data
No full textThe goal of the current study was to combine a time-encoded pseudo-continuous arterial spin labeling (te-pCASL) scheme with a golden angle radial readout for simultaneous acquisition of angiography and perfusion images from one single dataset both in a very flexible single-slice approach as well as within a multi-slice setting.
A te-pCASL labeling preparation and the golden angle radial readout were both used as a temporal resolution tool to retrospectively choose the temporal window for the reconstruction of both angiography and perfusion images from a single-slice dataset. The temporal window could be chosen retrospectively and adjusted to the hemodynamics of the volunteer on the scanner for the single-slice dataset. Angiographic images were reconstructed at a minimum temporal resolution of 69 ms. For the perfusion phase only the densely sampled center of k-space was included in the reconstruction.
For a multi-slice acquisition, the golden angle radial readout allowed reconstruction of images with different spatial resolutions to provide angiographic and perfusion information over 10 slices. The te-pCASL preparation was used as the only source for dynamic information. The multi-slice acquisition shows the ability of the golden angle radial readout to display the inflow of the labeled blood into the arteries as well as the perfusion in the tissue with full brain coverage.
By combining a te-pCASL preparation with a golden angle radial readout, single-slice high temporal resolution angiography and good quality perfusion images were reconstructed in a flexible manner from a single dataset. Optimizing the golden angle radial readout for reconstructions at multiple spatial resolutions, allows for multi-slice acquisition
Protocol for an observational cohort study investigating personalised medicine for intensification of treatment in people with type 2 diabetes mellitus: the PERMIT study
No full textIntroduction For people with type 2 diabetes mellitus (T2DM) who require an antidiabetic drug as an add-on to metformin, there is controversy about whether newer drug classes such as dipeptidyl peptidase-4 inhibitors (DPP4i) or sodium-glucose co-transporter-2 inhibitors (SGLT2i) reduce the risk of long-term complications compared with sulfonylureas (SU). There is widespread variation across National Health Service Clinical Commissioning Groups (CCGs) in drug choice for second-line treatment in part because National Institute for Health and Care Excellence guidelines do not specify a single preferred drug class, either overall or within specific patient subgroups. This study will evaluate the relative effectiveness of the three most common second-line treatments in the UK (SU, DPP4i and SGLT2i as add-ons to metformin) and help target treatments according to individual risk profiles.
Methods and analysis The study includes people with T2DM prescribed one of the second-line treatments-of-interest between 2014 and 2020 within the UK Clinical Practice Research Datalink linked with Hospital Episode Statistics and Office of National Statistics. We will use an instrumental variable (IV) method to estimate short-term and long-term relative effectiveness of second-line treatments according to individuals’ risk profiles. This method minimises bias from unmeasured confounders by exploiting the natural variation in second-line prescribing across CCGs as an IV for the choice of prescribed treatment. The primary outcome to assess short-term effectiveness will be change in haemoglobin A1c (%) 12 months after treatment initiation. Outcome measures to assess longer-term effectiveness (maximum ~6 years) will include microvascular and macrovascular complications, all-cause mortality and hospital admissions during follow-up.
Ethics and dissemination This study was approved by the Independent Scientific Advisory Committee (20-064) and the London School of Hygiene & Tropical Medicine Research Ethics Committee (21395). Results, codelists and other analysis code will be made available to patients, clinicians, policy-makers and researchers
MS Marsh 396: Zīj of Ulugh Beg And ʿAlī Qūshjī’s Sharḥ
No full textSelections photographed by Dr. Julia Bray for the project "Arabic Books and Astronomy in 17th C Oxford
Importance of patient bed pathways and length of stay differences in predicting COVID-19 hospital bed occupancy in England
No full textBackground: Predicting bed occupancy for hospitalised patients with COVID-19 requires understanding of length of stay (LoS) in particular bed types. LoS can vary depending on the patient’s “bed pathway” - the sequence of transfers of individual patients between bed types during a hospital stay. In this study, we characterise these pathways, and their impact on predicted hospital bed occupancy.
Methods: We obtained data from University College Hospital (UCH) and the ISARIC4C COVID-19 Clinical Information Network (CO-CIN) on hospitalised patients with COVID-19 who required care in general ward or critical care (CC) beds to determine possible bed pathways and LoS. We developed a discrete-time model to examine the implications of using either bed pathways or only average LoS by bed type to forecast bed occupancy. We compared model-predicted bed occupancy to publicly available bed occupancy data on COVID-19 in England between March and August 2020.
Results: In both the UCH and CO-CIN datasets, 82% of hospitalised patients with COVID-19 only received care in general ward beds. We identified four other bed pathways, present in both datasets: “Ward, CC, Ward”, “Ward, CC”, “CC” and “CC, Ward”. Mean LoS varied by bed type, pathway, and dataset, between 1.78 and 13.53 days.
For UCH, we found that using bed pathways improved the accuracy of bed occupancy predictions, while only using an average LoS for each bed type underestimated true bed occupancy. However, using the CO-CIN LoS dataset we were not able to replicate past data on bed occupancy in England, suggesting regional LoS heterogeneities.
Conclusions: We identified five bed pathways, with substantial variation in LoS by bed type, pathway, and geography. This might be caused by local differences in patient characteristics, clinical care strategies, or resource availability, and suggests that national LoS averages may not be appropriate for local forecasts of bed occupancy for COVID-19